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Boomerangs versus Javelins: The Impact of Polarization on Climate Change Communication

Filed under: — mike @ 7 June 2016

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.
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Unforced Variations: June 2016

Filed under: — group @ 1 June 2016

June already? Cripes…

Usual rules apply.

Do regional climate models add value compared to global models?

2016-05-20 10.08.17

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.

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AMOC slowdown: Connecting the dots

Filed under: — stefan @ 19 May 2016

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.

Saba_Fig4

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.

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Comparing models to the satellite datasets

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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