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Observations, Reanalyses and the Elusive Absolute Global Mean Temperature

One of the most common questions that arises from analyses of the global surface temperature data sets is why they are almost always plotted as anomalies and not as absolute temperatures.

There are two very basic answers: First, looking at changes in data gets rid of biases at individual stations that don’t change in time (such as station location), and second, for surface temperatures at least, the correlation scale for anomalies is much larger (100’s km) than for absolute temperatures. The combination of these factors means it’s much easier to interpolate anomalies and estimate the global mean, than it would be if you were averaging absolute temperatures. This was explained many years ago (and again here).

Of course, the absolute temperature does matter in many situations (the freezing point of ice, emitted radiation, convection, health and ecosystem impacts, etc.) and so it’s worth calculating as well – even at the global scale. However, and this is important, because of the biases and the difficulty in interpolating, the estimates of the global mean absolute temperature are not as accurate as the year to year changes.

This means we need to very careful in combining these two analyses – and unfortunately, historically, we haven’t been and that is a continuing problem.

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Predicting annual temperatures a year ahead

Filed under: — gavin @ 16 September 2016

I have a post at Nate Silver’s 538 site on how we can predict annual surface temperature anomalies based on El Niño and persistence – including a (by now unsurprising) prediction for a new record in 2016 and a slightly cooler, but still very warm, 2017.

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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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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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Nenana Ice Classic 2016

Filed under: — gavin @ 23 April 2016

Just a quick note since I’ve been tracking this statistic for a few years, but the Nenana Ice Classic tripod went down this afternoon (Apr 23, 3:39 Alaska Standard Time). See the earlier post for what this is and why it says something about the climate (see posts on 2014 and 2015 results).

With this unofficial time, this year places 4th earliest for the breakup of ice in the Tanana river. It is unsurprising that it was early given the exceptional warmth in Alaska this year.

The exact ranking of years depends a little on how one accounts for leap-year and other calendrical effects. The raw date is the 4th earliest, but given that this year is a leap year, it would be the 5th earliest counting Julian days from the start of the year. Tying the season to the vernal equinox is more stable, which again leads to the 4th earliest. But regardless of that detail, and consistent with local climate warming, the ice break-up date have advanced about 7 days over the last century.

As a side bet, I predict (based on previous years) that despite enormous attention in the skeptic-osphere given the Nenana result in 2013 (when it was remarkably late), it won’t be mentioned there this year.