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

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 dynami...

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Bibliographic Details
Published in:Geoscience data journal 2023-10, Vol.10 (4), p.429-446
Main Authors: Lauer, Abraham, Devaney, Jesse, Kieu, Chanh, Kravitz, Ben, O'Brien, Travis A., Robeson, Scott M., Staten, Paul W., Vu, The Anh
Format: Article
Language:English
Subjects:
Air temperature
Atmospheric research
Bias
Climate change
Climate studies
Convection
Datasets
downscaling
Energy consumption
Energy usage
Environmental protection
ENVIRONMENTAL SCIENCES
midwest
Precipitation
Public health
Regional planning
Simulation
Temperature
Temperature gradients
Trends
Urban heat islands
Urban planning
Value added
Water resources
Weather
Weather forecasting
WRF
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Summary: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.
ISSN:2049-6060
2049-6060
DOI:10.1002/gdj3.188