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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.

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.

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.

Through orographic forcing, mountains effectively harness water vapor into freshwater in the form of precipitation, a large fraction of which is stored in mountain snowpack during winter and released as [...]

The southern slope of the Tibetan Plateau (SSTP) is one of the rainiest regions in the world where geological hazards caused by extreme precipitation often occur. This study investigates the characteristics and mechanisms of extreme precipitation over SSTP from June to September during 2001–2020 using Global Precipitation Measurement satellite observation. The extreme precipitation days are defined as the days with top 5% of regional‐mean daily precipitation over SSTP in this period, which has an average precipitation of 27.2 mm/d. Averaging over the extreme precipitation days, precipitation peaks at an altitude of about 300 m, coinciding with the climatological maximum precipitation, but with a much larger value of 37.2 mm/d than the climatology of 11.9 mm/d. Composite analysis of circulations on extreme days reveals significant circulation anomalies in both the lower and upper troposphere. Specifically, the lower‐tropospheric circulations are characterized by significant westerly anomalies over northern India, and the upper‐tropospheric circulations are characterized by northerly anomalies over the central Tibetan Plateau, which are statistically independent. The lower‐tropospheric westerly anomalies blowing toward SSTP are blocked by the topography, favoring extreme precipitation over SSTP. The upper‐tropospheric northerly anomalies, on the other hand, correspond to anomalous northeasterlies north of SSTP in the middle troposphere and southeasterlies to the south in the lower troposphere, and the convergence of these circulation anomalies favors extreme precipitation over SSTP. Lastly, the lower‐tropospheric westerly and upper‐tropospheric northerly anomalies, respectively, correspond to less precipitation over the South Asian monsoon region and the Tibetan Plateau. Plain Language Summary: The southern slope of the Tibetan Plateau (SSTP) is one of the rainiest regions in the world, because huge amount of water vapor transported by the South Asian monsoon are blocked by the steep Himalayas. Extreme precipitation occurs frequently over SSTP, which causes geological hazards and threatens people's lives there. A comprehensive understanding of the disaster‐producing extreme precipitation over SSTP will be helpful for weather prediction and disaster mitigation. In this study, the extreme precipitation days are defined as the days with top 5% of regional‐mean daily precipitation over SSTP from June to September during 2001–2020. Spatiotemporal characteristics of extreme precipitation over SSTP and the associated atmospheric circulations are investigated. Results show that extreme precipitation peaks at about 300 m in elevation, where it is about triple the climatological mean, and the precipitation decreases sharply as elevation increases. The composited circulations on extreme days exhibit significant lower‐tropospheric westerly anomalies over northern India and upper‐tropospheric northerly anomalies over the central Tibetan Plateau. These two wind anomalies are climatologically independent of each other and both are significantly positively correlated with the SSTP extreme precipitation. Key Points: The 95% extreme precipitation over the southern slope of Tibetan Plateau (SSTP) peaks at ∼300 m where it is triple the climatological meanExtreme precipitation over SSTP is associated with lower‐tropospheric westerly and upper‐tropospheric northerly anomaliesThese circulation anomalies favor the extreme rainfall through topographic blocking and lower‐mid‐tropospheric convergence [ABSTRACT FROM AUTHOR]

The East African March–April–May (MAM, "long rains") precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline. Plain Language Summary: In this article the inter‐annual variability and the observed long‐term decline of Boreal spring rainfall over Eastern Africa are examined. Previous research on the causes of these droughts focused on sea surface temperature variability over tropical oceans. In this work through extensive analysis of observations and global modeling we show that land‐surface feedbacks over Northwestern Eurasia are the primary culprit. More broadly the work highlights the importance of teleconnections between regional climates through remote impacts of land surface feedbacks. Key Points: We demonstrate a co‐variability of East African March–April–May rainfall (long rains) and surface temperature over Northwestern EurasiaAnalysis of model simulations show warming Northwestern Eurasia weakens the local Hadley cell and introduces subsidence over East AfricaThe observed decline in East African March–April–May rainfall observed during 1980–2014 can be explained by this warming [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

In this study, we evaluate mesoscale convective system (MCS) simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using PyFLEXTRKR algorithm, which defines MCSs based on both cloud top brightness temperature (Tb) and surface precipitation. Results from using only Tb to define MCSs are also discussed to understand the impact of different MCS tracking algorithms on MCS evaluation and provide additional insights into model errors in simulating MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in tropical and extratropical regions. This is mainly attributed to the underestimated MCS genesis and underestimated precipitation intensity in EAMv2. Comparing the two MCS tracking methods, simulated MCS precipitation is increased if MCSs are defined with only cloud top Tb. The Tb‐based MCS tracking method, however, includes cloud systems with very weak precipitation. This illustrates the model issues in simulating heavy precipitation even though the convective cloud shield is overall well simulated from the moist convective processes. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. The new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are not significantly improved. This suggests that simulating MCSs still remain a challenge for the next version of E3SM. Plain Language Summary: Mesoscale convective systems (MCSs) are one of the largest forms of deep convective storms, which play an important role in the earth system. It is imperative for global climate models to reasonably simulate MCS properties. This study aims to evaluate simulated MCS properties in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). We utilized two different approaches to define MCSs in the model and observations for consistent comparisons. Our results show that the E3SMv2 model underestimates MCS precipitation in the tropical and subtropical regions. The too few MCSs and overly weak precipitation intensity in individual MCSs are the primary reasons for this MCS precipitation bias. The simulated MCS precipitation becomes more comparable to the observations when surface precipitation is not included in the MCS definition. However, many cloud systems with very weak precipitation characteristics are included. This comparison illustrates the model issues in precipitation formation while convective cloud structures are overall well simulated. In addition, by examining the impact of new physics parameterizations that are developed for the next generation of E3SM model on the MCS simulation, we find simulating MCSs will remain a challenge for the next version of E3SM model. Key Points: Simulated mesoscale convective system (MCS) precipitation is substantially underestimated in E3SMv2 due to insufficient MCS genesis and rain rate in individual MCSsUtilizing different MCS tracking methods provides a more complete picture about the model capability in simulating MCSsMCS properties in E3SMv2 are not significantly improved with the new cloud and convection parameterizations developed for E3SMv3 [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. Plain Language Summary: Current global Earth System Models (ESMs) used in climate simulation and projection represent the land surface using coarse grids with limited ability to resolve the surface variability within the grid. This study introduced a new subgrid structure and methods to downscale atmospheric variables such as precipitation and temperature from the atmosphere grid to the land subgrid units in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve its ability of representing the effects of topography induced subgrid surface variability on land surface processes. Effects of the new developments on simulations of land surface processes are evaluated using the conterminous United States (CONUS) as a case study. ELM simulations with the new developments generally yield higher snowfall, snow water equivalent and runoff over CONUS. Effects of the new developments are found to be larger over regions dominated by higher elevation landscapes and regions receiving their maximum precipitation during cool seasons. Also, the new developments improved ELM's ability to reproduce observed snow water equivalent at the SNOTEL sites. The new developments have important implications for streamflow modeling and water resources management dependent on hydrologic processes in mountainous regions. Key Points: A topography‐based subgrid scheme and downscaling of atmospheric forcing have been implemented in the E3SM Land Model componentThese new methods have more pronounced effects in high subgrid elevation areas that receive their major precipitation during cool seasonsThe new methods improve model capability to reproduce the observed snow water equivalent at 83% of the SNOTEL sites [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: A machine learning model is employed to bias correct the large‐scale dynamical and thermodynamical fields simulated by a low‐resolution global climate model. The impact of the machine learning model on the large‐scale storm environment associated with tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) was evaluated. It is found that the ML bias correction can effectively reduce the mean climate biases in large‐scale wind, temperature, and humidity fields associated with the three types of high‐impact weather systems. For storms such as ETCs and ARs that can be partly resolved by the low‐resolution climate models, the machine learning bias correction shows skills in improving the long‐term statistics of these weather events. For storms such as TCs that can not be well resolved in the low‐resolution climate models, the machine learning approach produces more realistic statistics of the tropical cyclone tracks by providing more realistic large‐scale steering winds for downscaling approaches. By reducing model biases without affecting the climate change signals in large‐scale storm environments derived from the low‐resolution climate model simulations, machine learning bias correction has the potential to provide more reliable projections for assessing future changes in extreme weather events. Key Points: A machine learning approach with a long short‐term memory network is used to bias correct climate simulations from the E3SM atmosphere modelThe approach effectively reduces biases in the simulated large‐scale model states while preserving their responses to climate changeImproving large‐scale storm environment with bias correction facilitates modeling of high‐impact weather systems and their future changes [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: As ocean levels rise, seawater threatens to intrude into coastal freshwater aquifers that millions of people depend on for drinking water and irrigation. While regional studies have examined the impacts of sea‐level rise (SLR) on coastal groundwater systems, current Earth system models (ESMs) overlook the exchange of water between ocean and groundwater. Our work addresses this gap by developing a water exchange process between ocean and land components in a state‐of‐the‐science ESM. This coupling scheme includes lateral exchanges between seawater and groundwater, as well as vertical seawater infiltration resulting from oceanic inundation. We used this new coupled model to assess SLR impacts on the global coastal groundwater table under a higher CO2 emission scenario. We found that SLR will raise groundwater levels and intensify the hydrological cycle by midcentury mainly due to increased seawater infiltration. Further, while SLR triggers this increased seawater infiltration, topography and warming temperature play more significant roles in determining its magnitude. Key Points: A novel land‐ocean coupling scheme is developed and implemented in Energy Exascale Earth System Model version 2 to evaluate the hydrologic exchange at the land‐ocean interfaceThe impact of land‐ocean coupling on the groundwater table is dominated by seawater infiltration, while lateral exchange is negligibleFuture sea‐level rise will induce significant seawater intrusion into the coastal groundwater system [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Tropical cyclones (TCs) can induce strong mixing in the upper ocean and upwelling of cold, nutrient‐rich water from subsurface to the surface. This process can induce significant sea surface cooling and enhance primary productivity. Utilizing satellite observations, we found evidence that the sea surface cooling associated with TCs has been increasing significantly in the near‐shore regions of United States (U.S.) Southeast Coast. However, corresponding trends in the U.S. Gulf Coast are not significant. Here we show that the enhanced TC‐induced SST cooling along the U.S. Southeast Coast has occurred in response to changes in the atmospheric circulation associated with global warming. These findings shed light on the potential changes in the interactions between TCs and the upper ocean conditions under a non‐stationary climate. Key Points: The tropical cyclone induced sea surface temperature cooling has significantly increased near the U.S. Southeast Coast in recent decadesThe increasing trend in TC‐induced cold wake is primarily attributed to subsurface cooling driven by the change in atmospheric circulationClimate models suggest greenhouse gases are likely responsible for the atmospheric circulation changes [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: As a basic mode of climate variability, the diurnal cycle is a key metric that has been used to benchmark climate models. The current state‐of‐the‐art GCMs struggle to accurately represent the precipitation diurnal cycle, frequently peaking too early compared to the observations. Due to the coarse resolution and the use of convection parameterization, GCMs are also unable to simulate organized convective storms, which often exhibit a distinct diurnal cycle. With the emergence of global storm‐resolving models at kilometer‐scale resolution, this study evaluates the diurnal cycle of precipitation simulated by global storm‐resolving models and high‐resolution GCMs. We find that at resolutions between ∼25 and 250 km, increasing resolution has limited effects on the precipitation diurnal cycle but global CPMs can reproduce the observed precipitation diurnal cycle much better as they can better represent organized convective storms. Key Points: Precipitation diurnal cycle is systematically evaluated in DYAMOND simulations from global convection‐permitting models (CPMs) and CMIP6 modelsBoth the amplitude and phase of the precipitation diurnal cycle are better simulated by CPMs than CMIP6 high‐resolution modelsThe global CPMs excel in reproducing the precipitation diurnal cycle owing to better simulated mesoscale convective systems [ABSTRACT FROM AUTHOR]

Most current land models approximate terrestrial hydrological processes as one‐dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer‐scale land models. To test the effect of incorporating lateral flow toward three‐dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF‐Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic‐tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF‐Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event‐water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event‐water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration‐excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope‐derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation. Key Points: A water tracer model has been integrated with WRF‐Hydro to examine the role of lateral flow in terrestrial hydrological processesAccounting for lateral flow lengthens the transit times of precipitation in conditions with limited hydrology connectivity and vice versaModeled results underestimate transit times compared to isotope‐derived estimates in the H.J. Andrews Experimental Forest [ABSTRACT FROM AUTHOR]

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters, and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated dataset includes data collected from 236 flights (766.4 hours), which covered the Arctic, the U.S. Southern Great Plains (SGP), the U.S. West Coast, the Eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states and properties for addressing essential science questions in Earth system process studies. This manuscript describes the DOE ARM merged G-1 datasets, including information on the acquisition, collection, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package. [ABSTRACT FROM AUTHOR]

Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, and they are as hazardous and fatal as many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study proposes a physically based definition of derechos that contains the key features of derechos described in the literature and allows their automated objective identification using either observations or model simulations. The automated detection is composed of three algorithms: the Flexible Object Tracker algorithm to track mesoscale convective systems (MCSs), a semantic segmentation convolutional neural network to identify bow echoes, and a comprehensive algorithm to classify MCSs as derechos or non-derecho events. Using the new approach, we develop a novel high-resolution (4 km and hourly) observational dataset of derechos over the United States east of the Rocky Mountains from 2004 to 2021. The dataset is analyzed to document the derecho climatology in the United States. Many more derechos (increased by ~50–400 %) are identified in the dataset (~31 events per year) than in previous estimations (~6–21 events per year), but the spatial distribution and seasonal variation patterns resemble earlier studies with a peak occurrence in the Great Plains and Midwest during the warm season. In addition, around 20 % of damaging gust (≥ 25.93 m s-1) reports are produced by derechos during the dataset period over the United States east of the Rocky Mountains. The dataset is available at https://doi.org/10.5281/zenodo.10884046 (Li et al., 2024). [ABSTRACT FROM AUTHOR]

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree. [ABSTRACT FROM AUTHOR]

Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

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. The authors show that the degree of amplified warming in the Arctic relative to the rest of the globe can be analytically understood from a simple nonlinear formula of five key physical parameters. [ABSTRACT FROM AUTHOR]

When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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 currently at hand, and working with stakeholders to co‐produce the information that they require. 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 the next generation of climate services. Key Points: Facing the uncharted territory of a changed climate and increasingly complex socio‐economic systems requires the best possible predictionsScientific and technological opportunities are at hand to accelerate progress on U.S. climate predictions and projectionsCapitalizing on the most transformative opportunities calls for an unprecedented level of coordination and resources [ABSTRACT FROM AUTHOR]

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-tonoise 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 FROM AUTHOR]

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. Plain Language Summary: The implementation and evaluation of the Slab Ocean Model (SOM) in the Energy Exascale Earth System Model version 2 (E3SMv2) is described in this study. The SOM is evaluated by comparing its climate simulation to that of the full version of the model that uses a dynamic ocean model instead of a SOM. The SOM reproduces the baseline climate of the full E3SMv2, as well as the equilibrium surface global temperature response to CO2 doubling of the full model reasonably well. The SOM is further used to test the sensitivity of the E3SM model to various ocean heat transport strengths. The results show that E3SMv2 has a large surface temperature sensitivity to ocean heat transport changes, particularly over the Southern Ocean. This large sensitivity occurs due to changes in marine low‐level clouds, which cause shortwave radiation changes that reach the surface and enhance the surface temperature changes. Atmosphere heat transport also responds to and compensates ocean heat transport changes, and as a result of the large temperature response in the Southern Ocean, this compensation is also greater there. We also find that increases in ocean heat transport reduce the equilibrium surface temperature response to CO2 doubling. Key Points: Slab Ocean Model implementation in E3SMv2 reproduces the model's baseline climate and equilibrium climate sensitivity very wellE3SMv2 Slab model experiments show a large surface temperature sensitivity to ocean heat transports, particularly in the Southern HemisphereTemperature sensitivity to ocean heat transports is enhanced by the shortwave cloud radiative effect due to marine low‐level cloud changes [ABSTRACT FROM AUTHOR]