Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)
Dasari, Hari Prasad
El Mohtar, Samah
Mishra, Saroj K
KAUST DepartmentApplied Mathematics and Computational Science Program
Biological and Environmental Science and Engineering (BESE) Division
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Office of the VP
Physical Science and Engineering (PSE) Division
Red Sea Research Center (RSRC)
KAUST Grant NumberREP/1/3268-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/669324
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AbstractUncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud forcing, ice and liquid water path). A first-order sensitivity analysis reveals that dcs contributes approximately 40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute approximately 20% and 11%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). Our study has implications for enhancing our understanding of the physical mechanisms associated with cloud processes leading to uncertainty in model simulations and further helps to improve the models used for their assessment.
CitationPathak, R., Dasari, H. P., El Mohtar, S., Subramanian, A. C., Sahany, S., Mishra, S. K., … Hoteit, I. (2021). Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6). Frontiers in Climate, 3. doi:10.3389/fclim.2021.670740
SponsorsFunding :The research reported in this paper was supported by the office Q13 of Sponsor Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (REP/1/3268-01-01) and the Saudi ARAMCO Marine Environmental Research Center at KAUST.
The DST Center of Excellence in Climate Modeling (RP03350) and Indian Institute of Technology Delhi, India, are acknowledged for their partial support. We thank NCAR for providing the Single-Column Community Atmosphere Model (SCAM)
JournalFrontiers in Climate
CollectionsArticles; Biological and Environmental Science and Engineering (BESE) Division; Red Sea Research Center (RSRC); Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as Copyright © 2021 Pathak, Dasari, El Mohtar, Subramanian, Sahany, Mishra, Knio and Hoteit. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.