Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

Aneesh C. Subramanian,Omar Knio,Raju Pathak,Ibrahim Hoteit,Samah El Mohtar,Sandeep Sahany,Hari Prasad Dasari,Saroj Kanta Mishra

doi:10.3389/fclim.2021.670740

Abstract

Uncertainty 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 radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~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 ~7 and 20%, 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). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

Highlights

Uncertainty quantification (UQ) in weather and climate models enables to evaluate model sensitivities and to reduce inconsistencies between the model outputs and observations (e.g., Allen et al, 2000; Murphy et al, 2004; Stainforth et al, 2005; Lopez et al, 2006; Jackson et al, 2008; Le Maitre and Knio, 2010; Covey et al, 2013)
We found that dcs contribution is highest, about 32, 50, 18, 23, 11, 84, and 28% for PRECT, longwave cloud radiative effect (LWCF), Short-wave Cloud Radiative Effect (SWCF), Latent Heat Flux (LHFLX), liquid water path (LWP), Ice Water Path (IWP), and convective available potential energy (CAPE), respectively. dcs contributes about 17, 42, and 42% for the medium-level cloud (CLDMED), the high-level cloud (CLDHGH), and for the total cloud (CLDTOT), respectively
This study used an efficient multi-objective UQ framework to assess the sensitivity of National Center for Atmospheric Research (NCAR) SCAM6 outputs to cloud microphysics and macrophysics (CMP) parameterization schemes

Summary

Introduction

Uncertainty quantification (UQ) in weather and climate models enables to evaluate model sensitivities and to reduce inconsistencies between the model outputs and observations (e.g., Allen et al, 2000; Murphy et al, 2004; Stainforth et al, 2005; Lopez et al, 2006; Jackson et al, 2008; Le Maitre and Knio, 2010; Covey et al, 2013). In global climate models (GCMs), subgridscale processes (e.g., cloud characteristics and convection) are often parameterized using various schemes and assumptions depending on empirical parameters These introduce different levels of uncertainty in the parameterization of subgrid-scales and, in the eventual model simulations (e.g., Warren and Schneider, 1979). Parameter estimation can be undertaken through a series of model simulations that perturb the parameters individually and determine the predictive skill of the model for each simulation (e.g., Zaehle and Friend, 2010; Anand et al, 2018; Ricciuto et al, 2018) Such methods cannot treat the non-linear interactions between the input parameters and model outputs (Tarantola, 2004; Hourdin et al, 2017). UQ has been demonstrated as an effective method to determine the interactive effects of model parameters (e.g., Jackson et al, 2008; Collins et al, 2011; Yang et al, 2012, 2013; Covey et al, 2013; Zou et al, 2014; Guo et al, 2015; Qian et al, 2015; Sraj et al, 2016)

Methods

Results

Conclusion