Parameter uncertainty and calibration of regional climate models

The study of the Earth climate system requires, due to its high complexity, the use of computerbased models which integrate the current knowledge about the underlying processes in the climate system. Such climate models have proven to reproduce many aspects of the observed climate. Yet inherent uncertainties obscure our current understanding of the climate system and in particular of climatic changes as a response to increasing greenhouse gas and aerosol concentrations. One source of this model uncertainty originates from unconfined semi-empirical model parameters employed in model parametrizations. Recent climate research revealed a main contribution of this parameter uncertainty to the overall uncertainty of climate change projections, and highlighted the need for objective approaches to constrain these model parameters using past observations in order to sharpen our understanding of the current and future climate. While the parameter uncertainty of low-resolution and/or idealized global climate models has been assessed intensively in recent research, so far no consensus on how to estimate these parameters in computationally expensive climate models has emerged. The risks and implications of presently adopted subjective approaches are largely unknown, and therefore a matter of constant debate in the climate modeling community. Furthermore, at the present stage little effort has been devoted to systematically assess the parameter uncertainty of regional climate models (RCMs) which are increasingly employed to provide high-resolution climate projections. The goal of this thesis is therefore to assess the importance of the parameter uncertainty of regional climate models (RCMs) and to constrain the determined parameter uncertainty based on an objective calibration approach applicable to computationally expensive models. The objective calibration approach is finally used to systematically assess the implications of parameter estimation for the projection of climate change signals using RCMs, and to identify structural deficiencies of the models. The parameter uncertainty of the RCM COSMO-CLM is assessed by generating perturbed physics ensembles of a large set of uncertain model parameters. The evaluation addresses the mean, the interannual variability and selected extremes over regional sub-domains of the European climate. The ensembles reveal a dominant component of the model uncertainty related to uncertain model parameters which exceeds the uncertainty associated with internal variability and observational errors. In order to quantify the agreement between the simulated and observed climates, an extension of an existing performance metric is proposed that allows for consideration of observational uncertainty and internal variability, and that integrates multivariate model errors based on variety of observational datasets. It is shown that the consideration of observational uncertainty, which is presently neglected in most metrics, has proven to be crucial at regional scales for total cloud cover and high precipitation intensities. The ensembles further demonstrate that parameters in the land surface scheme, the microphysics and convection parametrization dominate the parameter uncertainty in the model under consideration. Based on the knowledge gained from exploring perturbed physics ensembles, we demonstrate the feasibility of an objective calibration approach that takes into account the computa-