A convection‐permitting dynamically downscaled dataset over the Midwestern United States
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 n...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Geoscience Data Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/gdj3.188 |
| _version_ | 1797660773439242240 |
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| author | Abraham Lauer Jesse Devaney Chanh Kieu Ben Kravitz Travis A. O'Brien Scott M. Robeson Paul W. Staten The Anh Vu |
| author_facet | Abraham Lauer Jesse Devaney Chanh Kieu Ben Kravitz Travis A. O'Brien Scott M. Robeson Paul W. Staten The Anh Vu |
| author_sort | Abraham Lauer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:34:52Z |
| format | Article |
| id | doaj.art-4f9a1f78fe9042ab96c70891c27f8b00 |
| institution | Directory Open Access Journal |
| issn | 2049-6060 |
| language | English |
| last_indexed | 2024-03-11T18:34:52Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geoscience Data Journal |
| spelling | doaj.art-4f9a1f78fe9042ab96c70891c27f8b002023-10-13T04:28:49ZengWileyGeoscience Data Journal2049-60602023-10-0110442944610.1002/gdj3.188A convection‐permitting dynamically downscaled dataset over the Midwestern United StatesAbraham Lauer0Jesse Devaney1Chanh Kieu2Ben Kravitz3Travis A. O'Brien4Scott M. Robeson5Paul W. Staten6The Anh Vu7Department of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Geography Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USADepartment of Earth and Atmospheric Sciences Indiana University Bloomington Indiana USAAbstract 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.https://doi.org/10.1002/gdj3.188climate changedownscalingmidwestWRF |
| spellingShingle | Abraham Lauer Jesse Devaney Chanh Kieu Ben Kravitz Travis A. O'Brien Scott M. Robeson Paul W. Staten The Anh Vu A convection‐permitting dynamically downscaled dataset over the Midwestern United States Geoscience Data Journal climate change downscaling midwest WRF |
| title | A convection‐permitting dynamically downscaled dataset over the Midwestern United States |
| title_full | A convection‐permitting dynamically downscaled dataset over the Midwestern United States |
| title_fullStr | A convection‐permitting dynamically downscaled dataset over the Midwestern United States |
| title_full_unstemmed | A convection‐permitting dynamically downscaled dataset over the Midwestern United States |
| title_short | A convection‐permitting dynamically downscaled dataset over the Midwestern United States |
| title_sort | convection permitting dynamically downscaled dataset over the midwestern united states |
| topic | climate change downscaling midwest WRF |
| url | https://doi.org/10.1002/gdj3.188 |
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