Nasser Najibi , Alejandro J. Perez , Wyatt Arnold , Andrew Schwarz , Romain Maendly , Scott Steinschneider
{"title":"用于加利福尼亚州基于过程的自下而上气候风险评估的全州范围、基于天气时间的随机天气生成器 - 第 I 部分:模型评估","authors":"Nasser Najibi , Alejandro J. Perez , Wyatt Arnold , Andrew Schwarz , Romain Maendly , Scott Steinschneider","doi":"10.1016/j.cliser.2024.100489","DOIUrl":null,"url":null,"abstract":"<div><p>This study is the first of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 1 of this series, we present the details of model development and validation. The model is based on the identification and simulation of weather regimes, or large-scale patterns of atmospheric flow, which are then used to condition the simulation of local, daily weather at a 6 km resolution across the state. We conduct a thorough validation of a baseline, 1000-year model simulation to evaluate its ability to accurately simulate daily precipitation and minimum and maximum temperature at various spatial scales (grid cell, river basin) and temporal scales (daily, event-based, monthly, annual, inter-annual to decadal). Results show that the model effectively reproduces a large suite of climate statistics at these scales across the entire state, including moments, spells, dry and wet extremes, and extreme hot and cold periods. Moreover, the model successfully maintains spatial correlations and inter-variable relationships, enabling the use of model simulations in hydrologic and water resources analyses that span multiple watersheds across California. The weather generator can simulate physically plausible extreme events (e.g., multi-day extreme precipitation and severe drought) that extend beyond the worst case conditions observed historically, independent of climate change. Thus, the baseline simulation can be used to understand the impacts of natural climate variability on both flood and drought risk in regional water systems. Scenarios of climate change are discussed in Part 2.</p></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"34 ","pages":"Article 100489"},"PeriodicalIF":4.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240588072400044X/pdfft?md5=ad013a1ec283b2ae097b7f24b0aedf03&pid=1-s2.0-S240588072400044X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part I: Model evaluation\",\"authors\":\"Nasser Najibi , Alejandro J. Perez , Wyatt Arnold , Andrew Schwarz , Romain Maendly , Scott Steinschneider\",\"doi\":\"10.1016/j.cliser.2024.100489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study is the first of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 1 of this series, we present the details of model development and validation. The model is based on the identification and simulation of weather regimes, or large-scale patterns of atmospheric flow, which are then used to condition the simulation of local, daily weather at a 6 km resolution across the state. We conduct a thorough validation of a baseline, 1000-year model simulation to evaluate its ability to accurately simulate daily precipitation and minimum and maximum temperature at various spatial scales (grid cell, river basin) and temporal scales (daily, event-based, monthly, annual, inter-annual to decadal). Results show that the model effectively reproduces a large suite of climate statistics at these scales across the entire state, including moments, spells, dry and wet extremes, and extreme hot and cold periods. Moreover, the model successfully maintains spatial correlations and inter-variable relationships, enabling the use of model simulations in hydrologic and water resources analyses that span multiple watersheds across California. The weather generator can simulate physically plausible extreme events (e.g., multi-day extreme precipitation and severe drought) that extend beyond the worst case conditions observed historically, independent of climate change. Thus, the baseline simulation can be used to understand the impacts of natural climate variability on both flood and drought risk in regional water systems. 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A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part I: Model evaluation
This study is the first of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 1 of this series, we present the details of model development and validation. The model is based on the identification and simulation of weather regimes, or large-scale patterns of atmospheric flow, which are then used to condition the simulation of local, daily weather at a 6 km resolution across the state. We conduct a thorough validation of a baseline, 1000-year model simulation to evaluate its ability to accurately simulate daily precipitation and minimum and maximum temperature at various spatial scales (grid cell, river basin) and temporal scales (daily, event-based, monthly, annual, inter-annual to decadal). Results show that the model effectively reproduces a large suite of climate statistics at these scales across the entire state, including moments, spells, dry and wet extremes, and extreme hot and cold periods. Moreover, the model successfully maintains spatial correlations and inter-variable relationships, enabling the use of model simulations in hydrologic and water resources analyses that span multiple watersheds across California. The weather generator can simulate physically plausible extreme events (e.g., multi-day extreme precipitation and severe drought) that extend beyond the worst case conditions observed historically, independent of climate change. Thus, the baseline simulation can be used to understand the impacts of natural climate variability on both flood and drought risk in regional water systems. Scenarios of climate change are discussed in Part 2.
期刊介绍:
The journal Climate Services publishes research with a focus on science-based and user-specific climate information underpinning climate services, ultimately to assist society to adapt to climate change. Climate Services brings science and practice closer together. The journal addresses both researchers in the field of climate service research, and stakeholders and practitioners interested in or already applying climate services. It serves as a means of communication, dialogue and exchange between researchers and stakeholders. Climate services pioneers novel research areas that directly refer to how climate information can be applied in methodologies and tools for adaptation to climate change. It publishes best practice examples, case studies as well as theories, methods and data analysis with a clear connection to climate services. The focus of the published work is often multi-disciplinary, case-specific, tailored to specific sectors and strongly application-oriented. To offer a suitable outlet for such studies, Climate Services journal introduced a new section in the research article type. The research article contains a classical scientific part as well as a section with easily understandable practical implications for policy makers and practitioners. The journal''s focus is on the use and usability of climate information for adaptation purposes underpinning climate services.