Nicolás A. Vásquez, Pablo A. Mendoza, Wouter J. M. Knoben, Louise Arnal, Miguel Lagos-Zúñiga, Martyn Clark, Ximena Vargas
{"title":"气候影响评估中时间分层对 GCM 偏差校正的关键作用","authors":"Nicolás A. Vásquez, Pablo A. Mendoza, Wouter J. M. Knoben, Louise Arnal, Miguel Lagos-Zúñiga, Martyn Clark, Ximena Vargas","doi":"10.1029/2023EF004242","DOIUrl":null,"url":null,"abstract":"<p>Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 8","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EF004242","citationCount":"0","resultStr":"{\"title\":\"The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments\",\"authors\":\"Nicolás A. Vásquez, Pablo A. Mendoza, Wouter J. M. Knoben, Louise Arnal, Miguel Lagos-Zúñiga, Martyn Clark, Ximena Vargas\",\"doi\":\"10.1029/2023EF004242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.</p>\",\"PeriodicalId\":48748,\"journal\":{\"name\":\"Earths Future\",\"volume\":\"12 8\",\"pages\":\"\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EF004242\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earths Future\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2023EF004242\",\"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":"Earths Future","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2023EF004242","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}
The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments
Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.
期刊介绍:
Earth’s Future: A transdisciplinary open access journal, Earth’s Future focuses on the state of the Earth and the prediction of the planet’s future. By publishing peer-reviewed articles as well as editorials, essays, reviews, and commentaries, this journal will be the preeminent scholarly resource on the Anthropocene. It will also help assess the risks and opportunities associated with environmental changes and challenges.