Identifying weather regimes for regional‐scale stochastic weather generators
Identifying weather regimes for regional‐scale stochastic weather generators
复制标题
DOI：
10.1002/joc.6969
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
N. Najibi;S. Mukhopadhyay;S. Steinschneider
中科院分区：
文献类型：
--
作者：
N. Najibi;S. Mukhopadhyay;S. Steinschneider
最近，基于天气状况的随机天气发生器(WR-SWGs)被提出作为一种工具，以更好地了解多部门对深度不确定气候变化的脆弱性。WR-SWGs可以区分和模拟在未来预测中具有不同程度不确定性的不同类型的气候变化，包括热力学变化(例如，气温上升、极端降水的克劳修斯-克拉珀龙尺度)和动态变化(例如，环流和风暴路径的变化)。这些模型要求准确确定既代表大气环流的历史模式又代表看似合理的未来模式的WR，同时保留复杂的天气过程时空变异性。这项研究提出了一种新的框架，基于广泛地理区域的WR-SWG性能来识别此类WR，并将该框架应用于加利福尼亚州的一个案例研究。我们测试了WR-SWG设计的两个组件，包括用于WR识别的方法(隐马尔可夫模型(HMM)与K-均值聚类)和WR的数量。对于这些组件的不同组合，我们使用14个指标评估了寒冷季节期间加州13个主要河流流域的多站点WR-SWG的性能。结果表明，使用HMM识别的少量WR(4-5)时，性能最好。然后，我们将基于WR-SWG性能选择的WR的数量与使用大气场亚稳态分析确定的区域的数量进行比较。结果表明，两种方法在亚稳区的数目上有很强的一致性，这表明亚稳区的使用可以为WR-SWG的设计提供参考。最后，我们讨论了扩展这一框架的潜力，以增加WR-SWG的设计参数和空间尺度。
Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.