用于加拿大防暑降温的偏差调整和降尺度湿度预测

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Kenneth Kin Cheung Chow, Housseyni Sankaré, Emilia P. Diaconescu, Trevor Q. Murdock, Alex J. Cannon
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引用次数: 0

摘要

为帮助加拿大公共卫生和安全系统做好适应气候变化的准备工作,我们计算了湿度指数(humidex)和三个基于阈值的湿度指数(湿度指数大于 30、35 和 40 的年天数),这些指数是对加拿大气候变化预测的多模型集合进行的。该集合由 19 个耦合模式相互比较项目第 6 阶段(CMIP6)全球气候模式各运行一次组成,提供了从 1950 年开始的历史模拟和到 2100 年共享社会经济路径(SSP)的未来预测:SSP1-2.6、SSP2-4.5 和 SSP5-8.5。使用多变量偏差校正-N 维概率密度函数转换(MBCn),以 ERA5-Land 的每小时数据为目标数据集,并按照 Diaconescu 等人(2023 年;《国际气候学杂志》,43,837)提出的方法,从每日气候模式输出中计算湿度,对每个集合成员进行了偏差调整和统计降尺度。本文详细介绍了数据制作步骤,包括评估目标历史网格数据和选择降尺度方法,并介绍了本世纪末的一些湿度预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Bias-adjusted and downscaled humidex projections for heat preparedness and adaptation in Canada

Bias-adjusted and downscaled humidex projections for heat preparedness and adaptation in Canada

Bias-adjusted and downscaled humidex projections for heat preparedness and adaptation in Canada

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold-based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi-model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5 and SSP5-8.5. Each ensemble member was bias-adjusted and statistically downscaled using the Multivariate bias correction—N-dimensional probability density function transform (MBCn) with hourly data from ERA5-Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
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.
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