Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

Journal Article · Fri Jul 30 00:00:00 EDT 2021 · Journal of Geophysical Research: Atmospheres

DOI:https://doi.org/10.1029/2020JD034361· OSTI ID:1811352

Xu, Zexuan  ^[1]; Di Vittorio, Alan  ^[1]; Zhang, Jie ^[2]; Rhoades, Alan  ^[1]; Xin, Xiaoge ^[2]; Xu, Hongmei ^[2]; Xiao, Chan ^[2]

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1. Climate and Ecosystem Science Division Lawrence Berkeley National Laboratory Berkeley CA USA
2. National Climate Center China Meteorological Association Beijing China

Climate models are critical tools to study earth system processes and are often further downscaled to provide refined-resolution data sets for regional water-energy impact analyses. In this study, the Variable-Resolution Community Earth System Model (VR-CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR-CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR-CESM simulates +2°C warm bias in the low-elevation regions in China but -2°C cold bias in mountains. However, DJF cold bias (2.23°C to-3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (-15%) and slow bias of surface wind speeds (-16% to -29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR-CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water-energy studies.

• Research Organization: Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC); USDOE Office of International Affairs (IA). National Key Research and Development Program of China; USDOE Office of International Affairs (IA)
• Grant/Contract Number: DE‐IA0000018; DE‐SC0016605; IA0000018; SC0016605; AC02-05CH11231; 2018YFE0196000
• OSTI ID: 1811352
• Alternate ID(s): OSTI ID: 1811354; OSTI ID: 1817739; OSTI ID: 1819185
• Journal Information: Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Vol. 126 Journal Issue: 15; ISSN 2169-897X
• Publisher: American Geophysical Union (AGU)
• Country of Publication: United States
• Language: English

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54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES