Huan Guo, Levi G. Silvers, David Paynter, Wenhao Dong, Songmiao Fan, Xianwen Jing, Ryan Kramer, Kristopher Rand, Kentaroh Suzuki, Yuying Zhang, Ming Zhao
{"title":"使用卫星模拟器包COSP评估GFDL AM4.0中不同微物理参数化的云","authors":"Huan Guo, Levi G. Silvers, David Paynter, Wenhao Dong, Songmiao Fan, Xianwen Jing, Ryan Kramer, Kristopher Rand, Kentaroh Suzuki, Yuying Zhang, Ming Zhao","doi":"10.1029/2024EA004053","DOIUrl":null,"url":null,"abstract":"<p>We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two-moment Morrison-Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under-prediction of total and low-level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over-prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high-level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004053","citationCount":"0","resultStr":"{\"title\":\"Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP\",\"authors\":\"Huan Guo, Levi G. Silvers, David Paynter, Wenhao Dong, Songmiao Fan, Xianwen Jing, Ryan Kramer, Kristopher Rand, Kentaroh Suzuki, Yuying Zhang, Ming Zhao\",\"doi\":\"10.1029/2024EA004053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two-moment Morrison-Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under-prediction of total and low-level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over-prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high-level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.</p>\",\"PeriodicalId\":54286,\"journal\":{\"name\":\"Earth and Space Science\",\"volume\":\"12 6\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004053\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth and Space Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2024EA004053\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space Science","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024EA004053","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP
We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two-moment Morrison-Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under-prediction of total and low-level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over-prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high-level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.
期刊介绍:
Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.