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A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations. Significance Statement Tropical cyclogenesis is a process by which a less organized weather system in the tropics develops into a tropical cyclone (TC). Observations indicate that thunderstorms occurring prior to the tropical cyclogenesis often show a distinct quasi-diurnal cycle, while the related physical mechanisms are still unclear. In this study, we used a novel high-resolution dataset to investigate the diurnal thunderstorms occurring prior to the genesis of TC Mora (2017). We find that the pregenesis diurnal thunderstorms played a crucial role in spinning up the circulation of the atmosphere and provided a favorable environment for the rapid formation of Mora. It is likely that gravity waves emitted by afternoon thunderstorms over the inland region were responsible for regulating the diurnal variation of pregenesis thunderstorms over the ocean.

This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

Compound hazards are more destructive than the individual ones. Using observational and reanalysis datasets during 1960–2019, this study shows a remarkable concurrent relationship between extreme heatwaves (HWs) over southeastern coast of China (SECC) and tropical cyclone (TC) activities over western North Pacific (WNP). Overall, 70% of HWs co-occurred with TC activities (TC–HWs) in the past 60 years. Although the total frequency of TCs over WNP exhibited a decreasing trend, the occurrences of TC–HWs over SECC have been increasing, primarily due to the increasing HWs in the warming climate. In addition, TC–HWs are stronger and longer lasting than HWs that occur alone (AHWs). And in the long-term perspective, both AHWs and TC–HWs exhibit increasing trends, especially since the mid-1980s. The enhancement on HWs caused by TC activities is sustained until TCs make their landfalls and then collapse. Based on composite analysis, TC activities enhance HWs by modulating atmospheric circulations and triggering anomalous descending motion over southern China mainland which intensifies the western Pacific subtropical high (WPSH) and favors increased temperatures therein. Given the severe adverse impacts of TC–HWs on coastal populations, more research is needed to assess the future projections of TC–HWs, as HWs are expected to be more frequent and stronger as the climate warms, whereas TCs over WNP may occur less often.

Mesoscale convective systems (MCSs) are the dominant rainfall producer over the U.S. 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 U.S. 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 multi-column parcel model employing those parameterizations captures readily the cold pool-induced upscale growth feature. It simulates smaller mesoscale clusters over the central U.S. under global warming due to gust front slowdown and subsidence strength enhancement, which is 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 life time lengthening, and cold pool mechanical lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes.

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.

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.

Energy balance models (EBMs) have been widely used in a range of climate problems, but the assumption of constant diffusivity in the parameterization of the moist static energy (MSE) flux can hardly be justified. We demonstrate in this study that the diffusive MSE flux can be derived from the basic energy balance equation with a few tolerable assumptions. The estimated diffusivity is both spatially and seasonally dependent, and its midlatitude average is then tested against several scaling theories for the midlatitude eddy diffusivity. The result supports the diffusivity theory of Held and Larichev modified for the moist atmosphere, affording a dynamics-based parameterization of MSE diffusivity. The implementation of the parameterization in an EBM leads to an interactive MSE diffusivity that accounts for the midlatitude eddy response to climate forcing perturbations. Under a uniform radiative forcing, the EBM with a diffusivity so parameterized produces a weakening of the midlatitude diffusivity and a modestly polar-amplified surface temperature response as an inevitable outcome under the dual constraints of the nonlinear Clausius–Clapeyron relation and the temperature gradient-dependent diffusivity, even in the absence of any poleward-amplifying radiative feedbacks. As the consequence of more isothermal temperature and reduced diffusivity, the variance of the midlatitude surface temperature also decreases with warming.

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

The Earth's land surface features spatial and temporal heterogeneity over a wide range of scales below those resolved by current Earth system models (ESMs). State-of-the-art land and atmosphere models employ parameterizations to represent their subgrid heterogeneity, but the land–atmosphere coupling in ESMs typically operates on the grid scale. Communicating the information on the land surface heterogeneity with the overlying atmospheric boundary layer (ABL) remains a challenge in modeling land–atmosphere interactions. In order to account for the subgrid-scale heterogeneity in land–atmosphere coupling, we implement a new coupling scheme in the Energy Exascale Earth system model version 1 (E3SMv1) that uses adjusted surface variances and covariance of potential temperature and specific water content as the lower boundary condition for the atmosphere model. The new lower boundary condition accounts for both the variability of individual subgrid land surface patches and the inter-patch variability. The E3SMv1 single-column model (SCM) simulations over the Atmospheric Radiation Measurement (ARM) Southern Great Plain (SGP) site were performed to assess the impacts. We find that the new coupling parameterization increases the magnitude and diurnal cycle of the temperature variance and humidity variance in the lower ABL on non-precipitating days. The impacts are primarily attributed to subgrid inter-patch variability rather than the variability of individual patches. These effects extend vertically from the surface to several levels in the lower ABL on clear days. We also find that accounting for surface heterogeneity increases low cloud cover and liquid water path (LWP). These cloud changes are associated with the change in cloud regime indicated by the skewness of the probability density function (PDF) of the subgrid vertical velocity. In precipitating days, the inter-patch variability reduces significantly so that the impact of accounting for surface heterogeneity vanishes. These results highlight the importance of accounting for subgrid heterogeneity in land–atmosphere coupling in next-generation ESMs.

A distinct feature of the atmospheric circulation response to increasing greenhouse gas forcing is the poleward shift of the zonal-mean westerly jet. The dynamical mechanisms of the zonal-mean poleward jet shift have been extensively studied in literature. At seasonal/regional scales, however, the westerly jets can shift equatorward, such as in the early-summer Asia–Pacific region, the late-winter America–Atlantic region, and the winter/spring east Pacific. These equatorward jet shifts imply climate impacts distinct from those of the poleward shifts, yet their causes are not well understood. Here, based on a hierarchy of coupled, prescribed-SST, and aquaplanet simulations, we attribute the seasonal/regional equatorward jet shifts to the enhanced tropical upper-level warming (ETUW), which arises from both the tropical moist adiabat and the enhanced equatorial surface warming. By steepening the meridional temperature gradient in the subtropical upper-to-middle level and assisted by positive eddy feedback, the ETUW increases the zonal wind equatorward of the climatological jet. When the regional/seasonal meridional temperature gradients (or equivalently the westerly jets) are weak and peak close to the tropics, the ETUW effect overcomes the poleward jet-shift mechanisms and leads to the equatorward jet shifts. This climatological-state dependency is consistently seen in the decomposed jet responses to uniform warming and surface warming pattern, and further demonstrated through idealized aquaplanet experiments with designed climatological states. For uniform warming, the ETUW arising from moist adiabat makes the general poleward jet shifts insignificant in the aforementioned favorable regions/seasons. For warming pattern, the ETUW from enhanced equatorial warming drives substantial equatorward jet shifts in these favorable seasons/regions.

The linear relationship between gross primary productivity (GPP) and evapotranspiration (ET), evidenced by site-scale observations, is well recognized as an indicator of the close interactions between carbon and hydrologic processes in terrestrial ecosystems. However, it is not clear whether this relationship holds at the catchment scale, and if so, what are the controlling factors of its slope and intercept. This study proposes and examines a generalized GPP-ET relationship at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., based on monthly remote sensing-based GPP data, vegetation phenology, and several hydrometeorological variables. We demonstrate the validity of this GPP-ET relationship at the catchment scale, with Pearson’s r ≥ 0.6 for 97% of the 380 catchments. Furthermore, we propose a regionalization strategy for estimating the slope and intercept of the generalized GPP-ET relationship at the catchment scale by linking the parameter values a priori with hydrometeorological data. We validate the monthly GPP predicted from the relationship and regionalized parameters against remote-sensing based GPP product, yielding Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. Finally, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, demonstrating that the new relationship is transferable across the site, catchment, and regional scales. The relationship will be valuable for diagnosing coupled water–carbon simulations in land surface and Earth system models and constraining remote-sensing based estimation of monthly ET.

Seasonal snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of meltwater delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) that can be caused by low total precipitation (dry drought) or low proportion of precipitation falling as snowfall (warm drought), often combined with an early melt. The Standardized SWE Index (SWEI) ranks the current status of SWE for a given location compared to a baseline condition and identifies the existence, but not the cause, of snow drought. In this work, we use estimates of SWE, temperature, and precipitation from nine Coupled Model Intercomparison Project phase 6 (CMIP6) models to quantify the frequency, severity, and type of snow droughts globally for historical and future scenarios. Compared to a historical baseline (1850--1900) total snow drought frequency more than doubles under SSP2-4.5 and SSP5-8.5; all of the increase in snow drought frequency comes from an increase in warm droughts. The probability distribution of future SWEI in major snowy basins around the world are likely to be centered on more negative values, which corresponds to more severe drought and, with only moderate changes in distribution spread, more frequent drought. CMIP6 simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.

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

Here we provide the source code ofthe SWM for the paper entitled "A global increase in nearshore tropical cyclone intensification" submitted to Nature Climate Changein 2023. The model source code is included in the compressed file named "SWM_Isla_July2015.zip". For detail information regardinghow to compile, prepare the input data, and run the SWM, please read the fileentitled "README.txt".

This dataset includes the time-averaged outputson the pressure level(i.e., output_plev_XXXX.nc) and the original daily outputs on the model sigma level (i.e., swmodelout_XXXX.nc) from the SWM for the paper entitled "A global increase in nearshore tropical cyclone intensification" submitted to Nature Climate Changein 2023. The purpose of these experiments was to elucidate the factors leading to the projected decrease in vertical wind shear across the majority of coastal regions in the Northern Hemisphere. output_plev_CTRL.nc; swmodelout_CTRL.nc - simulates the large-scale winds and vertical wind shearin near future(2015-2034) output_plev_Future.nc; swmodelout_Future.nc - simulates the large-scale winds and vertical wind shear at the end of the 21st century(2081-2100) output_plev_CTRL_plus_delta_BS.nc; swmodelout_CTRL_plus_delta_BS.nc - investigates the effect of future changes in zonal-mean basic state (e.g., pressure, wind, temperature) onthe large-scale winds and vertical wind shear output_plev_CTRL_plus_enhanced_upper_warming; swmodelout_CTRL_plus_enhanced_upper_warming -a experiment thatonly retains the enhanced future upper-level warming (i.e., projected increase in static stability). This experiment was conducted to clarify the influenceofamplified warming in the upper troposphere on the reduced vertical wind shearover Northeast Asia and the North Indian Ocean output_plev_CTRL_plus_delta_DH.nc; swmodelout_CTRL_plus_delta_DH.nc - investigates the effect of future changes in diabatic heating (i.e., precipitation)on the large-scale winds and vertical wind shear output_plev_CTRL_plus_delta_TranF.nc; swmodelout_CTRL_plus_delta_TranF.nc - investigates the effect of future changes in synoptic transient eddies onthe large-scale winds and vertical wind shear Detail information of different experiments isdescribed in the text of the paper, as well as text S1in the supporting information.

The effective radiative forcing of anthropogenic aerosols (ERFaer) is an important measure of the anthropogenic aerosol effects simulated by a global climate model. Here we analyze ERFaer simulated by the E3SM version 1 (E3SMv1) atmospheric model using both century-long free-running atmosphere–land simulations and short nudged simulations. We relate the simulated ERFaer to characteristics of the aerosol composition and optical properties, and we evaluate the relationships between key aerosol and cloud properties. In terms of historical changes from the year 1870 to 2014, our results show that the global mean anthropogenic aerosol burden and optical depth increase during the simulation period as expected, but the regional averages show large differences in the temporal evolution. The largest regional differences are found in the emission-induced evolution of the burden and optical depth of the sulfate aerosol: a strong decreasing trend is seen in the Northern Hemisphere high-latitude region after around 1970, while a continued increase is simulated in the tropics. The relationships between key aerosol and cloud properties (relative changes between pre-industrial and present-day conditions) also show evident changes after 1970, diverging from the linear relationships exhibited for the period of 1870–1969. In addition to the regional differences in the simulated relationships, a reduced sensitivity in cloud droplet number and other cloud properties to aerosol perturbations is seen when the aerosol perturbation is large. Consequently, the global annual mean ERFaer magnitude does not increase after around 1970. The ERFaer in E3SMv1 is relatively large compared to the recently published multi-model estimates; the primary reason is the large indirect aerosol effect (i.e., through aerosol–cloud interactions). Compared to other models, E3SMv1 features large relative changes in the cloud droplet effective radius in response to aerosol perturbations. Large sensitivity is also seen in the liquid cloud optical depth, which is determined by changes in both the effective radius and liquid water path. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 are found to have a strong correlation, as the evolution of anthropogenic sulfate aerosols affects both the liquid cloud formation and the homogeneous ice nucleation in cirrus clouds (that causes a large effect on longwave ERFaer). As suggested by a previous study, the large ERFaer appears to be one of the reasons why the model cannot reproduce the observed global mean temperature evolution in the second half of the 20th century. Sensitivity simulations are performed to understand which parameterization and/or parameter changes have a large impact on the simulated ERFaer. The ERFaer estimates in E3SMv1 for the shortwave and longwave components are sensitive to the parameterization changes in both liquid and ice cloud processes. When the parameterization of ice cloud processes is modified, the top-of-model forcing changes in the shortwave and longwave components largely offset each other, so the net effect is negligible. This suggests that, to reduce the magnitude of the net ERFaer, it would be more effective to reduce the anthropogenic aerosol effect through liquid or mixed-phase clouds.

Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

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.

Several pathways for how climate change may influence the U.S. coastal hurricane risk have been proposed, but the physical mechanisms and possible connections between various pathways remain unclear. Here, future projections of hurricane activity (1980–2100), downscaled from multiple climate models using a synthetic hurricane model, show an enhanced hurricane frequency for the Gulf and lower East coast regions. The increase in coastal hurricane frequency is driven primarily by changes in steering flow, which can be attributed to the development of an upper-level cyclonic circulation over the western Atlantic. The latter is part of the baroclinic stationary Rossby waves forced mainly by increased diabatic heating in the eastern tropical Pacific, a robust signal across the multimodel ensemble. Last, these heating changes also play a key role in decreasing wind shear near the U.S. coast, further aggravating coastal hurricane risk enhanced by the physically connected steering flow changes.

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

Increasing temperature and water cycle changes due to warming climate may increase wildfire activities. Reliable projections of fire emissions are critical for informing fire management to address fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation (SHAP) to predict fire PM emissions change and understand their drivers over the contiguous US (CONUS) in the mid-21st century under a high greenhouse gas emissions scenario (SSP5-8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), future population density, and present-day land use and land cover (LULC) as input to the NN model, the total fire PM emissions over CONUS are projected to increase by 4-75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June-July-August (JJA) than in other seasons and regions, with the median ratios of future to present-day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23-29%) and decreasing soil moisture (2-10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%-83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.

Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

This study investigates the responses of the hydroclimate and extremes in the U.S. Midwest to global warming, based on ensemble projections of phase 6 of the Coupled Model Intercomparison Project and the multimodel initial-condition large-ensemble simulations. The precipitation response features a seasonally dependent change with increased precipitation in April–May but reduced precipitation in July–August. The late-spring wetting is attributed to the enhanced low-level moisture-transporting southerlies, which are induced by regional sea level pressure anomalies linked to the poleward shift of the North American westerly jet (NAWJ). The late-summer drying is attributed to the weakened storm track, which is also linked to the poleward NAWJ shift. The seasonally dependent future changes of the Midwest precipitation are analogous to its climatological seasonal progression, which increases over late spring as the NAWJ approaches the Midwest and decreases over late summer as the NAWJ migrates away. In response to the mean precipitation changes, extremely wet late springs (April–May precipitation above the 99th percentile of the historical period) and extremely dry late summers (below the 1st percentile) will occur much more frequently, implying increased late-spring floods and late-summer droughts. Future warming in the Midwest is amplified in late summer due to the reduced precipitation. With amplified background warming and increased occurrence, future late-summer droughts will be more devastating. Our results highlight that, under a time-invariant poleward jet shift, opposite precipitation changes arise before and after the peak rainy month, leading to substantial increases in the subseasonal extremes. The severity of such climate impacts is obscured in projections of the rainy-season mean.

Convective vertical transport is critical in the monsoonal overturning, but the relative roles of different convective systems are not well understood. This study used a cloud classification and tracking technique to decompose a convection-permitting simulation of the South Asian summer monsoon (SASM) into subregimes of mesoscale convective systems (MCSs), non-MCS deep convection (non-MCS), congestus, and shallow convection/clear sky. Isentropic analysis is adopted to quantify the contributions of different convective systems to the total SASM vertical mass, water, and energy transports. The results underscore the crucial roles of MCSs in the SASM vertical transports. Compared to non-MCSs, the total mass and energy transports by MCSs are at least 1.5 times stronger throughout the troposphere, with a larger contributing fraction from convective updrafts compared to upward motion in stratiform regions. Occurrence frequency of non-MCSs is around 40 times higher than that of MCSs. However, per instantaneous convection features, the vertical transports and net moist static energy (MSE) exported by MCSs are about 70–100 and 58 times stronger than that of non-MCSs. While these differences are dominantly contributed by differences in the per-feature MCS and non-MCS area coverage, MCSs also show stronger transport intensities than non-MCSs over both ocean and land. Oceanic MCSs and non-MCSs show more obvious top-heavy structures than their inland counterparts, which are closely related to the widespread stratiform over ocean. Compared to the monsoon break phase, MCSs occur more frequently (~1.6 times) but their vertical transport intensity slightly weakens (by ~10%) during the active phases. These results are useful for understanding the SASM and advancing the energetic framework.

The distribution of latent heating released by mesoscale convective systems (MCSs) plays a crucial role in global energy and water cycles. To investigate the characteristics of MCS latent heating, five years (2014–19) of Global Precipitation Measurement (GPM) Ku-band Precipitation Radar observations and latent heating retrievals are combined with a newly developed global high-resolution (~10 km, hourly) MCS tracking dataset. The results suggest that midlatitude MCSs are shallower and have a lower maximum precipitation rate than tropical MCSs. However, MCSs occurring in the midlatitudes have larger precipitation areas and higher stratiform rain volume fraction, in agreement with previous studies. With substantial spatial and seasonal variability, MCS latent heating profiles are top-heavier in the middle and high latitudes than those in the tropics. Larger magnitudes of latent heating in the stratiform regions are found over the ocean than over land, which is the case for both the tropics and midlatitudes. The larger magnitude is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core typically associated with land systems. A majority of midlatitude MCSs have a relatively high (>70%) stratiform fraction while this is not the case for tropical MCSs, suggesting that midlatitude MCSs tend to produce more stratiform rain while tropical MCSs are more convective. Importantly, the results of this study indicate that storm intensity, latent heating, and rainfall are different metrics of MCSs that can provide multiple constraints to inform development of convection parameterizations in global models.

Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30kt, the MCE exhibits a critical success index (CSI) score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable towards RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3K. In E3SMv2, ECS is reduced to 4.0K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change. Copyright © 2022 Smith, Gillett, Simpson, Athanasiadis, Baehr, Bethke, Bilge, Bonnet, Boucher, Findell, Gastineau, Gualdi, Hermanson, Leung, Mignot, Müller, Osprey, Otterå, Persad, Scaife, Schmidt, Shiogama, Sutton, Swingedouw, Yang, Zhou and Ziehn.