REPLY: Go for it. I’m jammed up today, fires are threatening again. 1/4 mile visibility due to smoke.
Added reference: A number of comments ask why the historical numbers change. I wrote a post on that earlier this year, which was publicized on this blog and Climate Audit. I am not saying it is OK that they change, only describing why they change.
In relation to the GISS figures and also Hadcrut I saw this over at realclimate
http://www.realclimate.org/index.php/archives/2008/07/global-trends-and-enso/
“The differences in the two products (HadCRUT3v and GISTEMP) are mostly a function of coverage and extrapolation procedures where there is an absence of data. Since one of those areas with no station coverage is the Arctic Ocean, (which as you know has been warming up somewhat), that puts in a growing difference between the products. HadCRUT3v does not extrapolate past the coast, while GISTEMP extrapolates from the circum-Arctic stations – the former implies that the Arctic is warming at the same rate as the rest of the globe, while the latter assumes that the Arctic is warming as fast as the highest measured latitudes”
I am not sure about the significance or otherwise of this difference in treatment of the polar regions
GISTEMP anomaly at 250km smoothing:
June 1988 = 0.30 deg C
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2008&month_last=6&sat=4&sst=1&type=anoms&mean_gen=06&year1=1988&year2=1988&base1=1951&base2=1980&radius=250&pol=reg
June 2008 = 0.29 deg C
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2008&month_last=6&sat=4&sst=1&type=anoms&mean_gen=06&year1=2008&year2=2008&base1=1951&base2=1980&radius=250&pol=reg
GISTEMP anomaly at 1200km smoothing:
June 1988 = 0.38 deg C
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2008&month_last=6&sat=4&sst=1&type=anoms&mean_gen=06&year1=1988&year2=1988&base1=1951&base2=1980&radius=1200&pol=reg
June 2008 = 0.25 deg C
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2008&month_last=6&sat=4&sst=1&type=anoms&mean_gen=06&year1=2008&year2=2008&base1=1951&base2=1980&radius=1200&pol=reg
It’s cooler now than it was in 1988 no matter how they look at it.
JG, is it possible to plot the adjustments in some way so we can see what the scale and number of them is? Or has this already been done? Even a sample to show for a given station year, how the readings had varied over the last 20 years in consequence of the adjustments.
Or is the raw data simply not available any more?
Check out this GISS map: It shows the movement in GISS temperatures around the world from 1988 to 2008.
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2008&month_last=6&sat=4&sst=0&type=trends&mean_gen=1203&year1=2007&year2=2008&base1=1987&base2=1988&radius=1200&pol=reg
(This map is for the winter months. For some reason, GISS does not allow me to make the comparison for Spring or for June, and I have not yet understood the explanation of why. Of course, a reference to a GISS map is not be understood as an endorsement of GISS methodology.)
Here is a little exercise I did yesterday and you can play too!
Go to NOAA’sUnited States Climate Summary page (it says May but June’s numbers are in the database). Fill in the form as follows:
Leave “Data Type” at it’s default of Mean Temperature
Period: “Most Recent 12-month Period”
First Year To Display: 1998
Last Year To Display: 2008
Base Period Beg Yr: 1977
EndYr: 2007
Leave other data fields at their default and click the “Select” button to plot the graph.
And there you have it. A trend of -0.63C per decade or a cooling rate of 6 degrees per century since 1998. Now that looks to me like some significant “Global Cooling”. Keep in mind that those are only for North America, not global data.
Latest GISTEMP vs. UAH/RSS (Hadley not yet out for June), with adjusted baselines:
http://www.woodfortrees.org/plot/hadcrut3vgl/last:12/offset:-0.15/plot/gistemp/last:12/offset:-0.24/plot/uah/last:12/plot/rss/last:12
REPLY: Odd. GISS goes down while UAH and RSS go up slightly. – Anthony
This may be slightly OT but I have just been reading Steve McIntyre’s post of yesterday on Climate Audit – http://www.climateaudit.org/?p=3255#more-3255 – and it seems pretty important to me, but as a layman fairly new to this debate, I should welcome a view from people here as to just how significant this is? It suggests to me, at least, that a key tenet of the IPCC argument may well be seriously flawed.
Maybe Hansen has bitten the dust. BTW there is no way NH ice is close to last years melting getting to 1 million Km2 above now
http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/current.365.jpg
From CA, the response to Steve Mc’s request for Jones’ Russian station data:
“C) The only data a U.S. government author used in the cited paper was for U.S. An exact replicate of those data are no longer available having undergone continued update and adjustment including the addition of new data and metadata. The earliest digital version of the HCN data is the 1996 release which is updated continuously and available (no charge) at: http://ftp.ncdc.noaa.gov/pub/data/ushcn/urban_mean_fahr.Z. This document will need to be opened with UNIX uncompressed command. From that dataset, the listed stations can be extracted as either raw or adjusted data (adjusted data were used in the Jones et al. article). The data will not exactly match those used in the Jones et al article, but for purposes of testing the analysis as described in that article, there were no major changes in adjustment methods in the data through 1984. For data after 1984 there was an additional correction for introduction of the Maximum Minimum Temperature Sensor (MMTS) measurements at Cooperative Observing Stations, some of which are stations in the HCN.”
So, we cannot check their work or do our own (ok, not me, those of you who get these things done)? Does GISS then adjust adjusted data?
It is still unfathomable to me that historical data is revised every month. I can’t think of a real life analogy for this circumstance. The closest thing that comes to mind is the “fish that got away” whose size tends to be directly related to number of times the tale is told.
Imagine if stock charts were backward revised every month.
I believe someone recently referred to this as the worst scandal in science history going on right under our noses. Piltdown Man didn’t threaten to bankrupt entire economies. Its just waiting for a significant climate cool-down to expose it, I guess, they’re not even trying to hide their re-writing of temperature history.
Maybe they forgot that nature is going to do whatever its going to do no matter what mankind reaches consensus about.
It appears the continuous re-writing is not a conscious attempt at deception, but an artifact of the “adjustment” process.
Don’t confuse bias and group think with maliciousness.
Or as the famous quote goes:
Don’t assume malice when stupidity will explain it.
Despite their many degrees and many “peer-reviewed” papers, the mob psychology that has infected the team is not unusual in the history of science.
Dittos to what jeez and Bill Marsh have said. As an economist, I’m very familiar with preliminary estimates being later revised into final figures. But that’s not what is taking place here, when recent data causes revisions to data going back decades and decades. There is obviously a serious methodological issue with this. Since this is “official US Government data,” why can we not get a Congressional investigation going to look into this? Is anybody in a position to plant a bug in Senator Inhofe or one of his staff and have somebody in a position of .gov influence to look into this?
To follow up to what jeez posted while I was writing, I realize that this is an artifact of the adjustment process used by GISS. Which is all the more reason why the adjustments need to be more transparent. What kind of adjustment process really justifies data revisions like this?
Can someone answer me this:
The “average” daily temperature is taken to be (highest + lowest )/2. Isn’t this, rather the median temperature? Isn’t the average a more important indicator, but would require integrating the temperature samples over the day and then dividing by the number of the samples. Wouldn’t this give a different result, more indicative of the heat of the day?
Robert Wood,
No, it’s the average because only two temperatures are taken per day (the high and the low) using minimum and maximum thermometers. Technically it also is the median, but not in the sense you are thinking as in the case when more frequent reading are taken. Remember this process was started well over 100 years ago when readings were done manually. It took some effort just to get the high and low every day. Only with automated technology can you integrate a lot of reading over the course of a day.
Isn’t this, rather the median temperature?
No, that would be like taking hourly samples and taking the 6th warmest (or coolest) as the measure and paying no attention to how much warmer or cooler anything else was.
Actually, the Min+Max/2 system seems to work quite well and compares favorably with systems that measure temperatures hourly and do the HourSum/24.
But having said that, it is obviously a better procedure to use the latter method. The new NOAA/CRN system is set up to do just that (it’s automated).
What is interesting about the past data adjustments is that net total adjustment to date adds up to +0.7C which is essentially the amount that temperatures have increased since 1900.
So not only are temperatures increasing (falling lately actually) less than predicted by the global warming models, all of that increase is just Hansen and Hadley playing with the historical records (some of which might be justified if we could just find out what they actually did.)
Robert Wood,
(highest+lowest)/2 doesn’t have to be the mean or the median. Although it will be both if you assume a sinusoidal temperature function. The problem is that historically temperatures were not taken every minute or hour. Instead, they used two thermometers that would measure the minimum and maximum temperatures during a specific time period. An observer would check the thermometers once a day, and record them. Thus the best estimate of the mean daily temperature would be (highest+lowest)/2.
by John Goetz
The GISS Ts+dSST numbers are in.
June comes in at 26, continuing the downward trend at GISS and making it the seventh lowest anomaly this decade.
Lots of history was rewritten by the June temperature, with 89 monthly adjustments upward and 22 downward. Most of the downward adjustments were made this decade, and most of the upward adjustments were made pre-1941. At an annual level, 9 years before 1928 were adjusted upward, and 2007 was adjusted downward.
As for 2008, Jan and Feb were unchanged, Mar up 2, Apr up 1, and May up 3. The uplifts in M-A-M surprised me some, because I would have expected out of season months (such as June) to have no effect. Such is the GISS method.
I will post up a plot later today, unless Anthony beats me to it (I like his format).