There is an increasing demand for weather and climate information on different spatio-temporal scales, ranging from regional to local scales, and from short-term predictions to long-term climate projections. While weather and climate information from Global Climate Models (GCMs) are usually too coarse for decision makers from various disciplines, such as hydrology, agriculture, and energy, Regional Climate Models (RCMs) or Regional Earth System Models (RESMs) provide feasible solutions for downscaling GCM output to required spatiotemporal scales. However, it is well known that the performance of regional simulations depends a lot on the physical parametrization, and may vary from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs/RESMs include radiation, convection, and cloud microphysics, partly with complex interactions between the different parametrizations. Precipitation generation, for instance, involves many coupled processes between cumulus convection, cloud microphysics, radiation, land and ocean surface, and the planetary boundary layer.
Before conducting RCM/RESMs simulations, it is therefore indispensable to identify a suitable physics parametrization combination for long-term climate projections or to perform multi-physics ensemble forecasts for short-term weather or seasonal climate forecasts. In the selection process of suitable parameterization combinations, it is usually focused on the evaluation of single variables, which might lead to complex bias structures between the variables. Therefore, it might become crucial to apply a bias correction before passing the RCM/RESM output to climate impact models. Various correction methods of different complexity exist, ranging from very simple linear and univariate approaches to complex and multivariate approaches. It is assumed that the selection of a suitable setup of the RCM/RESM may reduce the need for applying complex post-processing bias correction methods.
This Research Topic seeks contributions of the following themes:
•Development and application RCMs/RESMs, in particular, multi-physics RCM/RESM experiments for different regions worldwide to support weather forecaster and climate modelers for modeling studies on different temporal scales, ranging from short-term weather predictions and medium-term seasonal predictions, towards long-term climate projections;
•Innovative methods and techniques to assess the model performance considering the spatial representation of hydrometeorological patterns (e.g. precipitation patterns due to different ENSO phase or typical storm tracks) or techniques accounting for the covariance structure between different hydrometeorological variables such as between temperature, precipitation and humidity;
•Evaluating different post-processing bias correction methods of different complexity for improving the representation of hydrometeorological variables in RCM/RESM simulations (for both deterministic and ensemble forecast systems). It can be tested whether or not different approaches may compensate for complex biases introduced by non-suitable RCM/RESM physics parameterization.
We would like to acknowledge
Dr. Tien Duc Du who has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.
