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.
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机译：气候模型的参数不确定度代表通过扰乱其经验和非整合参数来获得的结果的频谱来表示用于代表底图级过程。为了评估模型的可靠性并更好地了解其对不同物理过程的局限性和敏感性，需要仔细研究模型参数的传播。这对于区域气候模型（RCMS）尤其如此，其性能是域依赖的。在这项研究中，针对小型建模气候模式（COSMO-CLM）RCM的联盟参数空间为中亚协调区域气候缩小实验（CORDEX）域，使用通过表演获得的扰动物理集合（PPE）具有不同参数值的1年仿真。主要目标是表征模型的参数不确定性，并确定该区域最敏感的参数。 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.最后，本文探讨了不同域对不同域的模型参数敏感性，解决了RCM模型设置对不同区域区域的转移性问题。结果表明，只有模型参数的子集目前在不同参数值的模型性能中存在相关的变化。重要的是，对于几乎所有参数输入，该模型显示了不同群集和区域之间的相反行为。这表明，对校准模型进行观察，以确定中亚领域的最佳参数值尤为具有挑战性：在这种情况下，使用客观校准方法非常必要。最后，模型对中亚参数扰动的敏感性与欧洲观察到的人不同，这表明应重新调整RCM，并在为不同研究领域建立模型实验时正确调查其参数不确定性。
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