Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

Abstract

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

Grenzen und Herausforderungen der regionalen Klimamodellierung

“…Der Mehrwert der Regionalisierung ist besonders dort zu erwarten, wo es zu einer regionalen Beeinflussung von Klimakenngrößen kommt (Feser et al 2011). Neben der besseren räumlichen Wiedergabe kann die Regionalisierung aber auch dazu führen, dass überregionale Mittelwerte von Temperatur, Niederschlag und anderen Kenngrößen besser dargestellt werden als in den Globalmodellen (Feser 2006;Diaconescu und Laprise 2013;Di Luca et al 2013).…”

Projected Change—Models and Methodology

“…RCMs can resolve mesoscale atmospheric features explicitly and they add small-scale structures to the large-scale circulation provided by the driving model (Feser 2006). Local climate is influenced by large-scale dynamics, regional physiographic features such as local orography, land-sea contrasts land use, and soil type, as well as by small-scale atmospheric features such as frontal systems or convective cells (Lenderink et al 2007b;Feser et al 2011). This is particularly the case for the simulation of precipitation.…”

“…Evaluation of a hypothesis of AV implies a comparison of the performance of the RCM with that of the driving GCM (Feser et al 2011). To date, the number of studies in which the AV of RCMs is directly analysed is limited and only a few concerns the Baltic Sea region.…”

“…AV is small in the case of SLP and only for the scale well resolved by RCM, because it is the driving fields (from the GCM) that are the most relevant for SLP. For 2 m air temperature, regional and local factors exert a strong impact on its spatial distribution and the AV of RCMs can be significant (Feser et al 2011). Zahn et al (2008) have shown that RCMs can provide AV in describing mesoscale phenomena such as polar lows.…”

“…10.13 Bandpass-filtered mean sea-level pressure (isolines hPa) and 10 m wind speed anomalies (shaded) at 0600 UTC 15 October 1993: NCEP analysis, DWD (Deutscher Wetterdienst-German weather service) analysis data, and a simulation by the regional climate model CLM (after Zahn et al 2008). The position of the pressure minimum of the polar low in the CLM simulation is indicated (yellow dot), after Feser et al (2011) orography, and decreases when statistics are averaged over long time periods or over a wide spatial domain.…”