Geoscientific Model Development最新文献

{"title":"Balloon drift estimation and improved position estimates for radiosondes","authors":"Ulrich Voggenberger, L. Haimberger, F. Ambrogi, Paul Poli","doi":"10.5194/gmd-17-3783-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3783-2024","url":null,"abstract":"Abstract. When comparing model output with historical radiosonde observations, it is usually assumed that a radiosonde has risen exactly above its starting point and has not been displaced by wind. This changed only relatively recently with the availability of Global Navigation Satellite System (GNSS) receivers aboard radiosondes in the late 1990s, but even then the balloon trajectory data were often not transmitted, although this information was the basis for estimating the wind in the first place. Depending on the conditions and time of year, radiosondes can sometimes drift a few hundred kilometres, particularly at the middle latitudes during the winter months. The position errors can lead to non-negligible representation errors when the corresponding observations are assimilated. This paper presents a methodology to compute changes in the balloon position during its vertical ascent, using only limited information, such as the vertical profile of wind contained in the historical observation reports. The sensitivity of the method to various parameters is investigated, such as the vertical resolution of the input data, the assumption about the vertical ascent speed of the balloon, and the departure of the surface of Earth from a sphere. The paper considers modern GNSS sonde data reports for validation, for which the full trajectory of the balloon is available, alongside the reported wind. Evaluation is also conducted by comparison with ERA5 and by conducting low-resolution data assimilation experiments. Overall, the results indicate that the trajectory of the radiosondes can be accurately reconstructed from original data of varying vertical resolutions and that the more accurate balloon position reduces representation errors and, in some cases, systematic errors.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"65 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0","authors":"Jishi Zhang, Peter Bogenschutz, Qi Tang, P. Cameron-smith, Chengzhu Zhang","doi":"10.5194/gmd-17-3687-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3687-2024","url":null,"abstract":"Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes","authors":"A. Tai, David H. Y. Yung, T. Lam","doi":"10.5194/gmd-17-3733-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3733-2024","url":null,"abstract":"Abstract. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2>0.85 and R2>0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr−1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of +7 % in global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O3 is present and biases are reduced, especially for regions with high O3 damage. An increase in atmospheric CO2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emission ensemble approach to improve the development of multi-scale emission inventories","authors":"P. Thunis, J. Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, L. Gawuc, K. Szymankiewicz, Diego Guizardi, Monica Crippa, Susana López-Aparicio, Marc Guevara, A. de Meij, Sabine Schindlbacher, A. Clappier","doi":"10.5194/gmd-17-3631-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3631-2024","url":null,"abstract":"Abstract. Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"38 s171","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?","authors":"L. Menut, Bertrand Bessagnet, A. Cholakian, G. Siour, S. Mailler, R. Pennel","doi":"10.5194/gmd-17-3645-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3645-2024","url":null,"abstract":"Abstract. Meteorological and chemical modelling at the regional scale often involve the nudging of the modelled meteorology towards reanalysis fields and meteo-chemical coupling to properly consider the interactions between aerosols, clouds and radiation. Both types of processes can change the meteorology, but not for the same reasons and not necessarily in the same way. To assess the possible interactions between nudging and online coupling, several simulations are carried out with the WRF–CHIMERE (Weather Research and Forecasting) model in its offline and online configurations. Through comparison with measurements, we show that the use of nudging significantly improves the model performances. We also show that coupling changes the results much less than nudging. Finally, we show that when nudging is used, it limits the variability in the results due to coupling.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"117 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0","authors":"Xiaohui Zhong, Xing Yu, Hao Li","doi":"10.5194/gmd-17-3667-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3667-2024","url":null,"abstract":"Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"101 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method","authors":"Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, Hongliang Zhang","doi":"10.5194/gmd-17-3617-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3617-2024","url":null,"abstract":"Abstract. Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM2.5 by −19.25 to −2.66 µg m−3 in 2019, especially in winter and spring and during high-PM2.5 events. Secondary organic components showed the largest contribution to the PM2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"360 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec","authors":"S. Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, L. Minet, A. Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, M. Hatzopoulou","doi":"10.5194/gmd-17-3579-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3579-2024","url":null,"abstract":"Abstract. Air pollution is a major health hazard, and while air quality overall has been improving in industrialized nations, pollution is still a major economic and public health issue, with some species, such as ozone (O3), still exceeding the standards set by governing agencies. Chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D v1.11 CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model's capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales. The simulation by the model included three nested domains, at horizontal resolutions of 9 km by 9 km and 3 km by 3 km, as well as two 1 km by 1 km domains covering the cities of Montréal and Québec. We find that the model captures the spatial variability and seasonal effects and, to a lesser extent, the hour-by-hour or day-to-day temporal variability for a fixed location. The model at both the 3 km and the 1 km resolution struggled to capture high-frequency temporal variability and showed large variabilities in correlation and bias from site to site. When comparing the biases and correlation at a site-wide scale, the 3 km domain showed slightly higher correlation for carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO), while ozone (O3), sulfur dioxide (SO2), and PM2.5 showed slight increases in correlation at the 1 km domain. The performance of the Polair3D model was in line with other models over Canada and comparable to Polair3D's performance over Europe.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"52 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1","authors":"Rohith Thundathil, F. Zus, G. Dick, J. Wickert","doi":"10.5194/gmd-17-3599-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3599-2024","url":null,"abstract":"Abstract. In this study, we have incorporated tropospheric gradient observations from a Global Navigation Satellite Systems (GNSS) ground station network into the Weather Research and Forecasting (WRF) model through a newly developed observation operator. The experiments aim at testing the functionality of the developed observation operator and at analyzing the impact of tropospheric gradients on the sophisticated data assimilation (DA) system. The model was configured for a 0.1° mesh over Germany with 50 vertical levels up to 50 hPa. Our initial conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25° resolution, and conventional observations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), restricted to mainly surface stations and radiosondes. We selected approximately 100 GNSS stations with high data quality and availability covering Germany. We performed DA every 6 h for June and July 2021. Four experiments were conducted: (1) a control run assimilating only conventional observations; (2) an impact run assimilating zenith total delays (ZTDs) on top of the control run; (3) an impact gradient run assimilating ZTDs and gradients on top of the control run; and (4) a gradient run assimilating only gradients on top of the control run. The error for the impact run was reduced by 32 % and 10 % for ZTDs and gradients, whereas the error for the impact gradient run was reduced by 35 % and 18 %, respectively. The gradient errors for the gradient run were nearly equal to those of the impact gradient. Overall, the newly developed operator for the WRFDA system works as intended. In particular, the combined assimilation of gradients and the ZTDs led to a notable improvement in the humidity field at altitudes above 2.5 km. With the operator codes developed and freely available to the WRF users, we aim to trigger further GNSS tropospheric gradient assimilation studies.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"124 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model","authors":"T. Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shenmin Zhang, Hailong Wang, P. Ma","doi":"10.5194/gmd-17-3507-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3507-2024","url":null,"abstract":"Abstract. Emissions of anthropogenic aerosol and their precursors are often prescribed in global aerosol models. Most of these emissions are spatially heterogeneous at model grid scales. When remapped from low-resolution data, the spatial heterogeneity in emissions can be lost, leading to large errors in the simulation. It can also cause the conservation problem if non-conservative remapping is used. The default anthropogenic emission treatment in the Energy Exascale Earth System Model (E3SM) is subject to both problems. In this study, we introduce a revised emission treatment for the E3SM Atmosphere Model (EAM) that ensures conservation of mass fluxes and preserves the original emission heterogeneity at the model-resolved grid scale. We assess the error estimates associated with the default emission treatment and the impact of improved heterogeneity and mass conservation in both globally uniform standard-resolution (∼ 165 km) and regionally refined high-resolution (∼ 42 km) simulations. The default treatment incurs significant errors near the surface, particularly over sharp emission gradient zones. Much larger errors are observed in high-resolution simulations. It substantially underestimates the aerosol burden, surface concentration, and aerosol sources over highly polluted regions, while it overestimates these quantities over less-polluted adjacent areas. Large errors can persist at higher elevation for daily mean estimates, which can affect aerosol extinction profiles and aerosol optical depth (AOD). We find that the revised treatment significantly improves the accuracy of the aerosol emissions from surface and elevated sources near sharp spatial gradient regions, with significant improvement in the spatial heterogeneity and variability of simulated surface concentration in high-resolution simulations. In the next-generation E3SM running at convection-permitting scales where the resolved spatial heterogeneity is significantly increased, the revised emission treatment is expected to better represent the aerosol emissions as well as their lifecycle and impacts on climate.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"15 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}