Hot enough for you? The faster it warms the hotter it gets

An interesting if ominous paper was recently published in Nature Climate Change. It came out just before Christmas, at the height of the holiday season here in Australia while fires were raging. For some weeks I've been meaning to write about it. That moment has finally arrived.

The authors of the Nature Climate Change paper, Andrew D. King, Todd P. Lane, Benjamin J. Henley and Josephine R. Brown (from The University of Melbourne) tell us that it's up to us to a large degree (excuse the word play). We know that already, and we also know that recent history and current weather-related events in Australia, the UK, Africa and elsewhere demonstrate we've not yet been willing to take enough action.

However the authors weren't writing about our reluctance to do enough to save ourselves. They were in effect exploring what will happen if we can slow down global warming compared to if we let it continue to warm as quickly as it is. It probably won't surprise HotWhopper readers that the rate of warming makes quite a difference.

The relevance of (climate) models - increasing understanding

Climate science deniers in the main, do not understand why models are used in science. Nor do they typically understand how they are used, or how they are constructed.

Today Wondering Willis Eschenbach demonstrated this quite well (archived here). He wrote about a recent article in Science, by Professor Alex Hall. The article was discussing the merits and limitations of using General Circulation Models (GCMs) to model regional climate change, through a process known as down-scaling.

In his article, Dr Hall describes downscaling as follows:

The concept behind downscaling is to take a coarsely resolved climate field and determine what the finer-scale structures in that field ought to be. In dynamical downscaling, GCM data are fed directly to regional models. Apart from their finer grids and regional domain, these models are similar to GCMs in that they solve Earth system equations directly with numerical techniques. Downscaling techniques also include statistical downscaling, in which empirical relationships are established between the GCM grid scale and finer scales of interest using some training data set. The relationships are then used to derive finerscale fields from the GCM data.