Quite aside from the realclimatescientistsmoothingalgorithmparameterselectioncontroversy, another interesting aspect of Figure 3 of the Copnhagen Synthesis Report is the cone of model projections. Today I’ll show you how to do a similar comparison for an AR4 model of your choice. Unlike Rahmstorf, I’ll show how this is done, complete with turnkey code. I realize that this is not according to GARP (Generally Accepted Realclimatescientist Procedure), but even realclimatescientists publishing in peerreviewedliterature should be accountable for their methodology.
Here is Figure 3 from the Copenhagen Synthesis Report. I take it that the grey cone is the spread of model prokections – note that the caption says that these are from the Third Assessment Report. Raising the question – why the Third Assessment Report? Wouldn’t the Fourth Assessment Report be more relevant?
Figure 1: Copenhagen Synthesis Figure 3. “Changes in global average surface air temperature (smoothed over 11 years) relative to 1990. The blue line represents data from Hadley Center (UK Meteorological Office); the red line is GISS (NASA Goddard Institute for Space Studies, USA) data. The broken lines are projections from the IPCC Third Assessment Report, with the shading indicating the uncertainties around the projections3 (data from 2007 and 2008 added by Rahmstorf, S.).”
The IPCC AR4 Smear Graph
In April 2008, doctorrealclimatescientist Rahmstorf discussed models versus observation, excerpting IPCC Figure 1.1 which showed both the AR4 cone and the Tar cone as shown below. As I understand it, these cones are more or less spaghetti graphs with one color – grey.
Figure 2. IPCC Figure 1.1 shown by Rahmstorf at RC in April 2008.
The Canadian GCM
I thought that it would be interesting to do my own comparison from first principles. Rather than smearing everything into one big stew a la IPCC, I thought that it would be interesting to show results for individual models. And to simplify presentation, I’m only showing HadCRU, rather than spaghetti-ing it up with GISS. In making this presentation, I used my implementation in R of ssatrend (benchmarked against the original Matlab version – more on this later.) I also used my function to scrape model data from KNMI (this function constructs CGI commands at KNMI within R.)
Here’s a Rahmstorf-style plot for the Canadian GCM runs, chosen because I’m Canadian. (Actually I knew from Santer studies that it runs “hot”, so it wasn’t entirely a random selection. I wanted to see what this sort of model looked like.) I’ve only examined these plots for a few models – NCAR is another one that I looked at- but I can modify the script easily to make a pdf showing similar plots for all models and might do this some time.
I invite readers to consider whether there is a “remarkable similarity” between the geometry of the coherence of model and observations in this graphic and the coherence of model and observations in the Copenhagen Synthesis graphic. One noticeable difference between this graphic and the realclimatescientistgraphic is that it does not truncate the hindcast performance. In this case, one is inclined to say that the proprietors of this particular model haven’t gone out of their way to tune its performance to actual 20th century history. I’m pretty sure that this model is at the upper sensitivity end, but I don’t know this.
Readers may easily derive this graphic for themselves using the following turnkey code. First load functions to implement KNMI scrape and Rahmstorf smoothing and plotting:
#function to scrape from KNMI
#emulation of Rahmstorf smooth
Now load HadCRUT3v and Rahmsmooth it, using the realclimatescientistsmoothingparameter of Rahmstorf et al 2007.
source(“http://data.climateaudit.org/scripts/spaghetti/hadcru3v.glb.txt”) #hadcru3v, hadcru3v.ann
had_smooth= ssatrend(window(had,end=2008),M=11) #Rahmsmooth
Make sure that you register at KNMI. Then insert your registered email address in the R-code as shown below
email=Email= [##register and insert your email here
Now logon and set the field to “tas” (temperature at surface) and the scenario to A1B “sresa1b”. The code below will print out the available KNMI models according to a semi-manual collation from their webpage a few months ago. (KNMI needs to have a readable list of models !!)
Info #gives A1B models at KNMI
To generate the CCCMA figure in this post, look up their row-number of the model in the Info table generated above and simply execute the following command. This should scrape the data from KNMI. If it doesn’t, then there;s probably something wrong with your KNMI handshake.
Just change the number to generate other model comparisons. (I’ll use the pdf function to generate all the models in one document.)
Update: I modified the function a little and produced a pdf with all the models plotted in the above style. A pdf is at http://www.climateaudit.org/data/models/models_vs_hadcru.pdf . Virtually every model performed better than the Canadian GCM.
for (i in 1:nrow(Info)) plotf(i,gdd=FALSE) #CCCMA