Is Climate Change a Fraud?

In todays’ papers, there is a story about how climate change (aka global warming) puts billions of dollars in Canadian Assets at Risk.  Of course, this whole assessment by Tyndall Centre for Climate Change Research is based on the ASSUMPTION that the climate is changing…

Which it is becoming more clear from the evidence released last Friday is a fabrication.  Lorne Gunter gives a good overview of the hacked/released Hadley Climate Research Unit (CRU) emails, but I think it is the data files and code that make it very clear that the CRU scientists (and therefore the UN IPCC) have been making it all up.  Excerpt from the HARRY_READ_ME.txt file from the CRU dataset (bold my emphasis):

Deduce that .glo files, after the header, contain data taken row-by-row starting with the Northernmost, and presented as ’8E12.4′. The grid is from -180 to +180 rather than 0 to 360.

This should allow us to deduce the meaning of the co-ordinate pairs used to describe each cell in a .grim file (we know the first number is the lon or column, the second the lat or row – but which way up are the latitudes? And where do the longitudes break?

There is another problem: the values are anomalies, wheras the ‘public’ .grim files are actual values. So Tim’s explanations (in _READ_ME.txt) are incorrect…

And again:

So.. we don’t have the coefficients files (just .eps plots of something). But what are all those monthly files? DON’T KNOW, UNDOCUMENTED. Wherever I look, there are data files, no info about what they are other than their names. And that’s useless.. take the above example, the filenames in the _mon and _ann directories are identical, but the contents are not. And the only difference is that one directory is apparently ‘monthly’ and the other ‘annual’ – yet both contain monthly files.

And more!

I am seriously worried that our flagship gridded data product is produced by Delaunay triangulation – apparently linear as well. As far as I can see, this renders the station counts totally meaningless. It also means that we cannot say exactly how the gridded data is arrived at from a statistical perspective

And more!

The suggested way forward is to not use any observations after 1989, but to allow synthetics to take over. I’m not keen on this approach as it’s likely (imo) to introduce visible jumps at 1990, since we’re effectively introducing a change of data source just after calculating the normals. My compromise is to try it – but to also try a straight derivation from half-degree synthetics.

This one if particularly good…

Oh, GOD. What is going on? Are we data sparse and just looking at the climatology? How can a synthetic dataset derived from tmp and dtr produce the same statistics as an ‘real’ dataset derived from observations?

Ah, Bingo was his name-o! as I was hoping (well OK it’s a bad kind of hope), the reason it’s all the same is that it is by and large defaulting to the climatology. Which means that not much (any?) data is getting through, no matter if we use synthetic, observed, or both together. What’s odd about that conclusion is that the synthetic data is derived from TMP and DTR – two very well-populated datasets! So synthetics alone should pretty much fill the.. hang on, just though of something horrendous.. oh, okay, probably not that. I was wondering if glo2abs.for was factoring the normals so that the anomalies were insignificant, but the equation is: absgrid(ilon(i),ilat(i)) = * nint(anoms(ilon(i),ilat(i))*10) + normals(i,imo) ..so the anomaly is getting the weight! But still – - not a wise thing to leave to automatics. So glo2abs should prompt the user.. but with what? Just one anomaly and normal? Several? The same one from different timesteps? Eeek.

This would seem to indicate that the model being referred to here is using the climatology (the theory) and ignoring the data… good way to get the answer you want…

My favorite though:

The problem is that the synthetics are incorporated at 2.5-degrees, NO IDEA why, so saying they affect particular 0.5-degree cells is harder than it should be. So we’ll just gloss over that entirely ;0)

ARGH. Just went back to check on synthetic production. Apparently – I have no memory of this at all – we’re not doing observed rain days! It’s all synthetic from 1990 onwards. So I’m going to need conditionals in the update program to handle that. And separate gridding before 1989. And what TF happens to station counts?

OH **** THIS. It’s Sunday evening, I’ve worked all weekend, and just when I thought it was done I’m hitting yet another problem that’s based on the hopeless state of our databases. There is no uniform data integrity, it’s just a catalogue of issues that continues to grow as they’re found.

So, the CRU people:

• don’t actually have data in a format that is documented as to what it means
• are trying to DEDUCE the source and format of the data
• all of the modeling is based on SYNTHETIC (artificial / invented) data from 1990 onwards.
• The models may not use the data at all, but something in the code is overriding with the theory

Now, I admit that I do not know all of the context behind this stuff, but CRU had better release EVERYTHING so that real peer review can happen.  We need knowledgeable people to review all their files and see if this is as big a house of cards as it currently appears to be.

And as for Copenhagen, I would hope our politicians call it off until the science is better understood!