June already? Cripes…
Usual rules apply.
Global climate models (GCM) are designed to simulate earth’s climate over the entire planet, but they have a limitation when it comes to describing local details due to heavy computational demands. There is a nice TED talk by Gavin that explains how climate models work.
We need to apply downscaling to compute the local details. Downscaling may be done through empirical-statistical downscaling (ESD) or regional climate models (RCMs) with a much finer grid. Both take the crude (low-resolution) solution provided by the GCMs and include finer topographical details (boundary conditions) to calculate more detailed information. However, does more details translate to a better representation of the world?
The question of “added value” was an important topic at the International Conference on Regional Climate conference hosted by CORDEX of the World Climate Research Programme (WCRP). The take-home message was mixed on whether RCMs provide a better description of local climatic conditions than the coarser GCMs.
I want to revisit a fascinating study that recently came from (mainly) the Geophysical Fluid Dynamics Lab in Princeton. It looks at the response of the Atlantic Ocean circulation to global warming, in the highest model resolution that I have seen so far. That is in the CM2.6 coupled climate model, with 0.1° x 0.1° degrees ocean resolution, roughly 10km x 10km. Here is a really cool animation.
When this model is run with a standard, idealised global warming scenario you get the following result for global sea surface temperature changes.
Fig. 1. Sea surface temperature change after doubling of atmospheric CO2 concentration in a scenario where CO2 increases by 1% every year. From Saba et al. 2016.
How should one make graphics that appropriately compare models and observations? There are basically two key points (explored in more depth here) – comparisons should be ‘like with like’, and different sources of uncertainty should be clear, whether uncertainties are related to ‘weather’ and/or structural uncertainty in either the observations or the models. There are unfortunately many graphics going around that fail to do this properly, and some prominent ones are associated with satellite temperatures made by John Christy. This post explains exactly why these graphs are misleading and how more honest presentations of the comparison allow for more informed discussions of why and how these records are changing and differ from models.
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