用于 CMIP6 的加拿大多变量降尺度气候方案（CanDCS-M6）

IF 3.3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2024-07-10 DOI:10.1002/gdj3.257

Stephen R. Sobie, Dhouha Ouali, Charles L. Curry, Francis W. Zwiers

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引用次数: 0

摘要

第六次耦合模式相互比较项目（CMIP6）中的全球气候模式在加拿大范围内的降尺度统计模拟，采用了新的多元方法和改进的观测目标数据集，可用于 26 个模式。这些新的降尺度情景包括加拿大全境分辨率为 1/12° 的降水、最高气温和最低气温日模拟。26 个缩小尺度的全球气候模型中，每个模型的模拟都跨越了一个历史时期（1950-2014 年）和三个未来共享社会经济路径（SSP），分别代表 2015 年至 2100 年的低排放（SSP1 2.6）、中排放（SSP2 4.5）和高排放（SSP5 8.5）。对加拿大多变量降尺度方法的评估结果表明，与之前使用的单变量降尺度方法相比，多变量和复合气候指数的复制性能有所提高。这个用于 CMIP6 的加拿大多元降尺度气候情景（CanDCS-M6）数据集旨在促进气候影响评估、水文建模和用于展示气候预测的分析工具。

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Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6)

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Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6)

Canada-wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter-comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio-economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections.

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.