{"id":14810,"date":"2013-04-06T08:17:52","date_gmt":"2013-04-06T13:17:52","guid":{"rendered":"http:\/\/www.realclimate.org\/?p=14810"},"modified":"2013-06-29T17:27:23","modified_gmt":"2013-06-29T22:27:23","slug":"should-regional-climate-models-take-the-blame","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2013\/04\/should-regional-climate-models-take-the-blame\/","title":{"rendered":"Should regional climate models take the blame?"},"content":{"rendered":"
\n

Kerr (2013)<\/a><\/span> 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?<\/em> <\/p>\n

RCMs provide important input to many climate services<\/a>, 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<\/a> (GFCS) during the World Climate Conference 3<\/a> (WCC3) in Geneva 2009.<\/p>\n

<\/p>\n

Other activities include CORDEX<\/a>, and the International Conference on Climate Services 2<\/a> (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<\/a>, SPECS<\/a>, EUPORIAS<\/a>, IMPACT2C<\/a>, ECLISE<\/a>, CLIM-RUN<\/a>, IN-ENES<\/a>, BALTEX<\/a>, and ENSEMBLES<\/a>. Many of these projects rely on global and regional climate models.<\/p>\n

There are well-known limitations to both global and regional climate models, and many of these are described in Maslin and Austin (2012)<\/a><\/span> 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)<\/a><\/span> observed that:<\/p>\n

there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change.<\/p><\/blockquote>\n

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<\/em>. This finding is not surprising, however, it is important to establish this fact for the record.<\/p>\n

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<\/a>), they argued that such set-up was common in many climate change studies.<\/p>\n

This aspect takes us back to our previous post about the role of large-scale atmospheric circulation<\/a> 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.<\/p>\n

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.<\/p>\n

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. <\/p>\n

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.<\/p>\n

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. <\/p>\n

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<\/a> (ESD) is an alternative that does not require much computing power. ESD and RCMs have different strengths and weaknesses, and thus complement each other. <\/p>\n

\"\"<\/a>
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 F\u00f8rland et al. (2012)<\/em><\/figcaption><\/figure>\n

The figure above, taken from F\u00f8rland et al. (2012)<\/a><\/span> shows a comparison between ESD and RCM results for the Arctic island Spitsbergen<\/a> (a part of the Svalbard<\/a> archipelago), where the ESD has been applied to the entire 1900-2100 period as well as 48 different GCM simulations.<\/p>\n

Racherla et al. (2012)<\/a><\/span> 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<\/a>‘), by the results from a coarser GCM or observation-based data (reanalysis<\/a>). <\/p>\n

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<\/em>, where small features grow in spatial extent (not atypical for chaotic systems). <\/p>\n

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’. <\/p>\n

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

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’). <\/p>\n

Different ways of computing the cloud processes (cloud parameterisation) affect the condensation of vapour, the outgoing long-wave radiation, and precipitation. <\/p>\n

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.<\/p>\n

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

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. <\/p>\n

The figure above indicates that the GCMs (and the ESD results) underestimate some of the local natural variations in the past – which probably are connected with the Arctic sea ice (Benestad et al., 2002)<\/a><\/span>. The GCMs used in these calculations do not seem to capture the recent decline in the Arctic sea-ice cover (Stroeve et al., 2012)<\/a><\/span>.<\/p>\n

Another problem may be that the RCMs do not represent the precipitation statistics well, even when data based on real observations (the ERA40<\/a> reanalysis) are used to provide the boundary values (Orskaug et al., 2011)<\/a><\/span>. For climate services, it is important that the precipitation statistics is realistic, and in the past, systematic biases have been “fixed” (in a not very satisfactory way) by bias-correction. <\/p>\n

Boberg and Christensen (2012)<\/a><\/span> presented one type of validation analysis, that may partially meet the concerns expressed in Kerr (2013). They reported that many RCMs overestimate the temperature in warm and dry climates (e.g. around the Mediterranean). This temperature bias was greater with higher temperature. <\/p>\n

Most of the GCMs too had similar temperature biases, suggesting that the deficiencies seen in the RCM results were not different to those in the GCMs. Furthermore, these deficiencies cannot be explained in terms of the differences between the measured temperatures and the data used as boundary conditions for the RCMs (see figure below). <\/p>\n

\"\"<\/a>
The mean temperature from the thermometer measurements (E-OBS data; upper) and the mean difference between the E-OBS and reanalysis data used as boundary conditions for the RCMs (ERA40; below). There is no indication of systematic large differences in warmer regions, and the largest differences seen here are in the mountains, where the observations are made at different altitudes than the surface elevation used in the reanalysis.<\/em><\/figcaption><\/figure>\n

According to Kerr (2013), the RCMs need a more thorough validation, addressing the question whether they are able to describe changes in the local climate. <\/p>\n

It is also important to verify that they actually provide a consistent representation of the physics when embedded in the GCMs. Do the energy and mass (moisture) fluxes across the lateral and top boundaries of the RCM correspond with the fluxes through the cross-sections in the GCMs corresponding to the RCM’s boundaries? <\/p>\n

There are also new initiatives on proper validation of regional climate modelling (ESD and RCMs), and the European project VALUE<\/a> represents one notable example. <\/p>\n

(p.s. One of the references below has wrong author and title, but correct link and DOI).<\/em><\/p>\n

References<\/h2>\n
    \n
  1. <\/a>\nR.A. Kerr, \"Forecasting Regional Climate Change Flunks Its First Test\", Science<\/i>, vol. 339, pp. 638-638, 2013. http:\/\/dx.doi.org\/10.1126\/science.339.6120.638<\/a>\n\n\n<\/li>\n
  2. <\/a>\nM. Maslin, and P. Austin, \"Climate models at their limit?\", Nature<\/i>, vol. 486, pp. 183-184, 2012. http:\/\/dx.doi.org\/10.1038\/486183a<\/a>\n\n\n<\/li>\n
  3. <\/a>\nP.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\u2010ModelE2 and WRF models\", Journal of Geophysical Research: Atmospheres<\/i>, vol. 117, 2012. http:\/\/dx.doi.org\/10.1029\/2012JD018091<\/a>\n\n\n<\/li>\n
  4. <\/a>\nE.J. F\u00f8rland, R. Benestad, I. Hanssen-Bauer, J.E. Haugen, and T.E. Skaugen, \"Temperature and Precipitation Development at Svalbard 1900\u20132100\", Advances in Meteorology<\/i>, vol. 2011, pp. 1-14, 2011. http:\/\/dx.doi.org\/10.1155\/2011\/893790<\/a>\n\n\n<\/li>\n
  5. <\/a>\nR.E. Benestad, E.J. F\u00f8rland, and I. Hanssen\u2010Bauer, \"Empirically downscaled temperature scenarios for Svalbard\", Atmospheric Science Letters<\/i>, vol. 3, pp. 71-93, 2002. http:\/\/dx.doi.org\/10.1006\/asle.2002.0050<\/a>\n\n\n<\/li>\n
  6. <\/a>\nJ.C. Stroeve, V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W.N. Meier, \"Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations\", Geophysical Research Letters<\/i>, vol. 39, 2012. http:\/\/dx.doi.org\/10.1029\/2012GL052676<\/a>\n\n\n<\/li>\n
  7. <\/a>\nE. Orskaug, I. Scheel, A. Frigessi, P. Guttorp, J.E. Haugen, O.E. Tveito, and O. Haug, \"Evaluation of a dynamic downscaling of precipitation over the Norwegian\n mainland\", Tellus A: Dynamic Meteorology and Oceanography<\/i>, vol. 63, pp. 746, 2011. http:\/\/dx.doi.org\/10.1111\/j.1600-0870.2011.00525.x<\/a>\n\n\n<\/li>\n
  8. <\/a>\nF. Boberg, and J.H. Christensen, \"Overestimation of Mediterranean summer temperature projections due to model deficiencies\", Nature Climate Change<\/i>, vol. 2, pp. 433-436, 2012. http:\/\/dx.doi.org\/10.1038\/NCLIMATE1454<\/a>\n\n\n<\/li>\n<\/ol>\n\n<\/div> ","protected":false},"excerpt":{"rendered":"

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