Spatial datasets of CMIP6 climate change projections for Canada and the United States

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-02-01 DOI:10.1016/j.dib.2024.111246

Daniel W. McKenney , John H. Pedlar , Kevin Lawrence , Stephen R. Sobie , Kaitlin DeBoer , Tiziana Brescacin

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引用次数: 0

Abstract

Geospatial climate change projections are critical for assessing climate change impacts and adaptations across a wide range of disciplines. Here we present monthly-based grids of climate change projections at a 2-km resolution covering Canada and the United States. These data products are based on outputs from the 6th Coupled Model Intercomparison Project (CMIP6) and include projections for 13 General Circulation Models (GCMs), three Shared Socio-economic Pathways (SSP1 2.6, SSP2 4.5, and SSP5 8.5), four 30-year time periods (2011–2040, 2021–2050, 2041–2070, and 2071–2100), and a suite of climate variables, including monthly maximum and minimum temperature, precipitation, climate moisture index, and various bioclimatic summaries. The products employ a delta downscaling method, which combines historical normal values at climate stations with broad-scale change projections (or deltas) from GCMs, followed by spatial interpolation using ANUSPLIN. Various quality control efforts, described herein, were undertaken to ensure that the final products provided reasonable estimates of future climate.

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加拿大和美国CMIP6气候变化预估空间数据集。

地理空间气候变化预估对于评估气候变化影响和适应各种学科至关重要。在这里，我们以每月2公里的分辨率提供了覆盖加拿大和美国的气候变化预测网格。这些数据产品基于第6个耦合模式比对项目（CMIP6）的输出，包括13个一般环流模式（GCMs）、3个共享社会经济路径（SSP1 2.6、SSP2 4.5和SSP5 8.5）、4个30年周期（2011-2040、2021-2050、2041-2070和2071-2100）的预估，以及一系列气候变量，包括月最高和最低温度、降水、气候湿度指数和各种生物气候摘要。这些产品采用delta降尺度方法，将气候站的历史正常值与来自gcm的大尺度变化预估（或delta）相结合，然后使用ANUSPLIN进行空间插值。本文所述的各种质量控制工作是为了确保最终产品提供对未来气候的合理估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.