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Question is in the title. I'm doing impacts research and need to pick a couple of scenarios before downloading GCM runs. Want to pick a low one and a high one and one in the middle of the road. I'm an economist, rather than a climate scientist.

In my experience, HadGEM has been fairly pessimistic. But I'd appreciate a reference to something citable, ideally plotting climate sensitivity by model.

To add, I'll be downloadading a few of several model runs from a dataset of publicly available downscaling experiments. I'll then analyze each climate model, and then average the results of each experiment to form an ensemble. It is known that one should not average GCM runs until the final step in one's analysis.

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There is no model that consistently yields either highest or lowest warming because each model has strengths and weaknesses which depend upon the conceptual emphases. This varies with latitude, continentality, proximity of water/lakes, presence/absence of ice and mountains, regional significance of atmospheric processes - both physical and chemical, regional airflow strengths such as monsoonal and hurricanes, etc. Each model calibration works either brilliantly well, or downright dubiously, according to the model conceptualization. There is no 'one size fits all' (environments).

That said, however, I routinely use the CMIP-5 ensemble as listed in the World Bank Climate Change Knowledge Portal - just because it is convenient and readily available. You can find it at http://sdwebx.worldbank.org/climateportal/index.cfm - just click on the world map for your region / country / area of interest, and select the parameters you want. This is not the full ensemble, but 16 selected models that seem to work well - at least most of the time.

So here is a recent example from Nepal: scanning the maximum and minimum monthly temperatures, 'giss-r' generally yielded the hottest temperatures, although for occasional months the hottest was 'ccsm-4'. The coolest temperatures were mostly given by 'noresm 1-m' though for occasional months the coldest was from 'miroc esm'. However, this is just a random example. I do not believe that such conclusions are consistent everywhere in the world.

Why concentrate on the coolest and hottest models? Doesn't it make more sense to work with the ensemble median? I find that the relative distribution of model results is roughly similar regardless of the RCP. I usually quote RCP 4.5 or 6.0. RCP 8.5 is too pessimistic. Does anyone still seriously think that RCP 2.8 is attainable?

My advice is to spend a day picking temperature trends from the WB-CMIP model portal for a wide range of environments, and only then draw your conclusions.

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    $\begingroup$ "Does anyone still seriously think that RCP 2.8 is attainable?" - No. Looking around at my workplace and my neighbors: even RCP 4.5 is not realistic ... . $\endgroup$ – daniel.neumann Dec 11 '17 at 12:16
  • $\begingroup$ While climate models have tons of regional variation, they all have a scalar climate sensitivity, no? nature.com/articles/nature24672 $\endgroup$ – generic_user Dec 11 '17 at 13:20
  • $\begingroup$ Such a sensitivity would be an average change in temperature with a change in CO2, which, if I remember my coursework in climate science, can be inferred from outgoing longwave radiation at the top of the atmosphere (or something suchlike -- I'm not an expert). Averaged over the planet, this is a scalar. $\endgroup$ – generic_user Dec 11 '17 at 13:23

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