Benjamin W. Green, E. Sinsky, Shan Sun, V. Tallapragada, G. Grell
{"title":"亚季节统一预报系统模拟对对流、云微物理和行星边界层参数化变化的敏感性","authors":"Benjamin W. Green, E. Sinsky, Shan Sun, V. Tallapragada, G. Grell","doi":"10.1175/mwr-d-22-0338.1","DOIUrl":null,"url":null,"abstract":"\nNOAA has been developing a fully-coupled Earth system model under the Unified Forecast System framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.\nThis study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden-Julian Oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3-4, indicating that some subseasonal biases may be mitigated through tuning at shorter timescales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multi-year averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. The limitations of generalizing results when testing physics schemes are most acute in modeling systems that undergo rapid, intense development from myriad contributors – as is the case in (quasi) operational environments.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensitivities of Subseasonal Unified Forecast System Simulations to Changes in Parameterizations of Convection, Cloud Microphysics, and Planetary Boundary Layer\",\"authors\":\"Benjamin W. Green, E. Sinsky, Shan Sun, V. Tallapragada, G. Grell\",\"doi\":\"10.1175/mwr-d-22-0338.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nNOAA has been developing a fully-coupled Earth system model under the Unified Forecast System framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.\\nThis study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden-Julian Oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3-4, indicating that some subseasonal biases may be mitigated through tuning at shorter timescales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multi-year averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. 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Sensitivities of Subseasonal Unified Forecast System Simulations to Changes in Parameterizations of Convection, Cloud Microphysics, and Planetary Boundary Layer
NOAA has been developing a fully-coupled Earth system model under the Unified Forecast System framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.
This study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden-Julian Oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3-4, indicating that some subseasonal biases may be mitigated through tuning at shorter timescales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multi-year averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. The limitations of generalizing results when testing physics schemes are most acute in modeling systems that undergo rapid, intense development from myriad contributors – as is the case in (quasi) operational environments.
期刊介绍:
Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.