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

Numerous Earth system models exhibit excessive aerosol effective forcing at the top of the atmosphere (TOA), including the Department of Energy's Energy Exascale Earth System Model (E3SM). Here, in the context of the E3SM version 3 effort, the predicted particle property (P3) stratiform cloud microphysics scheme and an enhanced deep convection parameterization suite (ZM_plus) are implemented into E3SM. The ZM_plus includes a convective cloud microphysics scheme, a multi‐scale coherent structure parameterization for mesoscale convective systems, and a revised cloud base mass flux formulation considering impacts of the large‐scale environment. The P3 scheme improved cloud and radiation particularly over the Northern Hemisphere and the frequency of heavy precipitation over the tropics, and the ZM_plus improved clouds in the tropics. P3 decreases aerosol effective forcing by 0.15 W m−2, while the ZM_plus increases it by 0.27 W m−2, resulting from excessive direct (0.31 W m−2) and indirect forcing (−1.79 W m−2). The excessive aerosol forcings are due to aerosol overestimation associated with insufficient aerosol wet removal. By improving the physical treatments in the aerosol wet removal, we effectively mitigate anthropogenic aerosol overestimation and thus attenuate direct (0.09 W m−2) and indirect aerosol forcing (−1.52 W m−2). Adjustment to primary organic matter hygroscopicity reduces direct and indirect forcing to more reasonable values: −0.13 W m−2 and −1.31 W m−2, respectively. On climatology, improved aerosol treatments mitigate overestimation of aerosol optical depth. Plain Language Summary: The Energy Exascale Earth System Model (E3SM) exhibits strong direct and indirect aerosol forcings, after advanced stratiform and convective cloud treatments are implemented. This study identifies the primary cause of these excessive aerosol forcings as the significant overestimation of anthropogenic aerosols due to insufficient removal of aerosols by precipitation. To address this issue, we made aerosol wet removal representation more physical, which effectively reduced the overestimation of aerosols, bringing both direct and indirect forcings to the expected ranges. Furthermore, cloud and aerosol climatology are notably improved as the result of these developments in cloud and aerosol treatments. Key Points: The new cloud microphysics scheme P3 improves simulations of cloud properties in the NH and aerosol forcingInsufficient wet removal leads to an overly strong aerosol forcing after incorporating the enhanced deep convection parameterization suiteThe aerosol wet removal improvements lead to more reasonable aerosol forcing and improved aerosol climatology in Energy Exascale Earth System Model [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [ABSTRACT FROM AUTHOR]

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for "sweet spots" prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment. [ABSTRACT FROM AUTHOR]

The aim of this study is to reduce the manufacturing cost of a hydrophobic and heat-insulating silica aerogel and promote its industrial application in the field of thermal insulation. Silica aerogels with hydrophobicity and thermal-insulation capabilities were synthesized by using water-glass as the silicon source and supercritical drying. The effectiveness of acid and alkali catalysis is compared in the formation of the sol. The introduction of sodium methyl silicate for the copolymerization enhances the hydrophobicity of the aerogel. The resultant silica aerogel has high hydrophobicity and a mesoporous structure with a pore volume exceeding 4.0 cm3·g−1 and a specific surface area exceeding 950 m2·g−1. The obtained silica aerogel/fiber-glass-mat composite has high thermal insulation, with a thermal conductivity of less than 0.020 W·m−1·K−1. The cost-effective process is promising for applications in the industrial preparation of silica aerogel thermal-insulating material. [ABSTRACT FROM AUTHOR]

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy's Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance. [ABSTRACT FROM AUTHOR]

Lithium–sulfur (Li–S) batteries are considered as one of the top competitors to go beyond Li‐ion batteries. However, the shuttle effect triggered by soluble lithium polysulfides (LPSs) brings great troubles for understanding the solid–liquid–solid conversion process of the sulfur cathode. Herein, a new characterization technique is developed to deepen the understanding of such soluble LPSs shuttling, by integrating an electron‐conductive interlayer. The voltage of the interlayer exhibits a voltage‐adaptive effect to the cathode, indicating the true dependence of the open‐circuit voltages on the LPSs instead of on the solid cathodes. Furthermore, a quantitative method can be introduced to monitor the shuttling LPSs by such interlayer design, and it shows great potential to be a new standard technique, providing direct comparison of the shuttle effect between different studies. The newly developed interlayer design paves an avenue to gain new insight into the reaction process and improve the performance of Li–S batteries. [ABSTRACT FROM AUTHOR]

Poor cycling performance caused by massive volume expansion of silicon (Si) has always hindered the widespread application of silicon-based anode materials. Herein, bi-continuous silicon/carbon (Si/C) anode materials are prepared via magnesiothermic reduction of silica aerogels followed by pitch impregnation and carbonization. To fabricate the expected bi-continuous structure, mesoporous silica aerogel is selected as the raw material for magnesiothermic reduction. It is successfully reduced to mesoporous Si under the protection of NaCl. The as-obtained mesoporous Si is then injected with molten pitch via vacuuming, and the pitch is subsequently converted into carbon at a high temperature. The innovative point of this strategy is the construction of a bi-continuous structure, which features both Si and carbon with a cross-linked structure, which provides an area to accommodate the colossal volume change of Si. The pitch-derived carbon facilitates fast lithium ion transfer, thereby increasing the conductivity of the Si/C anode. It can also diminish direct contact between Si and the electrolyte, minimizing side reactions between them. The obtained bi-continuous Si/C anodes exhibit excellent electrochemical performance with a high initial discharge capacity of 1481.7 mAh g−1 at a current density of 300 mA g−1 and retaining as 813.5 mAh g−1 after 200 cycles and an improved initial Coulombic efficiency of 82%. The as-prepared bi-continuous Si/C anode may have great potential applications in high-performance lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Among the physical processes controlling aerosol vertical profiles, in‐cloud wet removal is of utmost importance while its representation in global climate models (GCMs) is crude. In this study, we implement into the Community Atmosphere Model version 6 (CAM6) a physically‐based aerosol wet removal parameterization scheme that explicitly treats aerosol activation, removal and resuspension. Evaluation against in‐situ observations shows that the default scheme substantially overestimates the upper tropospheric black carbon (BC) and sea salt mass. Our physically‐based scheme reduces BC and sea salt mass by a factor of 10 and 1,000, respectively, in better agreement with observations. Also, the new scheme slightly increases number of aerosol particles between 12 nm and 4.8 μm in diameter, thereby mitigating the aerosol number underestimation in the default scheme. Our new scheme reduces the overestimation of coarse‐mode aerosol (0.5–4.8 μm) number. Overall, the aerosol property changes (mass decrease and number increase) reduce the cloud condensation nuclei (CCN) concentration at low supersaturation (i.e., 0.02% and 0.1%), and increase CCN at high supersaturations (i.e., 0.5% and 1%). Consequently, the global annual mean cloud liquid water path increases by 1.89 g m−2 and the ice water path increases by 0.51 g m−2. The global annual mean shortwave, longwave, and net cloud radiative forcing change by −1.06, 0.57, and −0.48 W m−2, respectively. Further improvement is needed to reflect the real physics that the removal efficiencies for aerosol mass and number are disproportionate and to advect cloud‐borne (activated) aerosols for a complete aerosol lifecycle. Plain Language Summary: In‐cloud aerosol wet removal through aerosol particle activation to become cloud droplets and the subsequent rainfall formation removes airborne aerosol particles from the atmosphere, largely impacting aerosol amount and distribution in the atmosphere. Global climate models (GCMs) usually overestimate aerosol mass concentrations throughout the atmosphere, particularly in the upper troposphere, hampering an accurate assessment of aerosol‐cloud‐climate interactions. The main cause of aerosol overestimation lies in the oversimplified treatment of in‐cloud aerosol wet removal processes. In this study, we incorporate a new aerosol wet removal scheme in a GCM, the Community Atmosphere Model version 6. The evaluation against observations of the aerosol profile simulations with our new scheme shows significant improvements over the default scheme that is characterized by overestimation of aerosol and insufficient aerosol wet removal, highlighting the importance of an accurate representation of aerosol wet removal for aerosol vertical profiles. With the newly improved aerosol wet removal scheme, the global annual mean net cloud radiative forcing is changed by −0.48 W m−2, indicating that our new aerosol wet removal results in clouds that are more reflective on a global scale. Key Points: Improved representations of aerosol activation, removal, and resuspension in the wet removal scheme are implemented into Community Atmosphere Model version 6Our improved wet removal scheme effectively reduces the systematic overestimation of aerosol mass and mitigates the underestimation of total aerosol (12 nm to 4.8 μm) numberThe global mean net cloud radiative forcing is changed by −0.48 W m−2 due to our aerosol wet removal improvement in terms of activation, removal, and resuspension [ABSTRACT FROM AUTHOR]

Lithium–sulfur (Li–S) batteries are one of the most competitive candidates for high‐energy‐density batteries. However, the high energy density and high reactivity of the lithium anode and sulfur cathode make the safety issues of Li–S batteries particularly prominent. Herein, an early warning strategy for the short circuits in Li–S batteries is explored by integrating an in‐plane electron‐conductive separator. The internal short circuits caused by lithium dendrites or manufacturing defects can be quickly and accurately predicted before catastrophic thermal runaway, with enough time to take rescue measures. The separator with high thermal conductivity also facilitates the heat dissipation of the cell, thus reducing the risk of thermal runaway under abuse. The fast and reliable early warning strategy based on the in‐plane electron‐conductive separator builds the Great Wall of active defense against inherently unsafe Li–S batteries. [ABSTRACT FROM AUTHOR]

Dust transport and spatial distribution are poorly represented in current global climate models (GCMs) including the Community Atmosphere Model version 5 (CAM5). Particularly, models lack explicit representation of super‐coarse dust, which may have important implications for dust radiative forcing and impacts on biogeochemistry. A nine‐mode version of the modal aerosol model (MAM9) has been developed to address these issues. In this new aerosol scheme, four dust modes have been designed to treat dust particles of sizes up to 20 μm. The MAM9‐simulated results are compared with those from the default four‐mode version of MAM (MAM4) and also with the in situ surface measurements of dust concentration and deposition flux, satellite‐retrieved dust extinction profile, and in situ vertical measurements of dust concentrations from the NASA Atmosphere Tomography Mission (ATom). Overall, MAM9 improves the dust representation in remote regions while maintaining reasonably good results near the dust source regions. In addition, MAM9 reduces the fine dust burden and increases the coarse dust burden globally. The increased coarse dust burden has slightly increased the dust direct radiative effect by 0.01 W m−2 while it enhanced dust indirect radiative effect by 0.36 W m−2, globally. Plain Language Summary: Dust aerosol is the most abundant aerosol in the atmosphere. Although the current CESM‐CAM5 model can capture some dust aerosol distribution features, the long‐distance dust transport and coarse dust burden have been poorly represented. To address these issues, we have developed the nine‐mode version of the modal aerosol model (MAM9) and implemented it to CESM‐CAM5.4. The MAM9 has four dust modes to enhance the size resolution of dust in the model. The standard deviation in each mode is reset based on observational data. Also, dust and sea salt aerosols in our MAM9 are separated into different modes to reduce the wet scavenge across the Pacific and Atlantic Oceans. These changes improved dust transport in remote regions compared with MAM4. Moreover, the changes warm up the simulated atmosphere through the dust direct and indirect radiative effects. Key Points: A nine‐mode version of the modal aerosol model has been developed to improve the dust representation in Community Atmosphere ModelThe dust aerosols simulated by this new implement in remote regions better agree with observational dataThe increased coarse dust burden has increased the dust direct and indirect radiative effects resulting in a warmer atmosphere [ABSTRACT FROM AUTHOR]

Accurate prediction of cloud radiative effect (CRE) is important to weather forecast and climate projection, and solar energy production—a major renewable energy source toward decarbonization. Here, we evaluate the capability of the Weather Research and Forecast (WRF) model to simulate solar irradiance on a short‐term timescale (days) against observations in a remote region in north China. Results illustrate that our WRF simulation systematically underestimates the CRE and three error sources are identified: (a) incorrectly predicted cloud occurrence (i.e., missed clouds and false clouds), (b) underestimated cloud condensate mass, and (c) simplified parameterization of solar irradiance extinction. The incorrect cloud occurrence is the leading bias source, because it occurred most frequently and results in a substantial magnitude of errors. The cloud occurrence bias is subject to simulations of large‐scale air ascends and planetary boundary layer turbulence. Even when cloud occurrence is correctly simulated, our WRF simulation still underestimates CRE. This is because (a) the shallow convection scheme and cloud microphysics scheme underestimate cloud condensate mass and (b) cloud water path that feeds in the radiation scheme neglects precipitating cloud condensates (i.e., raindrops and graupels). Furthermore, an evaluation of cases with small bias in cloud condensate mass and effective radius demonstrates the parameterization of solar irradiance extinction for clouds induces a mean root mean square deviation of 110 W/m2. A possible reason is the simplified calculation of cloud extinction efficiency by applying Monte Carlo integration. The gained knowledge is important for understanding CRE simulation and solar irradiance forecast. Key Points: The Weather Research and Forecast model simulation is found to underestimate cloud radiative effect (CRE) in a remote and semiarid regionMissed clouds and false clouds, that occur 50% of the time and cause up to 400 W/m2 bias in CRE, are the leading bias sourcesOther nonignorable bias sources include underestimated cloud condensate mass and the oversimplified radiative transfer scheme [ABSTRACT FROM AUTHOR]

In order to provide paleofluid evidence of hydrocarbon accumulation periods in the Amu Darya Right Bank Block, microexperiments and simulations related to the Middle-Upper Jurassic Callovian-Oxfordian carbonate reservoirs were performed. On the basis of petrographic observation, the diagenetic stages were divided by cathodoluminescence, and the entrapment stages of fluid inclusions were divided by laser Raman experiment and UV epifluorescence. The hydrocarbon generation (expulsion) curve and burial (thermal) history curve of source rocks were simulated by using real drilling data coupled with geochemical parameters of source rocks, such as total organic carbon (TOC) and vitrinite reflectance ( R o ). The above results were integrated with microthermometry of fluid inclusions by inference the timing of hydrocarbon migration into the carbonate reservoirs. The horizon-flattening technique was used to process the measured seismic profile and restore the structural evolution profile. Four diagenetic periods and three hydrocarbon accumulation periods were identified. (i) For Syntaxial stage, the fluid captured by the overgrowing cement around particles is mainly seawater; (ii) for (Early) Mesogenetic burial stage, the calcite cements began to capture hydrocarbon fluids and show yellow fluorescence under UV illumination; (iii) for (Late) Mesogenetic burial stage, two sets of cleavage fissures developed in massive calcite cements, and oil inclusions with green fluorescence were entrapped in the crystal; (iv) for Telogenetic burial stage, blue fluorescent inclusions along with hydrocarbon gas inclusions developed in dully luminescent calcite veins. Based on the accurate division of hydrocarbon migration and charging stages, combined with the structural evolution history of the traps, the hydrocarbon accumulation model was established. Because two of the three sets of source rocks are of marine origin, resulting in the lack of vitrinite in the kerogen of those source rocks, there may be some deviation between the measured value of R o and the real value. Some systematic errors may occur in the thermal history and hydrocarbon generation (expulsion) history of the two sets of source rocks. Due to the limitations of seismic horizon-flattening technique—such as the inability to accurately recover the inclined strata thickness and horizontal expansion of strata—the final shape of the evolution process of structural profile may also deviate from the real state in geological history. The accumulation model established in this study was based upon the fluid inclusion experiments, which can effectively characterize the forming process of large condensate gas reservoirs in the Amu Darya Right Bank Block and quantify the timing of hydrocarbon charging. However, the hydrocarbon migration and accumulation model does not take the oil-source correlation into account, but only the relationship between the mature state of source rocks and the timing of hydrocarbon charging into the reservoirs. Subsequent research needs to conduct refined oil-source correlation to reveal the relationship between gas, condensate, source rocks, and recently discovered crude oil and more strictly constrain and modify the accumulation model, so as to finally disclose the origin of the crude oil and oil reservoir forming process in the Amu Darya Right Bank Block, evaluate the future exploration potential, and point out the direction of various hydrocarbon resources (condensate gas and crude oil). [ABSTRACT FROM AUTHOR]

Reliable ultra-short-term and short-term wind speed forecasting is pivotal for clean energy development and grid operation planning. During the wind forecasting process, decomposing the measured wind speed into data with different frequencies is a solution for overcoming the nonlinearity and the randomness of the natural wind. Existing forecasting methods, a hybrid method based on empirical mode decomposition and the back propagation neural network optimized by genetic algorithm (EMD-GA-BPNN), rely on partial decomposing the measured wind speed into data with different frequencies and subsequently achieving forecasting results from machine learning algorithms. However, such methods can roughly divide IMF signals in different frequency domains, but each frequency domain contains signals with multiple frequencies. The condition reflects that the method cannot fully distinguish wind speed into data with different frequencies and thus it compromises the forecasting accuracy. A complete decomposition of measured wind speed can reduce the complexity of machine learning algorithm, and has become a useful approach for precise simulations of wind speed. Here, we propose a novel hybrid method (CEEMDAN-GA-BPNN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) by completely decomposing the measured wind speed. The decomposition results are put into the back propagation neural network optimized by a genetic algorithm (GA-BPNN), and the final forecasting results are achieved by combining all the output values by GA-BPNN for each decomposition result from CEEMDAN. We benchmark the forecasting accuracy of the proposed hybrid method against EMD-GA-BPNN integrated by EMD and GA-BPNN. From a wind farm case in Yunnan Province, China, both for ultra-short-term forecasting (15 min) and short-term forecasting (1 h), the performance of the proposed method exceeds EMD-GA-BPNN in several criteria, including root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The forecasting accuracy in decomposed components of low frequencies outperform components of high and middle frequencies. Fine improvement of the error metric (in percentage) in ultra-short-term/short-term forecasting is found by the complete decomposition method CEEMDAN-GA-BPNN: RMSE (7.0% and 8.6%), MAE (7.41% and 7.9%), MAPE (11.0% and 8.7%), and R2 (2.2% and 11.0%), compared with the incomplete decomposing method EMD-GA-BPNN. Our result suggests that CEEMDAN-GA-BPNN could be an accurate wind speed forecasting tool for wind farms development and intelligent grid operations. Significance Statement: Nonlinearity and randomness of natural wind speed data are the limitations for short-term and ultra-short-term wind speed forecasting. By decreasing forecasting error in machine learning training process, data decomposition for the measured wind speed has become an effective method for overcoming this issue. Nonetheless, the normal incomplete decomposition method will compromise the extent of forecasting accuracy. We introduce a novel hybrid and complete decomposition method CEEMDAN-GA-BPNN (the complete decomposition method). Measured wind speed data from a wind farm in Yunnan Province, China, has been utilized. CEEMDAN-GA-BPNN outperforms EMD-GA-BPNN (the partial decomposition method) in forecasting accuracy both in the ultra-short-term and the short-term wind speed forecasting. [ABSTRACT FROM AUTHOR]

We introduce a physics‐based aerosol wet removal scheme with unified treatments of aerosol transport and removal by convective clouds into the Community Atmosphere Model version 6. Since several important physical processes are still neglected or poorly represented in this new physics‐based scheme, we develop secondary improvements to the parameterizations of aerosol activation, resuspension, and cloud‐borne aerosol detrainment in this new scheme. Changes in the aerosol wet removal scheme cause tropospheric aerosol concentrations to decrease to different extents: compared to the control run, the physics‐based scheme significantly decreases aerosol burdens by up to 60% over the southern Pacific Ocean, whereas the secondary improvements mitigate the decreasing tendency. The burden changes also depend on aerosol chemical components: the sulfate mass decrease is compensated by secondary production, black carbon (BC) is effectively removed via increasing the hygroscopicity of particulate organic matter from 0 to 0.2, and dust shows the most spatially heterogeneous changes. Simulated aerosol profiles are evaluated against aircraft‐based observations over the Pacific and Atlantic Oceans. The secondary‐improved scheme reduces the overestimations of upper tropospheric BC and sea salt concentrations by a factor of 10 and 1,000, respectively, and reproduces the dependence of BC mass decrease rates on cloud types. Consideration of convective cloud‐borne aerosol detrainment plays the most important role in enhancing the aerosol wet removal and decreasing the positive biases of tropospheric BC and sea salt concentrations. We also summarize unresolved issues related to convective cloud genesis and microphysics, cloud‐borne aerosol evolution, and BC and dust emissions. Key Points: Aerosol wet removal by deep convective clouds significantly impacts global aerosol vertical profilesExplicit convective cloud microphysics in wet removal scheme reduces overestimation of black carbon and sea salt concentrations in upper troposphereParameterization of cloud‐borne (activated) aerosol detrainment from convective clouds to stratiform clouds is an important step in improving the model performance [ABSTRACT FROM AUTHOR]

Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, "rimed ice," to the original four‐class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool‐International Cloud Experiment (TWP‐ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity. Key Points: Graupel is added to a convective microphysics scheme for global climate modelsNew convective ice particle terminal velocity schemes are implemented and convective snow is allowed to detrainVertical distribution of ice mass in the midtroposphere and upper troposphere is improved [ABSTRACT FROM AUTHOR]

The development of chemical transport models with advanced physics and chemical schemes could improve air-quality forecasts. In this study, the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, a comprehensive chemistry module incorporating gaseous chemistry and a size-segregated multicomponent aerosol algorithm, was coupled to the Weather Research and Forecasting (WRF) framework with chemistry (WRF-Chem) using an interface procedure to build the WRF/CUACE v1.0 model. The latest version of CUACE includes an updated aerosol dry deposition scheme and the introduction of heterogeneous chemical reactions on aerosol surfaces. We evaluated the WRF/CUACE v1.0 model by simulating PM 2.5 , O3 , NO2 , and SO2 concentrations for January, April, July, and October (representing winter, spring, summer and autumn, respectively) in 2013, 2015, and 2017 and comparing them with ground-based observations. Secondary inorganic aerosol simulations for the North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB) were also evaluated. The model captured well the variations of PM 2.5 , O3 , and NO2 concentrations in all seasons in eastern China. However, it is difficult to accurately reproduce the variations of air pollutants over SCB, due to its deep basin terrain. The simulations of SO2 were generally reasonable in the NCP and YRD with the bias at -15.5 % and 24.55 %, respectively, while they were poor in the Pearl River Delta (PRD) and SCB. The sulfate and nitrate simulations were substantially improved by introducing heterogeneous chemical reactions into the CUACE model (e.g., change in bias from -95.0 % to 4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate in the NCP). Additionally, The WRF/CUACE v1.0 model was revealed with better performance in simulating chemical species relative to the coupled Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and CUACE model. The development of the WRF/CUACE v1.0 model represents an important step towards improving air-quality modeling and forecasts in China. [ABSTRACT FROM AUTHOR]

Drought has become a major threat to local sustainable development in dryland Asia, one of the largest grassland ecosystems in the world. However, empirical‐ and science‐based evidence regarding the extent of drought changes and the future trends of these changes in dryland Asia is variable and incomplete. Here, we first investigate the historical variations in drought conditions in dryland Asia, as measured by the drought intensity and arid area, using three widely used drought indices (the Palmer Drought Severity Index, the Standardized Precipitation Index, and the Standardized Precipitation Evapotranspiration Index). Then, we use Bayesian model averaging to reproduce the future drought conditions under two representative concentration pathways (RCP2.6 and RCP4.5) from the Coupled Model Intercomparison Project Phase 5 Earth system models. The Palmer Drought Severity Index, Standardized Precipitation Index, and Standardized Precipitation Evapotranspiration Index illustrate that dryland Asia has experienced an overall drying trend and an expansion of arid areas over the past 100 years (1901–2016). Both temperature and precipitation are projected to increase under both the 1.5 and 2.0 °C warming scenarios compared with the values from the reference period (1986–2005). The projected drought conditions in the 1.5 and 2.0 °C warming scenarios will worsen, especially across Kazakhstan and Northwest China. We found that the drought conditions under the 2.0 °C warming conditions will not be as severe as those under the 1.5 °C warming conditions due to the mitigating effect of the projected precipitation increase under RCP4.5. These results call for short‐term and long‐term mitigation and adaptation measurements for drought events in dryland Asia. Plain Language Summary: To avoid the negative impacts of climate warming, the Paris Agreement aims to pursue efforts to maintain the global warming increase at well below 1.5 and even 2.0 °C until the end of the century. Questions have been raised regarding the climate extremes in dryland Asia. Will drought issues become more severe under the context of global warming? Are the existing drought indices able to quantify and characterize the drought intensity and arid area in this region? Answers to these questions are crucial for the livelihood of millions of individuals, as these people rely on grassland biomass to feed both animals and farmers; however, the answers remain unclear. Here, we found that the projected drought severity and arid area will persistently increase under both the 1.5 and 2.0 °C global warming scenarios. We also found that the drought conditions under the 2.0 °C warming scenario will be mitigated relative to those under the 1.5 °C warming scenario due to the beneficial effect of adequate precipitation under representative concentration pathway 4.5. Kazakhstan and Northwest China might be severely affected by drought. Therefore, understanding future changes in drought conditions in dryland Asia is critical for developing adaptation measures to cope with the challenges of rapid climate change. Key Points: Future global climate change will impose severe drought issues in dryland Asia, including drying trends and expanded arid areasDrought conditions in dryland Asia under the 2.0 °C warming will not be as severe as those under the 1.5 °C warmingFuture drought conditions in Kazakhstan and Northwest China will likely be more severe in dryland Asian countries [ABSTRACT FROM AUTHOR]

Significant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation. [ABSTRACT FROM AUTHOR]

Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu Province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting model with Chemistry (WRF-Chem), two simulation experiments were executed to assess the atmospheric environment change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that power plant emissions of PM2.5, PM10, SO2, and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs (non-methane volatile organic compounds) were higher in UEIPP relative to those in MEIC, reflecting a large discrepancy in the power plant emissions over East China. In accordance with the changes in UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC, and CO. Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced. This was reflected by increased oxidizing agents, e.g., O3 and OH, thus directly strengthening the chemical production from SO2 and NOx to sulfate and nitrate, respectively, which offset the reduction of primary PM2.5 emissions especially on haze days. This study indicates the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with implications on air quality and environmental changes. [ABSTRACT FROM AUTHOR]

To quantify the physical/chemical properties, and the formation and growth processes of aerosol particles on mountainous regions in Southeast China, an intensive field campaign was conducted from April to July 2008 on the top of Mt. Huang (1840 m above mean sea level). The average particle number concentration was 2.35 × 10 3 cm − 3 , and the ultrafine particles (< 0.1 μm) represented 70.5% of the total particle number concentration. Excluding the accumulation mode particles, the average daytime particle number concentrations were prominently higher than those measured at nighttime, suggesting there was a diurnal pattern of changes between planetary boundary layer and free troposphere air. The aerosol spectra were classified into two categories: the first category (FCS) exhibited a clear diurnal cycle, with relatively higher number concentration (3.19 × 10 3 cm − 3 ), smaller sizes and air masses from the inland; the second category (SCS) presented less obvious diurnal cycle, with lower number concentration (1.88 × 10 3 cm − 3 ), larger sizes and air masses from coastal regions. Air mass sources, weather conditions, and new particle formation (NPF) events were responsible for the differences of these two particle spectra. Six NPF events were identified, which usually began at 10:00–11:00 LT, with the estimated formation rate J 10 in the range of 0.09–0.30 cm − 3 s − 1 and the growth rate at 1.42–4.53 nm h − 1 . Wind speed, sulfur dioxide and ozone concentrations were higher on NPF days than those on non-NPF days, whereas temperature, relative humidity, concentrations of nitrogen oxide and carbonic oxide were lower on NPF days. Sulfur dioxide and ozone might be main potentially precursor gases for those NPF events. The NPF events at Mt. Huang corresponded closely to a southwest winds. These results are useful for improving our understanding of the main factors controlling the variation of aerosol size distribution and NPF events in this region. [ABSTRACT FROM AUTHOR]

Investigation of the number concentration of ice nucleating particles (INP) in the deposition nucleation mode during a dust event is reported. The results discussed in this paper are the first continuous INP measurements in Xinjiang, northwest of China, over a period with a strong dust event. The average INP concentration at − 20 °C and 22% of supersaturation with respect to ice during non-dust days is found around 11 particles per liter, but it reached several hundred per liter in a dust event. A close correlation is also found between the INP number concentration with the number concentration of aerosol particles larger than 0.5 μm in diameter measured during a dust event, which means that a higher concentration of larger particles induced higher INP number concentration. Parameterizations were developed based on measurements to represent the variations of INP concentration with temperature, supersaturation, and the number concentration of aerosol particles with size larger than 0.5 μm. It should be the first ever, as we have known so far, to measure ice nuclei and aerosol properties simultaneously in a desert area and to contrast INP concentrations in dust and dust-free days, and could advancing our understanding of the effects of dust particles on ice nucleation. [ABSTRACT FROM AUTHOR]

To explore the variations in atmospheric environment over mountainous areas, measurements were made from an intensive field observation at the summit of Mt. Huang (30.13°N, 118.15°E, 1841 m above sea level), a rural site located in East China, from June to August 2011. The measurements revealed a diurnal change of surface O 3 with low concentrations during the daytime and high concentrations during the nighttime. The causes of diurnal O 3 variations over the mountain peak in East China were investigated by using a fairly comprehensive WRF-Chem and HYSPLIT4 modeling approach with observational analysis. By varying model inputs and comparing the results to a baseline modeling and actual air quality observations, it is found that nearby ozone urban/anthropogenic emission sources were contributing to a nighttime increase in mountaintop ozone levels due to a regional transport lag and residual layer effects. Positive correlation of measured O 3 and CO concentrations suggested that O 3 was associated with anthropogenic emissions. Sensitivity modeling experiments indicated that local anthropogenic emissions had little impact on the diurnal pattern of O 3 . The diurnal pattern of O 3 was mainly influenced by regional O 3 transport from the surrounding urban areas located 100–150 km away from the summit, with a lag time of 10 h for transport. [ABSTRACT FROM AUTHOR]

Daily PM 2.5 samples were collected at a suburban site of Nanjing around 2014 Chinese Spring Festival (SF) and analyzed for 18 kinds of polycyclic aromatic hydrocarbons (PAHs) by GC–MS. Comparison of PAH concentrations during different periods, with different air mass origins and under different pollution situations was done. Sources were analyzed by diagnostics ratios and principal component analysis (PCA). The threat of PAHs was assessed by BaP equivalent concentrations (BaPeq) and incremental lifetime cancer risk (ILCR). The averaged PAHs for pre-SF, SF and after SF periods were 50.6, 17.2 and 29 ng m − 3 , indicating the variations of PAH sources, with reduced traffic, industrial and construction activities during SF and gradually re-starting of them after-SF. According to PAH mass concentrations, their relative abundance to particles, ratio of PAHs (3-ring + 4-ring)/PAHs(5-ring + 6-ring), mass concentrations of combustion-derived and carcinogenic PAHs, fireworks burning is an important source for PAHs during SF. The ILCR values for Chinese New Year day were 0.68 and 3.3 per 100,000 exposed children and adults. It suggested the necessity of controlling fireworks burning during Chinese SF period which was always companied with serious regional haze pollution. PAH concentrations exhibited decreasing trend when air masses coming from the following directions as North China Plain (63.9 ng m − 3 ) > Central China (53.0 ng m − 3 ) > Shandong Peninsula (46.6 ng m − 3 ) > Northwest China (18.8 ng m − 3 ) > Sea (15.8 ng m − 3 ). For different pollution situations, they decreased as haze (44.5 ng m − 3 ) > fog-haze (28.4 ng m − 3 ) > clear (12.2 ng m − 3 ) > fog day (9.2 ng m − 3 ). Coal combustion, traffic emission, industrial processes and petroleum (only for non-SF holiday periodss) were the main sources of PM 2.5 associated PAHs. Fireworks burning contributed 14.0% of PAHs during SF period. Directly measurement of PAHs from fireworks burning is urgently needed for source apportionment studies in the future. [ABSTRACT FROM AUTHOR]

A newly built static vacuum water vapor diffusion chamber was built to measure the concentration of ice nuclei (INs) at the top of Huangshan (the Yellow Mountains) in Southeast China. The experiments were conducted under temperatures between − 15 °C and − 23 °C and supersaturations with respect to ice between 4% and 25%. The results show that the average IN concentration was in the range of 0.27 to 7.02 L − 1 , when the temperature was varied from − 15 °C to − 23 °C. The changes in IN concentrations with time were correlated with the change of number concentration of the aerosol particles of 0.5–20 μm in diameter. The square correlation coefficients (R 2 ) between IN and coarse aerosol particles (0.5–20 μm in diameter) were all higher than 0.60, much higher than that (0.10) between IN and smaller particles (0.01–0.5 μm). The concentration of ice nuclei at 14:00 LST was significantly higher than that at 08:00 LST, which is correlated with the diurnal variation of the concentration of aerosol particles. A parametric equation was developed based on measurements to represent the variations of IN concentration with temperature and supersaturation. [ABSTRACT FROM AUTHOR]