So Wednesday was temperature series update day. The HadCRUT4, NOAA NCEI and GISTEMP time-series were all updated through to the end of 2018 (slightly delayed by the federal government shutdown). Berkeley Earth and the MSU satellite datasets were updated a couple of weeks ago. And that means that everyone gets to add a single additional annual data point to their model-observation comparison plots!
Model-Obs Comparisons
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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NOAA temperature record updates and the ‘hiatus’
In a new paper in Science Express, Karl et al. describe the impacts of two significant updates to the NOAA NCEI (née NCDC) global temperature series. The two updates are: 1) the adoption of ERSST v4 for the ocean temperatures (incorporating a number of corrections for biases for different methods), and 2) the use of the larger International Surface Temperature Initiative (ISTI) weather station database, instead of GHCN. This kind of update happens all the time as datasets expand through data-recovery efforts and increasing digitization, and as biases in the raw measurements are better understood. However, this update is going to be bigger news than normal because of the claim that the ‘hiatus’ is no more. To understand why this is perhaps less dramatic than it might seem, it’s worth stepping back to see a little context…
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References
- T.R. Karl, A. Arguez, B. Huang, J.H. Lawrimore, J.R. McMahon, M.J. Menne, T.C. Peterson, R.S. Vose, and H. Zhang, "Possible artifacts of data biases in the recent global surface warming hiatus", Science, vol. 348, pp. 1469-1472, 2015. http://dx.doi.org/10.1126/science.aaa5632
- B. Huang, V.F. Banzon, E. Freeman, J. Lawrimore, W. Liu, T.C. Peterson, T.M. Smith, P.W. Thorne, S.D. Woodruff, and H. Zhang, "Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons", Journal of Climate, vol. 28, pp. 911-930, 2015. http://dx.doi.org/10.1175/JCLI-D-14-00006.1
2012 Updates to model-observation comparisons
Time for the 2012 updates!
As has become a habit (2009, 2010, 2011), here is a brief overview and update of some of the most discussed model/observation comparisons, updated to include 2012. I include comparisons of surface temperatures, sea ice and ocean heat content to the CMIP3 and Hansen et al (1988) simulations.
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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.
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2010 updates to model-data comparisons
As we did roughly a year ago (and as we will probably do every year around this time), we can add another data point to a set of reasonably standard model-data comparisons that have proven interesting over the years.
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Updates to model-data comparisons
) It’s worth going back every so often to see how projections made back in the day are shaping up. As we get to the end of another year, we can update all of the graphs of annual means with another single datapoint. Statistically this isn’t hugely important, but people seem interested, so why not?
What the IPCC models really say
) () Over the last couple of months there has been much blog-viating about what the models used in the IPCC 4th Assessment Report (AR4) do and do not predict about natural variability in the presence of a long-term greenhouse gas related trend. Unfortunately, much of the discussion has been based on graphics, energy-balance models and descriptions of what the forced component is, rather than the full ensemble from the coupled models. That has lead to some rather excitable but ill-informed buzz about very short time scale tendencies. We have already discussed how short term analysis of the data can be misleading, and we have previously commented on the use of the uncertainty in the ensemble mean being confused with the envelope of possible trajectories (here). The actual model outputs have been available for a long time, and it is somewhat surprising that no-one has looked specifically at it given the attention the subject has garnered. So in this post we will examine directly what the individual model simulations actually show.