2011 Updates to model-data comparisons

And so it goes – another year, another annual data point. As has become a habit (2009, 2010), here is a brief overview and update of some of the most relevant model/data comparisons. We include the standard comparisons of surface temperatures, sea ice and ocean heat content to the AR4 and 1988 Hansen et al simulations.

First, a graph showing the annual mean anomalies from the IPCC AR4 models plotted against the surface temperature records from the HadCRUT3v, NCDC and GISTEMP products (it really doesn’t matter which). Everything has been baselined to 1980-1999 (as in the 2007 IPCC report) and the envelope in grey encloses 95% of the model runs.

The La Niña event that emerged in 2011 definitely cooled the year a global sense relative to 2010, although there were extensive regional warm extremes. Differences between the observational records are mostly related to interpolations in the Arctic (but we will check back once the new HadCRUT4 data are released). Checking up on our predictions from last year, I forecast that 2011 would be cooler than 2010 (because of the emerging La Niña), but would still rank in the top 10. This was true looking at GISTEMP (2011 was #9), but not quite in HadCRUT3v (#12) or NCDC (#11). However, this was the warmest year that started off (DJF) with a La Niña (previous La Niña years by this index were 2008, 2001, 2000 and 1999 using a 5 month minimum for a specific event) in the GISTEMP record, and the second warmest (after 2001) in the HadCRUT3v and NCDC indices. Given current indications of only mild La Niña conditions, 2012 will likely be a warmer year than 2011, so again another top 10 year, but not a record breaker – that will have to wait until the next El Niño.

People sometimes claim that “no models” can match the short term trends seen in the data. This is not true. For instance, the range of trends in the models for 1998-2011 are [-0.07,0.49] ºC/dec, with MRI-CGCM (run5) the laggard in the pack, running colder than observations.

In interpreting this information, please note the following (repeated from previous years):

  • Short term (15 years or less) trends in global temperature are not usefully predictable as a function of current forcings. This means you can’t use such short periods to ‘prove’ that global warming has or hasn’t stopped, or that we are really cooling despite this being the warmest decade in centuries.
  • The AR4 model simulations were an ‘ensemble of opportunity’ and vary substantially among themselves with the forcings imposed, the magnitude of the internal variability and of course, the sensitivity. Thus while they do span a large range of possible situations, the average of these simulations is not ‘truth’.
  • The model simulations use observed forcings up until 2000 (or 2003 in a couple of cases) and use a business-as-usual scenario subsequently (A1B). The models are not tuned to temperature trends pre-2000.
  • Differences between the temperature anomaly products is related to: different selections of input data, different methods for assessing urban heating effects, and (most important) different methodologies for estimating temperatures in data-poor regions like the Arctic. GISTEMP assumes that the Arctic is warming as fast as the stations around the Arctic, while HadCRUT3v and NCDC assume the Arctic is warming as fast as the global mean. The former assumption is more in line with the sea ice results and independent measures from buoys and the reanalysis products.
  • Model-data comparisons are best when the metric being compared is calculated the same way in both the models and data. In the comparisons here, that isn’t quite true (mainly related to spatial coverage), and so this adds a little extra structural uncertainty to any conclusions one might draw.

Page 1 of 3 | Next page