It has long been known that North America's mountains play a key role in shaping Northern Hemisphere atmospheric winter circulation and precipitation. However, much remains to be understood within this subject. Here, we further investigate how North America's topography alters winter (December-March) circulation and precipitation by performing and exploring a series of general circulation model experiments with altered topography over North America and analyzing observations. We find that the mountains of western North America divert the large-scale midlatitude westerly flow, leading to an anomalous ridge over the northern Rockies and a broad anomalous trough extending from southwest of the United States to eastern North America and the northwest Atlantic, contributing significantly to the observed high-amplitude atmospheric wave pattern over North America. The blocking and diversion of large-scale subtropical easterly flow by Mexico/Central America's mountains also have a substantial impact on large-scale circulation, contributing to a realistic separation of the North Atlantic and Pacific subtropical anticyclones. North American topography leads to strongly enhanced precipitation along the western slopes of the Rocky Mountains and eastern slopes of Central America where the large-scale low-level flow is forced upslope, with a drying over the interior of North America and a notable wetting over the southeast United States, where precipitation is increased by more than 100% in some areas when realistic topography is incorporated. In this work, we perform an in-depth dynamic analysis of our topography experiments and show how the simulated impact of North America's topography is consistent with observations.

Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a state-of-the-art dynamical forecast system and statistical predictions, such as the persistence forecast and model-analog forecasts. In the model-analog framework, we select analog states resembling the observed initial conditions from the model space, and the subsequent evolution of those initial model-analogs yields forecast ensembles. Dynamical forecasts demonstrate skillful predictions of VPD variability in the western US, exceeding the persistence forecast skill, which indicates additional sources of VPD predictability within the climate system. To quantify the contribution of different climate variables to VPD prediction, we develop a weighted model-analog forecast and evaluate its skill in comparison to VPD-only and unweighted forecasts. Our findings suggest that sea surface temperature is a critical source of VPD predictability over the western US. The optimally weighted model-analog exhibits forecast skill for VPD variability comparable to that of the dynamical forecast system.

Abstract Atmospheric rivers (ARs) are elongated areas of pronounced atmospheric water vapor transport that play an important role in the hydrological cycle over North America during winter. We investigate the sources of winter seasonal AR predictability over North America using average predictability time (APT) analysis. The skill of seasonal AR frequency predictions, in dynamical model forecasts provided by the Seamless System for Prediction and Earth System Research, is nearly entirely attributable to three physically interpretable APT modes that together represent about 19% of the total seasonal AR frequency variance. These three modes represent the AR response to the El Niño‐Southern Oscillation, anthropogenic forcing and equatorial heating over the eastern flank of the western Pacific warm pool, respectively. We further show that these three modes, calculated from AR frequency, explain nearly all winter seasonal precipitation forecast skill over North America.

To account for uncertainties in future projections associated with the level of greenhouse gas emissions, most climate models are run using different forcing scenarios, like the Shared Socioeconomic Pathways (SSPs). Although it is possible to compare real-world greenhouse gas concentrations with these hypothetical scenarios, it is less clear how to determine whether observed patterns of weather and climate anomalies align with individual scenarios, especially at the interannual timescale. As a result, this study designs a data-driven approach utilizing artificial neural networks (ANNs) that learn to classify global maps of annual-mean temperature or precipitation with a matching emission scenario using a high-resolution, single model initial-condition large ensemble. Here we construct our ANN framework to consider whether a climate map is from SSP1-1.9, SSP2-4.5, SSP5-8.5, a historical forcing scenario, or a natural forcing scenario using the Seamless System for Prediction and EArth System Research (SPEAR) by the NOAA Geophysical Fluid Dynamics Laboratory. A local attribution technique from explainable AI is then applied to identify the most relevant temperature and precipitation patterns used for each ANN prediction. The explainability results reveal that some of the most important geographic regions for distinguishing each climate scenario include anomalies over the subpolar North Atlantic, Central Africa, and East Asia. Lastly, we evaluate data from two overshoot simulations that begin in either 2031 or 2040, which are a set of future simulations that were excluded from the ANN training process. For the rapid mitigation experiment that starts a decade earlier, we find that the ANN links its climate maps to the lowest emission scenario by the end of the 21st century (SSP1-1.9) in comparison to the more moderate scenario (SSP2-4.5) that is selected for the later mitigation experiment. Overall, this framework suggests that explainable machine learning could provide one possible strategy for assessing observations with future climate change pathways.

Solar energy plays a crucial role in the transition towards a sustainable and resilient energy future. One challenge that remains is the considerable year-to-year variation in solar energy resources. As a result, precise seasonal solar energy predictions become pivotal for effective energy system planning and operation. This study employs GFDL’s GFDL’s Seamless System for Prediction and Earth System (SPEAR) to evaluate seasonal solar irradiance prediction across the United States. Notably, SPEAR demonstrates high skill in predicting solar irradiance particularly in the western United States. Furthermore, we conduct an advanced predictability analysis to pinpoint the underlying physical drivers contributing to this skillful solar energy prediction. The outcomes of this research offer substantial potential benefits to stakeholders within the energy sector by providing predictable information regarding year-to-year fluctuations in solar energy resources.

The seasonal prediction skill of tropical cyclone (TC) activity is evaluated using the Seamless System for Prediction and Earth System Research (SPEAR), a modeling system developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for experimental real-time seasonal forecasts. Compared with previous GFDL seasonal prediction models, SPEAR demonstrates improved skill in predicting TC activity for the western North Pacific, while exhibiting comparable or slightly degraded skill for the eastern North Pacific and North Atlantic. These changes in prediction skill do not always align with changes in prediction skill in large-scale variables, particularly over the North Atlantic. This study highlights that changes in the model's response of TCs to large-scale variables, as well as the changes in the amplitude of interannual variations in TC genesis frequency, are crucial for the changes in TC prediction skill. Using the predicted sea surface temperatures from SPEAR as lower boundary conditions, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR-S) model was employed to predict intense TCs, demonstrating skillful predictions of major hurricanes that are comparable to the previous HiFLOR coupled model predictions.

Increases in the intensity and frequency of heatwaves are already evident in the observational record, and these increases are expected to be further amplified in future climate projections with greater radiative forcing. However, it is unclear how temperature extremes will respond regionally to emissions reductions and declines of greenhouse gases later in the 21st century, such as through the implementation of global climate mitigation efforts. Here, we evaluate a set of large ensemble experiments that simulate hypothetical 21st century overshoot scenarios using the GFDL SPEAR climate model. While the two overshoot scenarios include a similar evolution of greenhouse gas reductions, they differ in the timing of this drawdown by about a decade. For this study, we then assess whether differences in the timing of starting climate mitigation influences summertime heat extremes across the contiguous United States (CONUS). By quantifying changes in extreme heat relative to the global mean surface temperature before and after the peak in greenhouse gas concentrations, we find significant decreases in the number of CONUS heat extreme days in response to mitigation. This is further amplified for the earlier overshoot scenario, which suggests a greater benefit (i.e., the time below an extreme temperature threshold) in reducing heat impacts by starting climate change mitigation even in as little as a decade sooner. The reductions in heat extremes are consistent with greater mean precipitation and humidity across most of CONUS for equivalent global warming levels. Changes to the global mean land-sea contrast are also found arising from differences in the rate of surface cooling following the greenhouse gas drawdowns. Our results also emphasize the importance of conducting more coordinated large ensemble modeling experiments to understand the range of possible effects of global climate mitigation efforts on changes to regional extreme events.

Coastal communities are increasingly vulnerable to long-term sea level rise and fluctuations driven by climate variability. While recent advances in coupled climate models enable sea level predictions several months in advance, further efforts are needed to assess and enhance seasonal prediction of coastal sea level. In this study, we evaluate seasonal prediction skill for large-scale and coastal sea level along the U.S. and Canadian West Coast using multiple forecast systems. Prediction skill peaks in the tropical Indo-Pacific and extends into the eastern North Pacific, declining from south to north along the coast. Using self-organizing maps (SOMs), a machine learning technique, we identify sources of large-scale sea level variability and predictability in the eastern tropical and North Pacific, closely linked to the El Niño–Southern Oscillation. Finally, we improve coastal sea level predictions from dynamical models by leveraging the connection between large-scale and coastal sea level through SOM-reconstructed and model-analog approaches.

Atmospheric vapor pressure deficit (VPD) is a useful proxy for meteorological fire-prone conditions in the southwestern United States (SWUS). Here, we demonstrate that the leading VPD mode, with a pronounced decadal component, displays a center of action in the SWUS. In its dry phase, this mode is characterized by reduced soil moisture, high pressure systems, and La Ni & ntilde;a-like conditions and connected to decadal sea surface temperature (SST) variability in the Pacific and Atlantic basins. Leveraging climate simulations, a backward model-analog technique reveals persistent SST signals a few seasons preceding the dry VPD mode. While state-of-the-art models can reasonably capture the spatial patterns of VPD, challenges emerge in representing the decadal component of the leading VPD mode, which predominantly arises from misrepresentations of the actual rather than saturation vapor pressure.

The Northeast US has faced the most rapidly increasing occurrences of extreme rainfall within the US in the past few decades. The latest fully-coupled 25-km GFDL SPEAR simulation, possessing 10 ensemble members, presents a good opportunity to study changes in regional extreme rainfall and relevant physical processes in both current and future climates. The surge in extreme rainfall over the Northeast US since the 1990s is primarily linked to events associated with tropical cyclones (TCs). In a future warming climate, the 25-km GFDL SPEAR SSP5-8.5 simulations project unprecedented rainfall events over the Northeast US, driven by increasing anthropogenic radiative forcing and distinguishable from natural variability, by the mid-21st century. Also, the occurrences of extreme rainfall related to both atmospheric rivers and TCs are projected to increase, even though the number of TC in the North Atlantic is projected to decrease in the 25-km GFDL SPEAR SSP5-8.5 simulations. Factors such as enhancing TC intensity, strengthening TC-related rainfall, or/and westward shift in TC tracks may offset the influence of declining TC numbers in the model projections, leading to more frequent TC-related extreme rainfall over the Northeast US in the future. On the other hand, the increase in extreme rainfall linked to atmospheric rivers is projected to outpace that associated with TCs. Given the distinct spatial patterns of rainfall resulting from atmospheric rivers and TCs, shifts in their relative contributions carry profound implications for risk prevention and mitigation strategies.

Santa Ana winds (SAWs), notorious for fueling the most destructive wildfires over Southern California, are exceptionally dry offshore katabatic winds that descend adiabatically from the inland arid plateau. Here, we investigate how model grid resolution affects the simulation of present and future SAWs using ensemble simulations from a newly developed climate model with varying atmospheric resolutions (100, 50, and 25 km). At finer resolutions, the model better resolves coastal mountains, strengthening the land‐sea temperature gradients conducive to SAWs and enhancing adiabatic warming over the leeward side. Consequently, the frequency and intensity of SAWs at 25‐km model resolution are significantly more realistic than at coarser resolution. As climate warms, while the occurrence of SAWs decreases across all resolutions (approximately −10%), the changes in their impacts are resolution dependent. At finer resolution, future SAWs are projected to further exacerbate wildfire risks over coastal Southern California due to an increased vapor pressure deficit.

Using tide gauge (TG) observations, we identify pronounced multidecadal fluctuations in sea level along the US Northeast Coast (USNEC) superimposed on a long-term increasing trend. This multidecadal sea level variability, largely arising from fluctuations in the buoyancy-driven Atlantic meridional overturning circulation (AMOC), substantially modulates the frequency of flood occurrences along the USNEC and serves as a source of multiyear predictability. Using an initialized dynamical downscaling decadal prediction system with a 1/12° ocean resolution, we demonstrate that flood frequency along the USNEC can be predicted on multiyear to decadal timescales. The long-term increasing trend in flood frequency, mainly driven by increasing greenhouse gases and associated radiative forcing changes, can be predicted a decade ahead. Furthermore, detrended flood frequency along the USNEC exhibits prediction skill for up to 3 years, as verified by TG observation. This multiyear prediction skill is achieved using prediction models that are initialized from our best estimate of observed AMOC.

The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.

Abstract The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.

Abstract A key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.

The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8-9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas.

Abstract A key challenge with the wind energy utilization is that winds, and thus wind power, are highly variable on seasonal to interannual timescales because of atmospheric variability. There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S. Great Plains during peak energy seasons (winter and spring), using seasonal prediction products from a climate model. The dominant source of that skillful prediction mainly comes from year-to-year variations of El Niño-Southern Oscillation in the tropical Pacific, which alters large-scale wind and storm track patterns over the United States. In the Southern Great Plains, the model can predict strong year-to-year wind energy changes with high skill multiple months in advance. Thus, this seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during peak energy production seasons.

Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction.

AbstractThe Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS.

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs on subseasonal timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on hindcast experiments from the Seamless System for Prediction and Earth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory, we evaluate the global subseasonal prediction skill of wintertime AR statistics. Overall, the results from SPEAR are comparable to the European Centre for Medium-Range Weather Forecasts (ECMWF). Higher forecast skill is detected for strong AR activities than weak AR activities, despite that the occurrence frequency for weak ARs exceeds that of strong ARs. Importantly, we assess the sources of predictability and find that three most predictable modes of ARs in the North Pacific sector can be interpreted as arising from the influence of the El Niño–Southern Oscillation, the Pacific North American and the Arctic Oscillation patterns. Subseasonal AR forecast skill in western North America is modulated by different phases of these modes of large-scale seasonal variability highlighting the potential windows of opportunity for subseasonal AR forecasting.