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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Drivers of Atlantic Tropical Cyclogenesis: African Easterly Waves and the Environment

Abstract Tropical cyclone (TC) genesis requires favorable environmental conditions and an initial disturbance, which, in the North Atlantic, is often an African easterly wave (AEW). Although studies have examined how AEWs and the environment affect TC genesis, their relative importance is less understood. Here, we examine whether AEW strength or sea surface temperatures (SSTs) are the primary drivers of TC genesis. Regional model simulations were performed to address how increased AEW strength and SSTs affect the frequency of AEWs that develop into TCs in a year with below‐average TCs with AEW origins and SSTs. We found that environmental favorability plays a larger role than AEW strength in driving the frequency of TCs with AEW origins. Strengthening the AEWs did not affect the frequency of AEWs that develop into TCs due to low environmental favorability. Warmer SSTs led to increased environmental favorability and a statistically significant increase in TCs with AEW origins.

Bercos‐Hickey, Emily [Climate and Ecosystem Scienc↗
DOE OSTI2025

Effect of Ice Number Concentration on the Evolution of Boundary Layer Clouds During Arctic Marine Cold‐Air Outbreaks

Abstract Marine cold‐air outbreaks (MCAOs) are crucial for Arctic Ocean heat loss, featuring convective cloud rolls that transition into convection cells downstream. Understanding factors controlling this transformation is the key for improving MCAO cloud representation in climate models. This study employs large‐eddy simulations to investigate how cloud ice number concentrations () affect cloud evolution using a case from the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. The simulations, performed in a Lagrangian framework following an air mass trajectory, are driven by ERA5 reanalysis data. Initially, all simulations produce similar cloud patterns, but higher leads to earlier breakup of cloud rolls. Between 4 and 10 hr, surface precipitation rates are similar across simulations, but precipitation initiates earlier, and the cloud‐base precipitation rates are higher when is higher. The stronger precipitation evaporation leads to increased stability of the boundary layer and reduced intensity of vertical mixing between the surface and cloud layer. An increased sink of cloud layer moisture via precipitation and decreased source through diminished vertical transport result in earlier cloud breakup in higher conditions. Simulations with different sea surface temperatures (SST) indicate that this cloud breakup mechanism remains valid for MCAOs of different strengths, although the cloud organization is more sensitive to SST changes in low environments. This work highlights the importance of accurate representations of ice processes in simulating MCAO clouds and suggests the need for observational constraints of ice nucleating particles and over the mixed‐phase cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Comparative Study of Cloud Properties Between Northern and Southern High Latitudes Based on ARM Observations and EAMv2 Simulations

Earth's high-latitude regions show a considerable amount of cloud coverage, which significantly affects global radiation budgets. Here, in this study, we examine ground- and ship-based remote sensing observations from two Atmospheric Radiation Measurement (ARM) field campaigns conducted in the high-latitude regions of Northern and Southern Hemispheres (NH and SH). A comparison of macrophysical properties for low, mid-level, high, and deep clouds shows that the cloud top heights and thickness values are higher in the Arctic for each cloud type compared with the Southern Ocean and Antarctic regions, possibly due to higher sea surface temperatures in the north Atlantic. The Arctic also shows lower ice phase occurrence frequencies compared with the SH high latitudes for low clouds. For high, mid-level and deep clouds, the cloud phase distributions as a function of temperature show small geographical variabilities. The observations are then compared with the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) to evaluate the representations of these cloud properties. Here, the results show that the model captures cloud base and top heights well and reproduces the differences in cloud macrophysical properties between the two hemispheres. But the model underestimates ice phase frequencies for all cloud types. Such model cloud phase bias is strongly correlated with positive relative humidity biases and negative aerosol number concentration biases, indicating an important role of relative humidity and aerosols in modulating cloud phase partitioning in the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhanced Simulation of Atmospheric Blocking in a High‐Resolution Earth System Model: Projected Changes and Implications for Extreme Weather Events

Atmospheric blocking is closely linked to the occurrence of extreme weather events. However, low-resolution Earth system models often underestimate the frequency of blocking, undermining confidence in future projections. Here, in this study, we use the high-resolution Community Earth System Model (CESM-HR; 25 km atm and 10 km ocean) to show that CESM-HR reduces biases in atmospheric blocking for both winter and summer, particularly for events lasting longer than 10 days. This improvement is partly due to reduced sea surface temperature biases at higher resolution. Additionally, applying a bias correction to the 500 hPa geopotential height further enhances blocking frequency simulations, highlighting the crucial role of the mean state. Under the Representative Concentration Pathway 8.5 scenario, CESM-HR projects a decrease in wintertime blocking over regions such as the Euro-Atlantic and Chukchi-Alaska, consistent with previous studies. In contrast, summer blocking is expected to become more frequent and persistent, driven by weakened zonal winds. The blocking center shifts from historical locations over Scandinavia and eastern Russia to central Eurasia, significantly increasing blocking over the Ural region. Summer blocking frequency over the Scandinavia-Ural region may eventually surpass historical winter blocking over the Euro-Atlantic. This increase in summer blocking could exacerbate summer heatwaves in a warming climate, making severe heatwaves, like those observed recently, more common in the future.

Atmospheric blocking↗
DOE OSTI2025

Ship‐Based Lidar Evaluation of Southern Ocean Low Clouds in the Storm‐Resolving General Circulation Model ICON and the ERA5 and MERRA‐2 Reanalyses

Global storm resolving models (GSRMs) represent the next generation of global climate models. One of them is a 5-km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid-scale clouds, are omitted, relying on explicit simulation but necessarily utilizing microphysics and turbulence parameterizations. Standard-resolution (10–100 km) models, which use convection and cloud parameterizations, have substantial cloud biases over the Southern Ocean (SO), adversely affecting radiation and sea surface temperature. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. We evaluated SO clouds in ICON and the ERA5 and MERRA-2 reanalyzes using approximately 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and a Macquarie Island station during 2010–2021, compared to the model and reanalyzes using a ground-based lidar simulator. We found that ICON and the reanalyzes underestimate the total cloud fraction by about 10% and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, associated with underestimated lower tropospheric stability and overestimated lifting condensation level. The reanalyzes strongly underestimate fog and very low-level clouds, and MERRA-2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in MERRA-2, implying a “too few, too bright” cloud problem. SO cloud and fog biases are a substantial issue in the analyzed model and reanalyzes and result in shortwave and longwave radiation biases.

Kuma, Peter [Stockholm Univ. (Sweden); Univ. of Ca↗
DOE OSTI2024

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

Abstract Can the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Duncan, James P. C.↗
DOE OSTI2025

The GFDL‐CM4X Climate Model Hierarchy, Part I: Model Description and Thermal Properties

We present the GFDL‐CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary application for CM4X is to investigate ocean and sea ice physics as part of a realistic coupled Earth climate model. CM4X utilizes an updated MOM6 (Modular Ocean Model version 6) ocean physics package relative to CM4.0, and there are two members of the hierarchy: one that uses a horizontal grid spacing of 0.25° (referred to as CM4X‐p25) and the other that uses a grid 0.125° (CM4X‐p125). CM4X also refines its atmospheric grid from the nominally 100 km (cubed sphere C96) of CM4.0–50 km (C192). Finally, CM4X simplifies the land model to allow for a more focused study of the role of ocean changes to global mean climate. CM4X‐p125 reaches a global ocean area mean heat flux imbalance of -0.02 W m -2 within $\mathcal{O}$ (150) years in a pre‐industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly 400 ZJ less ocean heat than present‐day, which corresponds to estimates for anthropogenic ocean heat uptake between 1870 and present‐day. CM4X‐p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly 1100 ZJ more heat than its early 21st century ocean initial state. Furthermore, the root‐mean‐square sea surface temperature bias for historical simulations is roughly 20% smaller in CM4X‐p125 relative to CM4X‐p25 (and CM4.0). We offer the mesoscale dominance hypothesis for why CM4X‐p125 shows such favorable thermal equilibration properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2026

ENSO Recharge Oscillator Theory Integrating the Southward Wind Shift

The El Niño–Southern Oscillation (ENSO) is one of the most‐well understood climate phenomena, and the Recharge Oscillator (RO) theory is widely used to conceptualize its physics in observations and models. ENSO‐associated equatorial zonal wind anomalies shift southward in boreal winter, contributing to ENSO termination. Thus far, this effect has not been explicitly described in the RO framework. Here we derive a new form of the RO under the low‐frequency limit that incorporates the seasonal migration of zonal wind anomalies. In our theory, the Bjerknes feedback depends on the central latitude of zonal wind anomalies, decaying exponentially as winds shift away from the equator. Meanwhile, wind anomalies centered off the equator induce equatorial wave adjustment and thereby have a delayed effect on sea surface temperature anomalies. A stochastic RO simulation with a prescribed wind shift reproduces about half the amplitude of ENSO seasonal synchronization as well as ENSO combination tones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Response of Atmospheric River Width and Intensity to Aquaplanet Warming: A Detection Algorithm‐ and Background Moisture‐Independent Approach

The width of an atmospheric river (AR) is an important parameter when evaluating its impact. Although previous research suggests ARs will widen with global warming, a precise response has been muddled by the large sensitivity of width to a diverse set of AR detection techniques (ARDTs). Here, we propose a methodology that removes the influence of the ARDT by modeling AR‐integrated vapor transport (IVT) profiles as idealized exponential curves with free parameters given by background IVT, intensity above background IVT, and profile width. Notably, our definition for AR profile width does not include any explicit numerical thresholds, relative or absolute, for IVT. We apply our approach to a series of idealized aquaplanet experiments, first with a baseline sea surface temperature (SST) distribution, and then with +2K, +4K, and +6K uniform warming, so as to determine the contributions of each free parameter to AR width. We also apply our approach to high‐resolution atmosphere‐only models forced with SSTs modified to preserve historical variability but following projected warming over 2016–2050. Our results show that contributions to impacts‐relevant AR widening comes primarily from enhancements in background IVT and AR intensity, as opposed to from dynamic width changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Resolving Low Cloud Feedbacks Globally With E3SM High‐Res MMF: Agreement With LES but Stronger Shortwave Effects

This study investigates low cloud feedback in a warmer climate using global simulations from the High-Resolution Multi-scale Modeling Framework (HR-MMF), which explicitly simulates small-scale eddies globally. Two 5-year simulations—one with present-day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W/m 2 /K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR-MMF results with predictions from idealized large-eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR-MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out-of-sample conditions found in HR-MMF. However, the HR-MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud-controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.

boundary-layer clouds↗
DOE OSTI2025

RCEMIP‐ACI: Aerosol‐Cloud Interactions in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations

Aerosol‐cloud interactions are a persistent source of uncertainty in climate research. This study presents findings from a model intercomparison project examining the impact of aerosols on clouds and climate in convection‐permitting radiative‐convective equilibrium (RCE) simulations. Specifically, 11 different modeling teams conducted RCE simulations under varying aerosol concentrations, domain configurations, and sea surface temperatures (SSTs). We analyze the response of domain‐mean cloud and radiative properties to imposed aerosol concentrations across different SSTs. Additionally, we explore the potential impact of aerosols on convective aggregation and large‐scale circulation in large‐domain simulations. The results reveal that the cloud and radiative responses to aerosols vary substantially across models. However, a common trend across models, SSTs, and domain configurations is that increased aerosol loading tends to suppress warm rain formation, enhance cloud water content in the mid‐troposphere, and consequently increase mid‐tropospheric humidity and upper‐tropospheric temperature, thereby impacting static stability. The warming of the upper troposphere can be attributed to reduced lateral entrainment effects due to the higher environmental humidity in the mid‐troposphere. However, models do not agree on aerosol impacts on convective updraft velocity based on the preliminary examination of high‐percentiles of vertical velocity at a single mid‐troposheric layer (500 hPa). In large‐domain simulations, where convection tends to self‐organize, aerosol loading does not consistently influence self‐organization but tends to reduce the intensity of large‐scale circulation forming between convective clusters and dry regions. This reduction in circulation intensity can be explained by the increase in static stability due to the upper tropospheric warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗
DOE OSTI2020

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ocean fronts and eddies force atmospheric rivers and heavy precipitation in western North America

Atmospheric rivers (ARs) are responsible for over 90% of poleward water vapor transport in the mid-latitudes and can produce extreme precipitation when making landfall. However, weather and climate models still have difficulty simulating and predicting landfalling ARs and associated extreme precipitation, highlighting the need to better understand AR dynamics. Here, using high-resolution climate models and observations, we demonstrate that mesoscale sea-surface temperature (SST) anomalies along the Kuroshio Extension can exert a remote influence on landfalling ARs and related heavy precipitation along the west coast of North America. Inclusion of mesoscale SST forcing in the simulations results in approximately a 40% increase in landfalling ARs and up to a 30% increase in heavy precipitation in mountainous regions and this remote impact occurs on two-week time scales. The asymmetrical response of the atmosphere to warm vs. cold mesoscale SSTs over the eddy-rich Kuroshio Extension region is proposed as a forcing mechanism that results in a net increase of moisture flux above the planetary boundary layer, prompting AR genesis via enhancing moisture transport into extratropical cyclones in the presence of mesoscale SST forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enhanced jet stream waviness induced by suppressed tropical Pacific convection during boreal summer

Abstract Consensus on the cause of recent midlatitude circulation changes toward a wavier manner in the Northern Hemisphere has not been reached, albeit a number of studies collectively suggest that this phenomenon is driven by global warming and associated Arctic amplification. Here, through a fingerprint analysis of various global simulations and a tropical heating-imposed experiment, we suggest that the suppression of tropical convection along the Inter Tropical Convergence Zone induced by sea surface temperature (SST) cooling trends over the tropical Eastern Pacific contributed to the increased summertime midlatitude waviness in the past 40 years through the generation of a Rossby-wave-train propagating within the jet waveguide and the reduced north-south temperature gradient. This perspective indicates less of an influence from the Arctic amplification on the observed mid-latitude wave amplification than what was previously estimated. This study also emphasizes the need to better predict the tropical Pacific SST variability in order to project the summer jet waviness and consequent weather extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate-driven changes in the predictability of seasonal precipitation

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

54 ENVIRONMENTAL SCIENCES↗