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Abstract Known as the Third Pole, the Tibetan Plateau (TP) significantly influences global weather and climate, but its potential for improving subseasonal-to-interannual predictions remains underexplored. Through coupled climate simulations and hindcast experiments, we uncovered interannual predictability of the 2003 European summer heatwave that persisted from June to August with devastating impacts. Hindcasts initialized from the atmosphere, land, and ocean states of a coupled simulation that assimilates soil moisture and soil temperature data over the TP show substantial skill in predicting this heatwave two years in advance. Hindcast sensitivity experiments isolated the indispensable role of the spring TP snow cover anomalies and their impact on the Atlantic and Pacific Oceans in exciting the Rossby waves that contributed to the anomalous European summer temperature. These findings highlight the dominant and remote influence of the TP and motivate research on its role in enhancing the predictability of extreme events worldwide.

Abstract The occurrence of extreme hot and dry summer conditions in the Pacific Northwest region of North America (PNW) has been known to be influenced by climate modes of variability such as the El Niño-Southern Oscillation and other variations in tropospheric circulation such as stationary waves and blocking. However, the extent to which the subseasonal remote tropical driver influences summer heat extremes and fire weather conditions across the PNW remains elusive. Our investigation reveals that the occurrence of heat extremes and associated fire-conducive weather conditions in the PNW is significantly heightened during the boreal summer intraseasonal oscillation (BSISO) phases 6-7, by ~50–120% relative to the seasonal probability. The promotion of these heat extremes is primarily attributed to the enhanced diabatic heating over the tropical central-to-eastern North Pacific, which generates a wave train traveling downstream toward North America, resulting in a prominent high-pressure system over the PNW. The ridge, subsequently, promotes surface warming over the region primarily through increased surface radiative heating and enhanced adiabatic warming. The results suggest a potential pathway to improving subseasonal-to-seasonal predictions of heatwaves and wildfire risks in the PNW by improving the representation of BSISO heating over the tropical-to-eastern North Pacific.

Abstract Diminishing Arctic sea ice has led to enhanced evaporation from the Arctic marginal seas (AMS), which is expected to alter precipitation over land. In this work, AMS evaporation is numerically tracked to quantify its contribution to cold-season (October–March) precipitation over land in the Northern Hemisphere during 1980–2021. Results show a significant 32% increase in AMS moisture contribution to land precipitation, corresponding to a 16% increase per million square km loss of sea ice area. Especially over the high-latitude land, despite the fractional contribution of AMS to precipitation being relatively low (8%), the augmented AMS evaporation contributed disproportionately (42%) to the observed upward trend in precipitation. Notably, northern East Siberia exhibited a substantial rise in both the amount and fraction of extreme snowfall sourced from the AMS. Our findings underscore the importance of the progressively ice-free Arctic as an important contributor to the escalating levels of cold-season precipitation and snowfall over northern high-latitude land.

Abstract Arctic Amplification (AA), the amplified surface warming in the Arctic relative to the globe, is a salient feature of climate change. While the basic physical picture of AA has been depicted, how its degree is determined has not been clearly understood. Here, by deciphering atmospheric heat transport (AHT), we build a two-box energy-balance model of AA and derive that the degree of AA is a simple nonlinear function of the Arctic and global feedbacks, the meridional heterogeneity in radiative forcing, and the partial sensitivities of AHT to global mean and meridional gradient of warming. The formula captures the varying AA in climate models and attributes the spread to models’ feedback parameters and AHT physics. The formula clearly illustrates how essential physics mutually determine the degree of AA and limits its range within 1.5-3.5. Our results articulate AHT as both forcing and feedback to AA, highlight its fundamental role in forming a baseline AA that exists even with uniform feedbacks, and underscore its partial sensitivities instead of its total change as key parameters of AA. The lapse-rate feedback has been widely recognized as a major contributor to AA but its effect is fully offset by the water-vapor feedback.

Abstract Lakes are important sentinels of climate change and may contribute over 30% of natural methane (CH4) emissions; however, no earth system model (ESM) has represented lake CH4 dynamics. To fill this gap, we refined a process‐based lake biogeochemical model to simulate global lake CH4 emissions, including representation of lake bathymetry, oxic methane production (OMP), the effect of water level on ebullition, new non‐linear CH4 oxidation kinetics, and the coupling of sediment carbon pools with in‐lake primary production and terrigenous carbon loadings. We compiled a lake CH4 data set for model validation. The model shows promising performance in capturing the seasonal and inter‐annual variabilities of CH4 emissions at 10 representative lakes for different lake types and the variations in mean annual CH4 emissions among 106 lakes across the globe. The model reproduces the variations of the observed surface CH4 diffusion and ebullition along the gradients of lake latitude, depth, and surface area. The results suggest that OMP could play an important role in surface CH4 diffusion, and its relative importance is higher in less productive and/or deeper lakes. The model performance is improved for capturing CH4 outgassing events in non‐floodplain lakes and the seasonal variability of CH4 ebullition in floodplain lakes by representing the effect of water level on ebullition. The model can be integrated into ESMs to constrain global lake CH4 emissions and climate‐CH4 feedback.

Abstract Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Abstract Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.

Abstract Sea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs.

Abstract The effects of small‐scale topography‐induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography‐based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5‐degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS‐level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional‐level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small‐scale surface heterogeneity in ESMs and motivate future research to understand their effects on land‐atmosphere interactions, streamflow, and water resources management over mountainous regions.

Abstract Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.

Abstract Tropical cyclones (TCs) induce substantial upper‐ocean mixing and upwelling, leading to sea surface cooling. In this study, we explore changes in TC‐induced cold wakes along the United States (U.S.) Southeast and Gulf Coasts during 1982–2020. Our study shows a significant increase in TC‐induced sea surface temperature (SST) cooling of about 0.20°C near the U.S. Southeast Coast over this period. However, for the U.S. Gulf Coast, trends in TC‐induced SST cooling are insignificant. Analysis of the large‐scale oceanic environments indicate that the increasing TC‐induced cold wakes near the Southeast coast have been predominantly caused by the cooling of subsurface waters in that region. This upper‐ocean change is attributed to the enhancement of surface pressure gradient across land‐sea boundary and the associated increase in alongshore winds over there. Further analysis with climate models reveals the important role of anthropogenic forcings in driving these changes in the atmospheric circulation response along the U.S. Southeast Coast.

Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change makes it more difficult for these ecosystems to adapt to the fluctuation in sea levels and other environmental changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. However, their divergent estimates of the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetlands (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model–data comparison, we showed the importance of using a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also found that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites in diverse environments can help reduce the model uncertainty.

Abstract Accurately representing the precipitation diurnal cycle has long been a challenge for global climate models (GCMs). Here we evaluate the precipitation diurnal cycle in the DYAMOND global convection‐permitting models (CPMs) and CMIP6 HighResMIP models. Comparison of the high‐ (25–50 km) and low‐resolution (100–250 km) models with parameterized convection in HighResMIP shows that simply increasing model resolution does not noticeably improve the precipitation diurnal cycle. In contrast, CPMs can better capture the observed amplitude and timing of precipitation diurnal cycle. However, the simulated spatial variation of timing in CPMs is smaller than observed, leading to an exaggeration of the spatially averaged diurnal amplitude. The better‐simulated precipitation diurnal cycle in the CPMs is tied to mesoscale convective systems (MCSs), which contribute about half of the total precipitation. The observed life cycle of MCSs, including initiation and mature stages, is well captured in the CPMs, leading to a more realistic precipitation diurnal cycle.

Abstract In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

Abstract Coastal zone compound flooding (CF) can be caused by the interactive fluvial and oceanic processes, particularly when coastal backwater propagates upstream and interacts with high river discharge. The modeling of CF is limited in existing Earth System Models (ESMs) due to coarse mesh resolutions and one‐way coupled river‐ocean components. In this study, we present a novel multi‐scale coupling framework within the Energy Exascale Earth System Model (E3SM), integrating global atmosphere and land with interactively coupled river and ocean models using different meshes with refined resolutions near the coastline. To evaluate this framework, we conducted ensemble simulations of a CF event (Hurricane Irene in 2011) in a Mid‐Atlantic estuary. The results demonstrate that the novel E3SM configuration can reasonably reproduce river discharge and sea surface height variations. The two‐way river‐ocean coupling improves the representation of coastal backwater effects at the terrestrial‐aquatic interface that are caused by the combined actions of tide and storm surge during the CF event, thus providing a valuable modeling tool for better understanding the river‐estuary‐ocean dynamics in extreme events under climate change. Notably, our results show that the most significant CF impacts occur when the highest storm surge generated by a tropical cyclone meets with a moderate river discharge. This study highlights the state‐of‐the‐art advancements developed within E3SM for simulating multi‐scale coastal processes.

Abstract Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 40,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with the observed spatial distribution of TC tracks and basin-scale TC statistics. Specifically along the coast, spatial variations in TC crossing probability and extreme winds upon landfall are well-reproduced by RAFT with R-squared values of 0.81 and 0.73, respectively. In summary, the synthetic TC database constructed with RAFT provides a reasonable pathway for the robust assessment of North Atlantic TC wind and rainfall risks.

Abstract This work describes the implementation and evaluation of the Slab Ocean Model component of the Energy Exascale Earth System Model version 2 (E3SMv2‐SOM) and its application to understanding the climate sensitivity to ocean heat transports (OHTs) and CO2 forcing. E3SMv2‐SOM reproduces the baseline climate and Equilibrium Climate Sensitivity (ECS) of the fully coupled E3SMv2 experiments reasonably well, with a pattern correlation close to 1 and a global mean bias of less than 1% of the fully coupled surface temperature and precipitation. Sea ice extent and volume are also well reproduced in the SOM. Consistent with general model behavior, the ECS estimated from the SOM (4.5 K) exceeds the effective climate sensitivity obtained from extrapolation to equilibrium in the fully coupled model (4.0 K). The E3SMv2 baseline climate also shows a large sensitivity to OHT strengths, with a global surface temperature difference of about 4.0°C between high‐/low‐OHT experiments with prescribed forcings derived from fully coupled experiments with realistic/weak ocean circulation strengths. Similar to their forcing pattern, the surface temperature response occurs mainly over the subpolar regions in both hemispheres. However, the Southern Ocean shows more surface temperature sensitivity to high/low‐OHT forcing due to a positive/negative shortwave cloud radiative effect caused by decreases/increases in mid‐latitude marine low‐level clouds. This large temperature sensitivity also causes an overcompensation between the prescribed OHTs and atmosphere heat transports. The SOM's ECS estimate is also sensitive to the prescribed OHT and the associated baseline climate it is initialized from; the high‐OHT ECS is 0.5 K lower than the low‐OHT ECS.

Abstract Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

Abstract To mitigate climate warming, many countries have committed to achieve carbon neutrality in the mid-21st century. Here, we assess the global impacts of changing greenhouse gases (GHGs), aerosols, and tropospheric ozone (O3) following a carbon neutrality pathway on climate and extreme weather events individually using the Community Earth System Model version 1 (CESM1). The results suggest that the future aerosol reductions significantly contribute to climate warming and increase the frequency and intensity of extreme weathers toward carbon neutrality and aerosol impacts far outweigh those of GHGs and tropospheric O3. It reverses the knowledge that the changing GHGs dominate the future climate changes as predicted in the middle of the road pathway. Therefore, substantial reductions in GHGs and tropospheric O3 are necessary to reach the 1.5 °C warming target and mitigate the harmful effects of concomitant aerosol reductions on climate and extreme weather events under carbon neutrality in the future.

Abstract Heat-dome-like stationary waves often lead to extreme heat events, such as the unprecedented heatwave in Northwestern North America during the summer of 2021. However, future changes in summer stationary waves over Northwestern North America and the underlying driving factors remain unclear. Here, we investigate the projected changes in the anticyclonic stationary wave circulation over Northwestern North America using data from the Coupled Model Intercomparison Project Phase 6 and diagnose the circulation changes using a stationary wave model. Our findings reveal a significant 95% increase in the summer stationary wave amplitude over Northwestern North America under the high-emission scenario in 2080–2099 relative to 1995–2014. The response is mainly driven by the diabatic heating changes over the tropical Pacific which induce a Rossby wave source in the northeastern tropical Pacific, and further supported by a northward expanded waveguide in North America, both enhancing wave activity flux into the Northwestern North America. The heat-dome-like stationary wave anomaly is expected to heighten the heatwave risk over the region.

Abstract The fractional increase in global mean precipitation ( $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ ) is a first-order measure of the hydrological cycle intensification under anthropogenic warming. However, $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ varies by a factor of more than three among model projections, hindering credible assessments of the associated climate impacts. The uncertainty in $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ stems from uncertainty in both hydrological sensitivity (global mean precipitation increase per unit warming) and climate sensitivity (global mean temperature increase per forcing). Here, by investigating hydrological and climate sensitivities in a unified surface-energy-balance perspective, we find that both sensitivities are significantly correlated with surface shortwave cloud feedback, which is further linked to the climatological pattern of cloud shortwave effect. The observed pattern of cloud effect thus constrains both sensitivities and consequently constrains $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ . The 5%-95% uncertainty range of $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ from 1979-2005 to 2080-2100 under the high-emission (moderate-emission) scenario is constrained from 6.34 $$\pm$$ ± 3.53% (4.19 $$\pm$$ ± 2.28%) in the raw ensemble-model projection to 7.03 $$\pm$$ ± 2.59% (4.63 $$\pm$$ ± 1.71%). The constraint thus suggests a higher most-likely $$\triangle \bar{P}/\bar{P}$$ △ P ¯ / P ¯ and reduces the uncertainty by ~25%, providing valuable information for impact assessments.

Abstract Light-absorbing particles (LAP) deposited on seasonal snowpack can result in snow darkening, earlier snowmelt, and regional climate change. However, their future evolution and contributions to snowpack change relative to global warming remain unclear. Here, using Earth System Model simulations, we project significantly reduced black carbon deposition by 2081-2100, which reduces the December-May average LAP-induced radiative forcing in snow over the Northern Hemisphere from 1.3 Wm−2 during 1995-2014 to 0.65 (SSP126) and 0.49 (SSP585) Wm−2. We quantify separately the contributions of climate change and LAP evolution on future snowpack and demonstrate that projected LAP changes in snow over the Tibetan Plateau will alleviate future snowpack loss due to climate change by 52.1 ± 8.0% and 8.0 ± 1.1% at the end of the century for the two scenarios, mainly due to reduced black carbon contamination. Our findings highlight a cleaner snow future and its benefits for future water supply from snowmelt especially under the sustainable development pathway of SSP126.

Abstract The Asian monsoon provides the freshwater that a large population in Asia depends on, but how anthropogenic climate warming may alter this key water source remains unclear. This is partly due to the prevailing point-wise assessment of climate projections, even though climate change patterns are inherently organized by dynamics intrinsic to the climate system. Here, we assess the future changes in the East Asian summer monsoon precipitation by projecting the precipitation from several large ensemble simulations and CMIP6 simulations onto the two leading dynamical modes of internal variability. The result shows a remarkable agreement among the ensembles on the increasing trends and the increasing daily variability in both dynamical modes, with the projection pattern emerging as early as the late 2030 s. The increase of the daily variability of the modes heralds more monsoon-related hydrological extremes over some identifiable East Asian regions in the coming decades.

Abstract The Tibetan Plateau (TP) is one of the most climate-sensitive regions around the world. Aerosols imported from adjacent regions reach their peak during the pre-monsoon season and play a vital role in the TP environment. However, the strong interannual variation in aerosols transported to the TP has not been fully understood. Here, we show that the interannual variability of pre-monsoon aerosols transported to the TP is influenced more by rainfall over the southern Himalayas than near-surface wind. Rainfall modulates fire events and biomass burning emissions and reduces aerosols over the TP by wet scavenging. Contrary to the role of wind in increasing aerosol transport, the positive correlation between wind and aerosols in the TP reported in previous studies is contributed by the negative interannual correlations between wind and rainfall and between rainfall and fire events over the southern Himalayas. This study highlights the co-variability of wind and rainfall and their confounding impacts on aerosols in the southern Himalayas and over the TP. With pre-monsoon rainfall projected to increase in adjacent regions of southern TP, aerosol transport to the TP may be mitigated in the future.

Abstract While some previous studies examined the contribution of Eastern Pacific (EP) hurricanes toward precipitation in the arid Southwest US (SWUS), their potential to influence wildfires in that region has not been explored. Here we show, using observations and simulations from the Energy Exascale Earth System Model (E3SM), that recurving EP hurricanes modulate the wildfire environment in the SWUS by increasing precipitation and soil moisture, and reducing the vapor pressure deficit. This is especially the case during late season months of September–October when the likelihood of storms to recurve and make landfall increases. Further, analysis of burnt area observations reveals that for the months of September–October, recurving EP hurricanes may significantly reduce the prevalence of wildfires in the SWUS. Finally, E3SM simulations indicate that late season EP hurricanes have been on the decline, with important implications for wildfires in the SWUS.

Abstract High‐resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain‐shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high‐resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming.

Abstract Tropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post‐hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM‐FATES) to study the processes and the key factors regulating post‐hurricane forest recovery. We implemented hurricane‐induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post‐hurricane forest recovery, and quantified the sensitivity of key model parameters to the post‐hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre‐hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post‐hurricane forest recovery in simulations. This research improves understanding of the ELM‐FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

Abstract Weather system clustering provides a high‐level summary of regional meteorological conditions. Most quantitative clustering schemes focus on precipitation alone, which does not sufficiently describe the meteorological conditions driving hydroclimate variability. This study presents the Weather Anomaly Clustering (WAC‐hydro), which extends the existing capability of predicting weather systems to predicting hydroclimate variability. Focusing on both precipitation and temperature predictions, WAC‐hydro identifies 12 clusters of daily weather anomaly modes in the US Pacific Northwest Puget Sound region during 1981–2020. The influence of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on regional precipitation can be well approximated by their modulation on the weather clusters. Within each weather cluster, local factors such as topography only play a secondary role in the hydrologic variability. The weather clusters highlight two types of flood‐inducing regional weather conditions, one causing floods by inducing positive precipitation anomalies and the other causing floods through combined precipitation and temperature‐induced rain‐on‐snow effect.

Abstract Solar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

Abstract Flow direction modeling consists of (a) an accurate representation of the river network and (b) digital elevation model (DEM) processing to preserve characteristics with hydrological significance. In part 1 of our study, we presented a mesh‐independent approach to representing river networks on different types of meshes. This follow‐up part 2 study presents a novel DEM processing approach for flow direction modeling. This approach consists of (a) a topological relationship‐based hybrid breaching‐filling method to conduct stream burning for the river network and (b) a modified depression removal method for rivers and hillslopes. Our methods reduce modifications to surface elevations and provide a robust two‐step procedure to remove local depressions in DEM. They are mesh‐independent and can be applied to both structured and unstructured meshes. We applied our new methods with different model configurations to the Susquehanna River Basin. The results show that topological relationship‐based stream burning and depression‐filling methods can reproduce the correct river networks, providing high‐quality flow direction and other characteristics for hydrologic and Earth system models.

Abstract The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

Abstract Relative humidity (RH) is projected to increase over ocean but decrease over land under anthropogenic warming. The land RH decrease was previously attributed to ocean influences on land changes. Here, we show that interactive soil moisture (SM) is necessary and may be sufficient for anthropogenic warming to reduce the land RH. The land RH decrease is absent in simulations with realistic land‐ocean geometry but fixed SM, and present in simulations with interactive SM but no oceans or changes in precipitation minus evaporation. The land RH decrease is coupled to the SM decline and the latter could be understood as a natural response of interactive SM to anthropogenic warming. Specifically, prior to adjustments in SM and RH, evaporation would increase by ∼6% K−1 following the Clausius–Clapeyron relation, outpacing the radiatively constrained ∼2% K−1 precipitation increase. This prior imbalance depletes SM and consequently reduces RH through the coupling between SM and RH.

Abstract Heatwaves are strongly associated with temperature distributions, but the mechanisms by which distributions are influenced by climate change remains unclear. Comparing simulations from a high‐spatial resolution Community Earth System Model (CESM‐HR) with those from low‐resolution models, we identify substantial improvements by CESM‐HR in reproducing observed Northern Hemisphere summer temperature skewness, as well as the frequency, intensity, persistence, and total heatwave days. Temperature skewness is strongly linked to land‐atmosphere interactions and atmospheric circulation. Under global warming projections, some regions exhibit enhanced temperature skewness, along with more frequent and persistent heatwaves of greater intensity. We find that in energy‐limited regimes, such as India, negative skewness in latent heat flux facilitates large positive skewness in sensible heat flux, which modulates near‐surface air temperatures. Skewness differences of latent and sensible heat fluxes are amplified under global warming, increasing the temperature skewness. We find that this contrasting flux mechanism is active in several heatwave‐prone regions.

Abstract Mesoscale convective systems (MCSs) contribute a majority of rainfall over tropical oceans. However, our understanding of the environmental controls on tropical oceanic MCS precipitation remains incomplete. Using 20‐year of satellite observations, reanalysis data, and MCS tracking, we found that MCSs initiating in a mesoscale environment with enhanced lower‐free‐tropospheric moisture, warmer middle troposphere, stronger low‐level ascent, and stronger deep‐layer (surface‐400 hPa) wind shear tend to produce more precipitation during their lifetimes. While most of these environmental factors are correlated with one another, the deep‐layer shear is not. A rapid pickup in MCS lifetime rainfall is found when the lower‐free‐tropospheric specific humidity exceeds 10 g kg−1. This nonlinearity is mostly dominated by the nonlinear increase in MCS area. On the other hand, both MCS area and rain rate increase quasi‐linearly with the deep‐layer shear. The increase in rain rate is related to the enhancement of heavy precipitating convective activity with deep‐layer shear.

Abstract The influence of the cross‐equatorial northerly surge (CES) on the eastward propagation of Madden‐Julian oscillation (MJO) during boreal winter is evaluated through the analysis of the column integrated moisture budget. Results show that the CES reinforces MJO's southward detour by increasing horizontal moisture convergence over the southern Maritime Continent (MC) region. Further analysis reveals that the zonal convergence by intraseasonal zonal wind anomalies acting upon background moisture is intensified in the presence of CES events, causing a stronger convective activity in the southern MC (SMC). The stronger moisture convergence in the SMC is associated with the CES‐induced intensification of low‐level northwesterly and westerly winds, which, in turn, strengthen zonal wind convergences and positive wind‐evaporation feedbacks onto the MJO convection. An improved process understanding of the link between the CES and MJO detours can help engender improvements in extreme weather forecasts and aid investigation biases in simulating MJO in climate models.

Abstract Aerosol‐cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX‐GDDP, a multimodel ensemble of high‐resolution (0.25°) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX‐GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models.

Abstract The seasonality of circumnavigation of Madden–Julian Oscillation (MJO) events is examined using 42 years of observations, reanalysis, and global model simulations. MJO events are most likely to propagate across the Atlantic Ocean, Africa and back to the equatorial Indian Ocean relatively unweakened during March–April–May and to a lesser extent during September–October–November. During these seasons, referred to as the “long‐rain” and “short‐rain” periods of East African Monsoon (EAM), precipitation is near the equator and extends farthest east toward the Indian Ocean. Moisture budget analysis of a circumnavigating MJO event under real and hypothetical seasonally varying background conditions indicates that the seasonal zonal movement of the zonal component of the background circulation regulates the moistening of MJO crossing from Eastern Africa to the Indian Ocean. Ensembles of Energy Exascale Earth System Model simulations of the MJO event under idealized seasonally varying background state confirm the observed drawbridge‐like role of the EAM in the propagation of MJO.

Abstract Mesoscale convective systems (MCSs) produce over 50% of tropical precipitation and account for the majority of extreme rainfall and flooding events. MCSs are considered the building blocks of larger‐scale convectively coupled equatorial waves (CCEWs). While CCEWs can provide favorable environments for convection, how CCEWs can systematically impact organized convection and thereby MCS characteristics is less clear. We examine this question by analyzing a global MCS tracking data set. During the active phase of CCEWs, MCS frequency increases and MCSs rain harder, produce more lifetime total rain, and grow larger in size. The probability of extreme MCSs also elevates. These changes are most pronounced when MCSs are associated with Kelvin waves and tropical depression‐type waves while less so with the Madden‐Julian Oscillation. These results can be benchmarks to improve model representation of MCS interactions with large‐scale circulations and can be leveraged for operational forecasts of high‐impact MCSs at extended lead times.

Abstract Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection.

Measurement(s) gridded precipitation Technology Type(s) weather station Sample Characteristic - Environment flood • snowmelt • hydrological process Sample Characteristic - Location contiguous United States of America

In the summer of 2020, China experienced record rainfall and flooding. Here, the authors investigate the connection between the rainfall and the abrupt decrease in emissions due to Covid-19 and find that the emission decrease may have contributed to the rainfall.

Abstract This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high‐resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud‐top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed.

Abstract Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.

Abstract River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model's spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model's spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship‐based river network representation method for spatially distributed hydrologic models. This novel method uses (a) graph theory algorithms to simplify real‐world vector‐based river networks and assist in mesh generation; and (b) a topological relationship‐based method to reconstruct conceptual river networks. The main advantages of our method are that (a) it combines the strengths of vector‐based and DEM raster‐based river network extraction methods; and (b) it is mesh‐independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

Spatial patterns in lake evaporation increase with global warming are closely linked to regional hydroclimate drying through energetic and aerodynamic effects, according to analyses of ensemble projections of lake and climate models.

There is a strong disagreement between climate models on how precipitation in California will change. Here, the authors show that much of this uncertainty originates from internal variability with the Interdecadal Pacific Oscillation linking the precipitation changes with the El Niño-like warming pattern through the westerly jet extension

Abstract While the Madden–Julian Oscillation (MJO) has been shown to affect tropical cyclones (TCs) worldwide through its modulation of large-scale circulation in the atmosphere, little or no role for the ocean has been identified to date in this influence of MJO on TCs. Using observations and numerical model simulations, we demonstrate that MJO events substantially impact TCs over the Maritime Continent (MC) region through an oceanic pathway. While propagating across the MC region, MJO events cause significant sea surface cooling with an area-averaged value of about 0.35 ± 0.12 °C. Hence, TCs over the MC region immediately following the passage of MJO events encounter considerably cooler sea surface temperatures. Consequently, the enthalpy fluxes under the storms are reduced and the intensification rates decrease by more than 50% on average. These results highlight an important role played by the ocean in facilitating MJO-induced sub-seasonal variability in TC activity over the MC region.