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

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Monthly Weather Review Pub Date : 2023-06-29 DOI:10.1175/mwr-d-22-0338.1
Benjamin W. Green, E. Sinsky, Shan Sun, V. Tallapragada, G. Grell
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引用次数: 0

摘要

美国国家海洋和大气管理局一直在统一预报系统框架下开发一个全耦合的地球系统模型,该模型将负责在0-35天内进行全球(整体)预报。在7年的时间里,总共168个病例的开发涉及了几个原型运行,包括两个月的初始化。本研究利用这些现有的(基线)原型来隔离对流、微物理和行星边界层在35天预报中替代(一次一个)参数化的影响。通过这些物理敏感性实验,我们发现,基于平均状态偏差和麦登-朱利安涛动、降水和2米温度的技能得分等几个指标,亚季节长度耦合模型的特定配置没有统一的更好或更差。重要的是,许多“一阶”偏差(例如对流对降水的影响)的空间格局在第一周结束和第3-4周之间非常相似,这表明一些亚季节偏差可以通过在更短的时间尺度上进行调整来减轻。这一结果虽然是第一次在不同物理方案的亚季节预测背景下显示出来,但与气候模式的发现一致,即在多年平均值中明显的一些平均状态偏差可以在几天内表现出来。另一个使用不同基线的对流参数化测试表明,试图在建模系统之间或系统内部推广结果可能是错误的。测试物理方案时推广结果的局限性在建模系统中最为严重,这些系统经历了来自无数贡献者的快速、激烈的开发——就像(准)操作环境中的情况一样。
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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.
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
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.
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