{"title":"加利福尼亚州逐年变化的高阶内部变异模式印记","authors":"Shiheng Duan, Giuliana Pallotta, Céline Bonfils","doi":"10.1038/s43247-024-01594-2","DOIUrl":null,"url":null,"abstract":"Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems. Machine learning techniques informed by CMIP6 models and historical observations suggest that higher order internal climate variability had a significant influence on streamflow, and therefore water resources, in California between 1981 and 2015.","PeriodicalId":10530,"journal":{"name":"Communications Earth & Environment","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43247-024-01594-2.pdf","citationCount":"0","resultStr":"{\"title\":\"Higher-order internal modes of variability imprinted in year-to-year California streamflow changes\",\"authors\":\"Shiheng Duan, Giuliana Pallotta, Céline Bonfils\",\"doi\":\"10.1038/s43247-024-01594-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems. Machine learning techniques informed by CMIP6 models and historical observations suggest that higher order internal climate variability had a significant influence on streamflow, and therefore water resources, in California between 1981 and 2015.\",\"PeriodicalId\":10530,\"journal\":{\"name\":\"Communications Earth & Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s43247-024-01594-2.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications Earth & Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.nature.com/articles/s43247-024-01594-2\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Earth & Environment","FirstCategoryId":"93","ListUrlMain":"https://www.nature.com/articles/s43247-024-01594-2","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Higher-order internal modes of variability imprinted in year-to-year California streamflow changes
Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems. Machine learning techniques informed by CMIP6 models and historical observations suggest that higher order internal climate variability had a significant influence on streamflow, and therefore water resources, in California between 1981 and 2015.
期刊介绍:
Communications Earth & Environment is an open access journal from Nature Portfolio publishing high-quality research, reviews and commentary in all areas of the Earth, environmental and planetary sciences. Research papers published by the journal represent significant advances that bring new insight to a specialized area in Earth science, planetary science or environmental science.
Communications Earth & Environment has a 2-year impact factor of 7.9 (2022 Journal Citation Reports®). Articles published in the journal in 2022 were downloaded 1,412,858 times. Median time from submission to the first editorial decision is 8 days.