Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict Pan-Arctic sea ice extent (SIE) on the seasonal timescale; 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 FLOR and 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 four 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.
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 GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) 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 U.S. 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. 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.
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.
The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans-Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric amenable to multidecadal hindcast evaluation. We show, using 1992-2017 retrospective predictions, that bias correction is necessary for the GFDL SPEAR forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the NE MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead-times less than two months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting significant error reduction might come from improved initialization and simulation of sub-annual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not trend anomaly forecast errors.
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.
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.
Chang, Chueh-Hsin, Nathaniel C Johnson, and Changhyun Yoo, February 2021: Evaluation of subseasonal impacts of the MJO/BSISO in the East Asian extended summer. Climate Dynamics, 56, DOI:10.1007/s00382-021-05656-53553-3568. Abstract
The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.
Hsu, Pang-Chi, Zhen Fu, Hiroyuki Murakami, June-Yi Lee, Changhyun Yoo, Nathaniel C Johnson, Chueh-Hsin Chang, and Yu Liu, June 2021: East Antarctic cooling induced by decadal changes in Madden-Julian oscillation during austral summer. Science Advances, 7(26), DOI:10.1126/sciadv.abf9903. Abstract
While West Antarctica has experienced the most significant warming in the world, a profound cooling trend in austral summer was observed over East Antarctica (30°W to 150°E, 70° to 90°S) from 1979 to 2014. Previous studies attributed these changes to high-latitude atmospheric dynamics, stratospheric ozone change, and tropical sea surface temperature anomalies. We show that up to 20 to 40% of the observed summer cooling trend in East Antarctica was forced by decadal changes of the Madden-Julian oscillation (MJO). Both observational analysis and climate model experiments indicate that the decadal changes in the MJO, characterized by less (more) atmospheric deep convection in the Indian Ocean (western Pacific) during the recent two decades, led to the net cooling trend over East Antarctica through modifying atmospheric circulations linked to poleward-propagating Rossby wave trains. This study highlights that changes in intraseasonal tropical climate patterns may result in important climate change over Antarctica.
Tseng, Kai-Chih, Nathaniel C Johnson, Eric Maloney, Elizabeth A Barnes, and Sarah B Kapnick, June 2021: Mapping large-scale climate variability to hydrological extremes: An application of the linear inverse model to subseasonal prediction. Journal of Climate, 34(11), DOI:10.1175/JCLI-D-20-0502.1. Abstract
The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.
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.
Wang, Xin-Yue, Jiang Zhu, Chueh-Hsin Chang, and Nathaniel C Johnson, et al., April 2021: Underestimated responses of Walker circulation to ENSO-related SST anomaly in atmospheric and coupled models. Geoscience Letters, 8, 17, DOI:10.1186/s40562-021-00186-8. Abstract
The Pacific Walker circulation (WC) is a major component of the global climate system. It connects the Pacific sea surface temperature (SST) variability to the climate variabilities from the other ocean basins to the mid- and high latitudes. Previous studies indicated that the ENSO-related atmospheric feedback, in particular, the surface wind response is largely underestimated in AMIP and CMIP models. In this study, we further investigate the responses in the WC stream function and the sea level pressure (SLP) to the ENSO-related SST variability by comparing the responses in 45 AMIP and 63 CMIP models and six reanalysis datasets. We reveal a diversity in the performances of simulated SLP and WC between different models. While the SLP responses to the El Niño-related SST variability are well simulated in most of the atmospheric and coupled models, the WC stream function responses are largely underestimated in most of these models. The WC responses in the AMIP5/6 models capture ~ 75% of those in the reanalysis, whereas the CMIP5/6 models capture ~ 58% of the responses. Further analysis indicates that these underestimated circulation responses could be partially attributed to the biases in the precipitation scheme in both the atmospheric and coupled models, as well as the biases in the simulated ENSO-related SST patterns in the coupled models. One should pay special attention to these biases when studying the WC or the tropical atmosphere–ocean interactions using numerical models.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), 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 (15 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.
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.
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.
In light of a warming climate, the complexity of the El Niño/Southern Oscillation (ENSO) makes its prediction a challenge. In addition to various flavors of ENSO, oceanic warming in the central and eastern tropical Pacific is not always accompanied by corresponding atmospheric anomalies; that is, the atmosphere and ocean remain uncoupled. Such uncoupled warm events as happened in 1979, 2004, 2014, and 2018 are rare and represent an unusual form of ENSO diversity. A weaker zonal sea surface temperature anomaly gradient across the tropical Pacific compared to a conventional El Niño may partially account for the decoupling. Also, the uncoupled warm events typically start late in the calendar year, which raises the possible influence of seasonality in background conditions for the lack of coupling. Without coupling, the impact of the warming in the central and eastern tropical Pacific on extratropical climate is different from that of its coupled counterpart.
Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic Subtropical High and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.
Widespread public and scientific interest in the recent global warming hiatus and related multidecadal climate variability stimulated a surge in our understanding of key metrics of global climate change. While seeking explanations for the nearly steady global mean temperature from late 1990s through the early 2010s, the scientific community also grappled with concomitant and seemingly inconsistent changes in other metrics. For example, over that period, Arctic sea ice experienced a rapid decline but Antarctic sea ice expanded slightly, summertime warm extremes continued to rise without evidence of a pause, and the expanding Hadley cell trend maintained its course. In this article, we review recent advances in understanding the multidecadal variability of these metrics of global climate change, focusing on how internal multidecadal variability may reconcile differences between projected and recently observed trends and apparent inconsistencies between recent trends in some metrics. We emphasize that the impacts of global scale, naturally occurring patterns on multidecadal timescales, most notably the Pacific and Atlantic Multidecadal Variability, tend to be more regionally heterogeneous than those of radiatively forced change, which weakens the relationship between local climate impacts and global mean temperature on multidecadal timescales. We conclude this review with a summary of current challenges and opportunities for progress.
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).
Mariotti, A, C Baggett, Elizabeth A Barnes, E Becker, Amy Butler, D C Collins, Paul A Dirmeyer, L Ferranti, and Nathaniel C Johnson, et al., May 2020: Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond. Bulletin of the American Meteorological Society, 101(5), DOI:10.1175/BAMS-D-18-0326.1. Abstract
Research points to strategic windows of opportunity for skillful forecasts on subseasonal, seasonal, and longer timescales with benefits to users when forecasts are increasingly geared accordingly.
There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0-14 day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer timescales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden-Julian Oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, re-evaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.
Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2‐4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode), and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence.
The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability.
Ding, Qinghua, A Schweiger, M L L'Heureux, E J Steig, D S Battisti, Nathaniel C Johnson, E Blanchard-Wrigglesworth, S Po-Chedley, Q Zhang, K J Harnos, and Mitchell Bushuk, et al., January 2019: Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nature Geoscience, 12(1), DOI:10.1038/s41561-018-0256-8. Abstract
The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence.
Fučkar, N S., V Guemas, Nathaniel C Johnson, and Francisco J Doblas-Reyes, March 2019: Dynamical prediction of Arctic sea ice modes of variability. Climate Dynamics, 52(5-6), DOI:10.1007/s00382-018-4318-9. Abstract
This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.
Johnson, Nathaniel C., et al., October 2019: On the delayed coupling between ocean and atmosphere in recent weak El Niño episodes. Journal of Geophysical Research, 46(20), DOI:10.1029/2019GL084021. Abstract
The recent borderline El Niño events of 2014/15 and 2018/19 provided operational centers with unique challenges because of the apparent absence of typical coupling between the tropical atmosphere and ocean before onset. The mismatch between atmosphere and ocean raises questions about its causes and predictability. Here we analyze observational data since 1979 to show that a sea surface temperature (SST) pattern characterized by an anomalous gradient in the western and central equatorial Pacific played a critical role in inhibiting the expected onset of central tropical Pacific deep convection during these events. This SST pattern, which produces an atmospheric response that opposes the response to elevated eastern Pacific SSTs, has become more prevalent over the past 40 years.
L'Heureux, M L., Michael K Tippett, Ken Takahashi, A Barnston, E Becker, G D Bell, T E Di Liberto, J Gottschalck, M S Halpert, Z-Z Hu, and Nathaniel C Johnson, et al., February 2019: Strength Outlooks for the El Niño–Southern Oscillation. Weather and Forecasting, 34(1), DOI:10.1175/WAF-D-18-0126.1. Abstract
Three strategies for creating probabilistic forecast outlooks for El Niño–Southern Oscillation (ENSO) are compared. One is subjective and is currently used by the NOAA/Climate Prediction Center (CPC) to produce official ENSO outlooks. A second is purely objective and is based on the North American Multimodel Ensemble (NMME). A new third strategy is proposed in which the forecaster only provides the expected value of the Niño-3.4 index, and then categorical probabilities are objectively determined based on past skill. The new strategy results in more confident probabilities compared to the subjective approach and higher verification scores, while avoiding the significant forecast busts that sometimes afflict the NMME-based objective approach. The higher verification scores of the new strategy appear to result from the added value that forecasters provide in predicting the mean, combined with more reliable representations of uncertainty, which is difficult to represent because forecasters often assume less confidence than is justified. Moreover, the new approach can produce higher-resolution probabilistic forecasts that include ENSO strength information and that are difficult, if not impossible, for forecasters to produce. To illustrate, a nine-category ENSO outlook based on the new strategy is assessed and found to be skillful. The new approach can be applied to other outlooks where users desire higher-resolution probabilistic forecasts, including the extremes.
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.
With a GFDL coupled model, the subseasonal prediction of wintertime (December‐February) surface air temperature (SAT) is investigated through the analysis of 11‐year hindcasts. Significant subseasonal week 3‐5 correlation skill exists over a large portion of the global land domain, and the predictability originates primarily from the eight most predictable SAT modes. The first three modes, identified as the El Niño‐Southern Oscillation mode, the North Atlantic Oscillation (NAO) mode, and the Eurasia Meridional Dipole (EMD) mode, can be skillfully predicted more than 5 weeks in advance. The NAO and EMD modes are strongly correlated with the initial stratospheric polar vortex strength, highlighting the role of stratosphere in subseasonal prediction. Interestingly, the Madden‐Julian Oscillation is not essential for the subseasonal land SAT prediction in the Northern Hemisphere extratropics. The spatial correlation skill exhibits considerable intraseasonal and interannual fluctuations, indicative of the importance to identify the time window of opportunity for subseasonal prediction.
The driving of tropical precipitation by variability of the underlying sea surface temperature (SST) plays a critical role in the atmospheric general circulation. To assess the precipitation sensitivity to SST variability, it is necessary to observe and understand the relationship between precipitation and SST. However, the precipitation – SST relationships from any coupled atmosphere-ocean system can be difficult to interpret due to the challenge of disentangling the SST-forced atmospheric response and the atmospheric intrinsic variability. This study demonstrates that the two components can be isolated using uncoupled atmosphere-only simulations, which extract the former when driven by time-varying SSTs and the latter when driven by climatological SSTs. With a simple framework that linearly combines the two types of uncoupled simulations, the coupled precipitation – SST relationships are successfully reproduced. Such a framework can be a useful tool for quantitatively diagnosing tropical air-sea interactions.
The precipitation sensitivity to SST variability is investigated with the use of uncoupled simulations with prescribed SST anomalies, where the influence of atmospheric intrinsic variability on SST is deactivated. Through a focus on local precipitation – SST relationships, the precipitation sensitivity to local SST variability is determined to be predominantly controlled by the local background SST. In addition, the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SSTs. These findings are supported by basic principles of moist static stability, from which a simple formula for precipitation sensitivity to local SST variability is derived.
Eastern North America contains densely populated, highly developed areas, making winter storms with strong winds and high snowfall among the costliest storm types. For this reason, it is important to determine how the frequency of high-impact winter storms, specifically those combining significant snowfall and winds, will change in this region under increasing greenhouse gas concentrations. This study uses a high-resolution coupled global climate model to simulate the changes in extreme winter conditions from the present climate to a future scenario with doubled-CO2 concentrations (2XC). In particular, this study focuses on changes in high snowfall, extreme wind (HSEW) events, which are defined as the occurrence of two-day snowfall and high winds exceeding thresholds based on extreme values from the control simulation where greenhouse gas concentrations remain fixed. Mean snowfall consistently decreases across the entire region, but extreme snowfall shows a more inconsistent pattern with some areas experiencing increases in the frequency of extreme snowfall events. Extreme wind events show relatively small changes in frequency with 2XC, with the exception of high-elevation areas where there are large decreases in frequency. As a result of combined changes in wind and snowfall, HSEW events decrease in frequency in the 2XC simulation for much of the eastern North America. Changes in the number of HSEW events in the 2XC environment are driven mainly by changes in the frequency of extreme snowfall events, with most of the region experiencing decreases in event frequency, except for certain inland areas at higher latitudes.
The recent levelling of global mean temperatures after the late 1990s, the so-called global warming hiatus or slowdown, ignited a surge of scientific interest into natural global mean surface temperature variability, observed temperature biases, and climate communication, but many questions remain about how these findings relate to variations in more societally relevant temperature extremes. Here we show that both summertime warm and wintertime cold extreme occurrences increased over land during the so-called hiatus period, and that these increases occurred for distinct reasons. The increase in cold extremes is associated with an atmospheric circulation pattern resembling the warm Arctic-cold continents pattern, whereas the increase in warm extremes is tied to a pattern of sea surface temperatures resembling the Atlantic Multidecadal Oscillation. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature.
Yoo, Changhyun, and Nathaniel C Johnson, et al., November 2018: Subseasonal Prediction of Wintertime East Asian Temperature Based on Atmospheric Teleconnections. Journal of Climate, 31(22), DOI:10.1175/JCLI-D-17-0811.1. Abstract
A composite-based statistical model utilizing Northern Hemisphere teleconnection patterns is developed to predict East Asian wintertime surface air temperature for lead times out to 6 weeks. The level of prediction is determined by using the Heidke skill score. The prediction skill of the statistical model is compared with that of hindcast simulations by a climate model, Global Seasonal Forecast System, version 5. When employed individually, three teleconnections (i.e., the east Atlantic/western Russian, Scandinavian, and polar/Eurasian teleconnection patterns) are found to provide skillful predictions for lead times beyond 4–5 weeks. When information from the teleconnections and the long-term linear trend are combined, the statistical model outperforms the climate model for lead times beyond 3 weeks, especially during those times when the teleconnections are in their active phases.
Black, J, and Nathaniel C Johnson, et al., July 2017: The Predictors and Forecast Skill of Northern Hemisphere Teleconnection Patterns for Lead Times of 3-4 Weeks. Monthly Weather Review, 145(7), DOI:10.1175/MWR-D-16-0394.1. Abstract
The Pacific/North American (PNA) pattern, North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are three dominant teleconnection patterns known to strongly affect December-February surface weather in the Northern Hemisphere. A partial least-squares regression (PLSR) method is adopted in this study to generate wintertime 2-week statistical forecasts of these three teleconnection pattern indices for lead times of up to five weeks over the 1980-2013 period. The PLSR approach generates forecasts for the teleconnection pattern indices by maximizing the variance explained by predictor indices determined as linear combinations of predictor fields, which include gridded outgoing longwave radiation (OLR), 300-hPa geopotential height (Z300), and 50-hPa geopotential height (Z50). Overall, the PLSR models yield statistically significant skill at all lead times up to five weeks. In particular, cross-validated correlations between the combined weeks 3-4 PLSR forecasts and verification for the PNA, NAO and AO indices are 0.34, 0.28 and 0.41. The PLSR approach also allows the authors to isolate a small number of predictor patterns that help shed light on the sources of prediction skill for each teleconnnection pattern. As expected, the results reveal the importance of tropical convection (OLR) for forecast skill in weeks 3-4, but the initial atmospheric flow (Z300) accounts for a substantial fraction of the skill as well. Overall, the results of this study provide promise for improving subseasonal-to-seasonal (S2S) forecasts and the physical understanding of predictability on these time scales.
Ding, Qinghua, A Schweiger, M L L'Heureux, D S Battisti, S Po-Chedley, and Nathaniel C Johnson, et al., April 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nature Climate Change, 7(4), DOI:10.1038/nclimate3241. Abstract
The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979.
L'Heureux, M L., Michael K Tippett, Arun Kumar, T Butler, L M Ciasto, Qinghua Ding, K J Harnos, and Nathaniel C Johnson, November 2017: Strong Relations Between ENSO and the Arctic Oscillation in the North American Multimodel Ensemble. Geophysical Research Letters, DOI:10.1002/2017GL074854. Abstract
Arctic Oscillation (AO) variability impacts climate anomalies over the middle to high latitudes of the Northern Hemisphere. Recently, state-of-the-art climate prediction models have proved capable of skillfully predicting the AO during the winter, revealing a previously unrealized source of climate predictability. Hindcasts from the North American Multimodel Ensemble (NMME) show that the seasonal, ensemble mean 200 hPa AO index is skillfully predicted up to 7 months in advance and that this skill, especially at longer leads, is coincident with previously unknown and strong relations (r > 0.9) with the El Niño–Southern Oscillation (ENSO). The NMME is a seasonal prediction system that comprises eight models and up to 100 members with forecasts out to 12 months. Observed ENSO-AO correlations are within the spread of the NMME member correlations, but the majority of member correlations are stronger than observed, consistent with too high predictability in the model, or overconfidence.
We determine robust modes of the northern hemisphere (NH) sea ice variability on interannual timescales disentangled from the long-term climate change. This study focuses on sea ice thickness (SIT), reconstructed with an ocean–sea-ice general circulation model, because SIT has a potential to contain most of the interannual memory and predictability of the NH sea ice system. We use the K-means cluster analysis—one of clustering methods that partition data into groups or clusters based on their distances in the physical space without the typical constraints of other unsupervised learning statistical methods such as the widely-used principal component analysis. To adequately filter out climate change signal in the Arctic from 1958 to 2013 we have to approximate it with a 2nd degree polynomial. Using 2nd degree residuals of SIT leads to robust K-means cluster patterns, i.e. invariant to further increase of the polynomial degree. A set of clustering validity indices yields K = 3 as the optimal number of SIT clusters for all considered months and seasons with strong similarities in their cluster patterns. The associated time series of cluster occurrences exhibit predominant interannual persistence with mean timescale of about 2 years. Compositing analysis of the NH surface climate conditions associated with each cluster indicates that wind forcing seem to be the key factor driving the formation of interannual SIT cluster patterns during the winter. Climate memory in SIT with such interannual persistence could lead to increased predictability of the Artic sea ice cover beyond seasonal timescales.
Johnson, Nathaniel C., and Yu Kosaka, December 2016: The impact of eastern equatorial Pacific convection on the diversity of boreal winter El Niño teleconnection patterns. Climate Dynamics, 47(12), DOI:10.1007/s00382-016-3039-1. Abstract
It is widely recognized that no two El Niño episodes are the same; hence the predictable variations of the climate impacts associated with El Niño remain an open problem. Through an analysis of observational data and of large ensembles from six climate models forced by the observed time-varying sea surface temperatures (SSTs), this study raises the argument that the most fundamental predictable variations of boreal wintertime El Niño teleconnection patterns relate to the distinction between convective (EPC) and non-convective eastern Pacific (EPN) events. This distinction is a consequence of the nonlinear relationship between deep convection and eastern Pacific SSTs, and the transition to a convective eastern Pacific has a predictable relationship with local and tropical mean SSTs. Notable differences (EPC minus EPN) between the teleconnection patterns include positive precipitation differences over southern North America and northern Europe, positive temperature differences over northeast North America, and negative temperature differences over the Arctic. These differences are stronger and more statistically significant than the more common partitioning between eastern Pacific and central Pacific El Niño. Most of the seasonal mean composite anomalies associated with EPN El Niño are not statistically significant owing to the weak SST forcing and small sample sizes; however, the EPN teleconnection is more robust on subseasonal timescales following periods when the EPN pattern of tropical convection is active. These findings suggest that the differences between EPC and EPN climate impacts are physically robust and potentially useful for intraseasonal forecasts for lead times of up to a few weeks.
Wang, Guihua, Lingwei Wu, Nathaniel C Johnson, and Zheng Ling, July 2016: Observed three‐dimensional structure of ocean cooling induced by Pacific tropical cyclones. Geophysical Research Letters, 43(14), DOI:10.1002/2016GL069605. Abstract
Sea surface cooling along tropical cyclone (TC) tracks has been well observed, but a complete understanding of the full three‐dimensional structure of upper ocean TC‐induced cooling is still needed. In this study, observed ocean temperature profiles derived from Argo floats and TC statistics from 1996 to 2012 are used to determine the three‐dimensional structure of TC‐induced cooling over the northwest Pacific. The average TC‐induced sea surface temperature change derived from Argo reaches −1.4°C, which agrees well with satellite‐derived estimates. The Argo profiles further reveal that this cooling can extend to a depth of ~30 m and can persist for about 20 days. The time scale of cooling recovery is somewhat longer in subsurface layers between a depth of ~10–15 m. Over the ocean domain where the mixed layer is shallower (deeper), the cooling is stronger (weaker), shallower (deeper), and more (less) persistent. The effect of initial MLD on the cooling derived from Argo observations may be only half of the idealized piecewise continuous model of tropical cyclone. These findings have implications for the total upper ocean heat content change induced by northwest Pacific TCs.
This study uses the method of self-organizing maps (SOMs) to categorize the June-August atmospheric teleconnections in the 500-hPa geopotential height field of the Southern Hemisphere (SH) extratropics. This approach yields 12 SOM patterns that provide a discretized representation of the continuum of SH teleconnection patterns from 1979 to 2012. These 12 patterns are large in spatial scale, exhibiting a mix of annular mode characteristics and wave trains of zonal wavenumber varying from 2 to 4. All patterns vary with intrinsic time scales of about 5-10 days, but some patterns exhibit quasi-oscillatory behavior over a period of 20-30 days, whereas still others exhibit statistically significant enhanced and suppressed frequencies up to about four weeks in association with the Madden-Julian oscillation. Two patterns are significantly influenced by El Nino-Southern Oscillation (ENSO) on interannual time scales. All 12 patterns have strong influences on surface air temperature and sea ice concentrations, with the sea ice response occurring over a time scale of about 2-4 weeks. The austral winter has featured a positive frequency trend in patterns that project onto the negative phase of the southern annular mode (SAM) and a negative frequency trend in positive SAM-like patterns. Such atmospheric circulation trends over 34 yr may arise through atmospheric internal variability alone, and, unlike other seasons in the SH, it is not necessary to invoke external forcing as a dominant source of circulation trends.
Horton, Daniel E., and Nathaniel C Johnson, et al., June 2015: Contribution of changes in atmospheric circulation patterns to extreme temperature trends. Nature, 522, DOI:10.1038/nature14550. Abstract
Surface weather conditions are closely governed by the large-scale circulation of the Earth’s atmosphere. Recent increases in the occurrence of some extreme weather phenomena1,2 have led to multiple mechanistic hypotheses linking changes in atmospheric circulation to increasing probability of extreme events3,4,5. However, observed evidence of long-term change in atmospheric circulation remains inconclusive6,7,8. Here we identify statistically significant trends in the occurrence of atmospheric circulation patterns, which partially explain observed trends in surface temperature extremes over seven mid-latitude regions of the Northern Hemisphere. Using self-organizing map cluster analysis9,10,11,12, we detect robust circulation pattern trends in a subset of these regions during both the satellite observation era (1979–2013) and the recent period of rapid Arctic sea-ice decline (1990–2013). Particularly substantial influences include the contribution of increasing trends in anticyclonic circulations to summer and autumn hot extremes over portions of Eurasia and North America, and the contribution of increasing trends in northerly flow to winter cold extremes over central Asia. Our results indicate that although a substantial portion of the observed change in extreme temperature occurrence has resulted from regional- and global-scale thermodynamic changes, the risk of extreme temperatures over some regions has also been altered by recent changes in the frequency, persistence and maximum duration of regional circulation patterns.
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.
Maloney, Eric, Suzana J Camargo, E K M Chang, B A Colle, R Fu, K L Geil, Qi Hu, Xianan Jiang, Nathaniel C Johnson, K B Karnauskas, J L Kinter, Ben P Kirtman, Sanjiv Kumar, B Langenbrunner, K Lombardo, L Long, A Mariotti, J E Meyerson, K Mo, J D Neelin, Zaitao Pan, R Seager, Yolande L Serra, A Seth, J Sheffield, J Stroeve, J Thibeault, Shang-Ping Xie, Chunzai Wang, Bruce Wyman, and Ming Zhao, March 2014: North American Climate in CMIP5 Experiments: Part III: Assessment of 21st Century Projections. Journal of Climate, 27(6), DOI:10.1175/JCLI-D-13-00273.1. Abstract
In Part 3 of a three-part study on North American climate in Coupled Model Intercomparison project (CMIP5) models, we examine projections of 21st century climate in the RCP8.5 emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. We also examine changes in eastern north Pacific and north Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe.
Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the Northern U.S., and Atlantic and east Pacific tropical cyclone activity.
Sheffield, J, Suzana J Camargo, R Fu, Qi Hu, Xianan Jiang, Nathaniel C Johnson, K B Karnauskas, Seon Tae Kim, J L Kinter, Sanjiv Kumar, B Langenbrunner, Eric Maloney, A Mariotti, J E Meyerson, J D Neelin, S Nigam, Zaitao Pan, A Ruiz-Barradas, R Seager, Yolande L Serra, D-Z Sun, Chunzai Wang, Shang-Ping Xie, J-Y Yu, Tao Zhang, and Ming Zhao, December 2013: North American Climate in CMIP5 Experiments. Part II: Evaluation of Historical Simulations of Intra-Seasonal to Decadal Variability. Journal of Climate, 26(23), DOI:10.1175/JCLI-D-12-00593.1. Abstract
This is the second part of a three-part paper on North American climate in CMIP5 that evaluates the 20th century simulations of intra-seasonal to multi-decadal variability and teleconnections with North American climate. Overall, the multi-model ensemble does reasonably well at reproducing observed variability in several aspects, but does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intra-seasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multi-model mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are under predicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with US winter temperatures. The models capture the spatial pattern of PDO variability and its influence on continental temperature and West coast precipitation, but less well for the wintertime precipitation. The spatial representation of the AMO is reasonable but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multi-decadal trends such as the warming hole over the central-southeast US and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability.
