Gu, Peng, Zhengyu Liu, and Thomas L Delworth, April 2024: Strong oceanic forcing on decadal surface temperature variability over global ocean. Geophysical Research Letters, 51(8), DOI:10.1029/2023GL107401. Abstract
Sea surface temperature (SST) variability on decadal timescales has been associated with global and regional climate variability and impacts. The mechanisms that drive decadal SST variability, however, remain highly uncertain. Many previous studies have examined the role of atmospheric variability in driving decadal SST variations. Here we assess the strength of oceanic forcing in driving decadal SST variability in observations and state-of-the-art climate models by analyzing the relationship between surface heat flux and SST. We find a largely similar pattern of decadal oceanic forcing across all ocean basins, characterized by oceanic forcing about twice the strength of the atmospheric forcing in the mid- and high latitude regions, but comparable or weaker than the atmospheric forcing in the subtropics. The decadal oceanic forcing is hypothesized to be associated with the wind-driven oceanic circulation, which is common across all ocean basins.
Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction.
Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.
The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8–9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas.
The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long-term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25-km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic-scale events, including atmospheric rivers (AR) and tropical cyclone (TC)-related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25-km GFDL-SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC-related events, especially those undergoing extratropical transitions. In the future, both AR-related and TC-related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5-8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC-related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC-related extreme precipitation over the NEUS.
The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi-decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse-resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.
To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time-evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station-based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.
A key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.
While the changes in ocean heat uptake in a warming climate have been well explored, the changes in response to climate mitigation efforts remain unclear. Using coupled climate model simulations, here we find that in response to a hypothesized reduction of greenhouse gases in the late 21st century, ocean heat uptake would significantly decline in all ocean basins except the North Atlantic, where a persistently weakened Atlantic meridional overturning circulation results in sustained heat uptake. These prolonged circulation anomalies further lead to interbasin heat exchanges, characterized by a sustained heat export from the Atlantic to the Southern Ocean and a portion of heat transfer from the Southern Ocean to the Indo-Pacific. Due to ocean heat uptake decline and interbasin heat export, the Southern Ocean experiences the strongest decline in ocean heat storage therefore emerging as the primary heat exchanger, while heat changes in the Indo-Pacific basin are relatively limited.
Antarctic sea ice exerts great influence on Earth’s climate by controlling the exchange of heat, momentum, freshwater, and gases between the atmosphere and ocean. Antarctic sea ice extent has undergone a multidecadal slight increase followed by a substantial decline since 2016. Here we utilize a 300-yr sea ice data assimilation reconstruction and two NOAA/GFDL and five CMIP6 model simulations to demonstrate a multidecadal variability of Antarctic sea ice extent. Stronger westerlies associated with the Southern Annular Mode (SAM) enhance the upwelling of warm and saline water from the subsurface ocean. The consequent salinity increase weakens the upper-ocean stratification, induces deep convection, and in turn brings more subsurface warm and saline water to the surface. This salinity-convection feedback triggered by the SAM provides favorable conditions for multidecadal sea ice decrease. Processes acting in reverse are found to cause sea ice increase, although it evolves slower than sea ice decrease.
Wave interference between transient waves and climatological stationary waves is a useful framework for diagnosing the magnitude of stationary waves. Here, we find that the wave interference over the North Pacific Ocean is an important driver of North American wintertime cold and heavy precipitation extremes in the present climate, but that this relationship is projected to weaken under increasing greenhouse gas emissions. When daily circulation anomalies are in-phase with the climatological mean state, the anomalous transport of heat and moisture causes the enhanced occurrence of cold extremes across the continental U.S. and a significant decrease of heavy precipitation extremes in the western U.S. In a future emissions scenario, the climatological stationary wave over the eastern North Pacific weakens and shifts spatially, which alters and generally weakens the relationship between wave interference and North American climate extremes. Our results underscore that the prediction of changes in regional wave interference is pivotal for understanding the future regional climate variability.
Sospedra-Alfonso, Reinel, William J Merryfield, Matthew Toohey, Claudia Timmreck, Jean-Paul Vernier, Ingo Bethke, Yiguo Wang, Roberto Bilbao, Markus G Donat, Pablo Ortega, Jason N S Cole, Woo-Sung Lee, Thomas L Delworth, David J Paynter, Fanrong Zeng, and Liping Zhang, et al., in press: Decadal prediction centers prepare for a major volcanic eruption. Bulletin of the American Meteorological Society. DOI:10.1175/BAMS-D-23-0111.1. October 2024. Abstract
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO.
A key challenge with the wind energy utilization is that winds, and thus wind power, are highly variable on seasonal to interannual timescales because of atmospheric variability. There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S. Great Plains during peak energy seasons (winter and spring), using seasonal prediction products from a climate model. The dominant source of that skillful prediction mainly comes from year-to-year variations of El Niño-Southern Oscillation in the tropical Pacific, which alters large-scale wind and storm track patterns over the United States. In the Southern Great Plains, the model can predict strong year-to-year wind energy changes with high skill multiple months in advance. Thus, this seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during peak energy production seasons.
The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.
Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.
Skillful prediction of wintertime cold extremes on seasonal time scales is beneficial for multiple sectors. This study demonstrates that North American cold extremes, measured by the frequency of cold days in winter, are predictable several months in advance in the Geophysical Fluid Dynamics Laboratory’s SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Three predictable components of cold extremes over the North American continent are found to be skillfully predicted on seasonal scales. One is a trend-like component, which shows a continent-wide decrease in the frequency of cold extremes and is primarily attributable to external radiative forcing. This trend-like component is predictable at least 9 months ahead. The second predictable component displays a dipole structure over North America, with negative signs in the northwest and positive signs in the southeast. This dipole component is predictable with significant correlation skill for 2 months and is a response to the central Pacific ENSO (El Niño-Southern Oscillation) as revealed from SPEAR AMIP-style simulations. The third component with the largest loadings over Canada and the northern US shows significant correlations with snow anomalies over mid-to-high latitudes of the North American continent. Predictions using only the three predictable components yield higher/comparable skill relative to the SPEAR raw forecasts.
The Kuroshio-Oyashio Extension (KOE) is the North Pacific oceanic frontal zone where air-sea heat and moisture exchanges allow strong communication between the ocean and atmosphere. Using satellite observations and reanalysis datasets, we show that the KOE surface heat flux variations are very closely linked to Kuroshio Extension (KE) sea surface height (SSH) variability on both seasonal and decadal time scales. We investigate seasonal oceanic and atmospheric anomalies associated with anomalous KE upper ocean temperature, as reflected in SSH anomalies (SSHa). We show that the ocean-induced seasonal changes in air-sea coupled processes, which are accompanied by KE upper-ocean temperature anomalies, lead to significant ocean-to-atmosphere heat transfer during November-December-January (i.e., NDJ). This anomalous NDJ KOE upward heat transfer has recently grown stronger in the observational record, which also appears to be associated with the enhanced KE decadal variability. Highlighting the role of KOE heat fluxes as a communicator between the upper-ocean and the overlying atmosphere, our findings suggest that NDJ KOE heat flux variations could be a useful North Pacific climate indicator.
The role of the Gulf of California (GoC) in the North American monsoon (NAM) is investigated using a global climate model with 50-km horizontal atmospheric resolution and prescribed SSTs. Specifically, two 135-yr simulations are compared to quantify the influence of the GoC on the NAM: in the first simulation a realistic representation of the GoC is incorporated, while in the second simulation the GoC is replaced with land surface. The results suggest that the GoC has a significant impact on circulation, with cooler surface air temperatures and lower surface friction allowing for south-southeasterly surface flow along the entire length of the GoC, in turn increasing low-level moisture fluxes into the NAM region. Cooler air over the GoC also leads to lower heights at 700–500 hPa, with a corresponding cyclonic moisture flux anomaly, further increasing moisture fluxes into the NAM region. Correspondingly, precipitation is substantially higher over the NAM region and even east of the Continental Divide in areas such as New Mexico and the U.S. Great Plains. July/August precipitation with a realistic GoC is generally 25%–50% greater in northwestern Mexico than the land-filled case, with precipitation 50% greater in much of the southwestern United States. Due to enhanced surface evaporation, areas with increased precipitation also tend to have lower surface temperatures, higher sea level pressure, and lower mid- to upper-tropospheric heights, thus altering the large-scale circulation. These results highlight the importance of the GoC in the NAM and demonstrate the necessity of resolving the GoC in climate simulations.
Extreme precipitation is among the most destructive natural disasters. Simulating changes in regional extreme precipitation remains challenging, partially limited by climate models’ horizontal resolution. Here, we use an ensemble of high-resolution global climate model simulations to study September–November extreme precipitation over the Northeastern United States, where extremes have increased rapidly since the mid-1990s. We show that a model with 25 km horizontal resolution simulates much more realistic extreme precipitation than comparable models with 50 or 100 km resolution, including frequency, amplitude, and temporal variability. The 25 km model simulated trends are quantitatively consistent with observed trends over recent decades. We use the same model for future projections. By the mid-21st century, the model projects unprecedented rainfall events over the region, driven by increasing anthropogenic radiative forcing and distinguishable from natural variability. Very extreme events (>150 mm/day) may be six times more likely by 2100 than in the early 21st century.
Liu, Zhengyu, Peng Gu, and Thomas L Delworth, January 2023: Strong red noise ocean forcing on Atlantic multidecadal variability assessed from surface heat flux: Theory and application. Journal of Climate, 36(1), DOI:10.1175/JCLI-D-22-0063.153-78. Abstract
The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ∼3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.
The frequency and intensity of heat extremes over the United States have increased since the mid-20th century and are projected to increase with additional anthropogenic greenhouse gas forcing. We define heat extremes as summertime (June–August) daily maximum 2m temperatures that exceed historical records. We examine characteristics of historical and near-future heat extremes using observations and past and future projections using 100 ensemble members from three coupled global climate models large ensemble simulations. We find that the large ensembles capture the trend and variability of heat extremes over the period 2006–2020 relative to the 1991–2005 climatology but overestimate the frequency at which the heat extremes occur. In future warming scenarios, heat extremes continue to increase over the next 30 years, with high amplitude records in the Northwest and Central US. After 2050, we find there is a spread in the frequency of heat extremes that is dependent on the emissions scenario, with a high emissions until mid-century followed by a high mitigation scenario showing a decrease in heat extremes by the end of the century. Although the frequency of future heat extremes is likely overestimated in the large ensembles, they are still a powerful tool for researching extreme temperatures in the climate system.
Using a state-of-the-art coupled general circulation model, physical processes underlying Antarctic sea ice multidecadal variability and predictability are investigated. Our model simulations constrained by atmospheric reanalysis and observed sea surface temperature broadly capture a multidecadal variability in the observed sea ice extent (SIE) with a low sea ice state (late 1970s–1990s) and a high sea ice state (2000s–early 2010s), although the model overestimates the SIE decrease in the Weddell Sea around the 1980s. The low sea ice state is largely due to the deepening of the mixed layer and the associated deep convection that brings subsurface warm water to the surface. During the high sea ice period (post-2000s), the deep convection substantially weakens, so surface wind variability plays a greater role in the SIE variability. Decadal retrospective forecasts started from the above model simulations demonstrate that the Antarctic sea ice multidecadal variability can be skillfully predicted 6–10 years in advance, showing a moderate correlation with the observation. Ensemble members with a deeper mixed layer and stronger deep convection tend to predict a larger sea ice decrease in the 1980s, whereas members with a larger surface wind variability tend to predict a larger sea ice increase after the 2000s. Therefore, skillful simulation and prediction of the Antarctic sea ice multidecadal variability require accurate simulation and prediction of the mixed layer, deep convection, and surface wind variability in the model.
The Model-Analogs technique is used in the present study to assess the decadal sea surface temperature (SST) prediction skill over the Southern Ocean (SO). The Model-Analogs here is based on reanalysis products and model control simulations that have ∼1° ocean/ice (refined to 0.5° at high latitudes) components and 100 km atmosphere/land components. It is found that the model analog hindcasts show comparable skills with the initialized retrospective decadal hindcasts south of 50°S, with even higher skills over the Weddell Sea at longer lead years. The high SST skills primarily arise from the successful capture of SO deep convection states. This deep ocean memory and the associated decadal predictability are also clearly seen when we assess the Model-Analogs technique in a perfect model context. Within 30°S–50°S latitudinal band, the model analog hindcasts show low skills. When we include the externally forced signals estimated from the large ensemble simulations, the model analog hindcasts and initialized decadal hindcasts show identical skills. The Model-Analogs method therefore provides a great baseline for developing future decadal forecast systems. It is unclear whether such analog techniques would also be successful with models that explicitly resolve ocean mesoscale eddies or other small-scale processes. This area of research needs to be explored further.
Long-term sea-level rise and multiyear to decadal sea level variations pose substantial risks for flooding and erosion in coastal communities. Here we use observations and climate model predictions to show that sea level variations along the U.S. East Coast are skillfully predictable 3 to 10 years in advance. The most predictable component of sea level is a basin scale upward trend, predictable a decade in advance and primarily a response to increasing greenhouse gases. Significant additional predictability comes from multidecadal variations of the Atlantic Meridional Overturning Circulation (AMOC). While perfect model simulations show AMOC-related sea level predictability of 5-7 years, model biases and initialization uncertainties reduce the realized predictive skill to 3-5 years, depending on location. Overall, greenhouse gas warming and predictable AMOC variations lead to multiyear to decadal prediction skill for sea level along the U.S. East Coast. Such skill could have significant societal benefit for planning and adaptation.
Barsugli, Joseph J., David R Easterling, Derek S Arndt, David A Coates, Thomas L Delworth, Martin P Hoerling, Nathaniel C Johnson, Sarah B Kapnick, Arun Kumar, Kenneth E Kunkel, Carl J Schreck, Russell S Vose, and Tao Zhang, March 2022: Development of a rapid response capability to evaluate causes of extreme temperature and drought events in the United States. Bulletin of the American Meteorological Society, 103(3), DOI:10.1175/BAMS-D-21-0237.1S14-S20.
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.
The Mediterranean is a projected hot spot for climate change, with significant warming and rainfall reductions. We use climate model ensembles to explore whether these Mediterranean rainfall declines could be reversed in response to greenhouse gas reductions. While the summer Mediterranean rainfall decline is reversed, winter rainfall continues to decline. The continued decline results from prolonged weakening of Atlantic Ocean poleward heat transport that combines with greenhouse gas reductions to cool the subpolar North Atlantic, inducing atmospheric circulation changes that favor continued Mediterranean drying. This is a potential “surprise” in the climate system, whereby changes in one component (Atlantic Ocean circulation) alter how another component (Mediterranean rainfall) responds to greenhouse gas reductions. Such surprises could complicate climate change mitigation efforts.
Hermanson, Leon, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco J Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav Kharin, William J Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicolí, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam A Scaife, Mark A Collier, Terence O'Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang A Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Thomas L Delworth, Xiaosong Yang, and Fanrong Zeng, et al., April 2022: WMO global annual to decadal climate update: A prediction for 2021–25. Bulletin of the American Meteorological Society, 103(4), DOI:10.1175/BAMS-D-20-0311.1E1117-E1129. Abstract
As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.
This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.
The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.
Understanding the behavior of western boundary current systems is crucial for predictions of biogeochemical cycles, fisheries, and basin-scale climate modes over the midlatitude oceans. Studies indicate that anthropogenic climate change induces structural changes in the Kuroshio Extension (KE) system, including a northward migration of its oceanic jet. However, changes in the KE temporal variability remain unclear. Using large ensembles of a global coupled climate model, we show that in response to increasing greenhouse gases, the time scale of KE sea surface height (SSH) shifts from interannual scales toward decadal and longer scales. We attribute this increased low-frequency KE variability to enhanced mid-latitude oceanic Rossby wave activity induced by regional and remote atmospheric forcing, due to a poleward shift of midlatitude surface westerly with climatology and an increase in the tropical precipitation activity, which lead to stronger atmospheric teleconnections from El Niño to the midlatitude Pacific and the KE region. Greenhouse warming leads to both a positive (elongated) KE state that restricts ocean perturbations (e.g., eddy activity) and stronger wind-driven KE fluctuations, which enhances the contributions of decadal KE modulations relative to short-time scale intrinsic oceanic KE variations. Our spectral analyses suggest that anthropogenic forcing may alter the future predictability of the KE system.
The impacts of the El Niño-Southern Oscillation (ENSO) are expected to change under increasing greenhouse gas concentrations, but the large internal variability of ENSO and its teleconnections makes it challenging to detect such changes in a single realization of nature. In this study, we explore both the internal variability and radiatively forced changes of boreal wintertime ENSO teleconnection patterns through the analysis of 30-member initial condition ensembles of the Seamless System for Prediction and EArth System Research (SPEAR), a coupled global climate model developed by the NOAA Geophysical Fluid Dynamics Laboratory. We focus on the projected changes of the large-scale circulation, temperature, and precipitation patterns associated with ENSO for 1951–2100 under moderate and high emissions scenarios (SSP2-4.5 and SSP5-8.5). We determine the time of emergence of these changes from the noise of internal climate variability, by determining the time when the amplitude of the ensemble mean change in the running 30-year ENSO composites first exceeds the 1951-1980 composite anomaly amplitude by at least one ensemble standard deviation. Overall, the high internal variability of ENSO teleconnection patterns primarily limits their expected emergence to tropical and subtropical regions before 2100, where some regions experience robust changes in ENSO-related temperature, precipitation, and 500 hPa geopotential height patterns by the middle of the twenty-first century. The earliest expected emergence generally occurs over tropical South America and Southeast Asia, indicating that an enhanced risk of ENSO-related extreme weather in that region could be detected within the next few decades. For signals that are expected to emerge after 2050, both internal climate variability and scenario uncertainty contribute similarly to a time of emergence uncertainty on the order of a few decades. We further explore the diversity of ENSO teleconnections within the SPEAR large ensemble during the historical period, and demonstrate that historical relationships between tropical sea surface temperatures and ENSO teleconnections are skillful predictors of projected changes in the Northern Hemisphere El Niño 500 hPa geopotential height pattern.
The frequency of large-scale anomalous precipitation events associated with heavy precipitation has been increasing in Japan. However, it is unclear if the increase is due to anthropogenic warming or internal variability. Also, it is challenging to develop an objective methodology to identify anomalous events because of the large variety of anomalous precipitation cases. In this study, we applied a deep learning technique to objectively detect anomalous precipitation events in Japan for both observations and simulations using high-resolution climate models. The results show that the observed increases in anomalous heavy precipitation events in Western Japan during 1977–2015 were not made only by internal variability but the increases in anthropogenic forcing played an important role. Such events will continue to increase in frequency this century. The increases are attributable to the increasing frequency of tropical cyclones and enhanced frontal rainbands near Japan. These results highlight the mitigation challenge posed by the increasing occurrence of unprecedented precipitation events in the future.
Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium-to-high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50-km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next-generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR-day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time-independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first-order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
Landfalling tropical cyclones (LTCs) are the most devastating disaster to affect the U.S., while the demonstration of skillful subseasonal (between 10 days and one season) prediction of LTCs is less promising. Understanding the mechanisms governing the subseasonal variation of TC activity is fundamental to improving its forecast, which is of critical interest to decision-makers and the insurance industry. This work reveals three localized atmospheric circulation modes with significant 10–30 days subseasonal variations: Piedmont Oscillation (PO), Great America Dipole (GAD), and the Subtropical High ridge (SHR) modes. These modes strongly modulate precipitation, TC genesis, intensity, track, and landfall near the U.S. coast. Compared to their strong negative phases, the U.S. East Coast has 19 times more LTCs during the strong positive phases of PO, and the Gulf Coast experiences 4–12 times more frequent LTCs during the positive phases of GAD and SHR. Results from the GFDL SPEAR model show a skillful prediction of 13, 9, and 22 days for these three modes, respectively. Our findings are expected to benefit the prediction of LTCs on weather timescale and also suggest opportunities exist for subseasonal predictions of LTCs and their associated heavy rainfalls.
The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in subseasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily gridcell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June-, July-, August-, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year, and IFD is the first date on which a grid cell is ice free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.
Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.
The recent multi-year 2015–2019 drought after a multi-decadal drying trend over Central America raises the question of whether anthropogenic climate change (ACC) played a role in exacerbating these events. While the occurrence of the 2015–2019 drought in Central America has been asserted to be associated with ACC, we lack an assessment of natural vs anthropogenic contributions. Here, we use five different large ensembles—including high-resolution ensembles (i.e., 0.5∘ horizontally)—to estimate the contribution of ACC to the probability of occurrence of the 2015–2019 event and the recent multi-decadal trend. The comparison of ensembles forced with natural and natural plus anthropogenic forcing suggests that the recent 40-year trend is likely associated with internal climate variability. However, the 2015–2019 rainfall deficit has been made more likely by ACC. The synthesis of the results from model ensembles supports the notion of a significant increase, by a factor of four, over the last century for the 2015–2019 meteorological drought to occur because of ACC. All the model results further suggest that, under intermediate and high emission scenarios, the likelihood of similar drought events will continue to increase substantially over the next decades.
Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (<5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (>3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal-to-decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction.
An extended period of high temperatures across the state of Alaska in June and July, 2019 set multiple temperature records. Here, we examine the extent to which human-driven climate change played a role in increasing the likelihood of experiencing such an extreme event. Using global climate models, we determine that human-driven climate change increased the probability of experiencing such high temperatures in 2019, and that the likelihood of similar or more extreme events will increase into the coming century.
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi-decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR-A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR-A1 significantly outperforms CM2.1 in predicting multi-decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.
Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.
Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
The decline of Arctic sea‐ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea‐ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically‐driven SIM export and negative feedbacks from sea‐ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice‐thickness observations collected after melt onset are particularly critical for summer Arctic sea‐ice predictions.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
Deser, Clara, Flavio Lehner, Keith B Rodgers, T R Ault, Thomas L Delworth, P DiNezio, and Arlene M Fiore, et al., April 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nature Climate Change, 10(4), DOI:10.1038/s41558-020-0731-2. Abstract
Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.
The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).
Moreno-Chamarro, Eduardo, J Marshall, and Thomas L Delworth, February 2020: Linking ITCZ migrations to AMOC and North Atlantic/Pacific SST decadal variability. Journal of Climate, 33(3), DOI:10.1175/JCLI-D-19-0258.1. Abstract
We examine the link between migrations in the Intertropical Convergence Zone (ITCZ) and changes in the Atlantic Meridional Overturning Circulation (AMOC), Atlantic Multidecadal Variability (AMV), and Pacific Decadal Oscillation (PDO). We use a coupled climate model which allows us to integrate over climate noise and assess underlying mechanisms. We use an ensemble of 10 300-year-long simulations forced by a 50-year oscillatory NAO-derived surface heat flux anomaly in the North Atlantic, and a 4000-year-long preindustrial control simulation performed with the GFDL’s CM2.1 climate model. In both setups, an AMV phase change induced by a change in the AMOC’s cross-equatorial heat transport forces an atmospheric interhemispheric energy imbalance which is compensated by a change in the cross-equatorial atmospheric heat transport due to a meridional ITCZ shift. Such linkages occur on decadal timescales in the ensemble driven by the imposed forcing, and internally on multicentennial timescales in the control. Regional precipitation anomalies differ between the ensemble and the control for a zonally averaged ITCZ shift of similar magnitude, which suggests a dependence on timescale. Our study supports observational evidence of an AMV–ITCZ link in the 20th century and further links it to the AMOC, whose long-timescale variability can influence the phasing of ITCZ migrations. In contrast to the AMV, our calculations suggest that the PDO does not drive ITCZ migrations, because the PDO does not modulate the interhemispheric energy balance.
Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.
Three consecutive dry winters (2015–2017) in southwestern South Africa (SSA) resulted in the Cape Town “Day Zero” drought in early 2018. The contribution of anthropogenic global warming to this prolonged rainfall deficit has previously been evaluated through observations and climate models. However, model adequacy and insufficient horizontal resolution make it difficult to precisely quantify the changing likelihood of extreme droughts, given the small regional scale. Here, we use a high-resolution large ensemble to estimate the contribution of anthropogenic climate change to the probability of occurrence of multiyear SSA rainfall deficits in past and future decades. We find that anthropogenic climate change increased the likelihood of the 2015–2017 rainfall deficit by a factor of five to six. The probability of such an event will increase from 0.7 to 25% by the year 2100 under an intermediate-emission scenario (Shared Socioeconomic Pathway 2-4.5 [SSP2-4.5]) and to 80% under a high-emission scenario (SSP5-8.5). These results highlight the strong sensitivity of the drought risk in SSA to future anthropogenic emissions.
Smith, D M., Adam A Scaife, Rosie Eade, Panos Athanasiadis, A Bellucci, Ingo Bethke, Roberto Bilbao, L F Borchert, Louis-Philippe Caron, François Counillon, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, V Estella-Perez, S Flavoni, Leon Hermanson, N Keenlyside, Viatcheslav Kharin, M Kimoto, William J Merryfield, J Mignot, T Mochizuki, K Modali, P-A Moneri, Wolfgang A Müller, Dario Nicolí, Pablo Ortega, Klaus Pankatz, Holger Pohlmann, J Robson, P Ruggieri, Reinel Sospedra-Alfonso, Didier Swingedouw, Yan Wang, S Wild, Stephen G Yeager, Xiaosong Yang, and Liping Zhang, July 2020: North Atlantic climate far more predictable than models imply. Nature, 583, DOI:10.1038/s41586-020-2525-0796-800. Abstract
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain. This leads to low confidence in regional projections, especially for precipitation, over the coming decades. The chaotic nature of the climate system may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.
Castruccio, Frederic, Yohan Ruprich-Robert, Stephen G Yeager, Gokhan Danabasoglu, Rym Msadek, and Thomas L Delworth, March 2019: Modulation of Arctic Sea Ice Loss by Atmospheric Teleconnections from Atlantic Multi-Decadal Variability. Journal of Climate, 32(5), DOI:10.1175/JCLI-D-18-0307.1. Abstract
Observed September Arctic sea ice has declined sharply over the satellite era. While most climate models forced by observed external forcing simulate a decline, few show trends matching the observations, suggesting either model deficiencies or significant contributions from internal variability. Using a set of perturbed climate model experiments, we provide evidence that atmospheric teleconnections associated with the Atlantic Multi-Decadal Variability (AMV) can drive low-frequency Arctic sea ice fluctuations. Even without AMV–related changes in ocean heat transport, AMV–like surface temperature anomalies lead to adjustments in atmospheric circulation patterns that produce similar Arctic sea ice changes in three different climate models. Positive AMV anomalies induce a decrease in the frequency of winter polar anticyclones, which is reflected both in the sea level pressure as a weakening of the Beaufort Sea High and in the surface temperature as warm anomalies in response to increased low-cloud cover. Positive AMV anomalies are also shown to favor an increased prevalence of an Arctic Dipole–like sea level pressure pattern in late winter / early spring. The resulting anomalous winds drive anomalous ice motions (dynamic effect). Combined with the reduced winter sea ice formation (thermodynamic effect), the Arctic sea ice becomes thinner, younger, and more prone to melt in summer. Following a phase shift to positive AMV, the resulting atmospheric teleconnections can lead to a decadal ice thinning trend in the Arctic Ocean of the order of 8-16% of the reconstructed long-term trend, and decadal trend (decline) in September Arctic sea ice area of up to 21% of the observed long-term trend.
The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including 8 typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, NICAM and MRI‐AGCM) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific, and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and MRI‐AGCM). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.
Smith, D M., Rosie Eade, Adam A Scaife, Louis-Philippe Caron, Gokhan Danabasoglu, T DelSole, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, Leon Hermanson, Viatcheslav Kharin, M Kimoto, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Stephen G Yeager, and Xiaosong Yang, May 2019: Robust skill of decadal climate predictions. npj Climate and Atmospheric Science, 2, 13, DOI:10.1038/s41612-019-0071-y. Abstract
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.
Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
Observed Southern Ocean surface cooling and sea-ice expansion over the past several decades are inconsistent with many historical simulations from climate models. Here we show that natural multidecadal variability involving Southern Ocean convection may have contributed strongly to the observed temperature and sea-ice trends. These observed trends are consistent with a particular phase of natural variability of the Southern Ocean as derived from climate model simulations. Ensembles of simulations are conducted starting from differing phases of this variability. The observed spatial pattern of trends is reproduced in simulations that start from an active phase of Southern Ocean convection. Simulations starting from a neutral phase do not reproduce the observed changes, similarly to the multimodel mean results of CMIP5 models. The long timescales associated with this natural variability show potential for skilful decadal prediction.
Mountain snowpack in the western United States provides a natural reservoir for cold season precipitation; variations in snowpack influence warm season water supply, wildfire risk, ecology, and industries like agriculture dependent on snow and downstream water availability. Efforts to understand snowpack variability have predominantly been focused on either weekly (weather) or decadal to centennial (climate variability and change) timescales. We focus on a timescale between these ranges by demonstrating that a global climate model suite can provide snowpack predictions 8 months in advance. The predictions from climate models outperform statistical methods from observations alone. Our results show that seasonal hydroclimate predictions are possible and highlight areas for future prediction system improvements.
We explore factors potentially linked to the enhanced major hurricane activity in the Atlantic during 2017. Using a suite of high-resolution model experiments, we show that the increase in 2017 major hurricanes was not primarily caused by La Niña conditions in the Pacific Ocean, but mainly by pronounced warm sea surface conditions in the tropical North Atlantic. It is further shown that, in the future, a similar pattern of North Atlantic surface warming, superimposed upon long-term increasing sea surface temperature from increases in greenhouse gas concentrations and decreases in aerosols, will likely lead to even higher numbers of major hurricanes. The key factor controlling Atlantic major hurricane activity appears to be how much the tropical Atlantic warms relative to the rest of the global ocean.
Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO2-induced warming remains little known, not least because the most widely available climate models do not currently resolve the relevant mesoscale dynamics due to their coarse resolution (100 km or more). In this study, a 50-km resolution global coupled model (FLOR) is used to address this question. It is found that the mean number of GoC surge events remains unchanged under CO2 doubling, but intermediate-to-high intensity surge-related precipitation tends to become less frequent, thus reducing the mean summertime rainfall. Lowlevel moisture fluxes associated with GoC surges as well as their convergence over land to the east of the GoC intensify, but the increases in low-level moisture are not matched by the larger increments in the near-surface saturation specific humidity due to amplified land warming. This results in a more unsaturated, low-level atmospheric environment which disfavors moist convection. These thermodynamic changes are accompanied by dynamics changes that are also less conducive to convective activity, with the mid-level monsoonal ridge projected to expand and move to the west of its present-day climatological maximum. Despite the overall reduction in precipitation, the frequency of very intense, localized daily surge-related precipitation in Arizona and surrounding areas is projected to increase, consistently with increased precipitable water.
Ruprich-Robert, Yohan, Thomas L Delworth, and Rym Msadek, et al., May 2018: Impacts of the Atlantic Multidecadal Variability on North American Summer Climate and Heat Waves. Journal of Climate, 31(9), DOI:10.1175/JCLI-D-17-0270.1. Abstract
The impacts of the Atlantic Multidecadal Variability (AMV) on summertime North American climate are investigated using three Coupled Global Climate Models (CGCMs) in which North Atlantic sea surface temperatures (SSTs) are restored to observed AMV anomalies. Large ensemble simulations are performed to estimate how AMV can modulate the occurrence of extreme weather like heat waves. We show that, in response to an AMV warming, all models simulate a precipitation deficit and a warming over northern Mexico and southern US that lead to an increased number of heat wave days by about 30% compared to an AMV cooling. The physical mechanisms associated with these impacts are discussed. The positive tropical Atlantic SST anomalies associated with the warm AMV drive a Matsuno-Gill-like atmospheric response that favors subsidence over northern Mexico and southern US. This leads to a warming of the whole tropospheric column, and to a decrease in relative humidity, cloud cover, and precipitation. Soil moisture response to AMV also plays a role in the modulation of heat wave occurrence. An AMV warming favors dry soil conditions over northern Mexico and southern US by driving year-round precipitation deficit through atmospheric teleconnections coming both directly from the North Atlantic SST forcing and indirectly from the Pacific. The indirect AMV teleconnections highlight the importance of using CGCMs to fully assess the AMV impacts on North America. Given the potential predictability of the AMV, the teleconnections discussed here suggest a source of predictability for the North American climate variability and in particular for the occurrence of heat waves at multi-year timescales.
Smith, D M., Adam A Scaife, E Hawkins, Roberto Bilbao, G J Boer, M Caian, Louis-Philippe Caron, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, R Doescher, Nick Dunstone, Rosie Eade, Leon Hermanson, Masao Ishii, Viatcheslav Kharin, M Kimoto, Torben Koenigk, Y Kushnir, D Matei, Gerald A Meehl, Martin Ménégoz, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Scott B Power, M Rixen, Reinel Sospedra-Alfonso, M Tuma, K Wyser, Xiaosong Yang, and Stephen G Yeager, November 2018: Predicted chance that global warming will temporarily exceed 1.5°C. Geophysical Research Letters, 45(21), DOI:10.1029/2018GL079362. Abstract
The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5oC above pre‐industrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5oC. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5oC above pre‐industrial levels in the coming five‐years. For the period 2017 to 2021 we predict a 38% and 10% chance respectively of monthly or yearly temperatures exceeding 1.5oC, with virtually no chance of the five‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.
The Geophysical Fluid Dynamics Laboratory (GFDL) has recently developed two global coupled GCMs, FLOR and HiFLOR, which are now being utilized for climate research and seasonal predictions. Compared to their predecessor CM2.1, the new versions have improved ocean/atmosphere physics and numerics, and refinement of the atmospheric horizontal grid from 220 km (CM2.1) to 55 km (FLOR) and 26 km (HiFLOR). Both FLOR and HiFLOR demonstrate greatly improved simulations of the tropical Pacific annual‐mean climatology, with FLOR practically eliminating any equatorial cold bias in sea surface temperature. An additional model experiment (LOAR1) using FLOR's ocean/atmosphere physics, but with the atmospheric grid coarsened toward that of CM2.1, is used to further isolate the impacts of the refined atmospheric grid versus the improved physics and numerics. The improved ocean/atmosphere formulations are found to produce more realistic tropical Pacific patterns of sea surface temperature and rainfall, surface heat fluxes, ocean mixed layer depths, surface currents, and tropical instability wave (TIW) activity; enhance the near‐surface equatorial upwelling; and reduce the inter‐centennial warm drift of the tropical Pacific upper ocean. The atmospheric grid refinement further improves these features, and also improves the tropical Pacific surface wind stress, implied Ekman and Sverdrup transports, subsurface temperature and salinity structure, and heat advection in the equatorial upper ocean. The results highlight the importance of nonlocal air‐sea interactions in the tropical Pacific climate system, including the influence of off‐equatorial surface fluxes on the equatorial annual‐mean state. Implications are discussed for improving future simulations, observations, and predictions of tropical Pacific climate.
A “typical” El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.
Over the 1997-2014 period, the mean frequency of western North Pacific (WNP) tropical cyclones (TCs) was markedly lower (~18%) than the period 1980-1996. Here we show that these changes were driven by an intensification of the vertical wind shear in the southeastern/eastern WNP tied to the changes in the Walker circulation, which arose primarily in response to the enhanced sea surface temperature (SST) warming in the North Atlantic, while the SST anomalies associated with the negative phase of the Pacific Decadal Oscillation (PDO) in the tropical Pacific and the anthropogenic forcing play only secondary roles. These results are based on observations and experiments using the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low-ocean Resolution Coupled Climate Model (FLOR) coupled climate model. The present study suggests a crucial role of the North Atlantic SST in causing decadal changes to WNP TC frequency.
Regional hydroclimate changes on decadal time scales contain substantial natural variability. This presents a challenge for the detection of anthropogenically forced hydroclimate changes on these spatiotemporal scales, because the “signal” of anthropogenic changes is modest compared to the “noise” of natural variability. However, previous studies have shown that this “signal to noise” ratio can be greatly improved in a large model ensemble where each member contains the same “signal” but different “noise”. Here using multiple state-of-the-art large ensembles from two climate models, we quantitatively assess the detectability of anthropogenically caused decadal shifts in precipitation-minus-evaporation (PmE) mean state against natural variability, focusing on North America during 2000-2050.
Anthropogenic forcing is projected to cause detectable (“signal” larger than “noise”) shifts in PmE mean state relative to the 1950-1999 climatology over 50-70% of North America by 2050. The earliest detectable signals include, during November-April, a moistening over northeastern North America and a drying over southwestern North America and, during May- October, a drying over central North America. Different processes are responsible for these signals. Changes in submonthly transient eddy moisture fluxes account for the northeastern moistening and central drying while monthly atmospheric circulation changes explain the southwestern drying. Our model findings suggest that, despite the dominant role of natural internal variability on decadal time scales, anthropogenic shifts in PmE mean state can be detected over most of North America before the middle of the current century.
Zhang, Honghai, and Thomas L Delworth, March 2018: Robustness of anthropogenically forced decadal precipitation changes projected for the 21st century. Nature Communications, 9, 1150, DOI:10.1038/s41467-018-03611-3. Abstract
Precipitation is characterized by substantial natural variability, including on regional and decadal scales. This relatively large variability poses a grand challenge in assessing the significance of anthropogenically forced precipitation changes. Here we use multiple large ensembles of climate change experiments to evaluate whether, on regional scales, anthropogenic changes in decadal precipitation mean state are distinguishable. Here, distinguishable means the anthropogenic change is outside the range expected from natural variability. Relative to the 1950–1999 period, simulated anthropogenic shifts in precipitation mean state for the 2000–2009 period are already distinguishable over 36–41% of the globe—primarily in high latitudes, eastern subtropical oceans, and the tropics. Anthropogenic forcing in future medium-to-high emission scenarios is projected to cause distinguishable shifts over 68–75% of the globe by 2050 and 86–88% by 2100. Our findings imply anthropogenic shifts in decadal-mean precipitation will exceed the bounds of natural variability over most of the planet within several decades.
The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean, or a slab ocean with no resolved ocean heat transport. On time scales less than ten years NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO.
On time scales longer than ten years a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic Multidecadal Oscillation (AMO), although with smaller than observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal scale response in the models. The simulated Atlantic Meridional Overturning Circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad scale warming (cooling).
We use additional simulations in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. We show that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the subpolar gyre.
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extra-tropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extra-tropical near surface land temperature. We show that most of the lead-0 month spring predictive skill of land temperature over extra-tropics, particularly over northern Eurasia, stems from stratospheric initialization. We further reveal that this predictive skill of extra-tropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
The 2015 hurricane season in the Eastern and Central Pacific Oceans (EPO and CPO), particularly around Hawaii, was extremely active – including a record number of tropical cyclones (TCs) and the first instance of three simultaneous Category 4 hurricanes in the EPO and CPO. A strong El Niño developed during the 2015 boreal summer season, and was attributed by some to be the cause of the extreme number of TCs. However, according to a suite of targeted high-resolution model experiments, the extreme 2015 EPO and CPO hurricane season was not primarily induced by the 2015 El Niño’s tropical Pacific warming, but by warming in the subtropical Pacific Ocean. This warming is not typical of El Niño, but rather the “Pacific Meridional Mode (PMM)” superimposed on long-term anthropogenic warming. Although the likelihood of such an extreme year depends on the phase of natural variability, the coupled GCM projects an increase in the frequency of such extremely active TC years over the next few decades for the EPO, CPO, and Hawaii due enhanced subtropical Pacific warming from anthropogenic greenhouse forcing.
Future changes in the North American monsoon, a circulation system that brings abundant summer rains to vast areas of the North American Southwest1, 2, could have significant consequences for regional water resources3. How this monsoon will change with increasing greenhouse gases, however, remains unclear4, 5, 6, not least because coarse horizontal resolution and systematic sea-surface temperature biases limit the reliability of its numerical model simulations5, 7. Here we investigate the monsoon response to increased atmospheric carbon dioxide (CO2) concentrations using a 50-km-resolution global climate model which features a realistic representation of the monsoon climatology and its synoptic-scale variability8. It is found that the monsoon response to CO2 doubling is sensitive to sea-surface temperature biases. When minimizing these biases, the model projects a robust reduction in monsoonal precipitation over the southwestern United States, contrasting with previous multi-model assessments4, 9. Most of this precipitation decline can be attributed to increased atmospheric stability, and hence weakened convection, caused by uniform sea-surface warming. These results suggest improved adaptation measures, particularly water resource planning, will be required to cope with projected reductions in monsoon rainfall in the American Southwest.
Ruprich-Robert, Yohan, Rym Msadek, Frederic Castruccio, Stephen G Yeager, Thomas L Delworth, and Gokhan Danabasoglu, April 2017: Assessing the Climate impacts of the observed Atlantic Mulitdecadal Variability using the GFDL CM2.1 and NCAR CESM1 Global Coupled Models. Journal of Climate, 30(8), DOI:10.1175/JCLI-D-16-0127.1. Abstract
The climate impacts of the observed Atlantic Multidecadal Variability (AMV) are investigated using the GFDL-CM2.1 and the NCAR-CESM1 coupled climate models. The model North Atlantic sea surface temperatures are restored to fixed anomalies corresponding to an estimate of the internally driven component of the observed AMV. Both models show that during boreal summer the AMV alters the Walker Circulation and generates precipitation anomalies over the whole tropical belt. A warm phase of the AMV yields reduced precipitation over western US, drier conditions over the Mediterranean basin, and wetter conditions over Northern Europe. During boreal winter, the AMV modulates by a factor of ~2 the frequency of occurrence of El Niño/La Niña events. This response is associated with anomalies over the Pacific that project onto the Interdecadal Pacific Oscillation pattern, i.e., Pacific Decadal Oscillation-like anomalies in the Northern hemisphere and a symmetrical pattern in the Southern Hemisphere. This winter response is a lagged adjustment of the Pacific Ocean to the AMV forcing in summer. Most of the simulated global-scale impacts are driven by the tropical part of the AMV, except for the winter North Atlantic Oscillation-like response over the North Atlantic/European region, which is driven by both the subpolar and the tropical parts of the AMV. The teleconnections between the Pacific and Atlantic basins alter the direct North Atlantic local response to the AMV, which highlights the importance of using a global coupled framework to investigate the climate impacts of the AMV. The similarity of the two model responses gives confidence that impacts described in this paper are robust.
Tommasi, Desiree, Charles A Stock, A J Hobday, R Methot, Isaac C Kaplan, J P Eveson, Kirstin Holsman, Timothy J Miller, Sarah K Gaichas, Marion Gehlen, A Pershing, Gabriel A Vecchi, Rym Msadek, Thomas L Delworth, C M Eakin, Melissa A Haltuch, Roland Séférian, C M Spillman, J R Hartog, Samantha A Siedlecki, Jameal F Samhouri, Barbara A Muhling, R G Asch, Malin L Pinsky, Vincent S Saba, Sarah B Kapnick, and Carlos F Gaitán, et al., March 2017: Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Progress in Oceanography, 152, DOI:10.1016/j.pocean.2016.12.011. Abstract
Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.
Temperature variability in the North Atlantic Ocean is the result of many competing physical processes, but the relative roles of these processes is a source of contention. Here, scientists present two perspectives on the debate.
The impact of multidecadal variations of the Atlantic meridional overturning circulation (AMOC) on the Southern Ocean (SO) is investigated in the current paper using a coupled ocean–atmosphere model. We find that the AMOC can influence the SO via fast atmosphere teleconnections and subsequent ocean adjustments. A stronger than normal AMOC induces an anomalous warm SST over the North Atlantic, which leads to a warming of the Northern Hemisphere troposphere extending into the tropics. This induces an increased equator-to-pole temperature gradient in the Southern Hemisphere (SH) upper troposphere and lower stratosphere due to an amplified tropical upper tropospheric warming as a result of increased latent heat release. This altered gradients leads to a poleward displacement of the SH westerly jet. The wind change over the SO then cools the SST at high latitudes by anomalous northward Ekman transports. The wind change also weakens the Antarctic bottom water (AABW) cell through changes in surface heat flux forcing. The poleward shifted westerly wind decreases the long term mean easterly winds over the Weddell Sea, thereby reducing the turbulent heat flux loss, decreasing surface density and therefore leading to a weakening of the AABW cell. The weakened AABW cell produces a temperature dipole in the SO, with a warm anomaly in the subsurface and a cold anomaly in the surface that corresponds to an increase of Antarctic sea ice. Opposite conditions occur for a weaker than normal AMOC. Our study here suggests that efforts to attribute the recent observed SO variability to various factors should take into consideration not only local process but also remote forcing from the North Atlantic.
This study explores the potential predictability of the Southern Ocean (SO) climate on decadal timescales as represented in the GFDL CM2.1 model using prognostic methods. We conduct perfect model predictability experiments starting from ten different initial states, and show potentially predictable variations of Antarctic bottom water formation (AABW) rates on time scales as long as twenty years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have comparable potential predictability as the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air-sea interaction. As a complement to our perfect predictability study, we also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multi-year time scales is found in the hindcast system. The success of the hindcasts, especially in reproducing observed surface cooling trends, is largely due to initializing the state of the AABW cell. A weak state of the AABW cell leads to cooler surface conditions and more extensive sea ice. Although there are considerable uncertainties regarding the observational data used to initialize the hindcasts, the consistency between the perfect model experiments and the decadal hindcasts at least gives us some indication as to where and to what extent skillful decadal SO forecasts might be possible.
The average predictability time (APT) method is used to identify the most predictable components of decadal sea surface temperature (SST) variations over the Southern Ocean (SO) in a 4000 year unforced control run of the GFDL CM2.1 model. The most predictable component shows significant predictive skill for periods as long as 20 years. The physical pattern of this variability has a uniform sign of SST anomalies over the SO, with maximum values over the Amundsen-Bellingshausen-Weddell Seas. Spectral analysis of the associated APT time series shows a broad peak on time scales of 70-120 years. This most predictable pattern is closely related to the mature phase of a mode of internal variability in the SO that is associated with fluctuations of deep ocean convection. The second most predictable component of SO SST is characterized by a dipole structure, with SST anomalies of one sign over the Weddell Sea and SST anomalies of the opposite sign over the Amundsen-Bellingshausen Seas. This component has significant predictive skill for periods as long as 6 years. This dipole mode is associated with a transition between phases of the dominant pattern of SO internal variability. The long time scales associated with variations in SO deep convection provide the source of the predictive skill of SO SST on decadal scales. These analyses suggest that if we could adequately initialize the SO deep convection in a numerical forecast model, the future evolution of SO SST and its associated climate impacts is potentially predictable.
The impact of the North Atlantic Oscillation (NAO) on the Atlantic Meridional Overturning Circulation (AMOC) and large-scale climate is assessed using simulations with three different climate models. Perturbation experiments are conducted in which a pattern of anomalous heat flux corresponding to the NAO is added to the model ocean. Differences between the perturbation experiments and a control illustrate how the model ocean and climate system respond to the NAO. A positive phase of the NAO strengthens the AMOC by extracting heat from the subpolar gyre, thereby increasing deepwater formation, horizontal density gradients, and the AMOC. The flux forcings have the spatial structure of the observed NAO, but the amplitude of the forcing varies in time with distinct periods varying from 2 to 100 years. The response of the AMOC to NAO variations is small at short time scales, but increases up to the dominant time scale of internal AMOC variability (20-30 years for the models used). The amplitude of the AMOC response, and associated oceanic heat transport, is approximately constant as the timescale of the forcing is increased further. In contrast, the response of other properties, such as hemispheric temperature or Arctic sea ice, continues to increase as the time scale of the forcing becomes progressively longer. The larger response is associated with the time integral of the anomalous oceanic heat transport at longer time scales, combined with an increased impact of radiative feedback processes. We show that NAO fluctuations, similar in amplitude to those observed over the last century, can modulate hemispheric temperature by several tenths of a degree.
Pronounced climate changes have occurred since the 1970s, including rapid loss of Arctic sea ice1, large-scale warming2 and increased tropical storm activity3 in the Atlantic. Anthropogenic radiative forcing is likely to have played a major role in these changes4, but the relative influence of anthropogenic forcing and natural variability is not well established. The above changes have also occurred during a period in which the North Atlantic Oscillation has shown marked multidecadal variations5. Here we investigate the role of the North Atlantic Oscillation in these rapid changes through its influence on the Atlantic meridional overturning circulation and ocean heat transport. We use climate models to show that observed multidecadal variations of the North Atlantic Oscillation can induce multidecadal variations in the Atlantic meridional overturning circulation and poleward ocean heat transport in the Atlantic, extending to the Arctic. Our results suggest that these variations have contributed to the rapid loss of Arctic sea ice, Northern Hemisphere warming, and changing Atlantic tropical storm activity, especially in the late 1990s and early 2000s. These multidecadal variations are superimposed on long-term anthropogenic forcing trends that are the dominant factor in long-term Arctic sea ice loss and hemispheric warming.
This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above normal (1983-2012) June-August 2-m temperature anomalies, and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) U.S.. Due to changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over U.S. exceeding the observed 2012 anomaly increases with time over the early 21st century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central U.S. are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs and radiative forcing are all important drivers of, and sources of predictability for U.S. summer heat waves.
Skillful seasonal forecasting of tropical cyclone (TC; wind speed ≥17.5 m s−1) activity is challenging, even more so when the focus is on major hurricanes (wind speed ≥49.4 m s−1), the most intense hurricanes (Category 4–5; wind speed ≥58.1 m s−1), and landfalling TCs. Here we show that a 25-km resolution global coupled model (HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL) has improved skill in predicting the frequencies of major hurricanes and Category 4–5 hurricanes in the North Atlantic, and landfalling TCs over the United States and Caribbean Islands a few months in advance, relative to its 50-km resolution predecessor climate model (FLOR). HiFLOR also shows significant skill in predicting Category 4–5 hurricanes in the western North Pacific and eastern North Pacific, while both models show comparable skills in predicting basin-total and landfalling TC frequency in the basins. The improved skillful forecasts of basin-total TCs, major hurricanes, and Category 4–5 hurricane activity in the North Atlantic by HiFLOR are obtained mainly by improved representation of the TCs and their response to climate from the increased horizontal resolution, rather than improvements in large-scale parameters.
The impact of atmosphere and ocean horizontal resolution on the climatology of North American Monsoon Gulf of California (GoC) moisture surges is examined in a suite of global circulation models (CM2.1, FLOR, CM2.5, CM2.6, HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These models feature essentially the same physical parameterizations, but differ in horizontal resolution in either the atmosphere (≃200, 50 and 25 km) or the ocean (≃1°, 0.25°, 0.1°). Increasing horizontal atmospheric resolution from 200 km to 50 km results in a drastic improvement in the model’s capability of accurately simulating surge events. The climatological near-surface flow and moisture and precipitation anomalies associated with GoC surges are overall satisfactorily simulated in all higher-resolution models. The number of surge events agrees well with reanalyses but models tend to underestimate July-August surge-related precipitation and overestimate September surge-related rainfall in the southwestern United States. Large-scale controls supporting the development of GoC surges, such as tropical easterly waves (TEWs), tropical cyclones (TCs) and trans-Pacific Rossby wave trains (RWTs), are also well captured, although models tend to underestimate the TEW/TC magnitude and number. Near-surface GoC surge features and their large-scale forcings (TEWs, TCs, RWTs) do not appear to be substantially affected by a finer representation of the GoC at higher ocean resolution. However, the substantial reduction of the eastern Pacific warm sea surface temperature bias through flux adjustment in the FLOR model leads to an overall improvement of tropical-extratropical controls on GoC moisture surges and the seasonal cycle of precipitation in the southwestern United States.
The Intergovernmental Panel on Climate Change (IPCC) fifth assessment of projected global and regional ocean temperature change is based on global climate models that have coarse (∼100-km) ocean and atmosphere resolutions. In the Northwest Atlantic, the ensemble of global climate models has a warm bias in sea surface temperature due to a misrepresentation of the Gulf Stream position; thus, existing climate change projections are based on unrealistic regional ocean circulation. Here we compare simulations and an atmospheric CO2 doubling response from four global climate models of varying ocean and atmosphere resolution. We find that the highest resolution climate model (∼10-km ocean, ∼50-km atmosphere) resolves Northwest Atlantic circulation and water mass distribution most accurately. The CO2 doubling response from this model shows that upper-ocean (0-300 m) temperature in the Northwest Atlantic Shelf warms at a rate nearly twice as fast as the coarser models and nearly three times faster than the global average. This enhanced warming is accompanied by an increase in salinity due to a change in water mass distribution that is related to a retreat of the Labrador Current and a northerly shift of the Gulf Stream. Both observations and the climate model demonstrate a robust relationship between a weakening Atlantic Meridional Overturning Circulation (AMOC) and an increase in the proportion of Warm-Temperate Slope Water entering the Northwest Atlantic Shelf. Therefore, prior climate change projections for the Northwest Atlantic may be far too conservative. These results point to the need to improve simulations of basin and regional-scale ocean circulation.
Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2°×2° grid cells (typical resolution in the CMIP5 archive) to 0.25°×.25° (tropical cyclone-permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities and seasonal timing. In response to 2×CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3-4% K−1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the CONUS southeast, this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.
This study aims to assess whether, and the extent to which, an increase in atmospheric resolution in versions of the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) with 50 km and HiFLOR with 25 km improves the simulation of the El Niño Southern Oscillation-tropical cyclone (ENSO-TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO-TC connections in the WNP including TC track density, genesis and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971-2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually-varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO-TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño/La Niña are responsible for the improved simulation of ENSO-TC connections in the WNP. The improved simulation of ENSO-TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño/La Niña. A better representation of ENSO-TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track and landfall, improve our understanding of the interannual variation of TC activity, and provide better projection of TC activity under climate change.
The impact of climate change on the Pacific Decadal Oscillation (PDO) is studied using a fully coupled climate model. The model results show that the PDO has a similar spatial pattern in altered climates, but its amplitude and time scale of variability change in response to global warming or cooling. In response to global warming the PDO amplitude is significantly reduced, with a maximum decrease over the Kuroshio-Oyashio-Extension (KOE) region. This reduction appears to be associated with a weakened meridional temperature gradient in the KOE region. In addition, reduced variability of North Pacific wind stress, partially due to reduced air-sea feedback, also helps to weaken the PDO amplitude by reducing the meridional displacements of the subtropical and subpolar gyre boundaries. In contrast, the PDO amplitude increases in response to global cooling.
In our control simulations the model PDO has an approximately bi-decadal peak. In a warmer climate the PDO timescale becomes shorter, changing from approximately 20 years to approximately 12 years. In a colder climate the timescale of the PDO increases to approximately 34 years. Physically, global warming (cooling) enhances (weakens) ocean stratification. The increased (decreased) ocean stratification acts to increase (reduce) the phase speed of internal Rossby waves, thereby altering the timescale of the simulated PDO.
Clement et al. (Reports, 16 October 2015, p. 320) claim that the Atlantic Multidecadal Oscillation (AMO) is a thermodynamic response of the ocean mixed layer to stochastic atmospheric forcing and that ocean circulation changes have no role in causing the AMO. These claims are not justified. We show that ocean dynamics play a central role in the AMO.
Zhang, Liping, and Thomas L Delworth, August 2016: Impact of the Antarctic bottom water formation on the Weddell Gyre and its northward propagation characteristics in GFDL model. Journal of Geophysical Research: Oceans, 121(8), DOI:10.1002/2016JC011790. Abstract
The impact of Antarctic bottom water (AABW) formation on the Weddell Gyre and its northward propagation characteristics are studied using a 4000-yr long control run of the GFDL CM2.1 model as well as sensitivity experiments. In the control run, the AABW cell and Weddell Gyre are highly correlated when the AABW cell leads the Weddell Gyre by several years, with an enhanced AABW cell corresponding to a strengthened Weddell Gyre and vice versa. An additional sensitivity experiment shows that the response of the Weddell Gyre to AABW cell changes is primarily attributed to interactions between the AABW outflow and ocean topography, instead of the surface wind stress curl and freshwater anomalies. As the AABW flows northward, it encounters topography with steep slopes that induce strong downwelling and negative bottom vortex stretching. The anomalous negative bottom vortex stretching induces a cyclonic barotropic streamfunction over the Weddell Sea, thus leading to an enhanced Weddell Gyre. The AABW cell variations in the control run have significant meridional coherence in density space. Using passive dye tracers, it is found that the slow propagation of AABW cell anomalies south of 35oS corresponds to the slow tracer advection time scale. The dye tracers escape the Weddell Sea through the western limb of the Weddell Gyre and then go northwestward to the Argentine Basin through South Sandwich Trench and Georgia Basin. This slow advection by deep ocean currents determines the AABW cell propagation speed south of 35oS. North of 35oS the propagation speed is determined both by advection in the deep western boundary current and through Kelvin waves.
This study aims to assess the connections between the El Niño Southern Oscillation (ENSO) and tropical cyclones near Guam (GuamTC) using the state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR). In observations, more (less) GuamTCs occur in El Niño (La Niña) years and the ENSO-GuamTC connections arise from TC genesis locations in ENSO phases. The observed ENSO-GuamTC connections are realistically simulated in the two control experiments that use two versions of FLOR, the standard version and another with flux adjustments (FLOR-FA). The ENSO-GuamTC connections in FLOR-FA are closer to observations than those in FLOR because of a better representation of TC genesis during ENSO phases. The physical mechanisms underlying the observed ENSO-GuamTC connections are further supported in the long-term control experiments with FLOR/FLOR-FA. The ENSO-GuamTC connections in sea surface temperature (SST)- and sea surface salinity (SSS)-restoring experiments with FLOR 1990 strongly resemble the observations, suggesting the ENSO-GuamTC connections arise substantially from the forcing of SST. The prediction skill of FLOR-FA for GuamTC frequency is quite promising in terms of correlation and root mean square error and is higher than that of FLOR for the period 1980-2014. This study shows the capability of global climate models (FLOR/FLOR-FA) in simulating the linkage between ENSO and TC activity near a highly localized region (i.e., Guam) and in predicting the frequency of TCs at the sub-basin scale.
Observed austral summertime (November through April) rainfall in southeastern South America (SESA)—including northern Argentina, Uruguay, southern Brazil and Paraguay—has exhibited substantial low-frequency variations with a multi-decadal moistening trend during the 20th century and a subsequent decadal drying trend during the current century. Understanding the mechanisms responsible for these variations is essential for predicting long-term rainfall changes. Here with a suite of attribution experiments using a pair of high-resolution global climate models—GFDL CM2.5 and FLOR_FA, we investigate the causes of these regional rainfall variations. Both models reproduce the 20th-century moistening trend, albeit with a weaker magnitude than observed, in response to the radiative forcing associated with increasing greenhouse gases. The increasing greenhouse gases drive tropical expansion; consequently, the subtropical dry branch of Hadley cell moves away from SESA, leading to the rainfall increase. The amplitude discrepancy between the observed and simulated rainfall changes suggests a possible underestimation by the models of the atmospheric response to the radiative forcing, as well as an important role for low-frequency internal variability in the observed moistening trend. Over the current century, increasing greenhouse gases drive a continuous SESA rainfall increase in the models. However, the observed decadal rainfall decline is largely (~60%) reproduced in response to the observed Pacific trade wind strengthening, which is likely associated with natural Pacific decadal variability. These results suggest that the recent summertime rainfall decline in SESA is temporary and the positive trend will resume in response to both increasing greenhouse gases and a return of Pacific trade winds to normal conditions.
Portions of western North America have experienced prolonged drought over the last decade. This drought has occurred at the same time as the global warming hiatus – a decadal period with little increase in global mean surface temperature. We use climate models and observational analyses to clarify the dual role of recent tropical Pacific changes in driving both the global warming hiatus and North American drought. When we insert observed tropical Pacific wind stress anomalies into coupled models, the simulations produce persistent negative sea surface temperature anomalies in the eastern tropical Pacific, a hiatus in global warming, and drought over North America driven by SST-induced atmospheric circulation anomalies. In our simulations the tropical wind anomalies account for 92% of the simulated North American drought during the recent decade, with 8% from anthropogenic radiative forcing changes. This suggests that anthropogenic radiative forcing is not the dominant driver of the current drought, unless the wind changes themselves are driven by anthropogenic radiative forcing. The anomalous tropical winds could also originate from coupled interactions in the tropical Pacific or from forcing outside the tropical Pacific. The model experiments suggest that if the tropical winds were to return to climatological conditions, then the recent tendency toward North American drought would diminish. Alternatively, if the tropical winds were to persist, then the impact on North American drought would continue; however, the impact of the enhanced Pacific easterlies on global temperature diminishes after a decade or two due to a surface reemergence of warmer water that was initially subducted into the ocean interior.
We characterize impacts on heat in the ocean climate system from transient ocean mesoscale eddies. Our tool is a suite of centennial-scale 1990 radiatively forced numerical climate simulations from three GFDL coupled models comprising the CM2-O model suite. CM2-O models differ in their ocean resolution: CM2.6 uses a 0.1° ocean grid, CM2.5 uses an intermediate grid with 0.25° spacing, and CM2-1deg uses a nominally 1.0° grid.
Analysis of the ocean heat budget reveals that mesoscale eddies act to transport heat upward in a manner that partially compensates (or offsets) for the downward heat transport from the time mean currents. Stronger vertical eddy heat transport in CM2.6 relative to CM2.5 accounts for the significantly smaller temperature drift in CM2.6. The mesoscale eddy parameterization used in CM2-1deg also imparts an upward heat transport, yet it differs systematically from that found in CM2.6. This analysis points to the fundamental role that ocean mesoscale features play in transient ocean heat uptake. In general, the more accurate simulation found in CM2.6 provides an argument for either including a rich representation of the ocean mesoscale in model simulations of the mean and transient climate, or for employing parameterizations that faithfully reflect the role of eddies in both lateral and vertical heat transport.
This study demonstrates skillful seasonal prediction of 2m air temperature and precipitation over land in a new high-resolution climate model developed by Geophysical Fluid Dynamics Laboratory, and explores the possible sources of the skill. We employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land, and demonstrate the predictive skill of these components. First, we show improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of NINO3.4 index and other aspects of interest. Then we measure the skill of temperature and precipitation in the high-resolution model for boreal winter and summer, and diagnose the sources of the skill. Lastly, we reconstruct predictions using a few most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, we find that the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer, and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2m air temperature and precipitation over land.
This study investigates the seasonality of the relationship between the Great Plains low-level jet (GPLLJ) and the Pacific Ocean from spring to summer, using observational analysis and coupled model experiments. The observed GPLLJ and El Niño-Southern Oscillation (ENSO) relation undergoes seasonal changes with a stronger GPLLJ associated with La Niña in boreal spring and El Niño in boreal summer. The ability of the GFDL FLOR global coupled climate model, which has the high-resolution atmospheric and land components, to simulate the observed seasonality in the GPLLJ-ENSO relationship is assessed. The importance of simulating the magnitude and phase-locking of ENSO accurately in order to better simulate its seasonal teleconnections with the Intra-Americas Seas (IAS) is demonstrated. This study explores the mechanisms for seasonal changes in the GPLLJ-ENSO relation in model and observations. It is hypothesized that ENSO affects the GPLLJ variability through the Caribbean low-level jet (CLLJ) during the summer and spring seasons. These results suggest that climate models with improved ENSO variability would advance our ability to simulate and predict seasonal variations of the GPLLJ and their associated impacts on the United States.
A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model (HiFLOR) has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises of high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean components. HiFLOR was developed from the Forecast Oriented Low Resolution Ocean model (FLOR) by decreasing the horizontal grid spacing of the atmospheric component from 50-km to 25-km, while leaving most of the sub-gridscale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, and a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden Julian Oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multi-century simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.
Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.
The seasonal predictability of extratropical storm tracks in Geophysical Fluid Dynamics Laboratory (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are ENSO-related spatial pattern for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm track changes (e.g., extreme low and high sea level pressure and extreme 2m air temperature) in response to different ENSO phases. These results point towards the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.
Zhang, Liping, and Thomas L Delworth, October 2015: Analysis of the Characteristics and Mechanisms of the Pacific Decadal Oscillation in a Suite of Coupled Models from the Geophysical Fluid Dynamics Laboratory. Journal of Climate, 28(19), DOI:10.1175/JCLI-D-14-00647.1. Abstract
North Pacific decadal oceanic and atmospheric variability is examined in a suite of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The models have ocean horizontal resolutions ranging from 1° to 0.1°, and atmospheric horizontal resolutions ranging from 200km to 50km. In all simulations the dominant pattern of decadal-scale sea surface temperature (SST) variability over the North Pacific is similar to the observed Pacific Decadal Oscillation (PDO). Simulated SST anomalies in the Kuroshio Oyashio Extension (KOE) region exhibit a significant spectral peak at approximately 20 years.
We use sensitivity experiments to show that: (i) the simulated PDO mechanism involves extratropical air-sea interaction and oceanic Rossby wave propagation, (ii) the oscillation can exist independent of interactions with the Tropics, but that such interactions can enhance the PDO, and (iii) ocean to atmosphere feedback in the extratropics is critical for establishing the approximately 20-year timescale of the PDO. The spatial pattern of the PDO can be generated from atmospheric variability that occurs independently of ocean-atmosphere feedback, but the existence of a spectral peak depends on active air-sea coupling. The specific interdecadal timescale is strongly influenced by the propagation speed of oceanic Rossby waves in the subtropical and subpolar gyres, as they provide a delayed feedback to the atmosphere.
The simulated PDO has a realistic association with precipitation variations over North America, with a warm phase of the PDO generally associated with positive precipitation anomalies over regions of the western United States. The seasonal dependence of this relationship is also reproduced by the model.
Precipitation in austral autumn and winter has declined over parts of southern and especially southwestern Australia in the past few decades. According to observations and climate models, at least part of this decline is associated with changes in large-scale atmospheric circulation, including a poleward movement of the westerly winds and increasing atmospheric surface pressure over parts of southern Australia. Here we use a high-resolution global climate model to analyse the causes of this rainfall decline. In our simulations, many aspects of the observed regional rainfall decline over southern and southwest Australia are reproduced in response to anthropogenic changes in levels of greenhouse gases and ozone in the atmosphere, whereas anthropogenic aerosols do not contribute to the simulated precipitation decline. Simulations of future climate with this model suggest amplified winter drying over most parts of southern Australia in the coming decades in response to a high-end scenario of changes in radiative forcing. The drying is most pronounced over southwest Australia, with total reductions in austral autumn and winter precipitation of approximately 40% by the late twenty-first century.
The high mountains of Asia, including the Karakoram, Himalayas and Tibetan Plateau, combine to form a region of perplexing hydroclimate changes. Glaciers have exhibited mass stability or even expansion in the Karakoram region1, 2, 3, contrasting with glacial mass loss across the nearby Himalayas and Tibetan Plateau1, 4, a pattern that has been termed the Karakoram anomaly. However, the remote location, complex terrain and multi-country fabric of high-mountain Asia have made it difficult to maintain longer-term monitoring systems of the meteorological components that may have influenced glacial change. Here we compare a set of high-resolution climate model simulations from 1861 to 2100 with the latest available observations to focus on the distinct seasonal cycles and resulting climate change signatures of Asia’s high-mountain ranges. We find that the Karakoram seasonal cycle is dominated by non-monsoonal winter precipitation, which uniquely protects it from reductions in annual snowfall under climate warming over the twenty-first century. The simulations show that climate change signals are detectable only with long and continuous records, and at specific elevations. Our findings suggest a meteorological mechanism for regional differences in the glacier response to climate warming.
Global tropical cyclone (TC) activity is simulated by the Geophysical Fluid Dynamics Laboratory (GFDL) CM2.5, which is a fully coupled global climate model with horizontal resolution of about 50km for atmosphere and 25 km for ocean. The present climate simulation shows fairly realistic global TC frequency, seasonal cycle, and geographical distribution. The model has some notable biases in regional TC activity, including simulating too few TCs in the North Atlantic. The regional biases in TC activity are associated with simulation biases in the large-scale environment such as sea surface temperature, vertical wind shear, and vertical velocity. Despite these biases, the model simulates the large-scale variations of TC activity induced by El Nino/Southern Oscillation fairly realistically.
The response of TC activity in the model to global warming is investigated by comparing the present climate with a CO2 doubling experiment. Globally, TC frequency decreases (-19%) while the intensity increases (+2.7%) in response to CO2 doubling, consistent with previous studies. The average TC lifetime decreases by -4.6%, while the TC size and rainfall increase by about 3% and 12%, respectively. These changes are generally reproduced across the different basins in terms of the sign of the change, although the percent changes vary from basin to basin and within individual basins. For the Atlantic basin, although there is an overall reduction in frequency from CO2 doubling, the warmed climate exhibits increased interannual hurricane frequency variability so that the simulated Atlantic TC activity is enhanced more during unusually warm years in the CO2-warmed climate relative to that in unusually warm years in the control climate.
Decadal prediction experiments were conducted as part of CMIP5 using the GFDL-CM2.1 forecast system. The abrupt warming of the North Atlantic subpolar gyre (SPG) that was observed in the mid 1990s is considered as a case study to evaluate our forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-90s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 90s show a warming of the SPG, however, only the ensemble mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predict the mid 90s warming. The successful initialized forecasts show an increased Atlantic Meridional Overturning Circulation and North Atlantic current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and subsequently a warming and spin down of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea-ice concentration over the Arctic, an enhanced warming over central US during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic Multidecadal Oscillation, which is encouraging for future predictions of North Atlantic climate.
In our original paper (Vecchi et al., 2013, hereafter V13) we stated “the skill in the initialized forecasts comes in large part from the persistence of the mid-1990s shift by the initialized forecasts, rather than from predicting its evolution”. Smith et al (2013, hereafter S13) challenge that assertion, contending that DePreSys was able to make a successful retrospective forecast of that shift. We stand by our original assertion, and present additional analyses using output from DePreSys retrospective forecasts to support our assessment.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
We investigate the influence of ocean component resolution on simulation of climate sensitivity using variants of the GFDL CM2.5 climate model incorporating eddy-resolving (1/10o) and eddy-parameterizing (1o) ocean resolutions. Two parameterization configurations of the coarse-resolution model are used yielding a three-model suite with significant variation in the transient climate response (TCR). The variation of TCR in this suite and in an enhanced group of 10 GFDL models is found to be strongly associated with the control climate Atlantic meridional overturning circulation (AMOC) magnitude and its decline under forcing. We find it is the AMOC behavior rather than resolution per se that accounts for most of the TCR differences. A smaller difference in TCR stems from the eddy-resolving model having more Southern Ocean surface warming than the coarse models.
Observations and climate simulations exhibit epochs of extreme El Niño/Southern Oscillation (ENSO) behavior that can persist for decades. Previous studies have revealed a wide range of ENSO responses to forcings from greenhouse gases, aerosols, and orbital variations – but they have also shown that interdecadal modulation of ENSO can arise even without such forcings. The present study examines the predictability of this intrinsically-generated component of ENSO modulation, using a 4000-year unforced control run from a global coupled GCM (GFDL-CM2.1) with a fairly realistic representation of ENSO. Extreme ENSO epochs from the unforced simulation are reforecast using the same (“perfect”) model, but slightly-perturbed initial conditions. These 40-member reforecast ensembles display potential predictability of the ENSO trajectory, extending up to several years ahead. However, no decadal-scale predictability of ENSO behavior is found. This indicates that multidecadal epochs of extreme ENSO behavior can arise not only intrinsically, but delicately, and entirely at random. Previous work had shown that CM2.1 generates strong, reasonably-realistic, decadally-predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO. However, those slow variations appear not to lend significant decadal predictability to this model’s ENSO behavior, at least in the absence of external forcings. While the potential implications of these results are sobering for decadal predictability, they also suggest an expedited approach to model evaluation and development – in which large ensembles of short runs are executed in parallel, to quickly and robustly evaluate simulations of ENSO. Further implications are discussed for decadal prediction, attribution of past and future ENSO variations, and societal vulnerability.
The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.
Response of climate conditions in the Atlantic Hurricane Main Development Region (MDR) to doubling of atmospheric CO2 has been explored, using the new high-resolution coupled Climate Model version 2.5 developed at the Geophysical Fluid Dynamics Laboratory (GFDL-CM2.5). In the annual mean, the SST in the MDR warms by about 2°C in the CO2 doubling run relative to the Control run, the trade winds become weaker in the northern tropical Atlantic, and the rainfall increases over the ITCZ and its northern region. The amplitude of the annual cycle of the SST over the MDR is not significantly changed by CO2 doubling. However, we find that the interannual variations show significant responses to CO2 doubling: the seasonal maximum peak of the interannual variations of the SST over the MDR is about 25% stronger than in the Control run. The enhancement of the interannual variations of the SST in the MDR is due to changes in effectiveness of the Wind-Evaporation-SST (WES) positive feedback: WES remains a positive feedback until boreal early summer in the CO2 doubling run. The enhancement of the interannual variability of the SST over the MDR in boreal early summer due to CO2 doubling could lead to serious damages associated with the Atlantic Hurricane count and drought (or flood) in the Sahel and South America in a future climate.
Goddard, L M., Rym Msadek, and Thomas L Delworth, et al., January 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics, 40(1-2), DOI:10.1007/s00382-012-1481-2. Abstract
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.
This study assesses the ability of a newly developed high-resolution coupled model from the Geophysical Fluid Dynamics Laboratory to simulate the cold-season hydroclimate in the present climate, and examines its response to climate change forcing. Output is assessed from a 280-yr control simulation based on 1990 atmospheric composition and an idealized 140-yr future simulation where atmospheric CO2 increases at 1% yr−1 until doubling in year 70 and then remains constant.
When compared to a low-resolution model, the high-resolution model is found to better represent the geographic distribution of snow variables in the present climate. In response to idealized radiative forcing changes, both models produce similar global-scale responses where global-mean temperature and total precipitation increase while snowfall decreases. Zonally, snowfall tends to decrease in the low to mid latitudes and increase in the mid to high latitudes.
At the regional scale, the high and low-resolution models sometimes diverge in the sign of projected snowfall changes; the high-resolution model exhibits future increases in a few select high altitude regions, notably the northwestern Himalaya region and small regions in the Andes and southwestern Yukon. Despite such local signals, there is an almost universal reduction in snowfall as a percent of total precipitation in both models. Using a simple multivariate model, temperature is shown to drive these trends by decreasing snowfall almost everywhere while precipitation increases snowfall in the high altitudes and mid to high latitudes. Mountainous regions of snowfall increases in the high-resolution model exhibit a unique dominance of the positive contribution from precipitation over temperature.
The impact of climate warming on the upper layer of the Bering Sea is investigated by using a high-resolution coupled global climate model. The model is forced by increasing atmospheric CO2 at a rate of 1% per year until CO2 reaches double its initial value (after 70 years), after which it is held constant. In response to this forcing, the upper layer of the Bering Sea warms by about 2�C in the southeastern shelf and by a little more than 1�C in the western basin. The wintertime ventilation to the permanent thermocline weakens in the western Bering Sea. After CO2 doubling, the southeastern shelf of the Bering Sea becomes almost ice-free in March, and the stratification of the upper layer strengthens in May and June. Changes of physical condition due to the climate warming would impact the pre-condition of spring bio-productivity in the southeastern shelf.
Msadek, Rym, W E Johns, Stephen G Yeager, Gokhan Danabasoglu, Thomas L Delworth, and Anthony Rosati, June 2013: The Atlantic Meridional Heat transport at 26.5° N and its relationship with the MOC in the RAPID array and the GFDL and NCAR coupled models. Journal of Climate, 26(12), DOI:10.1175/JCLI-D-12-00081.1. Abstract
The link at 26.5° N between the Atlantic meridional heat transport (MHT) and the Atlantic meridional overturning circulation (MOC) is investigated in two climate models, GFDL CM2.1 and NCAR CCSM4, and compared with the recent observational estimates from the RAPID-MOCHA array. Despite a stronger than observed MOC magnitude, both models underestimate the mean MHT at 26.5° N due to an overly diffuse thermocline. Biases result from errors in both overturning and gyre components of the MHT. The observed linear relationship between MHT and MOC at 26.5° N is realistically simulated by the two models and is mainly due to the overturning component of the MHT. Fluctuations in overturning MHT are dominated by Ekman transport variability in CM2.1 and CCSM4, whereas baroclinic geostrophic transport variability plays a larger role in RAPID. CCSM4 which has a parameterization of Nordic Sea overflows and thus a more realistic North Atlantic Deep Water (NADW) penetration shows smaller biases in the overturning heat transport than CM2.1 due to deeper NADW at colder temperatures. The horizontal gyre heat transport and its sensitivity to the MOC are poorly represented in both models. The wind-driven gyre heat transport is northward in observations at 26.5° N whereas it is weakly southward in both models, reducing the total MHT. We emphasize model biases that are responsible for the too weak MHT, particularly at the western boundary. The use of direct MHT observations through RAPID allows us to identify the source of the too weak MHT in the two models, a bias shared by a number of CMIP5 coupled models.
Retrospective predictions of multi-year North Atlantic hurricane frequency are explored, by applying a hybrid statistical-dynamical forecast system to initialized and non-initialized multi-year forecasts of tropical Atlantic and tropical mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of five-year mean and nine-year mean tropical Atlantic hurricane frequency show significant correlation relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts hurricane frequency over 1961-2011 yielding correlation coefficients that approach 0.9.
These encouraging retrospective multi-year hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits our confidence in distinguishing between the skill due to external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is due to improved representation of the multi-year tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994-1995 remains a critical issue for the success of multi-year forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.
Observational information has a strong geographic
dependence that may directly influence the quality of
parameter estimation in a coupled climate system. Using an
intermediate atmosphere-ocean-land coupled model, the
impact of geographic dependent observing system on
parameter estimation is explored within a ‘‘twin’’ experiment
framework. The ‘‘observations’’ produced by a ‘‘truth’’
model are assimilated into an assimilation model in which
the most sensitive model parameter has a different geographic
structure from the ‘‘truth’’, for retrieving the ‘‘truth’’
geographic structure of the parameter. To examine the
influence of data-sparse areas on parameter estimation, the
twin experiment is also performed with an observing system
in which the observations in some area are removed. Results
show that traditional single-valued parameter estimation
(SPE) attains a global mean of the ‘‘truth’’, while geographic
dependent parameter optimization (GPO) can retrieve the
‘‘truth’’ structure of the parameter and therefore significantly
improves estimated states and model predictability. This is
especially true when an observing system with data-void
areas is applied, where the error of state estimate is reduced
by 31 % and the corresponding forecast skill is doubled by
GPO compared with SPE.
The decadal predictability of sea surface temperature (SST) and 2m air temperature (T2m) in Geophysical Fluid Dynamics Laboratory (GFDL)'s decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL's Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model, allows identification of internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an inter-hemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bi-polar seesaw, with warm anomalies centered in Greenland, and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observations, indicates that the IMP of SST may be predictable up to 4 (10) year lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) years at 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments, in which radiative forcing is returned to 1961 values. These results point towards the possibility of meaningful decadal climate outlooks using dynamical coupled models, if they are appropriately initialized from a sustained climate observing system.
The non-Gaussian probability distribution of sea-ice concentration makes difficulties for directly assimilating sea-ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea-ice state, any direct assimilation adjustment on sea-ice states is easily overridden by model physics.A new approach implements sea-ice data assimilation in enthalpy space where a sea-ice model represents a nonlinear function that transforms a positive-definite space into the sea-ice concentration subspace.Results from observation-assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea-ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea-ice observations” into the model – while improving “sea-ice” variability itself, it consistently improves the estimates of all “climate” components.The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model.
Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently Booth et al (2012) showed that the HadGEM2-ES climate model closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the 20th century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al (2012) concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anti-correlation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.
We present results for simulated climate and climate change from a newly developed high-resolution global climate model (GFDL CM2.5). The GFDL CM2.5 model has an atmospheric resolution of approximately 50 Km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 Km in the tropics to 8 Km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes.
We present analyses based on the output of a 280 year control simulation; we also present results based on a 140 year simulation in which atmospheric CO2 increases at 1% per year until doubling after 70 years.
Results are compared to the GFDL CM2.1 climate model, which has somewhat similar physics but coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5.
The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.
Delworth, Thomas L., and Fanrong Zeng, July 2012: Multicentennial variability of the Atlantic Meridional Overturning Circulation and its climatic influence in a 4000 year simulation of the GFDL CM2.1 climate model. Geophysical Research Letters, 39, L13702, DOI:10.1029/2012GL052107. Abstract
We investigate decadal to multicentennial variability of Northern Hemisphere surface air temperature in a 4000-year control simulation of the GFDL CM2.1 climate model. Spectral analysis shows the presence of a distinct multicentennial timescle of temperature variability. The associated spatial pattern is broad, covering the entire Northern Hemisphere extratropics, but with enhanced amplitude in the Atlantic and Arctic sectors. This variability appears to be driven by interhemispheric fluctuations in oceanic heat transport associated with the Atlantic Meridional Overturning Circulation (AMOC). The AMOC variability is associated with century-scale propagation of salinity anomalies from the Southern Ocean to the subpolar North Atlantic, with out of phase transport variations between the upper ocean and deeper layers of the Atlantic. When positive (negative) upper ocean salinity anomalies reach the subpolar North Atlantic they strengthen (weaken) the AMOC by modulating upper ocean density and vertical stratification. The large-scale warming also appears to be enhanced by reductions in surface albedo associated with reduced sea-ice and low-level cloudiness, thereby increasing the absorption of shortwave radiation and amplifying the warming from AMOC changes. We speculate that such multicentennial variations in the AMOC could contribute to long-time scale climate fluctuations in the observed paleo record. This could arise purely as internal variability of the climate system, or through radiatively-induced changes to atmospheric circulation patterns, such as the NAO, that would in turn influence the AMOC.
Using two fully coupled ocean-atmosphere models of CM2.1 (the Climate Model version 2.1 developed at the Geophysical Fluid Dynamics Laboratory) and CM2.5 (a new high-resolution climate model based on CM2.1), the characteristics and sources of SST and precipitation biases associated with the Atlantic ITCZ have been investigated.
CM2.5 has an improved simulation of the annual mean and the annual cycle of the rainfall over the Sahel and the northern South America, while CM2.1 shows excessive Sahel rainfall and lack of northern South America rainfall in boreal summer. This marked improvement in CM2.5 is due to not only high-resolved orography, but also a significant reduction of biases in the seasonal meridional migration of the ITCZ. In particular, the seasonal northward migration of the ITCZ in boreal summer is coupled to the seasonal variation of the SST and a subsurface doming of the thermocline in the northeastern tropical Atlantic, known as the Guinea Dome. Improvements in the ITCZ allow for better representation of the coupled processes that are important for an abrupt seasonally phase-locked decay of the interannual SST anomaly in the northern tropical Atlantic.
Nevertheless, the differences between CM2.5 and CM2.1 were not sufficient to reduce the warm SST biases in the eastern equatorial region and Angola-Benguela Area. The weak bias of southerly winds along the southwestern African coast associated with the excessive southward migration bias of the ITCZ may be a key to improve the warm SST biases there.
Srokosz, M, and Thomas L Delworth, et al., November 2012: Past, present and future change in the Atlantic meridional overturning circulation. Bulletin of the American Meteorological Society, 93(11), DOI:10.1175/BAMS-D-11-00151.1. Abstract
Observations and numerical modelling experiments provide evidence for links between variability in the Atlantic Meridional Overturning Circulation (AMOC) and global climate patterns. Reduction in the strength of the overturning circulation is thought to have played a key role in rapid climate change in the past and may have the potential to significantly influence climate change in the future, as noted in the last two IPCC assessment reports (2001, 2007). Both IPCC reports also highlighted the significant uncertainties that exist regarding the future behaviour of the AMOC under global warming. Model results suggest that changes in the AMOC can impact surface air temperature, precipitation patterns and sea level, particularly in areas bordering the North Atlantic, thus affecting human populations. Here current understanding of past, present and future change in the AMOC and the effects of such changes on climate are reviewed. The focus is on observations of the AMOC, how the AMOC influences climate and in what way the AMOC is likely to change over the next few decades and the 21st century. The potential for decadal prediction of the AMOC is also discussed. Finally, the outstanding challenges and possible future directions for AMOC research are outlined.
Matei et al. (Reports, 6 January 2012, p. 76) claim to show skillful multiyear predictions of the
Atlantic Meridional Overturning Circulation (AMOC). However, these claims are not justified,
primarily because the predictions of AMOC transport do not outperform simple reference forecasts
based on climatological annual cycles. Accordingly, there is no justification for the "confident"
prediction of a stable AMOC through 2014.
Due to the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. Four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root mean square errors as 41%, 23%, 62% and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature and land surface temperature respectively. Consistently, the new parameter optimization greatly improves the model predictability due to the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.
Uncertainties in physical parameters of coupled models are an important source of model bias and adversely impact initialisation for climate prediction. Data assimilation using error covariances derived from model dynamics to extract observational information provides a promising approach to optimise parameter values so as to reduce such bias. However, effective parameter estimation in a coupled model is usually difficult because the error covariance between a parameter and the model state tends to be noisy due to multiple sources of model uncertainties. Using a simple coupled model consisting of the 3-variable Lorenz model and a slowly varying slab ‘ocean’, this study first investigated how to enhance the signal-to-noise ratio in covariances between model states and parameters, and then designed a data assimilation scheme for enhancive parameter correction (DAEPC). In DAEPC, parameter estimation is facilitated after state estimation reaches a ‘quasiequilibrium’ where the uncertainty of coupled model states is sufficiently constrained by observations so that the covariance between a parameter and the model state is signal dominant. The observation-updated parameters are applied to improving the next cycle of state estimation and the refined covariance of parameter and model state further improves parameter correction. Performing dynamically adaptive state and parameter estimations with speedy convergence, DAEPC provides a systematic way to estimate the whole array of coupled model parameters using observations, and produces more accurate state estimates. Forecast experiments show that the DAEPC initialisation with observation-estimated parameters greatly improves the model predictability - while valid ‘atmospheric’ forecasts are extended two times longer, the ‘oceanic’ predictability is almost tripled. The simple model results here provide some insights for improving climate estimation and prediction with a coupled general circulation model.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
Mahajan, S, Rong Zhang, and Thomas L Delworth, December 2011: Impact of the Atlantic Meridional Overturning Circulation (AMOC) on Arctic surface air temperature and sea-ice variability. Journal of Climate, 24(24), DOI:10.1175/2011JCLI4002.1. Abstract
The simulated impact of the Atlantic Meridional Overturning Circulation (AMOC) on the low frequency variability of the Arctic Surface Air temperature (SAT) and sea-ice extent is studied with a 1000 year-long segment of a control simulation of GFDL CM2.1 climate model. The simulated AMOC variations in the control simulation are found to be significantly anti-correlated with the Arctic sea-ice extent anomalies and significantly correlated with the Arctic SAT anomalies on decadal timescales in the Atlantic sector of the Arctic. The maximum anti-correlation with the Arctic sea-ice extent and the maximum correlation with the Arctic SAT occur when the AMOC Index leads by one year. An intensification of the AMOC is associated with a sea-ice decline in the Labrador, Greenland and Barents Seas in the control simulation, with the largest change occurring in the winter. The recent declining trend in the satellite observed sea-ice extent also shows a similar pattern in the Atlantic sector of the Arctic in the winter, suggesting the possibility of a role of the AMOC in the recent Arctic sea-ice decline in addition to anthropogenic greenhouse gas induced warming. However, in the summer, the simulated sea-ice response to the AMOC in the Pacific sector of the Arctic is much weaker than the observed declining trend, indicating a stronger role for other climate forcings or variability in the recently observed summer sea-ice decline in the Chukchi, Beaufort, East Siberian and Laptev Seas.
Recent studies have suggested that the leading modes of North Atlantic subsurface temperature (Tsub) and sea surface height (SSH) anomalies are induced by Atlantic meridional overturning circulation (AMOC) variations and can be used as fingerprints of AMOC variability. Based on these fingerprints of the AMOC in the GFDL CM2.1 coupled climate model, a linear statistical predictive model of observed fingerprints of AMOC variability is developed in this study. The statistical model predicts a weakening of AMOC strength in a few years after its peak around 2005. Here, we show that in the GFDL coupled climate model assimilated with observed subsurface temperature data, including recent Argo network data (2003–2008), the leading mode of the North Atlantic Tsub anomalies is similar to that found with the objectively analyzed Tsub data and highly correlated with the leading mode of altimetry SSH anomalies for the period 1993–2008. A statistical auto-regressive (AR) model is fit to the time-series of the leading mode of objectively analyzed detrended North Atlantic Tsub anomalies (1955–2003) and is applied to assimilated Tsub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled Tsub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled Tsub, SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed Tsub and modeled Tsub, SSH and AMOC variations.
Solomon, Amy, and Thomas L Delworth, et al., February 2011: Distinguishing the roles of natural and anthropogenically forced decadal climate variability: Implications for prediction US CLIVAR Decadal Predictability Working Group. Bulletin of the American Meteorological Society, 92(2), DOI:10.1175/2010BAMS2962.1. Abstract
Given that over the course of the next 10–30 years the magnitude of natural decadal variations may rival that of anthropogenically forced climate change on regional scales, it is envisioned that initialized decadal predictions will provide important information for climate-related management and adaptation decisions. Such predictions are presently one of the grand challenges for the climate community. This requires identifying those physical phenomena—and their model equivalents—that may provide additional predictability on decadal time scales, including an assessment of the physical processes through which anthropogenic forcing may interact with or project upon natural variability. Such a physical framework is necessary to provide a consistent assessment (and insight into potential improvement) of the decadal prediction experiments planned to be assessed as part of the IPCC's Fifth Assessment Report.
The study of climate impacts on Living Marine Resources (LMRs) has increased rapidly in recent years with the availability of climate model simulations contributed to the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Collaboration between climate and LMR scientists and shared understanding of critical challenges for such applications are essential for developing robust projections of climate impacts on LMRs. This paper assesses present approaches for generating projections of climate impacts on LMRs using IPCC-class climate models, recommends practices that should be followed for these applications, and identifies priority developments that could improve current projections. Understanding of the climate system and its representation within climate models has progressed to a point where many climate model outputs can now be used effectively to make LMR projections. However, uncertainty in climate model projections (particularly biases and inter-model spread at regional to local scales), coarse climate model resolution, and the uncertainty and potential complexity of the mechanisms underlying the response of LMRs to climate limit the robustness and precision of LMR projections. A variety of techniques including the analysis of multi-model ensembles, bias corrections, and statistical and dynamical downscaling can ameliorate some limitations, though the assumptions underlying these approaches and the sensitivity of results to their application must be assessed for each application. Developments in LMR science that could improve current projections of climate impacts on LMRs include improved understanding of the multi-scale mechanisms that link climate and LMRs and better representations of these mechanisms within more holistic LMR models. These developments require a strong baseline of field and laboratory observations including long time-series and measurements over the broad range of spatial and temporal scales over which LMRs and climate interact. Priority developments for IPCC-class climate models include improved model accuracy (particularly at regional and local scales), inter-annual to decadal-scale predictions, and the continued development of earth system models capable of simulating the evolution of both the physical climate system and biosphere. Efforts to address these issues should occur in parallel and be informed by the continued application of existing climate and LMR models.
Wu, S, Zhengyu Liu, Rong Zhang, and Thomas L Delworth, February 2011: On the observed relationship between the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation. Journal of Oceanography, 67(1), DOI:10.1007/s10872-011-0003-x. Abstract
We studied the relationship between the dominant patterns of sea surface temperature (SST) variability in the North Pacific and the North Atlantic. The patterns are known as the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO). In the analysis we used two different observational data sets for SST. Due to the high degree of serial correlation in the PDO and AMO time series, various tests were carried out to assess the significance of the correlations. The results demonstrated that the correlations are significant when the PDO leads the AMO by 1 year and when the AMO leads the PDO by 11–12 years. The possible physical processes involved are discussed, along with their potential implication for decadal prediction.
Zhang, D, Rym Msadek, Michael J McPhaden, and Thomas L Delworth, April 2011: Multidecadal variability of the North Brazil Current and its connection to the Atlantic Meridional Overturning Circulation. Journal of Geophysical Research: Oceans, 116, C04012, DOI:10.1029/2010JC006812. Abstract
The North Brazil Current (NBC) connects the North and South Atlantic and is the major pathway for the surface return flow of the Atlantic meridional overturning circulation (AMOC). Here, we calculate the NBC geostrophic transport time series based on 5 decades of observations near the western boundary off the coast of Brazil. Results reveal a multidecadal NBC variability that lags Labrador Sea deep convection by a few years. The NBC transport time series is coherent with the Atlantic Multidecadal Oscillation in sea surface temperature, which also has been widely linked to AMOC fluctuations in previous modeling studies. Our results thus suggest that the observed multidecadal NBC transport variability is a useful indicator for AMOC variations. The suggested connection between the NBC and AMOC is assessed in a 700 year control simulation of the Geophysical Fluid Dynamics Laboratory's CM2.1 coupled climate model. The model results are in agreement with observations and further demonstrate that the variability of NBC transport is a good index for tracking AMOC variations. Concerning the debate about whether a slowdown of AMOC has already occurred under global warming, the observed NBC transport time series suggests strong multidecadal variability but no significant trend.
The sensitivity of the North Atlantic Ocean Circulation to an abrupt change in the Nordic Sea overflow is investigated for the first time using a high resolution eddy-permitting global coupled ocean-atmosphere model (GFDL CM2.5). The Nordic Sea overflow is perturbed through the change of the bathymetry in GFDL CM2.5. We analyze the Atlantic Meridional Overturning Circulation (AMOC) adjustment process and the downstream oceanic response to the perturbation. The results suggest that north of 34N, AMOC changes induced by changes in the Nordic Sea overflow propagate on the slow tracer advection time scale, instead of the fast Kelvin wave time scale, resulting in a time lead of several years between subpolar and subtropical AMOC changes. The results also show that a stronger and deeper-penetrating Nordic Sea overflow leads to stronger and deeper AMOC, stronger northward ocean heat transport, reduced Labrador Sea deep convection, stronger cyclonic Northern Recirculation Gyre (NRG), westward shift of the North Atlantic Current (NAC) and southward shift of the Gulf Stream, warmer sea surface temperature (SST) east of Newfoundland and colder SST south of the Grand Banks, stronger and deeper NAC and Gulf Stream, and stronger oceanic eddy activities along the NAC and the Gulf Stream paths. A stronger/weaker Nordic Sea overflow also leads to a contracted/expanded subpolar gyre (SPG). This sensitivity study points to the important role of the Nordic Sea overflow in the large scale North Atlantic ocean circulation, and it is crucial for climate models to have a correct representation of the Nordic Sea overflow.
Simulations from a fine-resolution global coupled model, the Geophysical Fluid Dynamics Laboratory
Climate Model, version 2.4 (CM2.4), are presented, and the results are compared with a coarse version of the
same coupled model, CM2.1, under idealized climate change scenarios. A particular focus is given to the
dynamical response of the Southern Ocean and the role played by the eddies—parameterized or permitted—
in setting the residual circulation and meridional density structure. Compared to the case in which eddies are
parameterized and consistent with recent observational and idealized modeling studies, the eddy-permitting
integrations of CM2.4 show that eddy activity is greatly energized with increasing mechanical and buoyancy
forcings, buffering the ocean to atmospheric changes, and the magnitude of the residual oceanic circulation
response is thus greatly reduced. Although compensation is far from being perfect, changes in poleward eddy
fluxes partially compensate for the enhanced equatorward Ekman transport, leading to weak modifications in
local isopycnal slopes, transport by the Antarctic Circumpolar Current, and overturning circulation. Since the
presence of active ocean eddy dynamics buffers the oceanic response to atmospheric changes, the associated
atmospheric response to those reduced ocean changes is also weakened. Further, it is hypothesized that
present numerical approaches for the parameterization of eddy-induced transports could be too restrictive
and prevent coarse-resolution models from faithfully representing the eddy response to variability and change
in the forcing fields.
Farneti, Riccardo, and Thomas L Delworth, October 2010: The role of mesoscale eddies in the remote oceanic response to altered Southern Hemisphere winds. Journal of Physical Oceanography, 40(10), DOI:10.1175/2010JPO4480.1. Abstract
It has been suggested that a strengthening of the Southern Hemisphere winds would
induce a more vigorous overturning through an increased northward Ekman flux, bringing
more light waters into the oceanic basins and enhancing the upwelling of North
Atlantic Deep Water in the Southern Ocean, thereby increasing ocean ventilation. We
present here simulations from a coarse and a fine resolution version of a coupled model
subject to idealized wind stress changes in the Southern Ocean. In the fine resolution
eddy-permitting model, we find that changes in poleward eddy fluxes largely compensate
for the enhanced equatorward Ekman transport in the Southern Ocean. As a consequence,
northward transport of light waters, pycnocline depth, Northern Hemisphere
overturning and Southern Ocean upwelling anomalies are much reduced compared with
simulations in the coarse resolution model with parameterized eddies. These results
point to a relatively weak sensitivity of present-day global ocean
Climate model simulations run as part of the Climate Variability and Predictability (CLIVAR) Drought Working Group initiative were analyzed to determine the impact of three patterns of sea surface temperature (SST) anomalies on drought and pluvial frequency and intensity around the world. The three SST forcing patterns include a global pattern similar to the background warming trend, a pattern in the Pacific, and a pattern in the Atlantic. Five different global atmospheric models were forced by fixed SSTs to test the impact of these SST anomalies on droughts and pluvials relative to a climatologically forced control run.
The five models generally yield similar results in the locations of drought and pluvial frequency changes throughout the annual cycle in response to each given SST pattern. In all of the simulations, areas with an increase in the mean drought (pluvial) conditions tend to also show an increase in the frequency of drought (pluvial) events. Additionally, areas with more frequent extreme events also tend to show higher intensity extremes. The cold Pacific anomaly increases drought occurrence in the United States and southern South America and increases pluvials in Central America and northern and central South America. The cold Atlantic anomaly increases drought occurrence in southern Central America, northern South America, and central Africa and increases pluvials in central South America. The warm Pacific and Atlantic anomalies generally lead to reversals of the drought and pluvial increases described with the corresponding cold anomalies. More modest impacts are seen in other parts of the world. The impact of the trend pattern is generally more modest than that of the two other anomaly patterns.
The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to pre-industrial forcing. The response is characterized by an initial fast exponential decay with an e-folding time smaller than 5 years, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4C by 2100 in the A1B SRES scenario and then to 1.4C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO2 and by the excellent fit to the model's ensemble mean 20th century evolution with a simple one-box model with no long times scales.
Hurrell, J W., Gerald A Meehl, David Bader, Thomas L Delworth, Ben P Kirtman, and B A Wielicki, December 2010: Reply to Comments on “A Unified Modeling Approach to Climate System Prediction”. Bulletin of the American Meteorological Society, 91(12), DOI:10.1175/2010BAMS3118.1.
Understanding the plausible causes for the observed high dust concentrations in Antarctic ice cores during
the Last Glacial Maximum (LGM) is crucial for interpreting the Antarctic dust records in the past climates
and could provide insights into dust variability in future climates. Using the Geophysical Fluid Dynamics
Laboratory (GFDL) General Circulation Models, we conduct an investigation into the various factors
modulating dust emission, transport and deposition, with a view towards an improved quantification of the
LGM dust enhancements in the Antarctic ice cores. The model simulations show that the expansion of
source areas and changes in the Antarctic ice accumulation rates together can account for most of the
observed increase of dust concentrations in the Vostok, Dome C and Taylor Dome cores, but there is an
overestimate of the LGM-to-present ratio in the case of the Byrd core. The source expansion due to the
lowering of sea level yields a factor of 2–3 higher contribution than that due to the reduction of continental
vegetation. The changes in other climate parameters (e.g., SH precipitation change) are estimated to be
relatively less important within the context of this sensitivity study, while the model-simulated LGM
surface winds yield a 20–30 % reduction rather than an increase in dust deposition in Antarctica. This
research yields insights towards a fundamental understanding of the causes for the significant enhancement
of the dust deposition in the Antarctic ice cores during the LGM.
The North Atlantic is among the few places where decadal climate variations are considered potentially predictable. The physical mechanisms of the decadal variability are hypothesized to be associated with fluctuations of the Atlantic meridional overturning circulation (AMOC). Perfect model predictability experiments using the GFDL CM2.1 climate model are analyzed to investigate the potential predictability of the AMOC. Results indicate that the AMOC is predictable up to 20 years. We further connect AMOC predictability to readily observable fields. We show that modeled surface and subsurface signatures of AMOC variations defined by characteristic patterns of sea surface height, subsurface temperature, and upper ocean heat content anomalies, have a potential predictability similar to the AMOC's. Since we have longer observational records for these quantities than for direct measurements of the AMOC, our study highlights a potentially new promising method for monitoring AMOC variations, and hence assessing the predictability of the real climate system.
The Atlantic Meridional Overturning Circulation (AMOC) has an important influence on climate, and yet we lack adequate observations of this circulation. Here we assess the adequacy of past and current widely deployed routine observing systems for monitoring the AMOC and associated North Atlantic climate. To do so we draw on two independent simulations of the 20th century using an IPCC AR4 coupled climate model. We treat one simulation as “truth” and sample it according to the observing system we are evaluating. We then assimilate these synthetic “observations” into the second simulation within a fully-coupled system that instantaneously exchanges information among all coupled components and produces a nearly balanced and coherent estimate for global climate states including the North Atlantic climate system. The degree to which the assimilation recovers the “truth” is an assessment of the adequacy of the observing system being evaluated. As the coupled system responds to the constraint of the atmosphere or ocean, the assessment of the recovery for climate quantities such as Labrador Sea Water (LSW) and the North Atlantic Oscillation increases our understanding for the factors that determine AMOC variability. For example, we found the low-frequency sea-surface forcings provided by the atmospheric and sea-surface temperature observations can excite a LSW variation that governs the long time scale variability of the AMOC. When we use the most complete modern observing system consisting of atmospheric winds and temperature, along with Argo ocean temperature and salinity down to 2000 meters, a skill estimate of AMOC reconstruction is 90% (out of 100% maximum). Similarly encouraging results hold for other quantities, such as LSW. The past XBT observing system, in which deep ocean temperature and salinity were not available, has a lesser ability to recover the “truth” AMOC (the skill is reduced to 52%). While these results raise concerns about our ability to properly characterize past variations of the AMOC, they also hold promise for future monitoring of the AMOC and for initializing prediction models.
Hurrell, J W., Gerald A Meehl, David Bader, Thomas L Delworth, Ben P Kirtman, and B A Wielicki, December 2009: A unified modeling approach to climate system prediction. Bulletin of the American Meteorological Society, 90(12), DOI:10.1175/2009BAMS2752.1. Abstract
There is a new perspective of a continuum of prediction problems, with a blurring of the distinction between short-term predictions and long-term climate projections. At the heart of this new perspective is the realization that all climate system predictions, regardless of time scale, share common processes and mechanisms; moreover, interactions across time and space scales are fundamental to the climate system itself. Further, just as seasonal-to-interannual predictions start from an estimate of the state of the climate system, there is a growing realization that decadal and longer-term climate predictions could be initialized with estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface. Even though the prediction problem itself is seamless, the best practical approach to it may be described as unified: models aimed at different time scales and phenomena may have large commonality but place emphasis on different aspects of the system. The potential benefits of this commonality are significant and include improved predictions on all time scales and stronger collaboration and shared knowledge, infrastructure, and technical capabilities among those in the weather and climate prediction communities.
Schubert, S D., Thomas L Delworth, and Kirsten L Findell, et al., October 2009: A US CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results. Journal of Climate, 22(19), DOI:10.1175/2009JCLI3060.1. Abstract
The U.S. Climate Variability and Predictability (CLIVAR) working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land–atmosphere feedbacks on regional drought. The runs were carried out with five different atmospheric general circulation models (AGCMs) and one coupled atmosphere–ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Niño–Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic multidecadal oscillation (AMO), and a global trend pattern.
One of the key findings is that all of the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the United States tends to occur when the two oceans have anomalies of opposite signs. Further highlights of the response over the United States to the Pacific forcing include precipitation signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. The response to the positive SST trend forcing pattern is an overall surface warming over the world’s land areas, with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all of the models.
It is hoped that these early results, as well as those reported in the other contributions to this special issue on drought, will serve to stimulate further analysis of these simulations, as well as suggest new research on the physical mechanisms contributing to hydroclimatic variability and change throughout the world.
Sulfate aerosols resulting from strong volcanic explosions last for 2–3 years in the lower stratosphere. Therefore it was traditionally believed that volcanic impacts produce mainly short-term, transient climate perturbations. However, the ocean integrates volcanic radiative cooling and responds over a wide range of time scales. The associated processes, especially ocean heat uptake, play a key role in ongoing climate change. However, they are not well constrained by observations, and attempts to simulate them in current climate models used for climate predictions yield a range of uncertainty. Volcanic impacts on the ocean provide an independent means of assessing these processes. This study focuses on quantification of the seasonal to multidecadal time scale response of the ocean to explosive volcanism. It employs the coupled climate model CM2.1, developed recently at the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory, to simulate the response to the 1991 Pinatubo and the 1815 Tambora eruptions, which were the largest in the 20th and 19th centuries, respectively. The simulated climate perturbations compare well with available observations for the Pinatubo period. The stronger Tambora forcing produces responses with higher signal-to-noise ratio. Volcanic cooling tends to strengthen the Atlantic meridional overturning circulation. Sea ice extent appears to be sensitive to volcanic forcing, especially during the warm season. Because of the extremely long relaxation time of ocean subsurface temperature and sea level, the perturbations caused by the Tambora eruption could have lasted well into the 20th century.nd sea level, the perturbations caused by the Tambora eruption could last well into the 20th century.
Zhang, Rong, and Thomas L Delworth, March 2009: A new method for attributing climate variations over the Atlantic Hurricane Basin's main development region. Geophysical Research Letters, 36, L06701, DOI:10.1029/2009GL037260. Abstract
We propose a new approach to
decompose observed climate variations over the Atlantic Hurricane Basin's
main development region (MDR) into components attributable to radiative
forcing changes and to internal oceanic variability. Our attribution
suggests that the observed multidecadal anomalies of vertical shear (Uz) and
a simple index of maximum potential intensity (SIMPI) for tropical cyclones
are both dominated by internal variability, consistent with multidecadal
variations of Atlantic Hurricane activity; changes in radiative forcing led
to increasing Uz and decreasing SIMPI since the late 50's, unfavorable for
Atlantic Hurricane activity. Physically, at least for the GFDL model, sea
surface temperature (SST) anomalies induced by ocean heat transport
variations are more efficient in producing negative Uz anomalies than that
induced by altered radiative forcing.
Clark, P U., Thomas L Delworth, and A J Weaver, April 2008: Freshwater Forcing:Will History Repeat Itself?Science, 320(5874), 316.
Previous work has suggested that the strength and latitudinal position of the Southern Hemisphere (SH) mid-latitude westerly winds has an important impact on climate and the Atlantic Meridional Overturning Circulation (AMOC). We probe this hypothesis by conducting ensembles of experiments using the GFDL CM2.1 coupled ocean-atmosphere model with altered SH wind stress. We find, consistent with previous work, that enhanced (reduced) and poleward (equatorward) displaced SH westerly winds lead to an AMOC intensification (weakening). While the AMOC takes more than a century to respond fully to the altered SH winds, initial effects in the North Atlantic can occur within a few decades. The AMOC changes generate SST and surface air temperature responses in the North Atlantic and adjacent continental regions. In the Southern Hemisphere, the atmosphere responds to the altered ocean circulation with a further strengthening and poleward movement of the SH winds, thereby constituting a modest positive feedback.
Delworth, Thomas L., and Rong Zhang, et al., December 2008: The potential for abrupt change in the Atlantic Meridional Overturning Circulation In Abrupt Climate Change: Final Report, Synthesis & Assessment Product 3.4, CSSP, Reston, VA, U.S. Geological Survey, 258-359. PDF
Delworth, Thomas L., Rong Zhang, and M E Mann, 2007: Decadal to centennial variability of the Atlantic from observations and models In Ocean Circulation: Mechanisms and Impacts, Geophysical Monograph Series 173, Washington, DC, American Geophysical Union, 131-148. Abstract PDF
Some aspects of multidecadal Atlantic climate variability, and its impact on regional and hemispheric scale climate, are reviewed. Observational analyses have documented distinct patterns of Atlantic variability with decadal (8-12 years) and multidecadal (30-80 years) time scales. Numerical models have succeeded in capturing some aspects of this observed variability, but much work remains to understand the mechanisms of the observed variability. The impacts of the variability — particularly on the multidecadal time scale — are striking, including modulation of African and Indian summer monsoon rainfall, summer climate over North America and Europe, and a potential influence on Atlantic hurricane activity. Some of the observed variability, particularly in recent decades, is likely influenced by changing radiative forcings, of both anthropogenic and natural origin. This poses an important challenge for the detection, attribution and prediction of climate change.
Delworth, Thomas L., and Kirsten L Findell, 2007: Decadal to centennial scale changes in summer continental hydrology In Climate Variability and Change: Past, Present, and Future, John E. Kutzbach Symposium, Gisela Kutzbach, Ed., Madison, WI, Ctr. of Climatic Research, U. Wisconsin-Madison, 49-56. Abstract
Past
studies have suggested that increasing atmospheric CO2 will lead
to a substantial reduction of soil moisture during summer in the
extratropics. We revisit this topic using a new climate model developed at
NOAA's Geophysical Fluid Dynamics Laboratory. The new model has a horizontal
resolution of 2.5° longitude by 2.0° latitude, with 24 vertical levels, and
has both a diurnal and seasonal cycle of insolation. The model incorporates
substantially updated physics relative to previous versions.
Results from
earlier studies showed, among other things, an increase in wintertime
rainfall over most mid-latitude continental regions when CO2 is
doubled, an earlier snowmelt season and onset of springtime evaporation, and
a higher ratio of evaporation to precipitation in summer. These factors led
to large-scale increases in soil moisture in winter and decreases in summer
in mid-latitude in doubled-CO2 experiments. The new model shows
similar results, and the processes discussed above are important in this
model as well. In addition, we find that changes in atmospheric circulation
play an important role in regional hydrologic changes. Additional
experiments have been run to probe the causes of the circulation changes.
These simulations show that global scale sea surface temperature increases
caused by the CO2 doubling explain the majority of the
atmospheric circulation changes, while positive feedbacks from the land
surface have a secondary impact. These results highlight the importance of
global scale sea surface temperature changes for future regional hydrology
changes.
Meehl, Gerald A., C Covey, Thomas L Delworth, M Latif, B McAveney, J F B Mitchell, Ronald J Stouffer, and Karl E Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88(9), DOI:10.1175/BAMS-88-9-1383. Abstract
A coordinated set of global coupled climate model [atmosphere–ocean general circulation model (AOGCM)] experiments for twentieth- and twenty-first-century climate, as well as several climate change commitment and other experiments, was run by 16 modeling groups from 11 countries with 23 models for assessment in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Since the assessment was completed, output from another model has been added to the dataset, so the participation is now 17 groups from 12 countries with 24 models. This effort, as well as the subsequent analysis phase, was organized by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Coupled Models (WGCM) Climate Simulation Panel, and constitutes the third phase of the Coupled Model Intercomparison Project (CMIP3). The dataset is called the WCRP CMIP3 multimodel dataset, and represents the largest and most comprehensive international global coupled climate model experiment and multimodel analysis effort ever attempted. As of March 2007, the Program for Climate Model Diagnostics and Intercomparison (PCMDI) has collected, archived, and served roughly 32 TB of model data. With oversight from the panel, the multimodel data were made openly available from PCMDI for analysis and academic applications. Over 171 TB of data had been downloaded among the more than 1000 registered users to date. Over 200 journal articles, based in part on the dataset, have been published so far. Though initially aimed at the IPCC AR4, this unique and valuable resource will continue to be maintained for at least the next several years. Never before has such an extensive set of climate model simulations been made available to the international climate science community for study. The ready access to the multimodel dataset opens up these types of model analyses to researchers, including students, who previously could not obtain state-of-the-art climate model output, and thus represents a new era in climate change research. As a direct consequence, these ongoing studies are increasing the body of knowledge regarding our understanding of how the climate system currently works, and how it may change in the future.
While the Northern Hemisphere mean surface temperature has clearly warmed over the 20th century due in large part to increasing greenhouse gases, this warming has not been monotonic. The departures from steady warming on multidecadal timescales might be associated in part with radiative forcing, especially solar irradiance, volcanoes, and anthropogenic aerosols. It is also possible that internal oceanic variability explains a part of this variation. We report here on simulations with a climate model in which the Atlantic Ocean is constrained to produce multidecadal fluctuations similar to observations by redistributing heat within the Atlantic, with other oceans left free to adjust to these Atlantic perturbations. The model generates multidecadal variability in Northern Hemisphere mean temperatures similar in phase and magnitude to detrended observations. The results suggest that variability in the Atlantic is a viable explanation for a portion of the multidecadal variability in the Northern Hemisphere mean temperature record.
In this paper, we found that the Atlantic Multidecadal Oscillation (AMO) can contribute to the Pacific Decadal Oscillation (PDO), especially the component of the PDO that is linearly independent of El Niño and the Southern Oscillation (ENSO), i.e. the North Pacific Multidecadal Oscillation (NPMO), and the associated Pacific/North America (PNA) pattern. Using a hybrid version of the GFDL CM2.1 climate model, we show that the AMO provides a source of multidecadal variability to the North Pacific, and needs to be considered along with other forcings for North Pacific climate change. The lagged North Pacific response to the North Atlantic forcing is through atmospheric teleconnections and reinforced by oceanic dynamics and positive air-sea feedback over the North Pacific. The results indicate that a North Pacific regime shift, opposite to the 1976–77 shift, might occur now a decade after the switch of the observed AMO to a positive phase around 1995.
In many climate model simulations using realistic, time-varying climate change forcing agents for the 20th and 21st centuries, the North Atlantic thermohaline circulation (THC) weakens in the 21st century, with little change in the 20th century. Here we use a comprehensive climate model to explore the impact of various climate change forcing agents on the THC. We conduct ensembles of integrations with subsets of climate change forcing agents. Increasing greenhouse gases – in isolation – produce a significant THC weakening in the late 20th century, but this change is partially offset by increasing anthropogenic aerosols, which tend to strengthen the THC. The competition between increasing greenhouse gases and anthropogenic aerosols thus produces no significant THC change in our 20th century simulations when all climate forcings are included. The THC weakening becomes significant several decades into the 21st century, when the effects of increasing greenhouse gases overwhelm the aerosol effects.
The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.
Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.
The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.
Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).
Manuscript received 8 December 2004, in final form 18 March 2005
The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.
Hurrell, J W., M Visbeck, A Busalacchi, R A Clarke, Thomas L Delworth, R R Dickson, W E Johns, K P Koltermann, P J Kushner, David Marshall, C Mauritzen, M S McCartney, A Piola, C Reason, G Reverdin, F Schott, Rowan Sutton, I Wainer, and D Wright, 2006: Atlantic Climate Variability and Predictability: A CLIVAR Perspective. Journal of Climate, 19(20), DOI:10.1175/JCLI3902.1. Abstract
Three interrelated climate phenomena are at the center of the Climate Variability and Predictability
(CLIVAR) Atlantic research: tropical Atlantic variability (TAV), the North Atlantic Oscillation (NAO),
and the Atlantic meridional overturning circulation (MOC). These phenomena produce a myriad of impacts
on society and the environment on seasonal, interannual, and longer time scales through variability manifest
as coherent fluctuations in ocean and land temperature, rainfall, and extreme events. Improved understanding
of this variability is essential for assessing the likely range of future climate fluctuations and the
extent to which they may be predictable, as well as understanding the potential impact of human-induced
climate change. CLIVAR is addressing these issues through prioritized and integrated plans for short-term
and sustained observations, basin-scale reanalysis, and modeling and theoretical investigations of the
coupled Atlantic climate system and its links to remote regions. In this paper, a brief review of the state of
understanding of Atlantic climate variability and achievements to date is provided. Considerable discussion
is given to future challenges related to building and sustaining observing systems, developing synthesis
strategies to support understanding and attribution of observed change, understanding sources of predictability,
and developing prediction systems in order to meet the scientific objectives of the CLIVAR Atlantic
program.
Historical climate simulations of the period 1861–2000 using two new Geophysical Fluid Dynamics Laboratory (GFDL) global climate models (CM2.0 and CM2.1) are compared with observed surface temperatures. All-forcing runs include the effects of changes in well-mixed greenhouse gases, ozone, sulfates, black and organic carbon, volcanic aerosols, solar flux, and land cover. Indirect effects of tropospheric aerosols on clouds and precipitation processes are not included. Ensembles of size 3 (CM2.0) and 5 (CM2.1) with all forcings are analyzed, along with smaller ensembles of natural-only and anthropogenic-only forcing, and multicentury control runs with no external forcing.
Observed warming trends on the global scale and in many regions are simulated more realistically in the all-forcing and anthropogenic-only forcing runs than in experiments using natural-only forcing or no external forcing. In the all-forcing and anthropogenic-only forcing runs, the model shows some tendency for too much twentieth-century warming in lower latitudes and too little warming in higher latitudes. Differences in Arctic Oscillation behavior between models and observations contribute substantially to an underprediction of the observed warming over northern Asia. In the all-forcing and natural-only forcing runs, a temporary global cooling in the models during the 1880s not evident in the observed temperature records is volcanically forced. El Niño interactions complicate comparisons of observed and simulated temperature records for the El Chichón and Mt. Pinatubo eruptions during the early 1980s and early 1990s.
The simulations support previous findings that twentieth-century global warming has resulted from a combination of natural and anthropogenic forcing, with anthropogenic forcing being the dominant cause of the pronounced late-twentieth-century warming. The regional results provide evidence for an emergent anthropogenic warming signal over many, if not most, regions of the globe. The warming signal has emerged rather monotonically in the Indian Ocean/western Pacific warm pool during the past half-century. The tropical and subtropical North Atlantic and the tropical eastern Pacific are examples of regions where the anthropogenic warming signal now appears to be emerging from a background of more substantial multidecadal variability.
Stott, P, J F B Mitchell, M R Allen, Thomas L Delworth, Jonathan M Gregory, Gerald A Meehl, and B D Santer, 2006: Observational Constraints on Past Attributable Warming and Predictions of Future Global Warming. Journal of Climate, 19(13), DOI:10.1175/JCLI3802.1. Abstract
This paper investigates the impact of aerosol forcing uncertainty on the robustness of estimates of the twentieth-century warming attributable to anthropogenic greenhouse gas emissions. Attribution analyses on three coupled climate models with very different sensitivities and aerosol forcing are carried out. The Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3), Parallel Climate Model (PCM), and GFDL R30 models all provide good simulations of twentieth-century global mean temperature changes when they include both anthropogenic and natural forcings. Such good agreement could result from a fortuitous cancellation of errors, for example, by balancing too much (or too little) greenhouse warming by too much (or too little) aerosol cooling.
Despite a very large uncertainty for estimates of the possible range of sulfate aerosol forcing obtained from measurement campaigns, results show that the spatial and temporal nature of observed twentieth-century temperature change constrains the component of past warming attributable to anthropogenic greenhouse gases to be significantly greater (at the 5% level) than the observed warming over the twentieth century. The cooling effects of aerosols are detected in all three models.
Both spatial and temporal aspects of observed temperature change are responsible for constraining the relative roles of greenhouse warming and sulfate cooling over the twentieth century. This is because there are distinctive temporal structures in differential warming rates between the hemispheres, between land and ocean, and between mid- and low latitudes. As a result, consistent estimates of warming attributable to greenhouse gas emissions are obtained from all three models, and predictions are relatively robust to the use of more or less sensitive models. The transient climate response following a 1% yr−1 increase in CO2 is estimated to lie between 2.2 and 4 K century−1 (5–95 percentiles).
The climate response to idealized changes in the atmospheric CO2 concentration by the new GFDL climate model (CM2) is documented. This new model is very different from earlier GFDL models in its parameterizations of subgrid-scale physical processes, numerical algorithms, and resolution. The model was constructed to be useful for both seasonal-to-interannual predictions and climate change research. Unlike previous versions of the global coupled GFDL climate models, CM2 does not use flux adjustments to maintain a stable control climate. Results from two model versions, Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), are presented.
Two atmosphere–mixed layer ocean or slab models, Slab Model versions 2.0 (SM2.0) and 2.1 (SM2.1), are constructed corresponding to CM2.0 and CM2.1. Using the SM2 models to estimate the climate sensitivity, it is found that the equilibrium globally averaged surface air temperature increases 2.9 (SM2.0) and 3.4 K (SM2.1) for a doubling of the atmospheric CO2 concentration. When forced by a 1% per year CO2 increase, the surface air temperature difference around the time of CO2 doubling [transient climate response (TCR)] is about 1.6 K for both coupled model versions (CM2.0 and CM2.1). The simulated warming is near the median of the responses documented for the climate models used in the 2001 Intergovernmental Panel on Climate Change (IPCC) Working Group I Third Assessment Report (TAR).
The thermohaline circulation (THC) weakened in response to increasing atmospheric CO2. By the time of CO2 doubling, the weakening in CM2.1 is larger than that found in CM2.0: 7 and 4 Sv (1 Sv 106 m3 s−1), respectively. However, the THC in the control integration of CM2.1 is stronger than in CM2.0, so that the percentage change in the THC between the two versions is more similar. The average THC change for the models presented in the TAR is about 3 or 4 Sv; however, the range across the model results is very large, varying from a slight increase (+2 Sv) to a large decrease (−10 Sv).
Prominent multidecadal fluctuations of India summer rainfall, Sahel summer rainfall, and Atlantic Hurricane activity have been observed during the 20th century. Understanding their mechanism(s) will have enormous social and economic implications. We first use statistical analyses to show that these climate phenomena are coherently linked. Next, we use the GFDL CM2.1 climate model to show that the multidecadal variability in the Atlantic ocean can cause the observed multidecadal variations of India summer rainfall, Sahel summer rainfall and Atlantic Hurricane activity (as inferred from vertical wind shear changes). These results suggest that to interpret recent climate change we cannot ignore the important role of Atlantic multidecadal variability.
Observational analyses have documented increases in global ocean temperature, heat content, and sea level in the 20th century. Previous studies argued that the observed ocean warming is a response to increasing greenhouse gases. We use a new climate model to decompose simulated ocean temperature changes into components attributable to subsets of anthropogenic and natural influences. The model simulates a positive trend in global ocean volume mean temperature from the mid 1950s to 2000, consistent with observational estimates. We show that for the period 1861–2000 aerosols have delayed the onset of ocean warming by several decades and reduced the magnitude of the transient warming by approximately two-thirds when compared to the response that arises solely from increasing greenhouse gases. The simulated cooling signature from large volcanic eruptions in the late 19th and early 20th centuries is clearly visible in the subsurface ocean well into the middle part of the 20th century.
Past studies have suggested that increasing atmospheric CO2 will lead to a significant reduction of soil moisture during summer in the extratropics. These studies showed an increase in wintertime rainfall over most mid-latitude continental regions when CO2 is doubled, an earlier snowmelt season and onset of springtime evaporation, and a higher ratio of evaporation to precipitation in summer. These factors led to large-scale increases in soil moisture in winter and decreases in summer. We find that the above processes are important in simulated summer drying in a newly developed climate model. In addition to these thermodynamic processes, we find that changes in atmospheric circulation play an important role in regional hydroclimatic changes. Additional experiments show that the atmospheric circulation changes are forced by the CO2-induced warming of the ocean, particularly the tropical ocean. These results highlight the importance of sea surface temperature changes for regional hydroclimatic changes.
The Sahel, the transition zone between the Saharan desert and the rainforests of Central Africa and the Guinean Coast, experienced a severe drying trend from the 1950s to the 1980s, from which there has been partial recovery. Continuation of either the drying trend or the more recent ameliorating trend would have far-ranging implications for the economy and ecology of the region. Coupled atmosphere/ocean climate models being used to simulate the future climate have had difficulty simulating Sahel rainfall variations comparable to those observed, thus calling into question their ability to predict future climate change in this region. We describe simulations using a new global climate model that capture several aspects of the 20th century rainfall record in the Sahel. An ensemble mean over eight realizations shows a drying trend in the second half of the century of nearly half of the observed amplitude. Individual realizations can be found that display striking similarity to the observed time series and drying pattern, consistent with the hypothesis that the observations are a superposition of an externally forced trend and internal variability. The drying trend in the ensemble mean of the model simulations is attributable to anthropogenic forcing, partly to an increase in aerosol loading and partly to an increase in greenhouse gases. The model projects a drier Sahel in the future, due primarily to increasing greenhouse gases.
The Sahel region of Africa underwent a pronounced interdecadal drying trend in the latter half of the 20th century. In order to investigate this drying trend, several ensembles of numerical experiments are conducted using a recently developed atmospheric general circulation model (AM2, developed at NOAA's Geophysical Fluid Dynamics Laboratory). When the model is forced with the time series of observed SSTs and sea ice from 1950 to 2000, it successfully reproduces the observed interdecadal variability of Sahelian rainfall. Additional experiments are used to estimate the separate contributions to Sahel drought from SST anomalies in various ocean basins. In these, SST anomalies are applied only in the tropics, or only in the Atlantic, Indian and Pacific oceans separately. Forcing from the tropical oceans is dominant in driving the Sahelian rainfall trend. The response of Sahel rainfall to a general warming of the tropical oceans suggests a possible link to greenhouse gas-induced climate change.
In this study, a mechanism is demonstrated whereby a large reduction in the Atlantic thermohaline circulation (THC) can induce global-scale changes in the Tropics that are consistent with paleoevidence of the global synchronization of millennial-scale abrupt climate change. Using GFDL’s newly developed global coupled ocean–atmosphere model (CM2.0), the global response to a sustained addition of freshwater to the model’s North Atlantic is simulated. This freshwater forcing substantially weakens the Atlantic THC, resulting in a southward shift of the intertropical convergence zone over the Atlantic and Pacific, an El Niño–like pattern in the southeastern tropical Pacific, and weakened Indian and Asian summer monsoons through air–sea interactions.
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
It has been suggested that, unless a major effort is made, the atmospheric concentration of carbon dioxide may rise above four times the pre-industrial level in a few centuries. Here we use a coupled atmosphere-ocean-land model to explore the response of the global water cycle to such a large increase in carbon dioxide, focusing on river discharge and soil moisture. Our results suggest that water is going to be more plentiful in those regions of the world that are already `water-rich'. However, water stresses will increase significantly in regions and seasons that are already relatively dry. This could pose a very challenging problem for water-resource management around the world. For soil moisture, our results indicate reductions during much of the year in many semi-arid regions of the world, such as the southwestern region of North America, the northeastern region of China, the Mediterranean coast of Europe, and the grasslands of Australia and Africa. In some of these regions, soil moisture values are reduced by almost a factor of two during the dry season. The drying in semi-arid regions is likely to induce the outward expansion of deserts to the surrounding regions. Over extensive regions of both the Eurasian and North American continents in high and middle latitudes, soil moisture decreases in summer but increases in winter, in contrast to the situation in semi-arid regions. For river discharge, our results indicate an average increase of ~ 15% during the next few centuries. The discharges from Arctic rivers such as the Mackenzie and Ob' increase by much larger fractions. In the tropics, the discharges from the Amazonas and Ganga-Brahmaputra also increase considerably. However, the percentage changes in runoff from other tropical and many mid-latitude rivers are smaller.
We present results from a series of ensemble integrations of a global coupled atmosphere-ocean model for the period 1865-1997. Each ensemble consists of three integrations initialized from different points in a long-running GFDL R30 coupled model control simulation. The first ensemble includes time-varying forcing from greenhouse gases only. In the remaining three ensembles, forcings from anthropogenic sulfate aerosols, solar variability, and volcanic aerosols in the stratosphere are added progressively, such that the fourth ensemble uses all four of these forcings. The effects of anthropogenic sulfate aerosols are represented by changes in surface albedo, and the effects of volcanic aerosols are represented by latitude-dependent perturbations in incident solar radiation. Comparisons with observations reveal that the addition of the natural forcings (solar and volcanic) improves the simulation of global multidecadal trends in temperature, precipitation, and ocean heat content. Solar and volcanic forcings are important contributors to early twentieth century warming. Volcanic forcing reduces the warming simulated for the late twentieth century. Interdecadal variations in global mean surface air temperature from the ensemble of experiments with all four forcings are very similar to observed variations during most of the twentieth century. The improved agreement of simulated and observed temperature trends when natural climate forcings are included supports the climatic importance of variations in radiative forcing during the twentieth century.
The transient responses of two versions of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model to a climate change forcing scenario are examined. The same computer codes were used to construct the atmosphere, ocean, sea ice and land surface components of the two models, and they employ the same types of sub-grid-scale parameterization schemes. The two model versions differ primarily, but not solely, in their spatial resolution. Comparisons are made of results from six coarse-resolution R15 climate change experiments and three medium-resolution R30 experiments in which levels of greenhouse gases (GHGs) and sulfate aerosols are specified to change over time. The two model versions yield similar global mean surface air temperature responses until the second half of the 21st century, after which the R15 model exhibits a somewhat larger response. Polar amplification of the Northern Hemisphere's warming signal is more pronounced in the R15 model, in part due to the R15's cooler control climate, which allows for larger snow and ice albedo positive feedbacks. Both models project a substantial weakening of the North Atlantic overturning circulation and a large reduction in the volume of Arctic sea ice to occur in the 21st century. Relative to their respective control integrations, there is a greater reduction of Arctic sea ice in the R15 experiments than in the R30 simulations as the climate system warms. The globally averaged annual mean precipitation rate is simulated to increase over time, with both model versions projecting an increase of about 8% to occur by the decade of the 2080s. While the global mean precipitation response is quite similar in the two models, regional differences exist, with the R30 model displaying larger increases in equatorial regions.
Rutherford, S, M E Mann, Thomas L Delworth, and Ronald J Stouffer, 2003: Climate field reconstruction under stationary and nonstationary forcing. Journal of Climate, 16(3), 462-479. Abstract PDF
The fidelity of climate reconstructions employing covariance-based calibration techniques is tested with varying levels of sparseness of available data during intervals of relatively constant (stationary) and increasing (nonstationary) forcing. These tests employ a regularized expectation-maximization algorithm using surface temperature data from both the instrumental record and coupled ocean-atmosphere model integrations. The results indicate that if radiative forcing is relatively constant over a data-rich calibration period and increases over a data-sparse reconstruction period, the imputed temperatures in the reconstruction period may be biased and may underestimate the true temperature trend. However, if radiative forcing is stationary over a data-sparse reconstruction period and increases over a data-rich calibration period, the imputed values in the reconstruction period are nearly unbiased. These results indicate that using the data-rich part of the twentieth-century instrumental record (which contains an increasing temperature trend plausibly associated with increasing radiative forcing) for calibration does not significantly bias reconstructions of prior climate.
Visbeck, M, Eric P Chassignet, R G Curry, Thomas L Delworth, R R Dickson, and G Krahmann, 2003: The ocean's response to North Atlantic Oscillation variability In The North Atlantic Oscillation: Climatic Significance and Environmental Impact, Washington, DC, American Geophysical Union, 113-145. Abstract
The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability in the North Atlantic Sector. Basin scale changes in the atmospheric forcing significantly affect properties and circulation of the ocean. Part of the response is local and rapid (surface temperature, mixed-layer depth, upper ocean heat content, surface Ekman transport, sea ice cover). However, the geostrophically balanced large-scale horizontal and overturning circulation can take several years to adjust to changes in the forcing. The delayed response is non-local in the sense that waves and the mean circulation communicate perturbations at the air-sea interface to other parts of the Atlantic basin. A delayed and non-local response can potentially give rise to oscillatory behavior if there is significant feedback from the ocean to the atmosphere. We conjecture that, on decadal and longer time scales, changes in the ocean's heat storage and transport should have an increasingly important impact on the climate. Finally, changes in the ocean circulation and distribution of heat and freshwater will also alter ventilation rates and pathways. Thus we expect a change in the net uptake of gases (e.g., O2, CO2), altered nutrient balance, and changes in the dispersion of marine life. We review what is known about the oceanic response to changes in NAO-induced forcing from combined theoretical, numerical experimentation and observational perspectives.
A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75° longitude by 2.25° latitude. The ocean component has a resolution of approximately 1.875° longitude by 2.25° latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near milennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.
Radiative effects of anthropogenic changes in atmospheric composition are expected to cause climate changes, in particular an intensification of the global water cycle with a consequent increase in flood risk. But the detection of anthropogenically forced changes in flooding is difficult because of the substantial natural variability; the dependence of streamflow trends on flow regime further complicates the issue. Here we investigate the changes in risk of great floods- that is, floods with discharges exceeding 100-year levels from basins larger than 200,000 km2- using both streamflow measurements and numerical simulations of the anthropogenic climate change associated with greenhouse gases and direct radiative effects of sulphate aerosols. We find that the frequency of great floods increased substantially during the twentieth century. The recent emergence of a statistically significant positive trend in risk of great floods is consistent with results from the climate model, and the model suggests that the trend will continue.
The effect of changes in observational coverage on the association between the Arctic oscillation (AO) and extratropical Northern Hemisphere surface temperature is examined. A coupled atmosphere-ocean model, which produces a realistic simulation of the circulation and temperature patterns associated with the AO, is used as a surrogate for the real-climate system. The association between the AO and spatial mean-temperature, as quantified by regressing the latter on the AO index, is subject to a positive bias due to the incomplete spatial coverage of the observational network. The bias is largest during the early part of the twentieth century and decreases, but does not vanish, thereafter.
Kushner, P J., Isaac M Held, and Thomas L Delworth, 2001: Southern Hemisphere atmospheric circulation response to global warming. Journal of Climate, 14(10), 2238-2249. Abstract PDF
The response of the Southern Hemisphere (SH), extratropical, atmospheric general circulation to transient, anthropogenic, greenhouse warming is investigated in a coupled climate model. The extratropical circulation response consists of a SH summer half-year poleward shift of the westerly jet and a year-round positive wind anomaly in the stratosphere and the tropical upper troposphere. Along with the poleward shift of the jet, there is a poleward shift of several related fields, including the belt of eddy momentum-flux convergence and the mean meridional overturning in the atmosphere and in the ocean. The tropospheric wind response projects strongly onto the model's "Southern Annular Mode" (also known as the "Antarctic oscillation"), which is the leading pattern of variability of the extratropical zonal winds.
We compared the temporal variability of the heat content of the world ocean, of the global atmosphere, and of components of Earth's cryosphere during the latter half of the 20th century. Each component has increased its heat content (the atmosphere and the ocean) or exhibited melting (the cryosphere). The estimated increase of observed global ocean heat content (over the depth range from 0 to 3000 meters) between the 1950s and 1990s is at least one order of magnitude larger than the increase in heat content of any other component. Simulation results using an atmosphere-ocean general circulation model that includes estimates of the radiative effects of observed temporal variations in greenhouse gases, sulfate aerosols, solar irradiance, and volcanic aerosols over the past century agree with our observation-based estimate of the increase in ocean heat content. The results we present suggest that the observed increase in ocean heat content may largely be due to the increase of anthropogenic gases in Earth's atmosphere.
This lecture discusses the low-frequency variability of surface temperature using a coupled ocean-atmosphere-land-surface model developed at the Geophysical Fluid Dynamics Laboratory/NOAA. Despite the highly idealized parametrization of various physical processes, the coupled model simulates reasonably well the variability of local and global mean surface temperature. The first half of the lecture explores the basic physical mechanisms responsible for the variability. The second half examines the trends of local surface temperature during the last half century in the context of decadal variability simulated by the coupled model.
Stocker, T F., Thomas L Delworth, Stephen M Griffies, Isaac M Held, V Ramaswamy, and Brian J Soden, et al., 2001: Physical climate processes and feedbacks In Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK, Cambridge University Press, 418-470.
Allen, M R., P Stott, J F B Mitchell, R Schnur, and Thomas L Delworth, 2000: Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature, 407(6804), 617-620. Abstract PDF
Forecasts of climate change are inevitably uncertain. It is therefore essential to quantify the risk of significant departures from the predicted response to a given emission scenario. Previous analyses of this risk have been based either on expert opinion, perturbation analysis of simplified climate models or the comparison of predictions from general circulation models. Recent observed changes that appear to be attributable to human influence provide a powerful constraint on the uncertainties in multi-decadal forecasts. Here we assess the range of warming rates over the coming 50 years that are consistent with the observed near-surface temperature record as well as with the overall patterns of response predicted by several general circulation models. We expect global mean temperatures in the decade 2036-46 to 1-2.5 K warmer than in pre-industrial times under a 'business as usual' emission scenario. This range is relatively robust to errors in the models' climate sensitivity, rate of oceanic heat uptake or global response to sulphate aerosols as long as these errors are persistent over time. Substantial changes in the current balance of greenhouse warming and sulphate aerosol cooling would, however, increase the uncertainty. Unlike 50-year warming rates, the final equilibrium warming after the atmospheric composition stabilizes remains very uncertain, despite the evidence provided by the emerging signal.
Delworth, Thomas L., and Keith W Dixon, 2000: Implications of the recent trend in the Arctic/North Atlantic Oscillation for the North Atlantic Thermohaline Circulation. Journal of Climate, 13(21), 3721-3727. Abstract PDF
Most projections of greenhouse gas-induced climate change indicate a weakening of the thermohaline circulation (THC) in the North Atlantic in response to increased freshening and warming in the subpolar region. These changes reduce high-latitude upper-ocean density and therefore weaken the THC. Using ensembles of numerical experiments with a coupled ocean-atmosphere model, it is found that this weakening could be delayed by several decades in response to a sustained upward trend in the Arctic/North Atlantic oscillation during winter, such as has been observed over the last 30 years. The stronger winds over the North Atlantic associated with this trend extract more heat from the ocean, thereby cooling and increasing the density of the upper ocean and thus opposing the previously described weakening of the THC. This result is of particular importance if the positive trend in the Arctic/North Atlantic oscillation is a response to increasing greenhouse gases, as has been recently suggested.
Delworth, Thomas L., and R J Greatbatch, 2000: Multidecadal thermohaline circulation variability driven by atmospheric surface flux forcing. Journal of Climate, 13(9), 1481-1495. Abstract PDF
Previous analyses of an extended integration of the Geophysical Fluid Dynamics Laboratory coupled climate model have revealed pronounced multidecadal variations of the thermohaline circulation (THC) in the North Atlantic. The purpose of the current work is to assess whether those fluctuations can be viewed as a coupled air-sea mode (in the sense of ENSO), or as an oceanic response to forcing from the atmosphere model, in which large-scale feedbacks from the ocean to the atmospheric circulation are not critical.
A series of integrations using the ocean component of the coupled model are performed to address the above question. The ocean model is forced by suitably chosen time series of surface fluxes from either the coupled model or a companion integration of an atmosphere-only model run with a prescribed seasonal cycle of SSTs and sea-ice thickness. These experiments reveal that 1) the previously identified multidecadal THC variations can be largely viewed as an oceanic response to surface flux forcing from the atmosphere model, although air-sea coupling through the thermodynamics appears to modify the amplitude of the variability, and 2) variations in heat flux are the dominant term (relative to the freshwater and momentum fluxes) in driving the THC variability. Experiments driving the ocean model using either high- or low-pass-filtered heat fluxes, with a cutoff period of 20 yrs. show that the multidecadal THC variability is driven by the low-frequency portion of the spectrum of atmospheric flux forcing. Analyses have also revealed that the multidecadal THC fluctuations are driven by a spatial pattern of surface heat flux variations that bears a strong resemblance to the North Atlantic oscillation. No conclusive evidence is found that the THC variability is part of a dynamically coupled mode of the atmosphere and ocean models.
The observed global warming of the past century occurred primarily in two distinct 20-year periods, from 1925 to 1944 and from 1978 to the present. Although the latter warming is often attributed to a human-induced increase of greenhouse gases, causes of the earlier warming are less clear because this period precedes the time of strongest increases in human-induced greenhouse gas (radiative) forcing. Results from a set of six integrations of a coupled ocean-atmosphere climate model suggest that the warming of the early 20th century could have resulted from a combination of human-induced radiative forcing and an unusually large realization of internal multidecadal variability of the coupled ocean-atmosphere system. This conclusion is dependent on the model's climate sensitivity, internal variability, and the specification of the time-varying human-induced radiative forcing.
Delworth, Thomas L., and M E Mann, 2000: Observed and simulated multidecadal variability in the Northern Hemisphere. Climate Dynamics, 16(9), 661-676. Abstract PDF
Analyses of proxy based reconstructions of surface temperatures during the past 330 years show the existence of a distinct oscillatory mode of variability with an approximate time scale of 70 years. This variability is also seen in instrumental records, although the oscillatory nature of the variability is difficult to assess due to the short length of the instrumental record. The spatial pattern of this variability is hemispheric or perhaps even global in scale, but with particular emphasis on the Atlantic region. Independent analyses of multi-century integrations of two versions of the GFDL coupled atmosphere-ocean model also show the existence of distinct multidecadal variability in the North Atlantic region which resembles the observed pattern. The model variability involves fluctuations in the intensity of the thermohaline circulation in the North Atlantic. It is our intent here to provide a direct comparison of the observed variability to that simulated in a coupled ocean-atmosphere model, making use of both existing instrumental analyses and newly available proxy based multi-century surface temperature estimates. The analyses demonstrate a substantial agreement between the simulated and observed patterns of multidecadal variability in sea surface temperature (SST) over the North Atlantic. There is much less agreement between the model and observations for sea level pressure. Seasonal analyses of the variability demonstrate that for both the model and observations SST appears to be the primary carrier of the multidecadal signal.
Mehta, V M., E Lindstrom, A Busalacchi, J E Hansen, K M Lau, J Susskind, Thomas L Delworth, Clara Deser, Gerald A Meehl, L-L Fu, and S Levitus, et al., 2000: Proceedings of the NASA Workshop on Decadal Climate Variability. Bulletin of the American Meteorological Society, 81(12), 2983-2986. PDF
Mehta, V M., M J Suarez, J Y Manganello, and Thomas L Delworth, 2000: Oceanic influence on the North Atlantic Oscillation and associated Northern Hemisphere climate variations: 1959-1993. Geophysical Research Letters, 27(1), 121-124. Abstract PDF
The North Atlantic Oscillation (NAO) exhibits variations at interannual to multidecadal time scales and is associated with climate variations over eastern North America, the North Atlantic, Europe, and North Africa. Therefore, it is very important to understand causes of these NAO variations and assess their predictability. It has been hypothesized, based on observations, that sea surface temperature (SST) and sea-ice variations in the North Atlantic Ocean influence the NAO. We describe results of an ensemble of sixteen experiments with an atmospheric general circulation model in which we used observed SST and sea-ice boundary conditions globally during 1949-1993. We show that multiyear NAO and associated climate variations can be simulated reasonably accurately if results from a large number of experiments are averaged. We also show that the ambiguous results of previous NAO modeling studies were strongly influenced by the ensemble size, which was much smaller than that in the present study. The implications of these results for understanding and predictability of the NAO are discussed.
Barnett, T P., and Thomas L Delworth, et al., 1999: Detection and attribution of recent climate change: A status report. Bulletin of the American Meteorological Society, (80), 12, 2631-2660. Abstract PDF
This paper addresses the question of where we now stand with respect to detection and attribution of an anthropogenic climate signal. Our ability to estimate natural climate variability, against which claims of anthropogenic signal detection must be made, is reviewed. The current situation suggests control runs of global climate models may give the best estimates of natural variability on a global basis, estimates that appear to be accurate to within a factor of 2 or 3 at multidecadal timescales used in detection work.
Present uncertainties in both observations and model-simulated anthropogenic signals in near-surface air temperature are estimated. The uncertainty in model simulated signals is, in places, as large as the signal to be detected. Two different, but complementary, approaches to detection and attribution are discussed in the context of these uncertainties.
Applying one of the detection strategies, it is found that the change in near-surface, June through August air temperature field over the last 50 years is generally different at a significance level of 5% from that expected from model-based estimates of natural variability. Greenhouse gases alone cannot explain the observed change. Two of four climate models forced by greenhouse gases and direct sulfate aerosols produce results consistent with the current climate change observations, while the consistency of the other two depends on which model's anthropogenic fingerprints are used. A recent integration with additional anthropogenic forcings (the indirect effects of sulfate aerosols and tropospheric ozone) and more complete tropospheric chemistry produced results whose signal amplitude and pattern were consistent with current observations, provided the model's fingerprint is used and detection carried out over only the last 30 years of annually averaged data. This single integration currently cannot be corroborated and provides no opportunity to estimate the uncertainties inherent in the results, uncertainties that are thought to be large and poorly known. These results illustrate the current large uncertainty in the magnitude and spatial pattern of the direct and indirect sulfate forcing and climate response. They also show detection statements depend on model-specific fingerprints, time period, and seasonal character of the signal, dependencies that have not been well explored.
Most, but not all, results suggest that recent changes in global climate inferred from surface air temperature are likely not due solely to natural causes. At present it is not possible to make a very confident statement about the relative contributions of specific natural and anthropogenic forcings to observed climate change. One of the main reasons is that fully realistic simulations of climate change due to the combined effects of all anthropogenic and natural forcings mechanisms have yet to be computed. A list of recommendations for reducing some of the uncertainties that currently hamper detection and attribution studies is presented.
Changes in Heat Index (a combined measure of temperature and humidity) associated with global warming are evaluated based on the output from four extended integrations of the GFDL coupled ocean-atmosphere climate model. The four integrations are: a control with constant levels of atmospheric carbon dioxide (CO2), a second integration in which an estimate of the combined radiative forcing of greenhouse gases and sulfate aerosols over the period 1765-2065 is used to force the model, and a third (fourth) integration in which atmospheric CO2 increases at the rate of 1% per year to double (quadruple) its initial value, and is held constant thereafter. While the spatial patterns of the changes in Heat Index are largely determined by the changes in surface air temperature, increases in atmospheric moisture can substantially amplify the changes in Heat Index over regions which are warm and humid in the Control integration. The regions most prone to this effect include humid regions of the Tropics and summer hemisphere extra-tropics, including the southeastern United States, India, southeast Asia and northern Australia.
The mechanism by which the model-simulated North Atlantic thermohaline circulation (THC) weakens in response to increasing greenhouse gas (GHG) forcing is investigated through the use of a set of five multi-century experiments. Using a coarse resolution version of the GFDL coupled climate model, the role of various surface fluxes in weakening the THC is assessed. Changes in net surface freshwater fluxes (precipitation, evaporation, and runoff from land) are found to be the dominant cause for the model's THC weakening. Surface heat flux changes brought about by rising GHG levels also contribute to THC weakening, but are of secondary importance. Wind stress variations have negligible impact on the THC's strength in the transient GHG experiment.
Analyses are conducted to assess whether simulated trends in SST and land surface air temperature from two versions of a coupled ocean-atmosphere model are consistent with the geographical distribution of observed trends over the period 1949-1997. The simulated trends are derived from model experiments with both constant and time-varying radiative forcing. The models analyed are low-resolution (R15, ~4º) and medium-resolution (R30, ~2º) versions of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model. Internal climate variability is estimated from long control integrations of the models with no change of external forcing. The radiatively forced trends are based on ensembles of integrations using estimated past concentrations of greenhouse gases and direct effects of anthropogenic sulfate aerosols (G+S). For the regional assessment, the observed trends at each grid point with adequate temporal coverage during 1949-1997 are first compared with the R15 and R30 model unforced internal variability. Nearly 50% of the analyzed areas have observed warming trends exceeding the 95th percentile of trends from the control simulations. These results suggest that regional warming trends over much of the globe during 1949-1997 are very unlikely to have occurred due to internal climate variability alone and suggest a role for a sustained positive thermal forcing such as increasing greenhouse gases. The observed trends are then compared with the trend distributions obtained by combining the ensemble mean G+S forced trends with the internal variability "trend" distributions from the control runs. Better agreement is found between the ensemble mean G+S trends and the observed trends than between the model internal variability alone and the observed trends. However, the G+S trends are still significantly different from the observed trends over about 30% of the areas analyzed. Reasons for these regional inconsistencies between the simulated and the observed trends include possible deficiencies in (1) specified radiative forcings, (2) simulated responses to specified radiative forcings, (3) simulation of internal climate variability, or (4) observed temperature records.
Delworth, Thomas L., and V M Mehta, 1998: Simulated interannual to decadal variability in the tropical and sub-tropical North Atlantic. Geophysical Research Letters, 25(15), 2825-2828. Abstract PDF
The dominant pattern of tropical and sub-tropical North Atlantic sea surface temperature (SST) anomalies simulated in the GFDL coupled ocean-atmosphere model is identified and compared to observations. The spatial pattern and temporal variability of that pattern resemble observational results. On interannual time scales it is shown that anomalous surface heat fluxes, consistent with variations in the intensity of the sub-tropical high pressure system in the atmosphere and the associated Northeasterly Trade winds, appear to be the most important process for generating this SST pattern. This relationship is also true on decadal time scales, although the relative role of oceanic heat advection is somewhat larger than on the interannual time scales.
Pronounced oscillations of ocean temperature and salinity occur in the Greenland Sea in a 2000 year integration of a coupled ocean-atmosphere model. The oscillations, involving both the surface and subsurface ocean layers, have a timescale of approximately 40-80 years, and are associated with fluctuations in the intensity of the East Greenland Current. The Greenland Sea temperature and salinity variations are preceded by large-scale changes in near-surface salinity in the Arctic, which appear to propagate out of the Arctic through the East Greenland Current. These anomalies then propagate around the subpolar gyre into the Labrador Sea and the central North Atlantic. These oscillations are coherent with previously identified multi-decadal fluctuations in the intensity of the North Atlantic thermohaline circulation. The oscillations in the Greenland Sea are related to atmospheric variability. Negative (cold) anomalies of surface air temperature are associated with negative (cold) sea surface temperature (SST) anomalies in the Greenland Sea, with amplitudes up to 2°C near Greenland declining to several tenths of a degree C over northwestern Europe. The cold SST anomalies and intensified East Greenland Current are also associated with enhanced northerly winds over the Greenland Sea.
Delworth, Thomas L., 1996: North Atlantic interannual variability in a coupled ocean-atmosphere model. Journal of Climate, 9(10), 2356-2375. Abstract PDF
The primary mode of sea surface temperature variability in the North Atlantic on interannual timescales during winter is examined in a coupled ocean-atmosphere model. The model, developed at the Geophysical Fluid Dynamics Laboratory, is global in domain with realistic geography and a seasonal cycle of insolation. Analyses performed on a 1000-year integration of this model show that this mode is characterized by zonal bands of SST anomalies in the North Atlantic and bears a distinct resemblance to observational results. The largest anomalies in the model are to the southeast of Newfoundland.
The model SST variations appear to be related to a north-south dipole in the atmospheric 500-mb geopotential height field, which resembles the North Atlantic oscillation and the Western Atlantic pattern. Analyses are presented that show that this mode of SST variability is primarily driven by perturbations to the surface heat fluxes, which are largely governed by atmospheric variability. Changes in model ocean circulation also contribute to this mode of variability but appear to be of secondary importance.
Additional integrations are analyzed to examine the above conclusion. The same atmospheric model used in the above integration was coupled to a 50-m slab ocean and integrated for 500 years. The primary mode of SST variability in this model, in which there were no effects of ocean dynamics, resembles the primary mode from the coupled model, strengthening the conclusion that the surface fluxes are the primary mechanism generating this oceanic variability. One notable difference between the two models is related to the presence of deep vertical mixing at high latitudes in the model with a fully dynamic ocean. An additional 500-year integration of the atmospheric model with a prescribed seasonal cycle of SSTs lends further support to this conclusion, as do additional diagnostic calculations in which a 50-m slab ocean was forced by the time series of surface fluxes from both the prescribed SST and fully coupled model.
Delworth, Thomas L., and Syukuro Manabe, 1996: Climate variability and land surface processes In From Atmospheric Circulation to Global Change - Celebration of the 80th Birthday of Prof. YE Duzheng, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing: China, China Meteorological Press, 477-502. Abstract
The coupled ocean-atmosphere-land climate system is characterized by substantial amounts of variability at a wide range of spatial and temporal scales. This natural variability of climate increases the difficulty of detecting climate change attributable to increasing greenhouse gas concentrations. A key issue in climate research is obtaining a better description of this variability and the physical mechanisms responsible for it. One of the important physical processes contributing to this variability is the interaction between the land surface and the atmosphere. Through its effect on the surface energy flux components, the land surface can exert a pronounced effect on the variability of the atmosphere. The potential importance of such interactions for climate variability is examined through the use of numerical modeling studies. The physical mechanisms governing the time scales of soil moisture variability in the model are outlined, and observational evidence is presented supporting this analysis. In addition, it is shown that interactions between soil wetness and the atmosphere can both increase the total variability of the atmosphere and lengthen the time scales of near-surface atmospheric fluctuations.
Delworth, Thomas L., 1995: Commentary on the paper of Parker et al In Natural Climate Variability on Decade-to-Century Time Scales, Washington, DC, National Academy Press, 251.
Delworth, Thomas L., Syukuro Manabe, and Ronald J Stouffer, 1995: North Atlantic Interdecadal variability in a coupled model In Natural Climate Variability on Decade-to-Century Time Scales, Washington, DC, National Academy Press, 432-439; 440-441. Abstract
A fully coupled ocean-atmosphere model is shown to have irregular oscillations of the thermohaline circulation in the North Atlantic Ocean with a time scale of approximately 40 to 50 years. The fluctuations appear to be driven by density anomalies in the sinking region of the thermohaline circulation combined with much smaller density anomalies of opposite sign in the broad, rising region. Anomalies of sea surface temperature associated with this oscillation induce surface air temperature anomalies over the northern North Atlantic, the Arctic, and northwestern Europe. The spatial pattern of sea surface temperature anomalies bears an encouraging resemblance to a pattern of observed interdecadal variability in the North Atlantic.
Mehta, V M., and Thomas L Delworth, 1995: Decadal variability of the tropical Atlantic Ocean surface temperature in shipboard measurements and in a global ocean-atmosphere model. Journal of Climate, 8(2), 172-190. Abstract PDF
Numerous analyses of relatively short (25-30 years in length) time series of the observed surface temperature of the tropical Atlantic Ocean have indicated the possible existence of decadal timescale variability. It was decided to search for such variability in 100-yr time series of sea surface temperature (SST) measured aboard ships and available in the recently published Global Ocean Surface Temperature Atlas (GOSTA). Fourier and singular spectrum analyses of the GOSTA SST time series averaged over 11 subregions, each approximately 1 x 106 km2 in area, show that pronounced quasi-oscillatory decadal (~8-20 yr) and multidecadal (~30-40 yr) timescale variability exists in the GOSTA dataset over the tropical Atlantic.
Motivated by the above results, SST variability was investigated in a 200-yr integration of a global model of the coupled oceanic and atmospheric general circulations developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The second 100 yr of SST in the coupled model's tropical Atlantic region were analyzed with a variety of techniques. Analyses of SST time series, averaged over approximately the same subregions as the GOSTA time series, showed that the GFDL SST anomalies also undergo pronounced quasi-oscillatory decadal and multidecadal variability but at somewhat shorter timescales than the GOSTA SST anomalies. Further analyses of the horizontal structures of the decadal timescale variability in the GFDL coupled model showed the existence of two types of variability in general agreement with results of the GOSTA SST time series analyses. One type, characterized by timescales between 8 and 11 yr, has high spatial coherence within each hemisphere but not between the two hemispheres of the tropical Atlantic. A second type, characterized by timescales between 12 and 20 yr, has high spatial coherence between the two hemispheres. The second type of variability is considerably weaker than the first. As in the GOSTA time series, the multidecadal variability in the GFDL SST time series has approximately opposite phases between the tropical North and South Atlantic Oceans. Empirical orthogonal function analyses of the tropical Atlantic SST anomalies revealed a north-south bipolar pattern as the dominant pattern of decadal variability. It is suggested that the bipolar pattern can be interpreted as decadal variability of the interhemispheric gradient of SST anomalies.
The decadal and multidecadal timescale variability of the tropical Atlantic SST, both in the actual and in the GFDL model, stands out significantly above the background "red noise" and is coherent within each of the time series, suggesting that specific sets of processes may be responsible for the choice of the decadal and multidecadal timescales. Finally, it must be emphasized that the GFDL coupled ocean-atmosphere model generates the decadal and multidecadal timescale variability without any externally applied force, solar or lunar, at those timescales.
A fully coupled ocean-atmosphere model is shown to have irregular oscillations of the thermohaline circulation in the North Atlantic Ocean with a time scale of approximately 50 years. The irregular oscillation appears to be driven by density anomalies in the sinking region of the thermohaline circulation (approximately 52°N to 72°N) combined with much smaller density anomalies of opposite sign in the broad, rising region. The spatial pattern of sea surface temperature anomalies associated with this irregular oscillation bears an encouraging resemblance to a pattern of observed interdecadal variability in the North Atlantic. The anomalies of sea surface temperature induce model surface air temperature anomalies over the northern North Atlantic, Arctic, and northwestern Europe.
The coupled ocean-atmosphere-land climate system is characterized by substantial amounts of variability on a wide range of spatial and temporal scales. This natural variability of climate increases the difficulty of detecting climate change attributable to increasing greenhouse gas concentrations. A key issue in climate research is obtaining a better description of this variability and the physical mechanisms responsible for it. One of the important physical processes contributing to this variability is the interaction between the land surface and the atmosphere. Through its effect on the surface energy flux components, the land surface can exert a pronounced effect on the variability of the atmosphere. The potential importance of such interactions for climate variability is examined through the use of numerical modeling studies. The physical mechanisms governing the time scales of soil moisture variability in the model are outlined, and observational evidence is presented supporting this analysis. In addition, it is shown that interactions between soil wetness and the atmosphere can both increase the total variability of the atmosphere and lengthen the time scales of near-surface atmospheric fluctuations.
The temporal variability of soil wetness and its interactions with the atmosphere were studied using a general circulation model of the atmosphere. It was found that time series of soil wetness computed by the model contain substantial amounts of variance at low frequencies. Long time-scale anomalies of soil moisture resemble the red noise response of the soil layer to white noise rainfall forcing. The dependence of the temporal variability of soil moisture on potential evaporation and precipitation is discussed.
The influence of land surface processes on near-surface atmospheric variability on seasonal and interannual time scales is studied using output from two integrations of a general circulation model. In the first experiment, of 50 year s duration, soil moisture is predicted, thereby taking into consideration interactions between the surface moisture budget and the atmosphere. In the second experiment, of 25 years duration, the seasonal cycle of soil moisture is prescribed at each grid point based upon the results of the first integration, thereby suppressing these interactions. The same seasonal cycle of soil moisture is prescribed for each year of the second integration. Differences in atmospheric variability between the two integrations are due to interactions between the surface moisture budget and the atmosphere. Analyses of monthly data indicate that the surface moisture budget interacts with the atmosphere in such a way as to lengthen the time scales of fluctuation of near-surface relative humidity and temperature, as well as to increase the total variability of the atmosphere. During summer months at middle latitudes, the persistence of near-surface relative humidity, as measured by correlations of monthly mean relative humidity between successive months, increases from near zero in the experiment with prescribed soil moisture to as large as 0.6 in the experiment with interactive soil moisture, which corresponds to an e-folding time of approximately two months. The standard deviation of monthly mean relative humidity during summer is substantially larger in the experiment with interactive soil moisture than in the experiment with prescribed soil moisture. Surface air temperature exhibits similar changes, but of smaller magnitude. Soil wetness influences the atmosphere by altering the partitioning of the outgoing energy flux at the surface into latent and sensible heat compon ents. Fluctuations of soil moisture result in large variations in these fluxes, and thus significant variations in near surface relative humidity and temperature. Because anomalies of monthly mean soil moisture are characterized by seasonal and interannual time scales, they create persistent anomalous fluxes of latent and sensible heat, thereby increasing the persistence of near-surface atmospheric relative humidity and temperature.
Delworth, Thomas L., and Syukuro Manabe, 1988: Influence of potential evaporation on the variabilities of simulated soil wetness and climate. Journal of Climate, 1(5), 523-547. Abstract PDF
An atmospheric general circulation model with prescribed sea surface temperature and cloudiness was integrated for 50 years to study atmosphere-land surface interactions. The temporal variability of model soil moisture and precipitation has been studied in an effort to understand the interactions of these variables with other components of the climate system. Temporal variability analysis has shown that the spectra of monthly mean precipitation over land are close to white at all latitudes, with total variance decreasing poleward. In contrast, the spectra of soil moisture are red and become more red with increasing latitude. As a measure of this redness, half of the total variance of a composite tropical soil moisture spectrum occurs at periods longer than nine months, while at high latitudes, half of the total variance of a composite soil moisture spectrum occurs at periods longer than 22 months. The spectra of soil moisture also exhibit marked longitudinal variations.
These spectral results may be viewed in light of stochastic theory. The formulation of the GFDL soil moisture parameterization is mathematically similar to a stochastic process. According to this model, forcing of a system by an input white noise variable (precipitation) will yield an output variable (soil moisture) with a red spectrum, the redness of which is controlled by a damping term (potential evaporation). Thus, the increasingly red nature of the soil moisture spectra at higher latitudes is a result of declining potential evaporation values at higher latitudes. Physically, soil moisture excesses are dissipated more slowly at high latitudes, where the energy available for evaporation is small.
Some of the longitudinal variations in soil moisture spectra result from longitudinal variations in potential evaporation, while others are explicable in terms of the value of the ratio of potential evaporation to precipitation. Regions where this value is less than one are characterized by frequent runoff and short time scales of soil moisture variability. By preventing excessive positive anomalies of soil moisture, the runoff process hastens the return of soil moisture values to their mean state, thereby shortening soil moisture time scales.
Through the use of a second GCM integration with prescribed soil moisture, it was shown that interactive soil moisture may substantially increase summer surface air temperature variability. Soil moisture interacts with the atmosphere primarily through the surface energy balance. The degree of soil saturation strongly influences the partitioning of outgoing energy from the surface between the latent and sensible heat fluxes. Interactive soil moisture allows larger variations of these fluxes, thereby increasing the variance of surface air temperature. Because the flux of latent heat is directly proportional to potential evaporation under conditions of sufficient moisture, the influence of soil moisture on the atmosphere is greatest when the potential evaporation value is large. This occurs most frequently in the tropics and summer hemisphere extratropics.
