Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

Journal Article · Mon Nov 01 00:00:00 EDT 2021 · Atmospheric Research

DOI:https://doi.org/10.1016/j.atmosres.2021.105761· OSTI ID:1819924

Wang, Chen ^[1]; Qian, Yun  ^[2]; Duan, Qingyun ^[3]; Huang, Maoyi ^[4]; Yang, Zhao ^[2]; Berg, Larry K.  ^[2]; Gustafson, William I.  ^[2]; Feng, Zhe  ^[2]; Liu, Juxiu ^[5]; Quan, Jiping ^[6]

---

1. Chinese Academy of Sciences
2. BATTELLE (PACIFIC NW LAB)
3. Hohai University
4. NOAA
5. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, China
6. Institute of Urban Meteorology, China Meteorological Administration

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1819924
• Report Number(s): PNNL-SA-161024
• Journal Information: Atmospheric Research, Vol. 262
• Country of Publication: United States
• Language: English