Abstract
Abstract. The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent subgrid-scale processes. In order to assess a model's reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for regional climate models (RCMs), whose performance is domain dependent. In this study, the parameter space of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM is investigated for the Central Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, using a perturbed physics ensemble (PPE) obtained by performing 1-year simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for subregions characterized by different climate conditions, assessing by which degree it is possible to improve model performance by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a subset of model parameters present relevant changes in model performance for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behaviour among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameter perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be retuned, and its parameter uncertainty properly investigated, when setting up model experiments for different domains of study.
Highlights
Climate models are representations of the climate system based on well-understood physics combined with simplified descriptions of subgrid-scale processes called parameterizations (Hourdin et al, 2017)
It is clearly seen that model performance is sensitive to only a restricted set of parameters, which is in agreement with the findings of Bellprat et al (2012a)
Other parameters, which have some considerable impact on performance score (PS), are d_mom, the factor for turbulent momentum dissipation, v0snow, controlling the fall velocity of snow, radfac, which represents the fraction of cloud water/ice used in the radiation scheme, tkhmin, the minimum value for the turbulence heat diffusion coefficient, and rlam_heat, the scaling factor of the laminar boundary layer for heat
Summary
Climate models are representations of the climate system based on well-understood physics combined with simplified descriptions of subgrid-scale processes called parameterizations (Hourdin et al, 2017). These parameterizations usually depend on one or several empirical and unconfined parameters (Hourdin et al, 2017; Bellprat et al, 2012a; Tebaldi and Knutti, 2007) whose different values produce a wide spectrum of outcomes referred to as parameter uncertainty. The common approach to sample model parameter uncertainty is to use ensembles of model simulations, called perturbed physics ensembles (PPEs; Murphy et al, 2007; Bellprat, 2013; Tebaldi and Knutti, 2007; Paeth, 2015)
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