Sorry for the low rate of posts this summer. Lots of offline life going on. ;-)
Meantime, this paper by Hourdin et al on climate model tuning is very interesting and harks back to the FAQ we did on climate models a few years ago (Part I, Part II). Maybe it’s worth doing an update?
Some of you might also have seen some of the discussion of record temperatures in the first half of 2016. The model-observation comparison including the estimates for 2016 are below:
Guest commentary by Jack Zhou, Nicholas School of the Environment, Duke University
For advocates of climate change action, communication on the issue has often meant “finding the right message” that will spur their audience to action and convince skeptics to change their minds. This is the notion that simply connecting climate change to the right issue domains or symbols will cut through the political gridlock on the issue. The difficulty then lies with finding these magic bullet messages, figuring out if they talk about climate change in the context of with national security or polar bears or passing down a clean environment to future generations.
On highly polarized issues like climate change, however, communicating across the aisle may be more difficult than simply finding the right message. Here, the worst case scenario is not simply a message failing to land and sending you back to the drawing board. Instead, any message that your audience disagrees with may polarize that audience even further in their skepticism, leaving you in a worse position than you began. As climate change has become an increasingly partisan issue in American politics, this means that convincing Republicans to reject the party line of climate skepticism may be easier said than done. More »
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?