Regional Climate Modeling: Progress, Challenges, and Prospects

Abstract

Regional climate modeling using regional climate models (RCMs) has matured over the past decade to enable meaningful utilization in a broad spectrum of applications. In this paper, the latest progress in regional climate modeling studies is reviewed, including RCM development, applications of RCMs to Challenges and potential directions of future research in this important area are discussed, with focus on those that have received less attention previously, such as the importance of ensemble simulations, further development and improvement of the regional climate modeling approach, modeling extreme climate events and sub-daily variation of clouds and precipitation, model evaluation and diagnostics, applications of RCMs to climate process studies and seasonal predictions, and development of regional earth system models.It is believed that with the demonstrated credibility of RCMs in reproducing not only monthly to seasonal mean climate and interannual variability, but also the extreme climate events when driven by good quality reanalysis and continuous improvements in the skill of global general circulation models (GCMs) in simulating large-scale atmospheric circulation, regional climate modeling will remain an important dynamical downscaling tool for providing the needed information for assessing climate change impacts, and seasonal climate predictions, and a powerful tool for improving our understanding of regional climate processes. Internationally coordinated efforts can be developed to further advance regional climate modeling studies. It is also recognized that since the final quality of the results from nested RCMs depends in part on the realism of the large-scale forcing provided by GCMs, the reduction of errors and improvement in physics parameterizations in both GCMs and RCMs remain a priority for the climate modeling community.

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

“…• Dependent on GCM boundary forcing; affected by biases in underlying GCM • Domain size, climatic region and season affects downscaling skill within the western U.S., Europe, and New Zealand, where topographic effects on temperature and precipitation are prominent, often report more skilful dynamical downscaling than in regions such as the U.S., Great Plains and China where regional forcings are weaker (Wang et al, 2004). Variability in internal parameterizations also provides considerable uncertainty.…”

“…Frei et al, 2006). Applications to geographically diverse regions and model inter-comparison studies have allowed the strengths and weaknesses of dynamical downscaling to be better understood (Wang et al, 2004). This has recently proliferated their use in impact studies (e.g.…”

Evaluation of a WRF dynamical downscaling simulation over California

“…Although more physically defensible, this approach is much more computationally expensive and is itself subject to error due to imperfect parameterizations and numerics. Wang et al (2004), Fowler et al (2007), and Solomon et al (2007) provide an overview of current regional modeling techniques. The most popular of these is the nested-model, or dynamical downscaling approach, where GCM data are used to provide lateral boundary conditions, sea surface temperature (SST), and initial land-surface conditions for a limited-area model (typically based on an existent numerical weather prediction model).…”

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

“…In DD, the GCM outputs are used as boundary conditions to drive a Regional Climate Model or Limited Area Model and produce regional-scale information up to 5~50 km. This method has superior capability in complex terrain or with changed land cover (Wang et al 2004;Kite 1997). However, this method entails higher computation cost and relies strongly on the boundary conditions provided by GCMs.…”