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.
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 NOAA. SPEAR is an effort to develop a seamless system for prediction and research across timescales. 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 ocean data assimilation as 3‐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 SST forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño‐Southern Oscillation (ENSO).
Strazzo, S E., D C Collins, A Schepen, Q J Wang, E Becker, and Liwei Jia, February 2019: Application of a hybrid statistical-dynamical system to seasonal prediction of North American temperature and precipitation. Monthly Weather Review, 147(2), DOI:10.1175/MWR-D-18-0156.1. Abstract
Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian post-processing technique–Calibration, Bridging, and Merging (CBaM)–which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multi-Model Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO-precipitation teleconnection pattern compared to the ENSO-temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.
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.
Unprecedented high intensity flooding induced by extreme precipitation was reported over Chennai in India during November-December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in future and the related climate phenomena, for planning and mitigation purposes. Here, we make use of a suite of simulations from GFDL high-resolution coupled climate models to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in future. Climate of 20th century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fail to show evidence for increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither an El Niño nor La Niña enhances the probability of extreme floods over Chennai. However, warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. The atmospheric condition such as a tropical depression over Bay of Bengal favoring the transport of moisture from warm Bay of Bengal is conducive for intense precipitation.
Floods in the Mississippi basin can have large negative societal, natural and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-year return Lower-Mississippi River floods using a global coupled climate model with an integrated surface-water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land-surface data, transient climate). In the months preceding the model’s 100-year floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the build up of snowpack, making the river system prone to these major flooding events. The melt water from snowpack in the northern Missouri and Upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient-forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-year floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully-coupled land-atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.
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.
There is large uncertainty in the simulation of transient climate sensitivity. This study aims to understand how such uncertainty is related to the simulation of the base climate by comparing two simulations with the same model but in which CO2 is increased from either a preindustrial (1860) or a present-day (1990) control simulation. This allows different base climate ocean circulations that are representative of those in current climate models to be imposed upon a single model. As a result, the model projects different transient climate sensitivities that are comparable to the multimodel spread. The greater warming in the 1990-start run occurs primarily at high latitudes and particularly over regions of oceanic convection. In the 1990-start run, ocean overturning circulations are initially weaker and weaken less from CO2 forcing. As a consequence, there are smaller reductions in the poleward ocean heat transport, leading to less tropical ocean heat storage and less moderated high-latitude surface warming. This process is evident in both hemispheres, with changes in the Atlantic meridional overturning circulation and the Antarctic Bottom Water formation dominating the warming differences in each hemisphere. The high-latitude warming in the 1990-start run is enhanced through albedo and cloud feedbacks, resulting in a smaller ocean heat uptake efficacy. The results highlight the importance of improving the base climate ocean circulation in order to provide a reasonable starting point for assessments of past climate change and the projection of future climate change.
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.
This study examines the year-to-year modulation of the western North Pacific (WNP) tropical cyclones (TC) activity by the Atlantic Meridional Mode (AMM) using both observations and the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) global coupled model. 1. The positive (negative) AMM phase suppresses (enhances) WNP TC activity in observations. The anomalous occurrence of WNP TCs results mainly from changes in TC genesis in the southeastern part of the WNP. 2. The observed responses of WNP TC activity to the AMM are connected to the anomalous zonal vertical wind shear (ZVWS) caused by AMM-induced changes to the Walker circulation. During the positive AMM phase, the warming in the North Atlantic induces strong descending flow in the tropical eastern and central Pacific, which intensifies the Walker cell in the WNP. The intensified Walker cell is responsible for the suppressed (enhanced) TC genesis in the eastern (western) part of the WNP by strengthening (weakening) ZVWS. 3. The observed WNPTC–AMM linkage is examined by the long-term control and idealized perturbations experiment with FLOR-FA. A suite of sensitivity experiments strongly corroborate the observed WNPTC–AMM linkage and underlying physical mechanisms.
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.
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.
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.
Tian, D, M Pan, Liwei Jia, Gabriel A Vecchi, and Eric F Wood, July 2016: Assessing GFDL High-Resolution Climate Model Water and Energy Budgets from AMIP simulations over Africa. Journal of Geophysical Research: Atmospheres, 121(14), DOI:10.1002/2016JD025068. Abstract
This study assessed surface water and energy budgets in Atmospheric Model Intercomparison Project (AMIP) simulations of a coupled atmosphere-land model developed by Geophysical Fluid Dynamics Laboratory [Atmospheric General Circulation Model (AM2.5)]. The AM2.5 water and energy budget variables were compared with four reanalyses datasets and an observational-based reference, the Variable Infiltration Capacity model simulations forced by Princeton Global Meteorological Forcing (PGF/VIC) over 20-year period during 1991-2010 in nine African river basins. Results showed that AM2.5 have closed water and energy budgets. However, the discrepancies between AM2.5 and other datasets were notable in terms of their long-term averages. For the water budget, the AM2.5 mostly overestimated precipitation, evapotranspiration, and runoff compared to PGF/VIC and reanalyses. The AM2.5, reanalyses, and PGF/VIC showed similar seasonal cycles but discrepant amplitudes. For the energy budget, while the AM2.5 has relatively consistent net radiation with other datasets, it generally showed higher latent heat, lower sensible heat, and lower Bowen ratio than reanalyses and PGF/VIC. In addition, the AM2.5 water and energy budgets terms mostly had the smallest interannual variability compared to both reanalyses and PGF/VIC. The spatial differences of long-term mean precipitation, runoff, evapotranspiration, and latent heat between AM2.5 and other datasets were reasonably small in dry regions. On average, AM2.5 is closer to PGF/VIC than R2 and 20CR are to PGF/VIC, but is not as close as MERRA and CFSR to PGF/VIC. The bias in AM2.5 water and energy budget terms may be associated with the excessive wet surface and parameterization of moisture advection from ocean to land.
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 investigates the association between the Pacific Meridional Mode (PMM) and tropical cyclone (TC) activity in the western North Pacific (WNP). It is found that the positive PMM phase favors the occurrence of TCs in the WNP while the negative PMM phase inhibits the occurrence of TCs there. Observed relationships are consistent with those from a long-term pre-industrial control experiment (1000 years) of a high-resolution TC-resolving Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model. The diagnostic relationship between the PMM and TCs in observations and the model is further supported by sensitivity experiments with FLOR. The modulation of TC genesis by the PMM is primarily through the anomalous zonal vertical wind shear (ZVWS) changes in the WNP, especially in the southeastern WNP. The anomalous ZVWS can be attributed to the responses of the atmosphere to the anomalous warming in the northwestern part of the PMM pattern during the positive PMM phase, which resembles a classic Matsuno-Gill pattern. Such influences on TC genesis are strengthened by a cyclonic flow over the WNP. The significant relationship between TCs and the PMM identified here may provide a useful reference for seasonal forecasting of TCs and interpreting changes in TC activity in the WNP.
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.
DelSole, T, M K Tippett, and Liwei Jia, October 2015: Multi-year Prediction and Predictability In Climate Change: Multidecadal and Beyond, DOI:10.1142/9789814579933_0014. Abstract
This chapter reviews climate prediction and predictability on multi-year time scales. There is clear evidence from coupled atmosphere-ocean general circulation models that the climate system can be predicted on multi-year time scales based on changes in greenhouse gas concentration, aerosol concentration, and solar insolation, and on initial condition information primarily from the ocean subsurface. New statistical optimization techniques have substantially clarified the space-time structure of predictability on multi-year time scales. These techniques reveal not only predictability of ocean quantities, but also predictability of temperature and precipitation over land on multi-year time scales. A forecast system based on these predictable components is verified to have skill on multi-year time scales over the entire twentieth century observational record.
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.
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.
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.
DelSole, T, Liwei Jia, and M K Tippett, October 2013: Scale-Selective Ridge Regression for Multimodel Forecasting. Journal of Climate, 26(20), DOI:10.1175/JCLI-D-13-00030.1. Abstract
This paper proposes a new approach to linearly combining multimodel forecasts, called scale-selective ridge regression, which ensures that the weighting coefficients satisfy certain smoothness constraints. The smoothness constraint reflects the “prior assumption” that seasonally predictable patterns tend to be large scale. In the absence of a smoothness constraint, regression methods typically produce noisy weights and hence noisy predictions. Constraining the weights to be smooth ensures that the multimodel combination is no less smooth than the individual model forecasts. The proposed method is equivalent to minimizing a cost function comprising the familiar mean square error plus a “penalty function” that penalizes weights with large spatial gradients. The method reduces to pointwise ridge regression for a suitable choice of constraint. The method is tested using the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) hindcast dataset during 1960–2005. The cross-validated skill of the proposed forecast method is shown to be larger than the skill of either ordinary least squares or pointwise ridge regression, although the significance of this difference is difficult to test owing to the small sample size. The model weights derived from the method are much smoother than those obtained from ordinary least squares or pointwise ridge regression. Interestingly, regressions in which the weights are completely independent of space give comparable overall skill. The scale-selective ridge is numerically more intensive than pointwise methods since the solution requires solving equations that couple all grid points together.
