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Brown carbon (BrC) in China is of great interest to the regional and global climate due to its strong absorption of sunlight. However, the contribution of BrC to total carbonaceous aerosol light absorption and its direct radiative effects (DRE) in China remains largely uncertain. To better assess its climate impact in China, we develop an explicit BrC scheme and implement it in a global climate model, which includes optical parameters of primary BrC derived from local measurements, secondary BrC absorption, and a photobleaching parameterization of BrC. By comparing with multi‐type observational data, we find that with the implementation of this scheme, the model captures the seasonal variations of BrC light absorption well in China. The model estimates that BrC contributes 19% and 12% to the total light absorption of carbonaceous aerosol in China in winter and summer, resulting in 0.110 and 0.205 W m−2 of DRE, respectively. Plain Language Summary: While the existing research on the direct radiative effects (DRE) of brown carbon (BrC) predominantly focuses on biomass‐burning sources, little attention has been given to BrC originating from anthropogenic sources. Coal combustion and residential fuels used for cooking and heating release many brown carbon aerosols in China. Source of BrC in China is different from that in Europe and North America. Therefore, studying the light‐absorbing properties and climate effects of BrC in China is of great scientific significance. Here, we introduce local optical parameters of primary BrC based on observations in China, secondary BrC absorption, and chemical bleaching of BrC in a global climate model (GCM). We find that the model can simulate the strong seasonal variations of BrC absorption observed in China. With the help of the model and multi‐type measurement data, we estimate the contribution of BrC to the total carbonaceous aerosol absorption and the DRE of BrC in China in different seasons. This study is the first attempt to introduce an explicit BrC scheme in a GCM to estimate the DRE of BrC in China. Key Points: An explicit brown carbon (BrC) scheme is introduced in a climate model together with observations to constrain light absorption of BrCBrC contributes 19% and 12% of total carbonaceous aerosol absorption in China in winter and summer, respectivelySimulated direct radiative effects due to BrC absorption over China are 0.110 and 0.205 W m2 in the winter and summer seasons [ABSTRACT FROM AUTHOR]

In this study, a variable‐resolution version of the Community Earth System Model (VR‐CESM) with mesh refinement (∼0.125°) over East Asia is used to simulate the regional climate in this region. For the evaluation of model performance and sensitivity to model resolution, the simulated near‐surface temperature and precipitation are compared with observations and simulation results from a globally quasi‐uniform (∼1°) CESM (UN‐CESM). Results show that VR‐CESM better simulates the spatial patterns and seasonal variations of mean temperature and precipitation than UN‐CESM over China. For extreme events, VR‐CESM improves the simulation of the occurrence frequency of wintertime daily minimum temperature and heavy precipitation. In regions with complex terrains, VR‐CESM better resolves the topographic forcing and captures the observed fine‐scale spatial patterns of temperature and precipitation, although precipitation is still overestimated. For East Asian summer monsoon precipitation, both UN‐CESM and VR‐CESM tend to overestimate (underestimate) the precipitation over northern (southern) China, which is associated with too strong meridional water vapor transport in the models and biases in the large‐scale circulation in the middle and upper troposphere. Different from previous studies with different physics parameterizations and refined domains, as the model resolution increases, simulated monsoon precipitation evolution is not obviously improved, and convective precipitation intensity decreases over eastern China. Despite this, our results indicate that VR‐CESM simulates regional climate, topographical forcing, and large‐scale circulations over East Asia reasonably well, and thus it can be applied for the future climate projection in the region. Plain Language Summary: Horizontal resolution is one of the key factors that affect the climate model's fidelity. Current global climate models (GCMs) are usually run at a coarse resolution due to the limitation of computational resources. To compromise, variable‐resolution GCM with higher resolution over domains of interest is developed to reduce the computational costs of high‐resolution modeling. This study evaluates the impact of model horizontal resolution on simulating regional climate in terms of temperature and precipitation using the model with a regionally‐refined high resolution over East Asia. Compared to observational data sets and the coarse‐resolution model, the regionally‐refined model better simulates spatial distributions and seasonal variations of climatological mean temperature and precipitation over China, as well as the occurrence frequency of extreme cold events and heavy precipitation. The regionally‐refined model with high‐resolution terrain improves precipitation simulation in regions with complex terrains. Our results demonstrate the regionally‐refined model's capability in simulating the regional climate over East Asia. Key Points: Variable‐resolution CESM2 (VR‐CESM2) performs better in simulating seasonal variation of precipitation over China but overestimates its magnitude than the quasi‐uniform 1° CESM2VR‐CESM2 reasonably simulates frequency of cold temperature extremes and heavy precipitationPrecipitation improvements at higher resolution depend on model physics parameterizations and refined domain size [ABSTRACT FROM AUTHOR]

The dust direct radiative effect (DRE) depends strongly on the dust particle size distribution (PSD) and complex refractive index (CRI). Although recent studies constrained the dust PSD in the models, its CRI uncertainties are still large. As a result, whether dust warms or cools the climate system remains unclear. Here, we estimate the dust DRE by employing the regionally‐dependent dust CRI based on global measurements. We find that new dust CRI significantly enhances the scattering of dust in the shortwave while reduces its absorption in the longwave, which is opposite to that caused by increasing the coarse and giant dust fraction via constraining the PSD. Constraining both PSD and CRI ultimately leads to a net dust DRE of −0.68 W m−2, a cooling stronger than current model estimates. Plain Language Summary: Impacts of dust on the Earth's climate are sensitive to the size and composition of dust particles. Previous research found that dust composition varies among its source regions. Using a single dust complex refractive index by assuming a uniform dust particle composition is inadequate for accurate dust modeling. In this study, we develop a regionally‐dependent dust refractive index scheme based on global observations to represent the differences in dust composition among its source regions. We find that the optical and radiative properties of the modeled dust are much improved when compared with observations. Our results show an enhanced dust cooling effect when accounting for regional differences in the dust complex refractive index, which is opposite to that when increasing more large dust particles. As a result, the combined effect leads to a stronger dust cooling than our previous model estimate. This study emphasizes the need to constrain the dust size distribution and the refractive index in the model to more accurately quantify the impacts of dust on climate. Key Points: New dust simulations are constrained by a combination of observed dust size distributions and regionally‐dependent dust refractive indicesNew dust refractive indices increase dust scattering in the shortwave and reduce dust absorption in the longwaveNew dust refractive indices greatly enhance dust cooling and change the sign of the net dust direct radiative effect in its source regions [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are important precursors to ozone and secondary organic aerosols in the atmosphere, affecting air quality, clouds, and climate. However, the trend in BVOC emissions and driving factors for the emission changes in different geographic regions over the past 2 decades has remained unclear. Here, regional to global changes in BVOC emissions during 2001–2020 are simulated using the latest Model of Emission of Gases and Aerosols from Nature (MEGANv3.2) with the input of time-varying satellite-retrieved vegetation and reanalysis meteorology data. Comparison of model simulations with the site observations shows that the model can reasonably reproduce the magnitude of isoprene and monoterpene emission fluxes. The spatial distribution of the modeled isoprene emissions is generally comparable to the satellite retrievals. The estimated annual average global BVOC emissions are 835.4 Tg yr -1 with the emissions from isoprene, monoterpenes, sesquiterpenes, and other BVOC comprised of 347.7, 184.8, 23.3, and 279.6 Tg yr -1 , respectively. We find that the decrease in global isoprene emissions (-0.07 % per year) caused by the increase in CO 2 concentrations (-0.20 % per year) is stronger than that caused by changes in vegetation (-0.03 % per year) and meteorological factors (0.15 % per year). However, regional disparities are large. Isoprene emissions increase significantly in Europe, East Asia, and South Asia (0.37 % per year–0.66 % per year). Half of the increasing trend is contributed by increased leaf area index (LAI) (maximum over 0.02 m 2 m -2 yr -1) and tree cover. Changes in meteorological factors contribute to another half, with elevated temperature dominating in Europe and increased soil moisture dominating in East and South Asia. In contrast, in South America and Southeast Asia, shifts in vegetation type associated with the BVOC emission capacity, which partly results from the deforestation and agricultural expansion, decrease the BVOC emission and offset nearly half of the emission increase caused by changes in meteorological factors. Overall, isoprene emission increases by 0.35 % per year and 0.25 % per year in South America and Southeast Asia, respectively. In Central Africa, a decrease in temperature dominates the negative emission trend (-0.74 % per year). Global monoterpene emissions show a significantly increasing trend (0.34 % per year, 0.6 Tg yr -1) compared to that of isoprene (-0.07 % per year, -0.2 Tg yr -1) , especially in strong greening hotspots. This is mainly because the monoterpene emissions are more sensitive to changes in LAI and are not subject to the inhibition effect of CO 2. The findings highlight the important roles of vegetation cover and biomass, temperature, and soil moisture in modulating the temporal variations of global BVOC emissions in the past 2 decades. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are important precursors to ozone and secondary organic aerosols in the atmosphere, affecting air quality, clouds and climate. However, the trend of BVOC emissions and driving factors for the emission changes in different geographic regions over the past two decades has remained unclear. Here, regional to global changes in BVOC emissions during 2001–2020 are simulated using the latest Model of Emission of Gases and Aerosols from Nature (MEGANv3.2) with the input of time-varying satellite-retrieved vegetation and reanalysis meteorology data. Comparison of model simulations with the site observations shows that the model can reasonably reproduce the magnitude of isoprene and monoterpene emission fluxes. The spatial distribution of the modeled isoprene emissions is generally comparable to the satellite retrievals. The estimated annual average global BVOC emissions are 835.4 Tg yr-1 with the emissions from isoprene, monoterpenes, sesquiterpenes, and other BVOC comprised of 347.7, 184.8, 23.3, and 279.6 Tg yr-1, respectively. We find that the decrease in global isoprene emissions (-0.07 % yr-1) caused by increase in CO2 concentrations (-0.20 % yr-1) is stronger than that caused by changes in vegetation (-0.03 % yr-1) and meteorological factors (0.15 % yr-1). However, regional disparities are large. Isoprene emissions increase significantly in Europe, East Asia, and South Asia (0.37-0.66 % yr-1). The increasing trend is contributed by half from increased leaf area index (LAI) (maximum over 0.02 m2 m-2 yr-1) and tree cover. Changes in meteorological factors contribute to another half, with elevated temperature dominating in Europe and increased soil moisture dominating in East and South Asia. In contrast, in South America and Southeast Asia, shifts in vegetation type associated with the BVOC emission capacity, which partly results from the deforestation and agricultural expansion, decrease the BVOC emission and offset nearly half of the emission increase caused by changes in meteorological factors. Overall, isoprene emission increases by 0.35 % yr-1 and 0.25 % yr-1 in South America and Southeast Asia, respectively. In Central Africa, a decrease in temperature dominates the negative emission trend (-0.74 % yr-1). Global monoterpene emissions show a significantly increasing trend (0.34 % yr-1, 0.6 Tg yr-1) compared to that of isoprene (-0.07 % yr-1, -0.2 Tg yr-1), especially in strong greening hotspots. This is mainly because the monoterpene emissions are more sensitive to changes in LAI and are not subject to the inhibition effect of CO2. The findings highlight the important roles of vegetation cover and biomass, temperature, and soil moisture in modulating the temporal variations of global BVOC emissions in past two decades. [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]

Deep convective clouds and associated anvils exert opposite radiative effects. The impact of different aerosol types on these two categories of clouds remains a major challenge in understanding aerosol‐cloud interactions. Using 11‐year satellite retrievals, we find that cloud top height (CTH) and ice cloud fraction of deep convective clouds and anvil cirrus identified by Cloud‐Aerosol Lidar with Orthogonal Polarization increase with small aerosol loadings and level off or even decrease with further aerosol increase. Compared with continental aerosols, CTH affected by marine aerosols starts to decrease at smaller aerosol loadings. Moreover, cloud optical depth (COD) of deep convective clouds decreases with aerosol loadings. COD of anvil cirrus increases with increased loadings of most aerosol types but decreases with smoke aerosol. These relationships are mainly attributed to the aerosol effect rather than the meteorological effects. Our findings contribute to the development of models and better assessment of aerosol‐cloud radiative forcing. Plain Language Summary: By acting as the seeds of clouds, aerosols affect the formation and development of clouds, thereby affecting climate. Deep convective clouds and associated anvil cirrus are often accompanied with severe weather events. These two types of clouds have opposite climate effects: the former generally cools the Earth system while the latter warms the Earth system. Using 11 years of satellite data, we find that with the increase of aerosol loadings, the cloud top height (CTH) and cloud fraction of deep convective clouds and anvil cirrus identified by Cloud‐Aerosol Lidar with Orthogonal Polarization first increase and then remain virtually unchanged or even decrease. We also analyze the effects of different types of aerosols on deep convective clouds and anvil cirrus. We find that, compared with continental aerosols, CTH affected by marine aerosols starts to decrease at smaller aerosol loadings. As the aerosol loadings increase, the cloud optical depth of deep convective clouds decreases while the optical thickness of the anvil cirrus increases. Therefore, these two categories of clouds as well as the effects from various aerosol types should be carefully considered when quantifying the aerosol effects on deep convective cloud systems. Key Points: The height and amount of Cloud‐Aerosol Lidar with Orthogonal Polarization‐identified deep convective clouds and anvil cirrus first increase and then level off with aerosol increaseMarine aerosols start to decrease cloud top height when aerosol optical depth is relatively smaller than continental aerosolsWith increased aerosol loadings, deep convective clouds have a decreased cloud optical depth (COD) while anvils have an increased COD. One exception is for smoke [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) have been used widely to study radiative forcing and health risks of aerosols. A recent study using two GCMs found that light rain plays a dominant role in controlling aerosol loading. However, "too much light rain and too little heavy rain" is a longstanding bias in GCMs. It is unclear how much light rain affects aerosol‐cloud‐radiation interactions and health risks from air pollution. Here we show that, with the correction of the rainfall intensity spectrum in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 by introducing a stochastic deep convection scheme, the reduced frequency of light rain (1–20 mm d−1) results in changes of aerosol direct radiative effects (DRE) of up to −0.5 ± 0.03 W/m2 and aerosol cloud radiative effects (CRE) of up to −0.9 ± 0.03 W/m2. The total (CRE + DRE) radiative effects of light rain‐mediated aerosol changes exceed the present‐day anthropogenic forcing of aerosols relative to preindustrial levels from the Coupled Model Intercomparison Project (CMIP5&6) models. However, the correction of the rainfall intensity spectrum has little effect on anthropogenic aerosol forcing (defined as the radiative perturbation due to changes in aerosol concentrations between the industrial era and preindustrial levels). Due to increased exposure to fine particulates (PM2.5), the estimated global total premature mortality is much higher than previously estimated, by 300,000 ± 60,000 deaths per year, and is more severe in populous regions such as India and China. The findings in this study highlight the need to understand uncertainties in radiative effects and health risks of aerosols due to simulation biases of precipitation in GCMs. Plain Language Summary: All global climate models have a problem of "too much light rain and too little heavy rain". Climatologically, light rain plays a disproportionate role in controlling the aerosol loading and thus can affect aerosol radiative effects and health risks. Here, we show that correcting this issue in a global climate model leads to substantial increases of the radiative effects and health risks of aerosols. The resulting change of the radiative effects is even larger than present‐day anthropogenic forcing of aerosols relative to preindustrial levels. The global total premature mortality due to elevated air pollution level is increased significantly. Key Points: Suppression of light rain increases the total radiative effect of aerosols by −1.4 W/m2, even larger than aerosol anthropogenic effectsElevated air pollution increases the health risks, with estimated global premature mortality increased by 300,000 deaths per yearReduced excessive light rain by suppressing too frequent convection has little effect on anthropogenic aerosol forcing [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]

The dust emission parameterization in the Chinese Academy of Sciences Earth System Model version 2 (CAS‐ESM2) is described with emphasis on the implementation process and simulations of global dust cycle and East Asia dust event statistics. The parameterization is based on the scheme of Shao (2004; http://doi.org/10.1029/2003JD004372) which considers two major dust emission mechanisms, namely, saltation bombardment and aggregation disintegration. Shao (2004; http://doi.org/10.1029/2003JD004372) scheme was well tested against field observations and has been widely used in regional dust modeling. Here this scheme is implemented into a global climate model for the first time and thus provides an independent solution for simulating global dust emissions. With this scheme, CAS‐ESM2 reasonably simulates the main dust emission regions in the Earth and reproduces the observations of dust deposition flux and surface dust concentrations at most stations. Compared to the synoptic records of dust events, the model also captures the general patterns and seasonal variations of dust activities in East Asia. However, the model underestimates the frequency of strong dust events (instantaneous surface dust concentrations >1,000 μg m−3) due to the weaker surface winds simulated by the model. The model tends to simulate much weaker and longer‐lasting dust events in Eastern Sources (35–49°N, 94–126.5°E) of northern China, suggesting the weaker and slower‐moving synoptic weather systems associated with dust events in the model. Overall CAS‐ESM2 performs well in simulating the key aspects of global dust distribution and East Asian dust events, yet some biases remain to be improved. Plain Language Summary: Dust emitted from the bare soil is an important aerosol type, and dust cycle links the various components of Earth System including atmosphere, land, and oceans. Therefore, dust emission is an essential part of Earth system. In this study, we develop a dust emission module for Chinese Academy of Sciences Earth System Model version 2 (CAS‐ESM2) to simulate the global dust emission. The module incorporates advanced mechanisms of dust emission physics. The results show that with this module, CAS‐ESM2 reproduces well the global distribution of dust aerosol. We also evaluate the model's ability in simulating the individual dust events in terms of dust event frequency, intensity, and duration. We find that the model can reproduce the key regions of dust emission and the highest frequency of dust event in spring in East Asia. However, because the model underestimates surface wind speeds, it simulates weaker and longer‐lasting dust events. Overall CAS‐ESM2 provides a new and promising solution to simulate global dust emission, although there are still some biases for further improvements. Key Points: Chinese Academy of Sciences Earth System Model version 2 (CAS‐ESM2) with a physically based dust emission scheme provides a new and promising solution to simulate global dust emissionsCAS‐ESM2 captures the main global dust emission regions and reproduces observed dust depositions and surface dust concentrationsThe weaker dust events are compensated by overestimated dust event frequency and duration to produce reasonable means in East Asia [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]

Mesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convection within the cloud‐resolving model embedded in each GCM column, has been shown to be a promising tool for simulating MCSs, particularly over the Tropics. In this work, we use ground‐based radar‐observed precipitation, North American Regional Reanalysis data, and a high‐resolution Weather Research and Forecasting simulation to evaluate in detail the MCS‐associated precipitation over the central United States predicted by a prototype MMF simulation that has a 2° host‐GCM grid. We show that the prototype MMF with nudged winds fails to capture the convective initiation in three out of four major MCS events during May 201x1 and underpredicts the precipitation rates for the remaining event, because the model cannot resolve the mesoscale drylines/fronts that are important drivers for initiating convection over the Southern Great Plains region. By reducing the host‐GCM grid spacing to 0.25° in the MMF and nudging the winds, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the cloud‐resolving model embedded in each GCM column further helps to boost precipitation rate. Plain Language Summary: Massive thunderstorms contribute a large proportion of warm season rainfall over the central United States. Previous studies have shown that the Multiscale Modeling Framework (MMF), which embeds a cloud‐resolving model (CRM) into each global climate model grid column to simulate convection, provides a promising tool for simulating massive thunderstorms in the Tropics. However, it is unclear whether the MMF can simulate similar thunderstorms in midlatitudes such as in the central United States. Therefore, this study compares the MMF simulations with detailed available observations over the central United States. We find that the commonly used MMF with 2°‐host‐GCM (~200 km) grid spacing has difficulty in reproducing the observed rainfall because the host‐GCM grid spacing is too coarse to capture the mesoscale circulations (at scales of approximately tens of kilometers) that are important for triggering the convection. When the host‐GCM‐grid spacing is reduced to a quarter degree (~25 km), the model succeeds to trigger the convection, so the rainfall simulation is improved. This study shows the importance of better representation of mesoscale circulations in models for predicting massive thunderstorms. Key Points: Springtime MCS‐associated precipitation properties over Central U.S. are comprehensively evaluatedThe MMF simulation with a coarse host grid fails to capture convective initiation over the Southern Great Plain (SGP) regionA nudged MMF simulation with a 0.25° host grid improves convective initiation and better captures the precipitation over the SGP region [ABSTRACT FROM AUTHOR]

One of the grand challenges of climate science is understanding the changes of the tropical rain belts and monsoon systems owing to CO2‐induced warming. A promising path forward links the fluxes of energy and moisture to tropical circulation features. To this end, we make use of the Energy Exascale Earth System Model version 1, where the divergence of moist static energy and moisture have been calculated online, and employ a normalized gross moist stability (NGMS) diagnostic framework to understand the linkages between changes in the flow of energy and moisture within the monsoons. We focus on the Asian Summer Monsoon system and utilize a series of atmosphere‐land and atmosphere‐land‐ocean simulations to understand the connection between fluxes and monsoons. Uncoupled simulations with prescribed sea surface temperatures indicate that decreases in NGMS over land are important in explaining precipitation increases in response to both sea surface temperature and CO2 increases. In fully coupled experiments, NGMS decreases remain an important contributor to the increase in P‐E, but the coupled simulations highlight the importance of consistent ocean and land responses in interpreting the monsoon changes. This study indicates that transient eddy fluxes play an important role in NGMS decreases and that a time‐mean view of the monsoon circulations is insufficient to quantify the link between future changes in the fluxes of energy and moisture. Compensation between dynamic and thermodynamic components of vertical moist static energy advection occurs, with the thermodynamic contribution dominating. The compensation is shown to be sensitive to relative humidity, with higher relative humidity leading to a stronger thermodynamic component. Plain Language Summary: One of the challenges of climate science is understanding how warming will change monsoon rainfall. A promising path forward links the transport of energy to the transport of water vapor. This link, termed the normalized gross moist stability (NGMS), can be used to fingerprint monsoon rainfall changes coming from different energy sources as well as changes to NGMS itself. A fingerprint of the Asian Summer Monsoon rainfall response to warming is made for a general circulation model, which highlights the importance of the NGMS term for understanding the monsoon rainfall response to warming. By examining this NGMS term more carefully, evidence is shown for the importance of the ocean circulation toward explaining the monsoon rainfall response to warming. Further analysis of the NGMS term also suggests that the traditional time‐mean view of the monsoon is insufficient to explain its response to warming. The response of NGMS to warming is shown to be sensitive to the relative humidity of the atmosphere. Key Points: Normalized gross moist stability framework diagnoses key factors for increase in Asian Summer Monsoon water cycle with warmingPrescribing sea surface temperature increases have an inconsistent circulation response to simulations with an active ocean modelTransient eddies contribute significantly to the changing normalized gross moist stability with warming [ABSTRACT FROM AUTHOR]

The net radiative effects of tropical clouds are determined by the evolution of thick, freshly detrained anvil clouds into thin anvil clouds. Thick anvil clouds reduce Earth's energy balance and cool the climate, while thin anvil clouds warm the climate. To determine role of these clouds in climate change we need to understand how interactions of their microphysical and macrophysical properties control their radiative properties. We explore anvil cloud evolution using a cloud‐resolving model in three‐simulation setups of increasing complexity to disentangle the impacts of the various components of diabatic heating and their interaction with cloud‐scale motions. The first phase of evolution and rapid cloud spreading is dominated by latent heating within convective updrafts. After the convective detrainment stops, most of the spreading and thinning of the anvil cloud is driven by cloud radiative processes and latent heating. The combination of radiative cooling at cloud top, latent cooling due to sublimation at cloud base, latent heating due to deposition and radiative heating in between leads to a sandwich‐like, cooling‐heating‐cooling structure. The heating sandwich promotes the development of two within‐anvil convective layers and a double cell circulation, dominated by strong outflow at 12‐km altitude with inflow above and below. Our study reveals how small‐scale processes including convective, microphysical processes, latent and radiative heating interact within the anvil cloud system. The absence or a different representation of only one component results in a significantly different cloud evolution with large impacts on cloud radiative effects. Plain Language Summary: Clouds have a large influence on climate. Thick clouds reflect part of the solar (or shortwave) radiation back to space and therefore cool the climate. On the other hand, wispy and thin high clouds do not reflect much of solar radiation. They form high in the atmosphere at cold temperatures and therefore keep part of the terrestrial (or longwave) radiation within the atmosphere. They warm the climate, similarly to greenhouse gasses. The evolution of thunderstorm clouds is of particular interest as it involves a transition from the thick clouds that cool the climate to the thin high clouds that warm the climate. We study small‐scale processes that drive this transition and their delicate balance and interactions. Tiny differences in how ice crystals form, grow, shrink, or interact with solar or terrestrial radiation can lead to large differences in the climatic role of thunderstorm clouds. Such processes are currently not represented in models we use for climate projections. Our findings may ultimately lead to improvements in the representation of thunderstorm cloud life cycles in climate models and therefore increase the trust in projections of future climate. Key Points: Both radiative and latent heating within anvil clouds strongly influence the cloud radiative effect at the top of the atmosphereLatent and radiative heating drive turbulence and organized circulations within the anvil cloud structureRadiative heating dominates near the top of the cloud, while latent heating dominates near the base of the cloud [ABSTRACT FROM AUTHOR]

Light-absorbing particles (LAPs), mainly dust and black carbon, can significantly impact snowmelt and regional water availability over high-mountain Asia (HMA). In this study, for the first time, online aerosol–snow interactions are enabled and a fully coupled chemistry Weather Research and Forecasting (WRF-Chem) regional model is used to simulate LAP-induced radiative forcing on snow surfaces in HMA at relatively high spatial resolution (12 km , WRF-HR) compared with previous studies. Simulated macro- and microphysical properties of the snowpack and LAP-induced snow darkening are evaluated against new spatially and temporally complete datasets of snow-covered area, grain size, and impurity-induced albedo reduction over HMA. A WRF-Chem quasi-global simulation with the same configuration as WRF-HR but a coarser spatial resolution (1 ∘ , WRF-CR) is also used to illustrate the impact of spatial resolution on simulations of snow properties and aerosol distribution over HMA. Due to a more realistic representation of terrain slopes over HMA, the higher-resolution model (WRF-HR) shows significantly better performance in simulating snow area cover, duration of snow cover, snow albedo and snow grain size over HMA, as well as an evidently better atmospheric aerosol loading and mean LAP concentration in snow. However, the differences in albedo reduction from model and satellite retrievals is large during winter due to associated overestimation in simulated snow fraction. It is noteworthy that Himalayan snow cover has high magnitudes of LAP-induced snow albedo reduction (4 %–8 %) in pre-monsoon seasons (both from WRF-HR and satellite estimates), which induces a snow-mediated radiative forcing of ∼30 –50 Wm-2. As a result, the Himalayas (specifically the western Himalayas) hold the most vulnerable glaciers and mountain snowpack to the LAP-induced snow darkening effect within HMA. In summary, coarse spatial resolution and absence of snow–aerosol interactions over the Himalayan cryosphere will result in significant underestimation of aerosol effects on snow melting and regional hydroclimate. [ABSTRACT FROM AUTHOR]

Abstract: Modeling the aerosol lifecycle in traditional global climate models (GCM) is challenging for a variety of reasons, not the least of which is the coarse grid. The multiscale modeling framework (MMF), in which a cloud resolving model replaces conventional parameterizations of cloud processes within each GCM grid column, provides a promising framework to address this challenge. Here we develop a new version of MMF that for the first time treats aerosol processes at cloud scale to improve the aerosol‐cloud interaction representation in the model. We demonstrate that the model with the explicit aerosol treatments shows significant improvements of many aspects of the simulated aerosols compared to the previous version of MMF with aerosols parameterized at the GCM grid scale. The explicit aerosol treatments produce a significant increase of the column burdens of black carbon (BC), primary organic aerosol, and sulfate by up to 40% in many remote regions, a decrease of the sea‐salt aerosol burdens by 40% in remote regions. These differences are caused by the differences in aerosol convective transport and wet removal between these two models. The new model also shows reduced bias of BC surface concentration in North America and BC vertical profiles in the high latitudes. However, the biased‐high BC concentrations in the upper troposphere over the remote Pacific regions remain, requiring further improvements on other process representations (e.g., secondary activation neglected in the model). [ABSTRACT FROM AUTHOR]

Efficient simulation strategies are crucial for the development and evaluation of high-resolution climate models. This paper evaluates simulations with constrained meteorology for the quantification of parametric sensitivities in the Community Atmosphere Model version 5 (CAM5). Two parameters are perturbed as illustrating examples: the convection relaxation time scale (TAU), and the threshold relative humidity for the formation of low-level stratiform clouds (rhminl). Results suggest that the fidelity of the constrained simulations depends on the detailed implementation of nudging and the mechanism through which the perturbed parameter affects precipitation and cloud. The relative computational costs of nudged and free-running simulations are determined by the magnitude of internal variability in the physical quantities of interest, as well as the magnitude of the parameter perturbation. In the case of a strong perturbation in convection, temperature, and/or wind nudging with a 6 h relaxation time scale leads to nonnegligible side effects due to the distorted interactions between resolved dynamics and parameterized convection, while 1 year free-running simulations can satisfactorily capture the annual mean precipitation and cloud forcing sensitivities. In the case of a relatively weak perturbation in the large-scale condensation scheme, results from 1 year free-running simulations are strongly affected by natural noise, while nudging winds effectively reduces the noise, and reasonably reproduces the sensitivities. These results indicate that caution is needed when using nudged simulations to assess precipitation and cloud forcing sensitivities to parameter changes in general circulation models. We also demonstrate that ensembles of short simulations are useful for understanding the evolution of model sensitivities. [ABSTRACT FROM AUTHOR]

Atmospheric deposition of reactive nitrogen (N) species from air pollutants is a significant source of exogenous nitrogen in marine ecosystems. Here we use an atmospheric chemical transport model to investigate the supply of soluble organic nitrogen (ON) from anthropogenic sources to the ocean. Comparisons of modeled deposition with observations at coastal and marine locations show good overall agreement for inorganic nitrogen and total soluble nitrogen. However, previous modeling approaches result in significant underestimates of the soluble ON deposition if the model only includes the primary soluble ON and the secondary oxidized ON in gases and aerosols. Our model results suggest that including the secondary reduced ON in aerosols as a source of soluble ON contributes to an improved prediction of the deposition rates (g N m−2 yr−1). The model results show a clear distinction in the vertical distribution of soluble ON in aerosols between different processes from the primary sources and the secondary formation. The model results (excluding the biomass burning and natural emission changes) suggest an increase in soluble ON outflow from atmospheric pollution, in particular from East Asia, to the oceans in the twentieth century. These results highlight the necessity of improving the process-based quantitative understanding of the chemical reactions of inorganic nitrogen species with organics in aerosol and cloud water. [ABSTRACT FROM AUTHOR]

The atmospheric component of Energy Exascale Earth System Model version 1 has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one‐at‐a‐time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the Energy Exascale Earth System Model version 1 model behaviors and physics, we conducted a large number of short simulations (three days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection, and cloud macrophysics and microphysics were perturbed simultaneously using the Latin hypercube sampling method. From the perturbed parameter ensemble simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that perturbed parameter ensemble using short simulations has some bearing on understanding parametric sensitivity of longer simulations. Results from this analysis provide a more comprehensive picture of the Energy Exascale Earth System Model version 1 behavior. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (so‐called embedded errors) to inform future development efforts. Key Points: Short PPE simulations provide a comprehensive picture of the behaviors of a new Earth system model associated with uncertain parametersPPE using short simulations has some bearing on understanding parametric sensitivity of longer simulationsThe difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model embedded errors [ABSTRACT FROM AUTHOR]