A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets
Abstract
Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWEmore »
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), Climate and Environmental Sciences Division (SC-23.1)
- OSTI Identifier:
- 1619590
- Alternate Identifier(s):
- OSTI ID: 1581388
- Grant/Contract Number:
- SC0016605; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Name: Scientific Reports Journal Volume: 9 Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; cryospheric science; hydrology
Citation Formats
Xu, Yun, Jones, Andrew, and Rhoades, Alan. A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets. United Kingdom: N. p., 2019.
Web. doi:10.1038/s41598-019-52880-5.
Xu, Yun, Jones, Andrew, & Rhoades, Alan. A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets. United Kingdom. https://doi.org/10.1038/s41598-019-52880-5
Xu, Yun, Jones, Andrew, and Rhoades, Alan. Mon .
"A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets". United Kingdom. https://doi.org/10.1038/s41598-019-52880-5.
@article{osti_1619590,
title = {A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets},
author = {Xu, Yun and Jones, Andrew and Rhoades, Alan},
abstractNote = {Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.},
doi = {10.1038/s41598-019-52880-5},
journal = {Scientific Reports},
number = 1,
volume = 9,
place = {United Kingdom},
year = {2019},
month = {11}
}
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