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Abstract 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.
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A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States
Abstract Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.
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- Award ID(s):
- 1702273
- PAR ID:
- 10455606
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 55
- Issue:
- 8
- ISSN:
- 0043-1397
- Page Range / eLocation ID:
- p. 6923-6945
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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