NASA has responded to a 2 year old FOIA request
These were released as a result of the FOIA request put in by Chris Horner as described on WUWT:
It comes in four parts from this URL as pdf docs:
http://www.nasa.gov/centers/goddard/business/foia/GISS.html
Part one is:
Part two:
Part Three:
Part Four:
http://www.nasa.gov/centers/goddard/pdf/417760main_part4.pdf
A scanned in OCR’d a searchable set is at:
http://www.neutralpedia.com/wiki/NASA_FOIA_Emails
An Example
Subject: Re: Your Reply to: GISS Temperature Correction Problem?
From: Gavin Schmidt gschmidt@giss.nasa.gov
Date: 19 Feb 2008 14:38:47 -0500
To: rruedy@giss.nasa.gov
I had a look at the data, and this whole business seems to be related to the infilling of seasonal and annual means. There is no evidence for any step change in any of the individual months.
The only anomalous point (which matches nearby deltas) is for Set 2005. Given the large amount of missing data in lampasas this gets propagated to the annual (D-N) mean – I think – with a little more weight then in the nearby stations. The other factor might be that lampasas is overall cooling, if we use climatology to infill in recent years, that might give a warm bias. But I’m not sure on how the filling-in happens.
Gavin
So as I read this, the folks at NASA responsible for GIStemp are saying that large data dropouts (i.e. Zombie Stations for a while or Dropouts for longer periods) “gets propagated” to the means (and thus the map products) and that if “we use climatology” (i.e. the way GIS uses the relationships between areas ‘climatology’ as it calculates ‘offsets’ – that’s the jargon for their process) that might “give a warm bias”.
Gee.
Maybe I don’t need to convince them that missing data can lead to climatology based infill giving a warming bias. Maybe I only need to get them to ADMIT it publicly… Oh, wait, this FOIA email looks like it does that… Though I’m sure we will get quibbling about it being only one swallow and not a whole spring…
And this one admires the way that you can make up yearly data with just a collection of months data… and that it might have issues. But he’s “pretty sure” it is “just a fluke”… unless, of course, you have a constantly shrinking number of thermometers with ever more gaps in the data to be “made up” from ever less real data…
But including one month of dropped data would “only” drop the temperature by 0.4 C for the annual mean of the cell in question…
Subject: Re: [Fwd: Re: Your Reply to: GISS Temperature Correction Problem?]
From: Gavin Schmidt
Date: 20 Feb 2008 15:01:26 -0500
To: rruedy
That works.
That implies that the missing months are assumed to have the same mean anomaly as the other two months, and that the missing seasons are assumed to have the same mean anomaly as the seasons present. Hence, one strong anomaly in a couple of months (ie. Sept and Nov 2005) can have a large impact on the annual mean.
I’m pretty sure that the Lampasas spike is just a fluke of the annual average construction. There are only eight months – of which only 7 are used to calculate the annual mean. The missing month (May) has the smallest anomaly, and so including it would bring down the annual mean by about 0.4 deg C.
There may be some improvements that could be made here. i.e. annual means could use as many months as there are available (rather than just whether the seasons are available), and it should be made clearer that this is a Dec-Nov mean, not the calendar year mean, Somewhere it should also be stated that the seas/ann values in the printout and figures are not used in the gridded data.
Thanks
Gavin