Should regional climate models take the blame?

Running RCMs is computationally expensive and it may not be possible to let them compute results for many decades or many GCMs. However, empirical-statistical downscaling (ESD) is an alternative that does not require much computing power. ESD and RCMs have different strengths and weaknesses, and thus complement each other.

A comparison between RCM results (coloured symbols with error bars) and ESD results (pink region showing the 90% interval for the model ensemble). Here the ESD was applied to many CMIP3 models forced by historic and future (SRES A1b) greenhouse gas emissions, and for the entire time period 1900-2100. The actual observations are shown as black symbols. From Førland et al. (2012)

The figure above, taken from Førland et al. (2012) shows a comparison between ESD and RCM results for the Arctic island Spitsbergen (a part of the Svalbard archipelago), where the ESD has been applied to the entire 1900-2100 period as well as 48 different GCM simulations.

Racherla et al. (2012) also discussed another concern, which is how RCMs and GCMs are combined. Since RCM only cover a limited space, the values at their boundaries must be specified explicitly (referred to as ‘boundary conditions‘), by the results from a coarser GCM or observation-based data (reanalysis).

The GCMs used to force the RCMs, however, do not account for situations where they and the RCMs describe a different states (e.g. precipitation or wind). This problem arises in the situation called upscaling, where small features grow in spatial extent (not atypical for chaotic systems).

It is possible to remedy some of the inconsistencies between the large-scale flow in the RCMs and the embedding GCMs by imposing so-called ‘nudging’.

Furthermore, imposing boundary values on models like RCMs may also sometimes cause problems such as spurious oscillations, and are by some labelled as an “ill-posed problem“. These problems can nevertheless be alleviated by using a “buffer-zone” along the RCM’s boundaries.

A finer grid mesh in the RCMs gives an improved description of mountains over that in the GCM, and introduces further details sugh as higher mountain peaks. This improvement alters the way air is forced upward over mountains, compared to the coarser GCM, and the amount precipitated out (‘orographic precipitation’).

Different ways of computing the cloud processes (cloud parameterisation) affect the condensation of vapour, the outgoing long-wave radiation, and precipitation.

A finer spatial grid also affects the wind structure and the evaporation near the surface (which depends on the wind speed). Furthermore, the energy transported in the atmosphere through eddies may not correspond between models with fine and coarse resolutions respectively.

Such differences between RCMs and GCMs may lead to inconsistent physics, however, are these concerns important, or just second-order effects?

Once again, a comparison between ESD and RCM results will provide some idea, and in many cases, there is a fair degree of agreement between these downscaling strategies. The problems with RCMs are absent in ESD (which have different caveats), however, the important question is whether the GCMs, used to drive both, provide a realistic description of the regional climate.

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  1. E.J. Førland, R. Benestad, I. Hanssen-Bauer, J.E. Haugen, and T.E. Skaugen, "Temperature and Precipitation Development at Svalbard 1900–2100", Advances in Meteorology, vol. 2011, pp. 1-14, 2011.
  2. 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, pp. n/a-n/a, 2012.