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Understanding global monsoon (GM) variability and projecting its future changes rely heavily on climate models. However, climate models generally show pronounced biases in GM simulations, and the reasons for this remain unclear. Here, we evaluate the performance of 20 pairs of climate models that participated in both phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 of CMIP (CMIP6) and identify the sources of their GM simulation biases from an energy transport perspective. The multimodel mean improvement in CMIP6 compared to CMIP5 is demonstrated by the increasing skill scores for various GM metrics from 0.20–0.79 to 0.48–0.83. More specifically, the dry biases in the Northern Hemisphere Summer Monsoon (NHSM) precipitation in CMIP5 [root-mean-square error (RMSE): 1.85 mm day−1] are reduced in CMIP6 (RMSE: 1.66 mm day−1). This higher simulation skill is associated with higher skill in simulating the precipitation-solstitial mode, monsoon intensity, and monsoon domains. The improvement in the NHSM precipitation simulation results from that in the meridional transport of atmospheric energy. Atmospheric energy budget analysis shows that the negative biases in downward surface longwave radiation and northward energy transport are smaller in CMIP6 than in CMIP5 in the boreal summer, resulting in a more realistic interhemispheric thermal contrast and meridional gradient of moist static energy. However, a major weakness of the CMIP6 models is found in the Southern Hemisphere Summer Monsoon precipitation simulation due to the positive bias in the top-of-the-atmosphere downward longwave radiation. This study shows that reasonably reproducing the meridional global atmospheric energy transportation is necessary for skillful GM simulation. [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]

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]

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]

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. Plain Language Summary: Tropical cyclones (TCs) that intensify close to the coast pose a major socio‐economic threat and are a substantial challenge from an operational standpoint. Therefore understanding historical trends in nearshore storm intensification and how they may change in future is of considerable significance. Despite this, few studies examined this key aspect of TCs at the global scale. Here we show, using an analysis of observations and atmospheric reanalyses, that the mean TC intensification rate has increased significantly over the period 1979–2020 primarily aided by increases in relative humidity and decreases in vertical wind shear. Further, high‐resolution climate models, which explicitly resolve TCs, suggest that nearshore TC intensification will continue to increase in future. These increases in coastal TC intensification rates can mainly be attributed to stronger projected decreases in vertical wind shear. To better understand wind shear projections, a suite of idealized numerical experiments with an intermediate complexity model were conducted. The experiments indicate that enhanced warming in the upper‐troposphere and changing heating patterns are likely responsible. Key Points: Tropical cyclone (TC) intensification rates have increased in near coastal regions over the 42‐year period 1979‐2020Increases in relative humidity along with decreases in vertical wind shear are responsibleClimate models project a continued increase in nearshore TC intensification rates with decreasing wind shear playing a crucial role [ABSTRACT FROM AUTHOR]

The Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid‐latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity‐zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross‐EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter‐model comparisons of CRE‐denial experiments are important to build consensus on the dynamical feedback of CRE. Plain Language Summary: The annular mode is the most dominant mode of variability in the mid‐latitude atmospheric circulation system. Its origin, maintenance, and feedback mechanisms have long been the focus of atmospheric dynamics research. While its dry mechanisms are well understood, the role of the diabatic processes in the midlatitude storm tracks, especially the radiative effects of the cloud fields that evolve together with the storms, have not been quantified. Even the sign of the diabatic feedback to the annular mode remains a topic of debate. By disabling the cloud radiative feedback through a cloud‐locking technique that decorrelates the cloud fields with other dry and moist components in an atmospheric model, we are able to isolate the cloud radiative effect (CRE) throughout the life cycle of the Southern Annular Mode (SAM). Compared to the case with interactive CRE, the propagating SAM becomes more persistent and predictable in the cloud‐locking run, although the overall diabatic feedback to the standing dipole representation of the SAM is somewhat attenuated in this run. The diabatic and CRE feedbacks to the SAM identified here epitomize the sensitivity of the leading mode of the atmospheric variability to the model representation of the diabatic processes. Key Points: Cloud locking technique is used to isolate the interactive cloud radiative effect (CRE) on the Southern Annular Mode (SAM)Cloud radiative effect boosts the persistence of SAM as the leading EOF of the Southern Hemisphere zonal windThe interactive CRE feedback acts to weaken the cross‐EOF interaction and counter the poleward propagation of the zonal wind [ABSTRACT FROM AUTHOR]

While some previous studies examined the contribution of Eastern Pacific (EP) hurricanes toward precipitation in the arid Southwest US (SWUS), their potential to influence wildfires in that region has not been explored. Here we show, using observations and simulations from the Energy Exascale Earth System Model (E3SM), that recurving EP hurricanes modulate the wildfire environment in the SWUS by increasing precipitation and soil moisture, and reducing the vapor pressure deficit. This is especially the case during late season months of September–October when the likelihood of storms to recurve and make landfall increases. Further, analysis of burnt area observations reveals that for the months of September–October, recurving EP hurricanes may significantly reduce the prevalence of wildfires in the SWUS. Finally, E3SM simulations indicate that late season EP hurricanes have been on the decline, with important implications for wildfires in the SWUS. Plain Language Summary: A majority of Eastern Pacific (EP) hurricanes form and traverse westwards away from the North American coastline. Therefore, compared to tropical cyclones in some other basins, EP hurricanes have historically received less attention. However, some previous studies have shown that recurving EP hurricanes can contribute significantly to precipitation in the arid Southwest US (SWUS) region. Here, we build on these studies and demonstrate that recurving EP hurricanes significantly modulate the SWUS wildfire environment. During years with recurving EP hurricanes, the precipitation and soil moisture are enhanced, and the vapor pressure deficit is reduced, over the SWUS region. Therefore, during late season months of September–October, when recurving EP hurricanes occur, they make the environment less favorable for wildfires in the SWUS region. With climate models showing a long‐term decline in recurving EP hurricanes and the precipitation associated with them over the SWUS region, our study has substantial implications for autumn wildfires of that region. Key Points: Eastern Pacific (EP) hurricanes that recurve tend to enhance precipitation and reduce vapor pressure deficit in the Southwest US regionDuring September–October, recurving EP hurricanes may make the environment less favorable for wildfires in the Southwest USClimate model simulations suggest that Southwest US precipitation associated with recurving EP hurricanes has decreased [ABSTRACT FROM AUTHOR]

The Department of Energy (DOE)'s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity (w') (gamma) and the damping of the w' skewness (c8) near the coast but become more sensitive to the parameter affecting the damping of the w' variance (c1) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model. [ABSTRACT FROM AUTHOR]

High‐resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain‐shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high‐resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming. Plain Language Summary: Mountains supply about half of the fresh water used by humans globally and often regulate the seasonal cycle of stream flows by accumulating snowpack during the wet season and releasing it over the dry season. Regional simulations tend to predict large future changes in the spatial pattern of precipitation in mountainous regions, but the reasons for these changes are not well understood. Here we show that the predicted patterns of precipitation change in the western US can be explained by a simple mechanism that causes the influence of mountains to weaken with increasing temperatures. We introduce a simple model of precipitation change based on this effect, and show that its predictions closely match those of far more sophisticated regional simulations of future warming over the western US. Key Points: Orographic influence on precipitation is reduced in simulations of global warmingFractional precipitation change scaled by warming is reproducible with a simple model [ABSTRACT FROM AUTHOR]

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

Accelerating progress in climate modeling is urgent for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet are climatically important, such as turbulence and cloud formation. These processes are not explicitly resolvable, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary AI-based data-driven methods to model subgrid-scale processes. This strategy focuses on employing AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distributions, but it must still allow the generation of large ensembles for model calibration and the broad exploration of possible climate outcomes – currently O (10 km) horizontal resolutions are feasible. By synergizing decades of scientific development with advanced AI techniques, this approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

Convection‐Permitting Model (CPM) simulations of the Central United States climate for the summer of 2011 are studied to understand the causes of warm biases in 2‐m air temperature (T2m) and related underestimates of precipitation including that from mesoscale convective systems (MCSs). Based on 10 CPM simulations and 9 coarser‐resolution model simulations, we quantify contributions from evaporative fraction (EF) and radiation to the T2m bias with both types of models overestimating T2m largely because they underestimate EF. The performance of CPMs in capturing MCS characteristics (frequency, rainfall, propagation) varies. The pre‐summer precipitation bias has large correlation with mean summertime T2m bias but the relationship between summertime MCS mean rainfall bias and T2m bias is non‐monotonic. Analysis of lifting condensation level deficit and convective available potential energy suggests that models with T2m warm biases and low EF have too dry and stable boundary layers, inhibiting the formation of clouds, precipitation and MCSs. Among the CPMs with differing model formulations (e.g., transpiration, infiltration, cloud macrophysics and microphysics), evidence suggests that altering the land‐surface model is more effective than altering the atmospheric model in reducing T2m biases. These results demonstrate that land‐atmosphere interactions play a very important role in determining the summertime climate of the Central United States. Plain Language Summary: It is challenging for conventional coarse‐resolution weather and climate models to simulate summertime near‐surface air temperature (T2m) over the central United States. We thought that a large reduction in the warm T2m bias would occur only when the model resolution was sufficiently fine to resolve mesoscale convective systems (MCSs), which are responsible for around 30%–70% of summertime rainfall. Here, we examined T2m, MCSs, and other climatic features in a collection of kilometer‐scale model simulations which have the necessary resolution to simulate MCSs. However, we found that these models also simulate a warm T2m bias and the relationships with other variables are very similar to that of the coarse‐resolution models. In particular, variations in the warm bias are well related to variations in evaporative fraction, which is the fraction of surface heat fluxes that are in latent heat form as opposed to sensible heat. Our analysis suggests that land‐atmosphere interactions play an important role for kilometer‐scale models to simulate realistic summertime T2m and precipitation over the Central United States. Key Points: Convection Permitting Models have surface warm biases mainly because of evaporation underestimation, similar to coarser‐resolution modelsThe simulations of mesoscale convective systems can be degraded by the evaporative fraction biases, which also influence surface warm biasesThe land surface impacts precipitation via its influence on evapotranspiration whereas spring precipitation impacts summertime soil moisture [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) bring large amounts of rainfall and strong wind gusts to the midlatitude land regions, with significant impacts on local weather and hydrologic cycle. However, weather and climate models face a huge challenge in accurately modeling the MCS life cycle and the associated precipitation, highlighting an urgent need for a better understanding of the underlying mechanisms of MCS initiation and propagation. From a theoretical perspective, a suitable model to capture the realistic properties of MCSs and isolate the bare-bones mechanisms for their initiation, intensification, and eastward propagation is still lacking. To simulate midlatitude MCSs over land, we develop a simple moist potential vorticity (PV) model that readily describes the interactions among PV perturbations, air moisture, and soil moisture. Multiple experiments with or without various environmental factors and external forcing are used to investigate their impacts on MCS dynamics and mesoscale circulation vertical structures. The result shows that mechanical forcing can induce lower-level updraft and cooling, providing favorable conditions for MCS initiation. A positive feedback among surface winds, evaporation rate, and air moisture similar to the wind-induced surface heat exchange over tropical ocean is found to support MCS intensification. Both background surface westerlies and vertical westerly wind shear are shown to provide favorable conditions for the eastward propagation of MCSs. Last, our result highlights the crucial role of stratiform heating in shaping mesoscale circulation response. The model should serve as a useful tool for understanding the fundamental mechanisms of MCS dynamics. [ABSTRACT FROM AUTHOR]

The observed stratospheric quasi‐biennial oscillation (QBO) and the tropospheric Madden‐Julian oscillation (MJO) are strongly connected in boreal winter, with stronger MJO activity when lower‐stratospheric winds are easterly. However, the current generation of climate models with internally generated representations of the QBO and MJO do not simulate the observed QBO‐MJO connection, for reasons that remain unclear. This study builds on prior work exploring the QBO‐MJO link in climate models whose stratospheric winds are relaxed toward reanalysis, reducing stratospheric biases in the model and imposing a realistic QBO. A series of ensemble experiments are performed using four state‐of‐the‐art climate models capable of representing the MJO over the period 1980–2015, each with similar nudging in the stratosphere. In these four models, nudging leads to a good representation of QBO wind and temperature signals, however no model simulates the observed QBO‐MJO relationship. Biases in MJO vertical structure and cloud‐radiative feedbacks are investigated, but no conclusive model bias or mechanism is identified that explains the lack of a QBO‐MJO connection. Plain Language Summary: Observations show a strong link between the stratospheric quasi‐biennial oscillation (QBO)—the alternation of tropical stratospheric zonal winds between easterly and westerly phases—and the Madden‐Julian oscillation (MJO), an eastward propagating phenomenon in the tropical troposphere in which the circulation and convection are coupled. Stronger MJO activity is observed when lower‐stratospheric winds are easterly. This coupling is intriguing for many reasons, but most practically because it suggests that the stratosphere can potentially enhance surface weather and inform subseasonal climate prediction. However, current climate models do not show this observed connection. One reason may be related to biases in how models simulate stratospheric winds, which can be corrected for in an artificial way by relaxing the model simulated winds to better match observationally constrained data sets. One recent study, however, showed that correcting for this bias using this approach in one climate model still fails to produce credible QBO‐MJO coupling. Here we expand that analysis to include four climate models and find that no model produces a robust QBO‐MJO relationship like that seen in observations. Our results show that properly representing the QBO winds and temperatures via nudging is therefore not sufficient for reproducing the observed relationship. Furthermore, while biases in how models represent cloud processes may still be a likely culprit, any definitive model bias or missing mechanism remains elusive. Key Points: The link between the quasi‐biennial oscillation (QBO) and Madden‐Julian oscillation (MJO) is explored in four climate models with nudged stratospheric winds and free‐evolving tropospheresNo model shows as strong of a QBO‐MJO connection as in observationsModel biases in cloud‐radiative feedbacks and MJO vertical velocity are diagnosed, but neither conclusively explains the lack of a QBO‐MJO connection [ABSTRACT FROM AUTHOR]

The multi-year simulation of tropical cyclones (TCs) over the Western North Pacific (WNP) in the variable resolution (VR) CAM-MPAS model is studied. Experiments with the global quasi-uniform low resolution of 120 km (MPAS-UR) and the variable resolution mesh of 30–120 km refined over East Asia (MPAS-VR) are integrated from 1980 to 2005 following the Atmospheric Model Intercomparison Project protocol. By utilizing an objective detection method, TCs in ERA5 reanalysis and model simulations are tracked and compared against observations. MPAS-VR shows significant advantages over MPAS-UR as indicated by more realistic TC counts, intensities, lifetime distribution, and seasonal variation. The large-scale circulation and precipitation patterns associated with TCs are also improved in MPAS-VR relative to MPAS-UR. Based on the theory of Dynamic Genesis Potential Index, the multi-year TC records are further used to quantify the dependence of TC genesis on various dynamical environmental factors from the perspective of seasonal variation. We find that in ERA5, the relative contribution of the 500 hPa vertical pressure velocity term to TC genesis exceeds that of the 200–850 hPa vertical wind shear term, which is responsible for the August peak and strong seasonal variation of TC genesis. MPAS-UR fails to capture such relationship while MPAS-VR performs much better in this regard, suggesting that the higher skills in simulating the relative contributions from different dynamical environmental factors to the simulated seasonal cycle of TC genesis may explain the improvements from MPAS-UR to MPAS-VR. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) contribute a majority of rainfall over tropical oceans. However, our understanding of the environmental controls on tropical oceanic MCS precipitation remains incomplete. Using 20‐year of satellite observations, reanalysis data, and MCS tracking, we found that MCSs initiating in a mesoscale environment with enhanced lower‐free‐tropospheric moisture, warmer middle troposphere, stronger low‐level ascent, and stronger deep‐layer (surface‐400 hPa) wind shear tend to produce more precipitation during their lifetimes. While most of these environmental factors are correlated with one another, the deep‐layer shear is not. A rapid pickup in MCS lifetime rainfall is found when the lower‐free‐tropospheric specific humidity exceeds 10 g kg−1. This nonlinearity is mostly dominated by the nonlinear increase in MCS area. On the other hand, both MCS area and rain rate increase quasi‐linearly with the deep‐layer shear. The increase in rain rate is related to the enhancement of heavy precipitating convective activity with deep‐layer shear. Plain Language Summary: Cumulonimbus clouds over tropical oceans often organize into entities with rainfall covering a horizontal scale of hundreds of kilometers, which are called mesoscale convective systems (MCSs). Although oceanic MCSs contribute more than 60% of the total rainfall over the tropics, how their precipitation is affected by environmental conditions has not been systematically assessed. Using two decades of satellite observations, this study shows that tropical oceanic MCSs that occur in an environment with higher moisture content and larger vertical variations in horizontal winds tend to rain more during their lifetime. We found that the increase in ambient moisture helps MCSs last longer, grow over a larger area, and achieve a higher rainfall rate. On the other hand, larger vertical variations in horizontal winds lead to increased MCS area and MCS rainfall intensity. These results might inform future development of climate models to better represent MCSs over tropical oceans. Key Points: Mesoscale convective system (MCS) lifetime rainfall increases nonlinearly with initial low‐level moisture and quasi‐linearly with deep‐layer shearMCS lifetime/rain rate increase quasi‐linearly, while area grows nonlinearly with lower‐tropospheric moistureDeep‐layer shear significantly increases the MCS rain rate by enhancing heavy precipitating convective activity [ABSTRACT FROM AUTHOR]

Aerosol‐cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX‐GDDP, a multimodel ensemble of high‐resolution (0.25°) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX‐GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models. Plain Language Summary: We investigate the impacts of aerosol on India's wet and dry climate. High‐resolution downscaled CMIP5 models were used to calculate extreme indices like CDD (consecutive dry days), CWD (consecutive wet days), SDII (precipitation intensity). From the group of 22 models, 12 reliable models were chosen based on their fidelity to the observations. Amongst the reliable models, certain models incorporate only aerosol‐radiation interaction (REMADE), while others have both aerosol‐radiation and aerosol‐cloud interaction (REMALL). We found that the simulated trends in the REMAll were similar to the observed trends. In the current period (1975–2005), the aerosol‐cloud interactions led to the reduction in rainfall (both frequency and intensity wise) and enhanced the dry days, however in the future projections, the reduction in aerosol emissions leads to a wetter climate (increase in wet days and rainfall intensity) over India. Key Points: Using statistical metrics downscaled CMIP5 models are classified into reliable models for better simulating the wet and dry indices over IndiaDownscaled reliable models that included all aerosol effects (AIE + ADE) improved the simulated trends in the historical period than the models with only ADEThere is a general drying‐up (frequency‐wise and intensity‐wise) in the historical period. In the future projections, the reduction in aerosols lead to a wetter climate (frequency‐wise and intensity‐wise) [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes. [ABSTRACT FROM AUTHOR]

The seasonality of circumnavigation of Madden–Julian Oscillation (MJO) events is examined using 42 years of observations, reanalysis, and global model simulations. MJO events are most likely to propagate across the Atlantic Ocean, Africa and back to the equatorial Indian Ocean relatively unweakened during March–April–May and to a lesser extent during September–October–November. During these seasons, referred to as the "long‐rain" and "short‐rain" periods of East African Monsoon (EAM), precipitation is near the equator and extends farthest east toward the Indian Ocean. Moisture budget analysis of a circumnavigating MJO event under real and hypothetical seasonally varying background conditions indicates that the seasonal zonal movement of the zonal component of the background circulation regulates the moistening of MJO crossing from Eastern Africa to the Indian Ocean. Ensembles of Energy Exascale Earth System Model simulations of the MJO event under idealized seasonally varying background state confirm the observed drawbridge‐like role of the EAM in the propagation of MJO. Plain Language Summary: The Madden–Julian Oscillation (MJO) has been recognized as an important source of predictability in the tropics and globally. This is particularly true for strong and circumnavigating events that can initiate a subsequent MJO event cycle. In this work, we show that circumnavigation is regulated by the East African Monsoon. Through analysis of observations and global model simulations, we show circumnavigation is most likely in spring and fall seasons when precipitation associated with the East Africa Monsoon is near the equator and extends farthest east toward Indian Ocean. In other words, by creating favorable moisture and circulation conditions the monsoon serves as a drawbridge for the crossing of MJO events across Africa to Indian Ocean. Key Points: Circumnavigation of Madden–Julian Oscillation (MJO) is most likely in during spring and fall when East African Monsoon (EAM) is located near the equatorAnalysis of moisture budget indicates the zonal movement of the EAM favors the crossing of MJO events onto Indian OceanRealistic and idealized simulations using atmospheric model component of Exascale Earth System Model (E3SM) confirm this seasonality MJO precipitation [ABSTRACT FROM AUTHOR]

In this study, we investigated the changing characteristics of climatic scale (monthly) tropical extreme precipitation in warming climates using the Energy Exascale Earth System Model (E3SM). The results are from Atmospheric Model Intercomparison Project (AMIP)-type simulations driven by (a) a control experiment with the present-day sea surface temperature (SST) and CO2 concentration, (b) P4K, the same as in (a) but with a uniform increase of 4K in the SST globally, and (c) the same as in (a), but with an imposed SST and CO2 concentration from the outputs of the coupled E3SM forced by a 4xCO2 concentration. We found that as the surface warmed under P4K and 4xCO2, both convective and stratiform rain increased. Importantly, there was an increasing fractional contribution of stratiform rain as a function of the precipitation intensity, with the most extreme but rare events occurring preferentially over land more than the ocean, and more so under 4xCO2 than P4K. Extreme precipitation was facilitated by increased precipitation efficiency, reflecting accelerated rates of recycling of precipitation cloud water (both liquid and ice phases) in regions with colder anvil cloud tops. Changes in the vertical profiles of clouds, condensation heating, and vertical motions indicate increasing precipitation–cloud–circulation organization from the control and P4K to 4xCO2. The results suggest that large-scale ocean warming, that is, P4K, was the primary cause contributing to an organization structure resembling the well-known mesoscale convective system (MCS), with increased extreme precipitation on shorter (hourly to daily) time scales. Additional 4xCO2 atmospheric radiative heating and dynamically consistent anomalous SST further amplified the MCS organization under P4K. Analyses of the surface moist static energy distribution show that increases in the surface moisture (temperature) under P4K and 4xCO2 was the key driver leading to enhanced convective instability over tropical ocean (land). However, a fast and large increase in the land surface temperature and lack of available local moisture resulted in a strong reduction in the land surface relative humidity, reflecting severe drying and enhanced convective inhibition (CIN). It is argued that very extreme and rare "record-breaking" precipitation events found over land under P4K, and more so under 4xCO2, are likely due to the delayed onset of deep convection, that is, the longer the suppression of deep convection by CIN, the more severe the extreme precipitation when it eventually occurs, due to the release of a large amount of stored surplus convective available potential energy in the lower troposphere during prolonged CIN. [ABSTRACT FROM AUTHOR]

Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50% slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection. [ABSTRACT FROM AUTHOR]

This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high‐resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud‐top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed. Plain Language Summary: A new class of high‐resolution global atmosphere models is emerging for Earth system modeling. These new models can directly simulate convective storm systems and hold promises to improve the simulation of hydrological extremes such as flood‐producing rainfall and how they may change in future climate. This study assesses the fidelity of simulated convective storms from six global models against high‐resolution satellite observations. We find that the models simulate widely different frequency of convective storms in the tropics, but many do not produce storms that grow as large as observed. Several important aspects of observed storms such as the diurnal cycle, land‐ocean contrast, and storm rainfall amount are reasonably captured by the models; however, precipitation intensity is consistently overestimated and the storm rainfall area is too small. We further discussed potential causes for the model differences with observations and future model development needs. Key Points: Deep convective clouds and mesoscale convective systems are tracked in global convection‐permitting simulations and satellite observationsModels produce a diverse range of tropical deep convective systems and MCS frequencies and their proportions in key climate regionsModels reasonably simulate tropical MCS diurnal cycle and some MCS characteristics, but overestimate MCS precipitation intensity [ABSTRACT FROM AUTHOR]

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

Tropical precipitation in climate models presents significant biases in both the large-scale pattern (i.e., double intertropical convergence zone bias) and local-scale characteristics (i.e., drizzling bias with too frequent drizzle/convection and reduced occurrences of no and heavy precipitation). By untangling the coupled system and analyzing the biases in precipitation, cloud, and radiation, this study shows that local-scale drizzling bias in atmospheric models can lead to large-scale double-ITCZ bias in coupled models by inducing convective-regime-dependent biases in precipitation and cloud radiative effects (CRE). The double-ITCZ bias consists of a hemispherically asymmetric component that arises from the asymmetric SST bias and a nearly symmetric component that exists in atmospheric models without the SST bias. By increasing light rain but reducing heavy rain, local-scale drizzling bias induces positive (negative) precipitation bias in the moderate (strong) convective regime, leading to the nearly symmetric wet bias in atmospheric models. By affecting the cloud profile, local-scale drizzling bias induces positive (negative) CRE bias in the stratocumulus (convective) regime in atmospheric models. Because the stratocumulus (convective) region is climatologically more pronounced in the southern (northern) tropics, the CRE bias is deemed to be hemispherically asymmetric and drives warm and wet (cold and dry) biases in the southern (northern) tropics when coupled to ocean. Our results suggest that correcting local-scale drizzling bias is critical for fixing large-scale double-ITCZ bias. The drizzling and double-ITCZ biases are not alleviated in models with mesoscale (0.25°–0.5°) or even storm-resolving (∼3 km) resolution, implying that either large-eddy simulation or fundamental improvement in small-scale subgrid parameterizations is needed. [ABSTRACT FROM AUTHOR]

Observations of the seasonal mean precipitation averaged over a sub‐region of the U.S. mid‐Atlantic show significant increases in summer but no changes in winter during 1979–2019. Clarifying the drivers of these observed changes is important for understanding and interpreting future projections of regional precipitation by climate models. This study examines the relative effects of external forcing and internal variability in the observed multi‐decadal changes using the historical and RCP8.5/SSP585 simulations from 37 models in the Coupled Model Intercomparison Project phase 5 (CMIP5), 35 models in CMIP6, as well as large ensemble simulations from 6 models, each including more than 20 members. During 1979–2019, enhanced warming over land relative to ocean due to external forcing induces low pressure anomalies over land that contribute to the wetting summer. While similar wetting also occurs during winter, such effect is offset by the drying associated with the positive phase of the North Atlantic Oscillation (NAO) in recent decades. Internal variability produces large uncertainty in the multi‐decadal precipitation changes simulated by climate models through changes in large‐scale circulation associated with the NAO in winter and the North Atlantic Subtropical High in summer. These multi‐decadal relationships between precipitation and large‐scale circulation under internal variability also manifest on inter‐annual timescale. These relationships highlight enhanced moisture transport from the Gulf of Mexico and North Atlantic in winter and near the mid‐Atlantic coast in summer that modulates precipitation change in the study region. Plain Language Summary: The mid‐Atlantic region of the U.S. receives more precipitation in summer than winter. However, observations during 1979–2019 show that summer precipitation has increased significantly while the area‐averaged winter precipitation has not changed. This study investigates the drivers of the seasonal mean precipitation changes using observations and simulations. We find that enhanced warming over land relative to the ocean induced by external forcing contributes to the summer wetting by inducing low pressure anomalies over land. The same wetting mechanism also occurs in winter, but it is offset by the drying related to the positive phase of the North Atlantic Oscillation (NAO) in the past decades. Internal variability induces large uncertainty in simulating the precipitation changes as winter and summer precipitation are modulated by the large‐scale circulation associated with the NAO and North Atlantic Subtropical High, respectively. The same multi‐decadal relationships between precipitation and large‐scale circulation also work well on interannual timescale. The latter reveals anomalous convergence of moisture transported from the Gulf of Mexico and North Atlantic, favoring more winter precipitation, while increased moisture near the mid‐Atlantic coast favors more summer precipitation. These results improve understanding of the past precipitation changes and provide important context for understanding projections of future changes. Key Points: Precipitation in a sub‐region of U.S. mid‐Atlantic has increased in summer, contributed by external forcing that enhances warming over landArea averaged winter precipitation has no change, as wetting due to the external forcing is offset by drying due to the positive North Atlantic Oscillation (NAO) phaseWith internal variability, precipitation changes have large uncertainty due to modulation of large‐scale circulation by the NAO and North Atlantic Subtropical High [ABSTRACT FROM AUTHOR]

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. We specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results. [ABSTRACT FROM AUTHOR]

In climate science and applications, the term "metric" is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are "fit for purpose." Metrics can also provide a valuable reference point for co‐production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision‐making. This article is categorized under:Climate Models and Modeling > Knowledge Generation with ModelsThe Social Status of Climate Change Knowledge > Climate Science and Decision MakingAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Hurricanes often cause severe damage and loss of life, and storms that intensify close to the coast pose a particularly serious threat. While changes in hurricane intensification and environment have been examined at basin scales previously, near‐coastal changes have not been adequately explored. In this study, we address this using a suite of observations and climate model simulations. Over the 40‐year period of 1979–2018, the mean 24‐hr hurricane intensification rate increased by ∼1.2 kt 6‐hr−1 near the US Atlantic coast. However, a significant increase in intensification did not occur near the Gulf coast over the same period. The enhanced hurricane intensification along the Atlantic coast is consistent with an increasingly favorable dynamic and thermodynamic environment there, which is well simulated by climate models over the historical period. Further, multi‐model projections suggest a continued enhancement of the storm environment and hurricane intensification near the Atlantic coast in the future. Plain Language Summary: While hurricanes pose a significant socioeconomic threat in general, those that intensify close to the coast are particularly challenging for operational forecasters and decision makers. Past studies examined basin‐scale changes in hurricane intensification and the large‐scale environment in the Atlantic. However, near‐coastal changes in hurricane intensification have not been extensively studied. Herein, we address this using a combination of observations and numerical model simulations. Analysis of hurricane track data for the period 1979–2018 indicates that the mean hurricane intensification rate has increased significantly near the Atlantic coast. In contrast, significant increases in nearshore hurricane intensification are not seen for the Gulf coast. Observed changes in the ambient storm environment over the same period, which are in good agreement with these changes in hurricane intensification, are well reproduced by climate models. Finally, models project a continued enhancement of the hurricane environment near the Atlantic coast in the future. These results are well supported by direct simulation of hurricanes in high‐resolution climate models. Key Points: Hurricane intensification rate has significantly increased near the US Atlantic coast over the last 40 yearsClimate models accurately simulate the observed trends with an enhanced environment conducive for hurricanesMulti‐model projections suggest that the environment will increasingly favor hurricane intensification near the Atlantic coast [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are frequent over the US Great Plains during spring. The link between large‐scale environments and spring MCS initiation were well established. Here, historical and future changes of spring large‐scale environments favorable for MCS initiation are investigated using an MCS tracking data set, ERA5 reanalysis, and 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The frequency of Great Plains low‐level jet (GPLLJ)‐related MCS environments is found to have increased by ∼41% from 1979 to 2019, consistent with the enhanced GPLLJ and more frequent MCSs. Comparing CMIP6 AMIP and historical experiments, we find that the observed GPLLJ strengthening and more frequent MCS environments are mainly due to the decadal sea‐surface temperature variations rather than external forcings. Under a high emission scenario, the frequency of GPLLJ‐related environments favorable for MCS initiation will increase by ～65% during 2015–2100, along with a stronger GPLLJ, suggesting more frequent MCSs over the US Great Plains in a warming world. Plain Language Summary: Convective storm systems that extend over several hundred kilometers are frequently observed and contribute to a majority of the warm season rainfall over the US Great Plains. It is a major challenge for climate models to simulate these storms because the processes governing their initiation and evolution are not well resolved. However, large‐scale atmospheric circulations such as the Great Plains low‐level jet (GPLLJ) that provide favorable environments for these storms in spring are well observed and simulated by climate models. Therefore, the GPLLJ may be used to understand past and future changes of these storms. Here, we found GPLLJ‐related environments become more frequent from 1979 to 2019, related to the observed strengthening of the GPLLJ. To determine whether these changes are due to global warming, we compare simulations driven by the observed sea‐surface temperature variation with simulations driven only by anthropogenic forcing. The results suggest that the observed GPLLJ strengthening is mainly due to the natural decadal variation of sea‐surface temperature. However, with continued warming under a high greenhouse gas emission scenario, the GPLLJ is greatly enhanced during 2015–2100. Accompanied by more frequent occurrence of the GPLLJ‐related environments, the frequency of large convective systems may also increase in the future. Key Points: Low‐level jet‐related mesoscale convective system environments in spring over the US Great Plains became more frequent from 1979 to 2019The more frequent convection environments are a response to decadal sea‐surface temperature variation rather than to external forcingAlong with the enhanced low‐level jet under a high emission scenario, the low‐level jet‐related storm environment will become more frequent [ABSTRACT FROM AUTHOR]

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary Representation of deep convection has been an important component of weather and climate models for understanding the effect of deep cumulus clouds on the atmosphere and predicting the characteristics of precipitation realistically. The Zhang-McFarlane deep convection scheme (ZM) is used in the Community Atmosphere Model (CAM), one of the most commonly used models by climate scientists over the past few decades. In this study, we implemented the Grell-Freitas deep convection scheme (GF) in CAM and conducted idealized experiments to determine its advantages over the ZM scheme. Specifically, sensitivity of the schemes to resolution is investigated using two types of meshes. One is a uniform mesh with the same grid size everywhere, and the other is a variable-resolution (VR) mesh with a finer grid resolution in a specific region but coarse resolution elsewhere. GF simulates a mean climate that is comparable to results in previous studies, and it shows better performance in simulating the characteristics of tropical precipitation. The VR results with GF are promising in that convective activity is less affected by resolution than ZM. However, the tropical cloud amount is more sensitive to resolution compared to ZM. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are often accompanied by strong winds and torrential rains. While the winds associated with TCs tend to enhance mixing in the upper ocean, the freshwater input from rain can stratify the water column and limit mixing. However, the extent to which the stabilizing effect of rainfall can compete with wind‐induced mixing and to what degree it modulates TC‐induced sea surface cooling remain unknown. Here we show, using a suite of observations, that heavy rains under weak TCs can significantly reduce the magnitude of cold wakes induced by them. When compared to storms with low rain rates, the ocean surface under TCs with high rain rates freshens significantly and cools less. High‐resolution climate model simulations and idealized experiments with an ocean mixed layer model support these results and reveal that oceanic mixing processes are primarily responsible for reduced cooling under TCs, with a lesser role for surface fluxes. Plain Language Summary: Strong winds of tropical cyclones (TCs) induce substantial mixing in the upper ocean, a process that entrains colder deeper water into the mixed layer resulting in cooling of the sea surface temperature (SST). Hence, ocean density stratification plays a crucial role in the SST response to TCs and consequently, in their intensification. While most previous studies considered the role of prestorm stratification near the ocean surface on TC‐induced SST cooling, the role of rainfall from TCs in the SST response to storms has mostly remained unexplored. In this study, using a variety of observations, we show that the SST cooling is reduced significantly for weak TCs with large rain rates. Under high rain rates, the upper ocean freshens considerably and the salinity stratification is enhanced. Consequently, vertical mixing and the entrainment of subsurface water is reduced, and the SST cooling decreases. Similar results are obtained using high‐resolution simulations based on the Energy Exascale Earth System Model. Finally, a set of idealized numerical sensitivity experiments with the Price‐Weller‐Pinkel ocean mixed layer model confirm the role of vertical ocean mixing in the rainfall effect on TC‐induced SST change. These results have potential implications for improving our understanding of TC‐ocean interactions. Key Points: Analysis of a suite of observations suggests that high rain rates can reduce the sea surface cooling induced by weak tropical cyclones (TCs)Consistent results are obtained from high‐resolution climate model simulations and sensitivity experiments with an ocean mixed layer modelChanges in upper‐ocean stratification and mixing are primarily responsible for the rainfall effect on TC‐induced sea surface temperature cooling [ABSTRACT FROM AUTHOR]

For the Community Atmosphere Model version 6 (CAM6), an adjustment is needed to conserve dry air mass. This adjustment exposes an inconsistency in how CAM6's energy budget incorporates water—in CAM6 water in the vapor phase has energy, but condensed phases of water do not. When water vapor condenses, only its latent energy is retained in the model, while its remaining internal, potential, and kinetic energy are lost. A global fixer is used in the default CAM6 model to maintain global energy conservation, but locally the energy tendency associated with water changing phase violates the divergence theorem. This error in energy tendency is intrinsically tied to the water vapor tendency, and reaches its highest values in regions of heavy rainfall, where the error can be as high as 40 W m−2 annually averaged. Several possible changes are outlined within this manuscript that would allow CAM6 to satisfy the divergence theorem locally. These fall into one of two categories: 1) modifying the surface flux to balance the local atmospheric energy tendency and 2) modifying the local atmospheric tendency to balance the surface plus top-of-atmosphere energy fluxes. To gauge which aspects of the simulated climate are most sensitive to this error, the simplest possible change—where condensed water still does not carry energy and a local energy fixer is used in place of the global one—is implemented within CAM6. Comparing this experiment with the default configuration of CAM6 reveals precipitation, particularly its variability, to be highly sensitive to the energy budget formulation. Significance Statement: This study examines and explains spurious regional sources and sinks of energy in a widely used climate model. These energy errors result from not tracking energy associated with water after it transitions from the vapor phase to either liquid or ice. Instead, the model used a global fixer to offset the energy tendency related to the energy sources and sinks associated with condensed water species. We replace this global fixer with a local one to examine the model sensitivity to the regional energy error and find a large sensitivity in the simulated hydrologic cycle. This work suggests that the underlying thermodynamic assumptions in the model should be revisited to build confidence in the model-simulated regional-scale water and energy cycles. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) contribute a large fraction of warm‐season precipitation and generate hazardous weather with substantial socioeconomic impacts. Uncertainties in convection parameterizations in climate models limit our understanding of MCS characteristics and reliability of future projection. We examine MCSs simulated by the global 14 km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) without cumulus parameterization against satellite observation from Global Precipitation Measurement during 2001–2008. We focus on MCSs over the central United States and eastern China where MCSs are prevalent from March to August. A process‐oriented tracking method incorporating both cloud and precipitation criteria is used to identify and track MCSs. About 140/100 MCSs initiate in the central United States/eastern China per warm season and most of them initiate in the east of high mountains and in coastal regions. The frequency distribution of MCS lifetime is well captured by NICAM. However, the simulated MCSs have stronger precipitation, smaller precipitation area, and larger cold cloud system than that observed in both regions, which may be caused by weak entrainment as it is not well resolved at 14 km resolution. The simulated MCS number is also underestimated in summer. By examining the climatological and MCS large‐scale environments, the significant underestimation of MCS number in summer over the central United States may be attributed to the large climatological dry bias in the atmosphere. For China, mean moisture in summer is well simulated but deficiency in capturing the dynamic condition related to the coastal topography for triggering convection may have contributed to underestimation of MCS even in a sufficiently moist environment. Key Points: The Non‐hydrostatic Icosahedral Atmospheric Model underestimates mesoscale convective systems (MCSs) initiation number in the central United States in the warm season but the bias is small in eastern ChinaThe simulated MCSs have stronger precipitation, smaller precipitation area, and larger cold cloud system than observed in both regionsDeficiency in climatological moisture in the central United States and dynamic condition in South China contributes to underestimation of MCS [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Large thunderstorms called mesoscale convective systems (MCSs) are an important source of flood‐producing rainfalls. In this paper, we examine how well the Energy Exascale Earth System Model (E3SM) simulate MCSs over the central US in spring and summer by comparing against observations. We run two versions of E3SM: one is the standard E3SM that uses a traditional parameterization to simulate deep convection, the other is E3SM with a superparameterization (SP‐E3SM) that uses a cloud resolving model within each E3SM grid column to explicitly simulate deep convection. By using a novel detection and tracking algorithm to characterize MCS features, we find that SP‐E3SM can better simulate the number, rainfall amount and intensity, and the diurnal cycle of MCSs compared to E3SM. SP improves MCS simulations by improving the representation of the large‐scale circulations and convection initiation. Despite the improvement, SP‐E3SM still underestimates the MCS rainfall amount, which is largely related to the underestimation of MCS rainfall intensity. In contrast, the larger underestimation of rainfall amount in E3SM is attributed in large parts to the lower MCS frequency. This study highlights the importance of large‐scale moisture transport and convection initiation in simulating MCSs. Key Points: Mesoscale convective systems (MCSs) in E3SM at 25 km with and without superparameterization (SP) are evaluated the central USIncluding SP significantly improves MCS simulations in both spring and summer, although large dry biases are still apparent in summerImprovements from SP are contributed by improved simulations of the large‐scale environments and MCS number and precipitation frequency [ABSTRACT FROM AUTHOR]

Marked uncertainty in California (CA) precipitation projections challenges their use in adaptation planning in the region already experiencing severe water stress. Under global warming, a westerly jet extension in the North Pacific analogous to the El Niño-like teleconnection has been suggested as a key mechanism for CA winter precipitation changes. However, this teleconnection has not been reconciled with the well-known El Niño-like warming response or the controversial role of internal variability in the precipitation uncertainty. Here we find that internal variability contributes > 70% and > 50% of uncertainty in the CA precipitation changes and the El Niño-like warming, respectively, based on analysis of 318 climate simulations from several multi-model and large ensembles. The Interdecadal Pacific Oscillation plays a key role in each contribution and in connecting the two via the westerly jet extension. This unifying understanding of the role of internal variability in CA precipitation provides critical guidance for reducing and communicating uncertainty to inform adaptation planning. 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 FROM AUTHOR]

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. Plain Language Summary: Precipitation and associated cloud and radiation processes can affect the energy budgets and water supply over land and thus the livelihoods of billions of people over East Asia. However, traditional low‐resolution global climate models still have difficulty in realistically simulating the mean‐state and variability of precipitation over East Asia, partly due to their insufficient resolution to resolve the complex terrain and associated synoptic‐scale systems. In this work, we perform model experiments using the Community Atmosphere Model coupled with the atmospheric dynamical core from the Model for Prediction Across Scales with a variable resolution mesh to assess the impact of regional refinement on the precipitation simulation over East Asia. Experiment with higher resolution (30 vs. 120 km) over East Asia better simulates the total precipitation amount over and around the Tibetan Plateau (TP), as well as precipitation frequency, intensity, and interannual variability associated with the East Asian summer monsoon. The finer grid spacing is found to contribute more to the improvements in areas adjacent to TP than the use of finer terrain information, likely due to better resolved physical and dynamical processes associated with orographic precipitation. Key Points: The precipitation climatology over East Asia can be reasonably reproduced by a global climate model CAM‐MPASRegional refinement over East Asia improves the precipitation simulation over East Asia in various aspectsFiner grid spacing is more essential for precipitation simulation around large‐scale terrain than using finer‐scale terrain information [ABSTRACT FROM AUTHOR]

Predicting how regional precipitation will respond to future warming is among the most challenging undertaking in climate change projection. Despite sustained efforts to improve modeling and understanding of precipitation, the overall uncertainty in projecting regional precipitation has not been reduced substantially. Here, the potential for more robust precipitation projections is demonstrated through the use of discriminating metrics to subsample a multimodel ensemble. Using a two‐dimensional metric of precipitation and its relationship with large‐scale circulation indices in East Asia, 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are classified into three groups. Models in the top performing group projected statistically significant increasing trends in precipitation and the regional precipitation patterns are more similar to each other than to the patterns in the bottom performing group. In contrast, models in the bottom performing group projected diverse responses, with overall small drying or no significant trends in precipitation. Plain Language Summary: Precipitation is a fundamental driver of land surface hydrology and water resources, but large biases in climate model simulations of precipitation and large uncertainties in the future projections hinder the use of climate models in climate impact studies. Metrics of precipitation used in climate model evaluation usually focus on different precipitation characteristics, while ignoring important physical mechanisms linking precipitation with its environments. In this study, a two‐dimensional metric is proposed to evaluate both the simulated precipitation and its relationship with large‐scale circulation patterns in East Asia. This metric is applied to the Coupled Model Intercomparison Project Phase 5 (CMIP5) models to classify 31 models into three groups based on model performances. The metric is able to discriminate model responses: models in the top performing group show more consistent and similar responses of precipitation to future warming, featuring larger increases of future precipitation than the CMIP5 ensemble mean over China. Key Points: A two‐dimensional metric is developed to evaluate precipitation and its relationships with the large‐scale circulation in climate modelsThe new metric identifies a subset of models skillful in simulating both precipitation and its relationship with the monsoon circulationThis subset of models projects a more robust and larger increase of future precipitation than the multimodel ensemble mean over China [ABSTRACT FROM AUTHOR]

A process-oriented approach is developed to evaluate warm-season mesoscale convective system (MCS) precipitation and their favorable large-scale meteorological patterns (FLSMPs) over the United States. This approach features a novel observation-driven MCS-tracking algorithm using infrared brightness temperature and precipitation features at 12-, 25-, and 50-km resolution and metrics to evaluate the model large-scale environment favorable for MCS initiation. The tracking algorithm successfully reproduces the observed MCS statistics from a reference 4-km radar MCS database. To demonstrate the utility of the new methodologies in evaluating MCS in climate simulations with mesoscale resolution, the process-oriented approach is applied to two climate simulations produced by the Variable-Resolution Model for Prediction Across Scales coupled to the Community Atmosphere Model physics, with refined horizontal grid spacing at 50 and 25 km over North America. With the tracking algorithm applied to simulations and observations at equivalent resolutions, the simulated number of MCS and associated precipitation amount, frequency, and intensity are found to be consistently underestimated in the central United States, particularly from May to August. The simulated MCS precipitation shows little diurnal variation and lasts too long, while the MCS precipitation area is too large and its intensity is too weak. The model is able to simulate four types of observed FLSMP associated with frontal systems and low-level jets (LLJ) in spring, but the frequencies are underestimated because of low-level dry bias and weaker LLJ. Precipitation simulated under different FLSMPs peak during the daytime, in contrast to the observed nocturnal peak. Implications of these findings for future model development and diagnostics are discussed. [ABSTRACT FROM AUTHOR]

Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated Flexible Object Tracker algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by season, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems; quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events; and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at 10.25584/1632005 (Li et al., 2020). [ABSTRACT FROM AUTHOR]

Climate models project a wetting winter over California, distinct from the drying in all other Mediterranean climate regions under warming. Dynamic effect via an eastward extension of the westerly jet stream over the North Pacific is the root cause for the wetting California. Utilizing four sets of prescribed‐SST (sea surface temperature) simulations, the relative effects of direct CO2 radiative forcing, uniform SST warming, and SST change patterns are investigated. The wetting winter is dominated by uniform SST warming mainly through its effect on the jet extension. As a fundamental response to the enhanced upper‐tropospheric warming in the tropics, the jet extension in coupled simulations is well reproduced under uniform SST warming and robust among models. An El Niño‐like SST warming pattern has a secondary contribution to the jet extension via atmospheric teleconnection, but with large inter‐model spread. Relative to the SST forcing, the direct CO2 radiative effect is negligible. Plain Language Summary: California has a large population and is a major agricultural production region in the United States. With dry summer and wet winter climates, the region receives low annual precipitation amount. Winter precipitation dominates the yearly water supply. Previous studies identified an increase in winter precipitation over California under global warming, dominated by atmospheric circulation changes featuring an eastward jet extension and a deepened Aleutian low. Here we decomposed the total precipitation change into the climate response to direct CO2 radiative forcing, uniform SST warming, and SST change patterns. It is found that the wetting California as well as jet extension during winter are dominated by the uniform SST warming, while SST change with an El Niño‐like warming pattern has a secondary contribution via the tropical teleconnection to the North Pacific. With uncertainty in the tropical SST warming patterns among the coupled models, the effect of SST change patterns has large inter‐model spread. The results have crucial implications for the large population in California, which is already experiencing severe water stress. Key Points: The future wetting winter in California is dominated by uniform SST warming mainly through its effect on the eastward jet extensionJet extension in response to the enhanced upper‐tropospheric tropical warming is a fundamental and robust feature under uniform SST warmingUncertainty in the El Niño‐like warming pattern contributes to uncertainty in the jet extension and future wetting via teleconnection [ABSTRACT FROM AUTHOR]