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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 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]

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]