Added value in regional climate modeling. (27th November 2015)

• Record Type: Journal Article
• Authors: Rummukainen, Markku
• Is Part Of: Wiley interdisciplinary reviews. Volume 7:Number 1(2016)
• Page Start: 145
• Page End: 159
• Publication Date: 2015-11-27
• Subjects: Climatic changes -- Periodicals; Climatic changes; Periodicals; 363.7387405
• DOI: 10.1002/wcc.378
• Languages: English
• ISSNs: 1757-7780

Abstract

Abstract : Regional climate modeling is a dynamical downscaling technique applied to the results of global climate models (GCMs) in order to acquire more information on climate simulations and climate change projections. GCMs and regional climate models (RCMs) have undergone considerable development over the past few decades, and both have increased in resolution. The higher‐resolution edge of RCMs compared to GCMs still remains, however. This has been demonstrated in a number of specific studies. As GCMs operate on relatively coarse resolutions, they do not resolve more variable land forms and similar features that shape regional‐scale climates. RCMs operate on higher resolutions than GCMs, by a factor of 2–10. Some RCMs now explore resolutions down to 1–5 km. This adds value in regions with variable orography, land–sea and other contrasts, as well as in capturing sharp, short‐duration and extreme events. In contrast, large‐scale and time‐averaged fields, not least over smooth terrain and on scales that have been already skillfully resolved in GCMs, are not much affected. RCMs also generate additional detail compared to GCMs when in climate projection mode. Compared to the present‐day climate for which observations exist, here the added value aspect is more complex to evaluate. Nevertheless, added value is meaningfully underlined when there is a clear physical context for it to appear in. In addition to climate modeling and model evaluation‐related added value considerations, a significant relevant aspect of added value is the provision of regional scale information, including climate change projections, for climate impact, adaptation, and vulnerability research. WIREs Clim Change 2016, 7:145–159. doi: 10.1002/wcc.378 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models