Abstract
Abstract. Climate models contain closure parameters to which the model climate is sensitive. These parameters appear in physical parameterization schemes where some unresolved variables are expressed by predefined parameters rather than being explicitly modeled. Currently, best expert knowledge is used to define the optimal closure parameter values, based on observations, process studies, large eddy simulations, etc. Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (n=4) of closure parameters appearing in the ECHAM5 climate model. The parameters considered are related to clouds and precipitation and they are sampled by an adaptive random walk process of the MCMC. The parameter probability densities are estimated simultaneously for all parameters, subject to an objective function. Five alternative formulations of the objective function are tested, all related to the net radiative flux at the top of the atmosphere. Conclusions of the closure parameter estimation tests with a low-resolution ECHAM5 climate model indicate that (i) adaptive MCMC is a viable option for parameter estimation in large-scale computational models, and (ii) choice of the objective function is crucial for the identifiability of the parameter distributions.
Highlights
Atmospheric general circulation models (GCMs) consist of dynamical laws of atmospheric motions and physical parameterizations of sub-grid scale processes, such as cloud formation and boundary layer turbulence
We demonstrate the use of Markov chain Monte Carlo (MCMC) in the context of the atmospheric general circulation model ECHAM5
The MCMC tests with the low-resolution ECHAM5 climate model are discussed in the four subsections, with emphasis on general aspects of the results
Summary
Atmospheric general circulation models (GCMs) consist of dynamical laws of atmospheric motions and physical parameterizations of sub-grid scale processes, such as cloud formation and boundary layer turbulence. Resolved variables are expressed by predefined parameters rather than being explicitly modeled. In a first order closure, the transfer of a quantity q is assumed to be proportional to the gradient of q multiplied by a fixed diffusion coefficient – note that a whole hierarchy of closures of different orders exists, each with different closure parameters (Mellor and Yamada, 1974). Another example is cloud shortwave optical properties which depend on cloud optical thickness. The modelled shortwave radiation flux is sensitive to the specified value of this parameter, and it can act as an effective ”tuning handle” of the simulated climate
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