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Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, pattern shift, phase and wet/dry season length changes. In a warming climate, the amplitude of tropical rainfall annual cycle is enhanced, more evidently over ocean, while the phase of tropical rainfall annual cycle is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The pattern shift under warming is marked by two features: equatorward shift of the inter-tropical convergence zone throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter is suggested to be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress of the last decade, many gaps remain in understanding the mechanism, quantifying and attributing the emergence, narrowing the inter-model uncertainty, and evaluating the impact of tropical rainfall annual cycle changes, motivating future work guided by some directions proposed in this review.

Seasonal snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of meltwater delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) that can be caused by low total precipitation (dry drought) or low proportion of precipitation falling as snowfall (warm drought), often combined with an early melt. The standardized SWE index (SWEI) ranks the current status of SWE for a given location compared to a baseline condition and identifies the existence, but not the cause, of snow drought. In this work, we use estimates of SWE, temperature, and precipitation from nine coupled model intercomparison project phase 6 (CMIP6) models to quantify the frequency, severity, and type of snow droughts globally for historical and future scenarios. Compared to a historical baseline (1850–1900) total snow drought frequency more than doubles under socio-economic pathway (SSP)2-4.5 and SSP5-8.5; all of the increase in snow drought frequency comes from an increase in warm droughts. The probability distribution of future SWEI in major snowy basins around the world are likely to be centered on more negative values, which corresponds to more severe drought and, with only moderate changes in distribution spread, more frequent drought. CMIP6 simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.

Surface ozone is substantially affected by climate change through the modulation of key meteorological parameters such as temperature. While the changes in temperature under a warming climate manifest in changes of both the mean and higher-order statistical moments, their individual role in ozone concentration changes has not been broadly investigated. To address this gap, we use a novel approach to isolate the impacts of mean warming and changes in higher-order moments of temperature on ozone over the southeastern U.S. (SEUS) and western U.S. (WUS) by the mid-21st century based on simulations under Technology Driver Model A1B and B2 scenarios from a regional modeling framework (Weather Research and Forecasting model coupled with Chemistry (WRF/Chem)). Mean warming generally dominates the impacts of climate change on ozone, and higher-order moment temperature changes can also counteract 25% of the ozone exceedance of 70 ppbv over SEUS, and may offset 48% of the mean warming induced increase of ozone exceedance in heat waves during 2046–2055 under A1B. The opposite changes in the higher-order moments over SEUS and WUS lead to opposite impacts on ozone exceedance in the two regions. Our results suggest that improving prediction of both the mean and higher-order temperature changes may be crucial to constraining the future changes in ozone concentration to better inform air quality policy.

Temperature and water vapor have been considered as important factors affecting ozone concentrations, however, their synergistic effect on ozone, as well as its response to a warming climate remains unclear. Here, numerical experiments with different combinations of climate and emissions highlighted opposite changes of annual ozone over the contiguous U.S. under scenarios with high vs. low ozone precursor emissions in the future with warming. Comparing the occurrence of heat waves on higher vs. lower ozone days, we found significant suppression of the heat wave exacerbation of ozone by anthropogenic emissions reductions. Variations of ozone with temperature ( T ) and water vapor ( Q ) over the western U.S. and southeastern U.S. in the simulations are clearly revealed through the construction of an O _3 - T-Q diagram. Based on the diagram, we further identified a T -dominant regime at higher temperatures where ozone increases with temperature increase, and a Q -dominant regime at lower temperatures where ozone decreases with temperature increase due to the negative effect of water vapor. With larger anthropogenic emission reductions, ozone may increase at a lower rate with temperature in the T -dominant regime, or even decrease with temperature in the Q -dominant regime. Our results show that with large emission reductions, substantial ozone pollution due to warming and increasing heat waves may be counteracted by water vapor, partly turning the ‘climate penalty’ on ozone into ‘climate benefit’. Hence controlling anthropogenic emissions may be an increasingly important strategy in a warmer and wetter climate to improve air quality and public health.

Land aridity is often characterized by the aridity index (AI), which does not account for land surface water-energy interactions that are crucially important in determining regional climate. Such interactions can be captured by the evaporative fraction (EF, ratio of evapotranspiration to available energy) regimes. As EF is subject to energy and water limitations in humid and dry areas, respectively, EF regimes may be used to characterize land aridity to account for the influence of complex land characteristics and their impact on water availability. Here, we propose a simple framework to characterize land aridity by statistically ranking the coupling strength between EF and surface energy and water terms. The framework is demonstrated using gridded data and compared with AI over the U.S. and China. Results show that regionalization of aridity zones based on EF regimes and a two-tiered classification scheme may provide information such as surface energy and water variability complementary to the background aridity depicted by AI, with implications for extreme events.

Soil erosion delivers enormous amounts of macro-nutrients including nitrogen (N) and phosphorus (P) from land to rivers, potentially sustaining water column bioavailable nutrient levels for decades. In this study, we represent erosional N and P fluxes in the Energy Exascale Earth System Model (E3SM) and apply the model to the continental United States. We estimate that during 1991–2019 soil erosion delivers 775 Gg yr ^−1 (1 Gg = 10 ^9 g) of particulate N (PN) and 328 Gg yr ^−1 of particulate P (PP) on average to the drainage basins of the northern Gulf of Mexico, including the Mississippi/Atchafalaya River and other rivers draining to the Texas Gulf and the Eastern Gulf. Our model simulation shows that in these rivers PP is the dominant P constituent and over 55% of P exported by erosion comes from soil P pools that could become bioavailable within decades. More importantly, we find that during 1991–2019 erosional N and P fluxes increase at rates of about 15 Gg N yr ^−1 and 6 Gg P yr ^−1 , respectively, due to increased extreme rains in the Mississippi/Atchafalaya river basin, and this intensification of erosional N and P fluxes drive the significant increase of riverine PN and PP yields to the northern Gulf of Mexico. With extreme rains projected to increase with warming, erosional nutrient fluxes in the region would likely continue to rise in the future, thus complicating the effort of reducing eutrophication in the inland and coastal waters.

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.

Wintertime convective mixing plays a pivotal role in the sub-polar North Atlantic spring phytoplankton blooms by favoring phytoplankton survival in the competition between light-dependent production and losses due to grazing and gravitational settling. We use satellite and ocean reanalyses to show that the area-averaged maximum winter mixed layer depth is positively correlated with April chlorophyll concentration in the northern Labrador Sea. A simple theoretical framework is developed to understand the relative roles of winter/spring convection and gravitational sedimentation in spring blooms in this region. Combining climate model simulations that project a weakening of wintertime Labrador Sea convection from Arctic sea ice melt with our framework suggests a potentially significant reduction in the initial fall phytoplankton population that survive the winter to seed the region's spring bloom by the end of the 21st century.

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.

This study investigated the anthropogenic influence on the temporal variability of annual precipitation for the period 1950–2005 as simulated by the CMIP5 models. The temporal variability of both annual precipitation amount (PRCPTOT) and intensity (SDII) was first measured using a metric of statistical dispersion called the Gini coefficient. Comparing simulations driven by both anthropogenic and natural forcing (ALL) with simulations of natural forcing only (NAT), we quantified the anthropogenic contributions to the changes in temporal variability at global, continental and sub-continental scales as a relative difference of the respective Gini coefficients of ALL and NAT. Over the period of 1950–2005, our results indicate that anthropogenic forcing have resulted in decreased uniformity (i.e. increase in unevenness or disparity) in annual precipitation amount and intensity at global as well as continental scales. In addition, out of the 21 sub-continental regions considered, 14 (PRCPTOT) and 17 (SDII) regions showed significant anthropogenic influences. The human impacts are generally larger for SDII compared to PRCTOT, indicating that the temporal variability of precipitation intensity is generally more susceptible to anthropogenic influence than precipitation amount. The results highlight that anthropogenic activities have changed not only the trends but also the temporal variability of annual precipitation, which underscores the need to develop effective adaptation management practices to address the increased disparity.

Despite the importance of surface water to people and ecosystems, few studies have explored detectable changes in surface water supply in a changing climate, given its large natural variability. Here we analyze runoff projections from the Variable Infiltration Capacity hydrological model driven by 97 downscaled and bias-corrected Coupled Model Intercomparison Project Phase 5 climate projections over the conterminous United States (CONUS). Our results show that more than 40% of the CONUS land area will experience significant changes in the probability distribution functions (i.e. PDFs) of summer and winter runoff by the end of the 21st century, which may pose great challenges to future surface water supply. Sub-basin mean runoff PDFs are projected to change significantly after 2040s depending on the emission scenarios, with earliest occurrence in the Pacific Northwest and northern California regions. When examining the response as a function of changes in the global mean temperature (ΔGMT), a linear relationship is revealed at the 95% confidence level. Generally, 1 °C increase of GMT leads to 11% and 17% more lands experiencing changes in summer and winter runoff PDFs, respectively. Such changes in land fraction scale with ΔGMT at the country scale independent of emission scenarios, but the same relationship does not necessarily hold at sub-basin scales, due to the larger role of atmospheric circulation changes and their uncertainties on regional precipitation. Further analyses show that the emergence of significant changes in sub-basin runoff PDFs is indicative of the emergence of new hydrology regimes and it is dominated by the changes in variability rather than shift in the mean, regardless of the emission scenarios.

This study examines future changes of cold air outbreaks (CAOs) using a multi-model ensemble of global climate simulations from the Coupled Model Intercomparison Project Phase 5 and high resolution regional climate simulations. Overall, climate models agree on a dip in CAO duration across North America, but the percentage change is consistently smaller from western Canada to the upper mid-western US with historically more frequent CAO. By decomposing the changes of the probability density function of daily surface temperature into changes due to mean warming and changes in standard deviation (std) and skewness/higher order moments, the contributions of each factor to CAO changes are quantified. Results show that CAO changes can be explained largely by the mean warming, but the decrease in temperature std contributes to about 20% reduction of CAO from Alaska to northeastern US and eastern Canada possibly due to the Arctic amplification and weakening of storm track. A thermodynamical modulation of the skewness called the ‘0 °C mode’ effect is found to operate prominently along the 0 °C isotherm hemispherically and reduce CAO in western and northeastern US with winter snow cover by up to 10%. This effect also produces a manifold increase in CAO events over the Arctic sea ice. An increased frequency in atmospheric blocking also contributes to increases in CAO duration over Alaska and the Arctic region. Regional simulations revealed more contributions of existing snowpack to CAO in the near future over the Rocky Mountain, southwestern US, and Great Lakes areas through surface albedo effects. Overall, the multi-model projections emphasize that cold extremes do not completely disappear in a warming climate. Concomitant with the relatively smaller reduction in CAO events in northwestern US, the top five most extreme CAO events may still occur, and wind chill will continue to have societal impacts in that region.

Sea surface temperature (SST) is not the only oceanic parameter that can play a key role in the interannual variability of Northeast Pacific hurricane activity. Using several observational data sets and the statistical technique of multiple linear regression analysis, we show that, along with SST, the thermocline depth (TD) plays an important role in hurricane activity at interannual timescales in this basin. Based on the parameter that dominates, the ocean basin can be divided into two sub-regions. In the Southern sub-region, which includes the hurricane main development area, interannual variability of the upper-ocean heat content (OHC) is primarily controlled by TD variations. Consequently, the interannual variability in the hurricane power dissipation index (PDI), which is a measure of the intensity of hurricane activity, is driven by that of the TD. On the other hand, in the Northern sub-region, SST exerts the major control over the OHC variability and, in turn, the PDI. Our study suggests that both SST and TD have a significant influence on the Northeast Pacific hurricane activity at interannual timescales and that their respective roles are more clearly delineated when sub-regions along an approximate north–south demarcation are considered rather than the basin as a whole.

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.

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.

Abstract The Grell‐Freitas deep convection scheme (GF) has been implemented in the Community Atmosphere Model (CAM) with the nonhydrostatic Model for Prediction Across Scales dynamical core. Aquaplanet simulations have been performed with a global quasi‐uniform resolution mesh with ∼120 km grid spacing and a variable resolution mesh employing a circular refined region with ∼30 km grid spacing centering at the equator and ∼120 km elsewhere. GF produces overall patterns of the general circulations comparable to those of other aquaplanet experiments, including those produced using the default Zhang‐McFarlane convection scheme (ZM) in CAM Version 5.4. GF alleviates the overestimated occurrence of weak intensity precipitation characteristic of ZM due to GF's different closure methods and deep‐convection trigger conditions. Compared to ZM, the GF characteristics of the equatorial Kelvin waves are closer to observations. GF also better simulates precipitation at variable resolution, with no unrealistic wave distortion by the refinement. Overall, GF shows less sensitivity of convective precipitation and heating/moistening tendencies to changes in horizontal resolution compared to ZM. However, vertically overlapped total cloud fraction simulated with GF in the tropics is more sensitive to the resolution than ZM due to the larger resolution‐sensitivity of high‐level cloud fraction in the tropics.

Abstract Accurately representing mesoscale convective systems (MCSs) is crucial to simulating the energy and water cycles in global climate models. Using a novel MCS identification and tracking algorithm applied to observations and model simulations, we evaluate how well the Energy Exascale Earth System Model (E3SM) simulates MCSs over the central US. Simulations performed by E3SM at 25 km grid spacing with and without superparameterization (SP) are compared and evaluated against observations using multiple metrics highlighting important MCS characteristics. Compared to E3SM, the superparameterized model (SP‐E3SM) better simulates MCS number and MCS precipitation amount, diurnal cycle, propagation, and the probability distribution of precipitation rate in both spring and summer. The improvement from SP is partly contributed by improvement in simulating the large‐scale environments, featuring enhanced atmospheric low‐level moisture and larger moisture transport to the central US relative to E3SM. However, SP‐E3SM still underestimates MCS precipitation amount, particularly in summer. This underestimation is closely related to the negative bias in MCS precipitation intensity, although the drier environments simulated during summer also contributes to underestimation of MCS frequency. Without SP, the larger bias in MCS precipitation amount is closely related to the negative bias in MCS frequency, while the substantial dry bias in the large‐scale environments also contributes to underestimation of MCS intensity. Our results suggest that SP improves MCS simulation by improving modeling of the large‐scale environments and convection initiation, which are both major limiting factors in E3SM even at 25 km grid spacing where deep convection is represented by a cumulus parameterization.

Abstract In tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve (Bsw), play crucial roles in controlling forest diversity, with higher Bsw values (>7) favoring late successional species in competition, and lower Bsw values (1 ∼ 7) promoting the coexistence of early and late successional plants. Considering the additional impact of soil properties resolves a systematic bias of FATES in simulating sensible/latent heat partitioning with repercussion on water budget and plant coexistence. A greater fraction of deeper tree roots can help maintain the dry‐season soil moisture and plant gas exchange. As soil properties are as important as vegetation parameters in predicting tropical forest dynamics, more efforts are needed to improve parameterizations of soil functions and belowground processes and their interactions with aboveground vegetation dynamics.

Abstract Soil erosion produces enormous amounts of sediment, carbon, and nutrient fluxes from land to rivers, thus playing crucial roles in global biogeochemical cycles and food security. To predict soil erosion in the context of climate and land use changes, we explicitly parameterize cropland management actions (i.e., irrigation, conserved agriculture, and crop residue management) and geological factors (i.e., lithology and glacier) in the Energy Exascale Earth System Model (E3SM) soil erosion module. The erosion model is calibrated using a global‐scale regionalized parameter calibration method. The spatial variabilities of the modeled and the Revised Universal Soil Loss Equation (RUSLE) based benchmark soil erosion are consistent across vegetation, climate and soil properties. Compared with independent data, our model shows a bias reduction in 59% of the observations relative to the RUSLE‐based soil erosion, with 53% of the bias reduction exceeding 50%. This improvement is mainly due to a better representation of the topographic effect on soil erosion. Our results indicate that conserved agriculture practices have effectively reduced soil erosion in cropland by over 25% in the United States and Argentina. In contrast, irrigation has increased soil erosion in many Asian countries. For upland sediment flux, our model is consistent with the WBMsed benchmark data in inter‐basin variability but could be more skillful in simulating intra‐basin variability because it couples soil erosion and sediment flux explicitly. The developed model provides useful skills for more realistic predictions of soil erosion and river sediment dynamics under environmental changes.

Abstract This study evaluates the precipitation over East Asia simulated by the variable resolution CAM‐MPAS featuring the Model for Prediction Across Scales (MPAS) atmospheric dynamical core coupled with the physics parameterizations of the Community Atmosphere Model (CAM) version 5.4. Two CAM‐MPAS experiments, one with a global quasi‐uniform resolution mesh of 120 km (MPAS‐UR) and the other with a variable resolution mesh of 30–120 km refined over East Asia (MPAS‐VR), are conducted from 1989 to 2005 following the Atmospheric Model Intercomparison Project protocol. Two regional climate model simulations at ∼25 km resolution from the Coordinated Regional Downscaling Experiment East Asia second plan are also analyzed for comparison. Results show that CAM‐MPAS performs better than the selected regional models in simulating the precipitation climatology over East Asia. Compared with MPAS‐UR, MPAS‐VR with refinement over East Asia better simulates the precipitation over and around the Tibetan Plateau (TP), the upper‐level circulation, the precipitation frequency and intensity in various regions of China, and the interannual precipitation pattern associated with the East Asian summer monsoon. Both decreased grid spacing and the use of finer‐scale terrain information contribute to the improvements in MPAS‐VR relative to MPAS‐UR. However, decreased grid spacing is more essential for simulating the precipitation magnitude over TP and the location of peak precipitation south of TP, likely due to the better resolved physical and dynamical processes associated with orographic precipitation. The 30–120 km CAM‐MPAS model is shown to be a promising tool for precipitation simulation over East Asia.

Abstract Plants are expected to face increasing water stress under future climate change. Most land surface models, including Noah‐MP, employ an idealized “big‐leaf” concept to regulate water and carbon fluxes in response to soil moisture stress through empirical soil hydraulics schemes (SHSs). However, such schemes have been shown to cause significant uncertainties in carbon and water simulations. In this paper, we present a novel plant hydraulics scheme (PHS) for Noah‐MP (hereafter, Noah‐MP‐PHS), which employs a big‐tree rather than big‐leaf concept, wherein the whole‐plant hydraulic strategy is considered, including root‐level soil water acquisition, stem‐level hydraulic conductance and capacitance, and leaf‐level anisohydricity and hydraulic capacitance. Evaluated against plot‐level observations from a mature, mixed hardwood forest at the University of Michigan Biological Station and compared with the default Noah‐MP, Noah‐MP‐PHS better represents plant water stress and improves water and carbon simulations, especially during periods of dry soil conditions. Noah‐MP‐PHS also improves the asymmetrical diel simulation of gross primary production under low soil moisture conditions. Noah‐MP‐PHS is able to reproduce different patterns of transpiration, stem water storage and root water uptake during a 2‐week dry‐down period for two species with contrasting plant hydraulic behaviors, i.e., the “cavitation risk‐averse” red maple and the “cavitation risk‐prone” red oak. Sensitivity experiments with plant hydraulic capacitance show that the stem water storage enables nocturnal plant water recharge, affects plant water use efficiency, and provides an important buffer to relieve xylem hydraulic stress during dry soil conditions.

Abstract Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a “wetter‐South‐drier‐North” rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

Compound events (e.g., co-occurrence of two extreme weather events simultaneously) have the potential to aggravate the impacts of heat wave and stagnation on ozone through nonlinear effects. Using the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem), this study investigated the impact of compound extreme weather events on ozone enhancement in the contiguous U.S. The analyses are based on simulations for the historical (2001–2006) and future (2046–2055) periods previously performed, with emissions in the future following the A1B/B2 emission scenarios produced by the Technology Driver Model (TDM). While A1B reflects rapid economic growth, B2 emphasizes more on local environmental sustainability, hence lower anthropogenic emissions. With overall warming in the future, the frequency and intensity of compound extreme events increase more than that of heat waves. Thus compound events promote higher ozone concentration compared to the single extreme events of heat wave and stagnation in larger areas of the U.S. in the future relative to the present. However, averaged over the U.S., the amplification effect of compound events on ozone enhancement is diminished in the future. Sensitivity experiments to isolate the effect of climate change versus emission change showed that climate change under the future emission scenarios dominates the weakening amplification effect of compound events. Further analyses to examine the nonlinear effect of compound events (NLRE), defined as the difference between the enhanced effect on ozone during compound events and the additive effect of heat waves and stagnation relative to non-extreme, showed that regions prone to positive NLRE tend to have compound events marked by higher temperature and stronger stagnation than the single extreme events. Simulations showed reduced mean positive NLRE in the U.S. in the future, suggesting that future emission reduction may provide a previously unrecognized benefit in ozone pollution control by reducing the impacts of compound events on ozone.

Abstract Supported by the U.S. Department of Energy (DOE), the Energy Exascale Earth System Model (E3SM) project aims to optimize the use of DOE resources to address the grand challenge of actionable predictions of Earth system variability and change. This requires sustained advancement to (1) integrate model development with leading‐edge computational advances toward ultra‐high‐resolution modeling; (2) represent the coupled human‐Earth system to address energy sector vulnerability to variability and change; and (3) address uncertainty in model simulations and projections. Scientific development of the E3SM modeling system is driven by the simulation requirements in three overarching science areas centering on understanding the Earth's water cycle, biogeochemistry, and cryosphere systems and their future changes. This paper serves as an introduction to the E3SM special collection, which includes 50 papers published in several AGU journals. It provides an overview of the E3SM project, including its goals and science drivers. It also provides a brief history of the development of E3SM version 1 and highlights some key findings from papers included in the special collection.

Abstract Irrigation supports agricultural production, but widespread use of irrigation can perturb the regional and global water cycle. The one‐way coupled irrigation scheme used in some land surface models and Earth system models assumes that surface water demand is always met and ignores the surface water constraints, leading to overestimation of surface water usage, underestimation of groundwater pumping, and unrealistic simulation of their seasonal variability. To better represent the irrigation processes, a two‐way coupled irrigation scheme is developed within the Energy Exascale Earth System Model (E3SM). The new irrigation scheme simulates irrigation water demand and applies irrigation water in E3SM Land Model (ELM), which is coupled to a river routing model and a water management model (MOSART‐WM) that simulate streamflow, reservoir operations, and irrigation water supply. With two‐way coupling, surface water irrigation is constrained by the available runoff, streamflow, and reservoir storage. Simulations were performed for 1979–2008 at 0.5° spatial resolution to estimate irrigation surface water and groundwater use and their seasonality in global and large river basin scales. Compared to one‐way coupling, the two‐way coupling scheme (1) estimates less surface water withdrawal and less return flow due to the surface water constraint; (2) better represents groundwater recharge and groundwater level decline at global scale; and (3) is able to capture the seasonal dynamics of irrigation water allocations which reflect the local water conditions. The new development is an important step to more realistically account for the interactions between human water use and the terrestrial water cycle in an Earth system model.

Abstract In this study, we analyze the realism with which tropical cyclones (TCs) are simulated in the fully coupled low‐ and high‐resolution Energy Exascale Earth System Model (E3SM) version 1, with a focus on the latter. Compared to the low‐resolution (grid spacing of ∼1°), the representation of TCs improves considerably in the high‐resolution configuration (grid spacing of ∼0.25°). Significant improvements are found in the global TC frequency, TC lifetime maximum intensities, and the relative distribution of TCs among the different basins. However, at both resolutions, spurious TC activity is found in some basins, notably in the subtropical regions. Contrasting the simulated large‐scale TC environment with observations reveals that the model environment is unrealistically conducive for TC development in those regions. Further analysis indicates that these biases are likely related to those in thermodynamic potential intensity, caused by systematic SST biases, and vertical wind shear in the coupled model. TC‐ocean interaction is also examined in the high‐resolution configuration of the model. The salient features of the ocean's response to TC‐induced mixing and the ocean's impact on TC intensification are well‐reproduced. Finally, an evaluation of the influence of El Niño Southern Oscillation (ENSO) on TCs in the high‐resolution configuration of the model reveals that the ENSO‐TC relationship in the model has the right sign and is significant for the North Atlantic and Northwest Pacific, albeit weaker than in observations. In summary, the high‐resolution configuration of the E3SM model simulates TC activity reasonably and hence could be a useful tool for TC‐related research.

Abstract The new Energy Exascale Earth System Model Version 1 (E3SMv1) developed for the U.S. Department of Energy has significant new treatments of aerosols and light‐absorbing snow impurities as well as their interactions with clouds and radiation. This study describes seven sets of new aerosol‐related treatments (involving emissions, new particle formation, aerosol transport, wet scavenging and resuspension, and snow radiative transfer) and examines how they affect global aerosols and radiative forcing in E3SMv1. Altogether, they give a reduced total aerosol radiative forcing (−1.6 W/m2) and sensitivity in cloud liquid water to aerosols, but an increased sensitivity in cloud droplet size to aerosols. A new approach for H2SO4 production and loss largely reduces a low bias in small particles concentrations and leads to substantial increases in cloud condensation nuclei concentrations and cloud radiative cooling. Emitting secondary organic aerosol precursor gases from elevated sources increases the column burden of secondary organic aerosol, contributing substantially to global clear‐sky aerosol radiative cooling (−0.15 out of −0.5 W/m2). A new treatment of aerosol resuspension from evaporating precipitation, developed to remedy two shortcomings of the original treatment, produces a modest reduction in aerosols and cloud droplets; its impact depends strongly on the model physics and is much stronger in E3SM Version 0. New treatments of the mixing state and optical properties of snow impurities and snow grains introduce a positive present‐day shortwave radiative forcing (0.26 W/m2), but changes in aerosol transport and wet removal processes also affect the concentration and radiative forcing of light‐absorbing impurities in snow/ice.