A convection-permitting dynamically downscaled dataset over the Midwestern United States

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Geoscience Data Journal Pub Date : 2023-05-11 DOI:10.1002/gdj3.188
Abraham Lauer, Jesse Devaney, Chanh Kieu, Ben Kravitz, Travis A. O'Brien, Scott M. Robeson, Paul W. Staten, The Anh Vu
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Abstract

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

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美国中西部上空允许对流的动态缩小数据集
气候变化预计将在全球和区域尺度上产生深远的影响,但局部影响很难从粗分辨率气候研究中确定。动态降尺度可以在局部尺度上提供对未来气候预测的见解。在这里,我们为印第安纳州及其周边地区提供了一个新的动态缩小数据集。社区地球系统模式(CESM)第1版的输出使用天气研究与预报模式(WRF)进行了缩小。模拟以24小时的重新初始化策略和12小时的自旋窗口运行。WRF输出使用改进的分位数映射方法对国家环境保护中心/国家大气研究中心40年再分析项目(NCEP)进行了偏差校正。偏差校正后的2米空气温度和累积降水是最初的重点,未来发布的版本还计划增加其他变量。区域气候变化信号与更大范围的全球研究结果一致,并且在结果数据集中可以看到局部的精细尺度特征,如城市热岛、锋面通道和地形温度梯度。该高分辨率气候数据集可用于关注整个领域影响的下游应用,如城市规划、能源使用、水资源、农业和公共卫生。
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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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