Hebatallah M Abdelmoaty, Chandra Rupa Rajulapati, Sofia D Nerantzaki, Simon Michael Papalexiou
{"title":"Bias-corrected high-resolution temperature and precipitation projections for Canada.","authors":"Hebatallah M Abdelmoaty, Chandra Rupa Rajulapati, Sofia D Nerantzaki, Simon Michael Papalexiou","doi":"10.1038/s41597-025-04396-z","DOIUrl":null,"url":null,"abstract":"<p><p>High-resolution precipitation and temperature projections are indispensable for informed decision-making, risk assessment, and planning. Here, we have developed an extensive database (SPQM-CMIP6-CAN) of high-resolution (0.1°) precipitation and temperature projections extending till 2100 at a daily scale for Canada. We employed a novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) projections for four Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing the observed marginal properties, trends, and variability according to future scenarios, while maintaining a smooth transition from observations to projected simulations. The SPQM-CMIP6-CAN database encompasses 693 simulations derived from 34 diverse climate models for precipitation. Similarly, for temperature projections, our database comprises 581 simulations from 27 climate models. These projections are valuable for hydrological, environmental, and ecological studies, offering a comprehensive resource for analyses within these domains. Furthermore, these projections serve as a vital tool for the quantification of uncertainties arising from climate models, their variant configurations, and future scenarios.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"191"},"PeriodicalIF":6.9000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787352/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04396-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
High-resolution precipitation and temperature projections are indispensable for informed decision-making, risk assessment, and planning. Here, we have developed an extensive database (SPQM-CMIP6-CAN) of high-resolution (0.1°) precipitation and temperature projections extending till 2100 at a daily scale for Canada. We employed a novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) projections for four Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing the observed marginal properties, trends, and variability according to future scenarios, while maintaining a smooth transition from observations to projected simulations. The SPQM-CMIP6-CAN database encompasses 693 simulations derived from 34 diverse climate models for precipitation. Similarly, for temperature projections, our database comprises 581 simulations from 27 climate models. These projections are valuable for hydrological, environmental, and ecological studies, offering a comprehensive resource for analyses within these domains. Furthermore, these projections serve as a vital tool for the quantification of uncertainties arising from climate models, their variant configurations, and future scenarios.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.