Hey, perhaps it’s a yellow submarine…

Outside well-traveled sea-lanes, largely in the NH, SMO’s are so sparse and irregularly made that the idea of constructing average SST monthly time-series for each 5-degree Marsden square is laughable. In many such squares there’s barely adequate data even for reliable monthly climatic summaries. QC is totally absent and it’s not uncommon to find ship reports where the longitude has been flipped in polarity. Without proper daily averages for each day of the month, strong temporal biases and aliasing are introduced. The locational bias in the coarse grid can be enormous and pass unrecognized. And then, of course, there’s the “correction” applied by Hadley to “homogenize” older data sampled by wooden buckets with more modern engine-intake measurements. Guess which way the trend of the “bucket correction” goes.

Anyone who believes that global temperatures rose sharply from a deep low in 1900 to a peak in the 1940′s, bottomed in 1956, and have been rising ever since is simply unacquainted with SMO reporting practice. Artificial series from SMOs constitute the bulk of the Hadcrut3 gridwork data.

While PCMDI archived Santer’s T2 and T2LT work product (including the goofy CRM version), they didn’t archive the corresponding surface data.

Although most people presume that Santer would use “conventional” temperature data sets (HadCRU, GISS, NOAA), he instead used things like ERSST sea surface and HadISST sea surface temperature. While KNMI provides air temperature (tas), it’s provision of SST (tos) is spotty.

Again this can doubtless be extracted from PCMDI, but once again we’re into GB of data merely to get modeled SST data which is less than 1MB in total.

One presumes that Santer’s results should, to some extent, apply to air temperature and this can be extracted from KNMI, as I’ve done. I can get very high cross-correlations for everything except the goofy CNRM series and an annoying problem with the CCSM3.0 series, where it looks like:
1) KNMI is missing a 20CEN version available in A1B. There is one more A1B series than 20CEN series at KNMI. Santer b30.030b has a 95+ correlation to A1B run2 but not to any 20CEN version at KNMI. So it looks like KNMI is clerically missing CCSM3.0 20CEN run 2 for some reason.

2) Santer b30.030d does not have a +70 correlation with any KNMI version. there are a few CCSM series at PCMDI and not at KNMI. It looks like Santer used a version not archived at KNMI, making use of this run impractical without a linked surface version.

If one were instead interested in differences between individual modeled and observed results, those differences would be plotted and plotted such that one could readily distinguish individual model run comparisons. That is not to say that the IPCC graphs do not provide information – about the mind set of those in charge at the IPCC.

Very reasonable comments. You’re right about considering what they were trying to do. And from that perspective, there’s no point objecting to their archive not meeting the demands that people subsequently placed on it.

Having said that, one can perhaps ask whether they have adequately responded to the demands subsequently placed on the archive – leaving aside Santer’s repugnant attitude.

Clearly climate is a major public concern and there are many people interested in the model runs who are not interested in downloading GB of data to get KB of what is relevant to them. They’ve known about this for a while. Most small companies would respond to changing market demand and arguably PCMDI hasn’t responded as nimbly as they might have. But let’s think about how they can improve things, as opposed to backbiting.

Improvements don’t need to cost very much. I’m sure that they could implement Geert’s interface at negligible cost – tweaking it a bit while the file was open. Not that I particularly like Java interfaces.

They could also implement something that provides the service of my scraping program in R to automate the radio buttons. Why not their own R package?

These sorts of things would improve the quality of service to outside parties. Maybe they should spend more money on things like that – recognizing the obligation that comes with their unique franchise as a data archive – and less on carrying out their own analyses of the data (the Santer sort of thing) where there’s nothing particularly unique about their services and where they don’t do a particularly good job – sometimes, as in Santer 2008, not even a satisfactory job.

There can be some flexibility. There is more flexibility in a pure science program and less in other programs. But if PCMDI didn’t propose creating interfaces, they aren’t going to exist. Given who the forsaw as users, they probably agreed on the scope of just collecting certain information, storing it and providing a web interface to let people download nearly unprocessed model data.

Of course, then the real world hit them. It turns out that loads and loads of people want to know something about what models predict.

Sorry, hit the “GO” button in midstream. Ironic that I have so many errors in a lecture about errors.

In more than half the (almost random) temperature comparisons I have made between BOM Australian rural data and NH records in databases derived therefrom, there have been discrepancies, some of the order of 1 deg C per year for several years in a row.

Also, some Australian southern stations like Macquarie Island, plus Casey, Davis and Mawson in Antarctica have shown nil to tiny changes in the past 40+ years and an insignificant 1998 spike. Strictly, if models cannot reconcile these differences from the global mean, they should be put in the “questionable” category. Logically, it would mean that other areas of the world had a large temperature increase to constrain the mean, and in places a huge 1998 peak. So, is there justification to call them “global” models when they do not seem to represent findings on the whole globe?