校正偏倚后的加拿大高分辨率温度和降水预测。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Hebatallah M Abdelmoaty, Chandra Rupa Rajulapati, Sofia D Nerantzaki, Simon Michael Papalexiou
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引用次数: 0

摘要

高分辨率降水和气温预测对于知情决策、风险评估和规划是不可或缺的。在此,我们开发了一个庞大的数据库(SPQM-CMIP6-CAN),其中包含加拿大直至 2100 年的日尺度高分辨率(0.1°)降水和气温预测。我们采用了一种新颖的半参数量化绘图(SPQM)方法,对耦合模式相互比较项目第六阶段(CMIP6)的四种共享社会经济路径预测进行偏差校正。SPQM 简单而稳健,可根据未来情景再现观测到的边际属性、趋势和变异性,同时保持从观测到预测模拟的平稳过渡。SPQM-CMIP6-CAN 数据库包含 34 种不同气候模式的 693 次降水模拟。同样,在温度预测方面,我们的数据库包括来自 27 个气候模式的 581 个模拟结果。这些预测对水文、环境和生态研究非常有价值,为这些领域的分析提供了全面的资源。此外,这些预测还是量化气候模型、其变异配置和未来情景所产生的不确定性的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias-corrected high-resolution temperature and precipitation projections for Canada.

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
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.
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