Should regional climate models take the blame?

Kerr (2013) recently provided a critical review of regional climate models (“RCMs”). I think his views have caused a stir in the regional climate model community. So what’s the buzz all about?

RCMs provide important input to many climate services, for which there is a great deal of vested interest on all levels. On the international stage, high-level talks lead to the establishment of a Global Framework for Climate Services (GFCS) during the World Climate Conference 3 (WCC3) in Geneva 2009.

Other activities include CORDEX, and the International Conference on Climate Services 2 (ICCS2). On a more regional multi-national level, there are several activities on climate services which have just started, and, looking only in Europe, there are several big projects: JPI-Climate, SPECS, EUPORIAS, IMPACT2C, ECLISE, CLIM-RUN, IN-ENES, BALTEX, and ENSEMBLES. Many of these projects rely on global and regional climate models.

There are well-known limitations to both global and regional climate models, and many of these are described in Maslin and Austin (2012) as “uncertainty”. Maslin and Austin highlighted several reasons for why the regional-scale predictions made by these models are only tentative, and Racherla et al. (2012) observed that:

there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change.

They acknowledged that the problem is not so much the RCMs, but the global climate models’ (GCMs) ability to predict climate changes on a regional scale. This finding is not surprising, however, it is important to establish this fact for the record.

Racherla et al. assessed the skill of an RCM and a GCMs, based on simulated and observed temperature and precipitation for two 10-year time slices (December 1968-December 1978 and December 1995 – December 2005). While they realised that estimating change from two different ten-year intervals is prone to errors caused by spontaneous natural year-to-year (and even slower undulations in temperature and precipitation – e.g. the AMO), they argued that such set-up was common in many climate change studies.

This aspect takes us back to our previous post about the role of large-scale atmospheric circulation associated with natural and ‘internal’ variations. The GCMs may in fact be able to reproduce many of the year-to-year variations and the slower variations, however, we know that these fluctuations are not synchronised with the real world.

The apparent lack of skill may not necessarily be a shortcoming of the individual climate models – indeed, they successfully compute the sensitivity of the subsequent large-scale atmospheric flow to small differences in their starting point.

These variations come on top of the historical long-term climate change trend. In the past, the regional natural variations have often been more pronounced than the regional climate change, and if they are out of synch, then we should expect neither a RCM nor a GCM to be able to predict the change between the two decades.

Hence, the fact that the past has been blurred by natural year-to-year variations does not invalidate the climate models. A proper evaluation of skill would involve looking at longer time scales or many different model runs. One important message is that one should never use a single GCM for making future regional climate projections.

For proper validation, we must look at a large number of different simulations with GCMs, and then apply a statistical test to see if the observed changes are outside the range of changes predicted by the models. By running many models, we get a statistical sample of natural variations following different courses.

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  1. R.A. Kerr, "Forecasting Regional Climate Change Flunks Its First Test", Science, vol. 339, pp. 638-638, 2013.
  2. M. Maslin, and P. Austin, "Uncertainty: Climate models at their limit?", Nature, vol. 486, pp. 183-184, 2012.
  3. P.N. Racherla, D.T. Shindell, and G.S. Faluvegi, "The added value to global model projections of climate change by dynamical downscaling: A case study over the continental U.S. using the GISS-ModelE2 and WRF models", Journal of Geophysical Research: Atmospheres, vol. 117, 2012.