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Title: Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Abstract

Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns tomore » the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.« less

Authors:
 [1]; ORCiD logo [2];  [3]; ORCiD logo [2]; ORCiD logo [2];  [4]; ORCiD logo [2]; ORCiD logo [5];  [6];  [7];  [8]
  1. UNIVERSITY PROGRAMS
  2. BATTELLE (PACIFIC NW LAB)
  3. Beijing Normal University
  4. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, US
  5. Nanjing University
  6. Institute of Urban Meteorology, China Meteorological Administration
  7. Korea Institute of Atmospheric Prediction Systems, Seoul, Korea
  8. Korea Institute of Atmospheric Prediction Systems
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1574383
Report Number(s):
PNNL-SA-149230
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Atmospheric Research
Additional Journal Information:
Journal Volume: 234
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Chen, Qian, Yun, Duan, Qingyun, Huang, Maoyi, Berg, Larry K., Shin, Hyeyum, Feng, Zhe, Yang, Ben, Quan, Jiping, Hong, Songyou, and Yan, Junhua. Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon. United States: N. p., 2020. Web. doi:10.1016/j.atmosres.2019.104738.
Wang, Chen, Qian, Yun, Duan, Qingyun, Huang, Maoyi, Berg, Larry K., Shin, Hyeyum, Feng, Zhe, Yang, Ben, Quan, Jiping, Hong, Songyou, & Yan, Junhua. Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon. United States. doi:10.1016/j.atmosres.2019.104738.
Wang, Chen, Qian, Yun, Duan, Qingyun, Huang, Maoyi, Berg, Larry K., Shin, Hyeyum, Feng, Zhe, Yang, Ben, Quan, Jiping, Hong, Songyou, and Yan, Junhua. Wed . "Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon". United States. doi:10.1016/j.atmosres.2019.104738.
@article{osti_1574383,
title = {Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon},
author = {Wang, Chen and Qian, Yun and Duan, Qingyun and Huang, Maoyi and Berg, Larry K. and Shin, Hyeyum and Feng, Zhe and Yang, Ben and Quan, Jiping and Hong, Songyou and Yan, Junhua},
abstractNote = {Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.},
doi = {10.1016/j.atmosres.2019.104738},
journal = {Atmospheric Research},
number = ,
volume = 234,
place = {United States},
year = {2020},
month = {4}
}