Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region
Abstract
Downscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. Here, we conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (Tmax) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extreme P and Tmax reasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extreme P and Tmax for both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extreme P projections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, whilemore »
- Authors:
-
[1];
- Incheon National University (Korea, Republic of)
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Univ. of Notre Dame, IN (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Modeling; USDOD; USEPA; National Science Foundation (NSF)
- OSTI Identifier:
- 1960309
- Grant/Contract Number:
- AC02-06CH11357; GL-00E02207; 2139316; 2139328
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Hydrometeorology
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 5; Journal ID: ISSN 1525-755X
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; climate change; climate models; downscaling; extreme events; North America
Citation Formats
Byun, Kyuhyun, Sharma, Ashish, Wang, Jiali, Tank, Jennifer L., and Hamlet, Alan F. Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region. United States: N. p., 2022.
Web. doi:10.1175/jhm-d-20-0282.1.
Byun, Kyuhyun, Sharma, Ashish, Wang, Jiali, Tank, Jennifer L., & Hamlet, Alan F. Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region. United States. https://doi.org/10.1175/jhm-d-20-0282.1
Byun, Kyuhyun, Sharma, Ashish, Wang, Jiali, Tank, Jennifer L., and Hamlet, Alan F. Sun .
"Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region". United States. https://doi.org/10.1175/jhm-d-20-0282.1. https://www.osti.gov/servlets/purl/1960309.
@article{osti_1960309,
title = {Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region},
author = {Byun, Kyuhyun and Sharma, Ashish and Wang, Jiali and Tank, Jennifer L. and Hamlet, Alan F.},
abstractNote = {Downscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. Here, we conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (Tmax) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extreme P and Tmax reasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extreme P and Tmax for both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extreme P projections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, while the changes projected by the HD approach exhibit moderate decreases overall. The WRF simulations at 4 km also show a pronounced decoupling effect between seasonal totals and extreme daily P for summer, which suggests that there could be more intense summer extremes at two different time scales, with more severe individual convective storms combined with longer summer droughts at the end of the twenty-first century.},
doi = {10.1175/jhm-d-20-0282.1},
journal = {Journal of Hydrometeorology},
number = 5,
volume = 23,
place = {United States},
year = {Sun May 01 00:00:00 EDT 2022},
month = {Sun May 01 00:00:00 EDT 2022}
}
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