NOTE: Please note that part 2 is now online, please see it here.
I recently plotted all four global temperature metrics (GISS, HadCRUT, UAH, RSS) to illustrate the magnitude of the global temperature drop we’ve seen in the last 12 months. At the end of that post, I mentioned that I’d like to get all 4 metrics plotted side-by-side for comparison, rather than individually.
Of course I have more ideas than time these days to collate such things, but sympathetic reader Earle Williams voluntarily came to my rescue by collating them and providing a nice data set for me in an Excel spreadsheet last night.
The biggest problem of course is what to do with 4 different data sets that have different time spans. The simplest answer, at least for a side by side comparison is to set their time scales to be the same. Satellite Microwave Sounder Unit (MSU) data from the University of Alabama, Huntsville (UAH), and Remote Sensing Systems (RSS) of Santa Rosa, CA only go back to 1979. So the January 1979-January 2008 period is what we’ll concentrate on for this exercise as it very nearly makes up a 30 year climate period. Yes, I know some may call this an arbitrary starting point, but it the only possible point that allows comparison between land-ocean data-sets and the satellite data-sets.
Here is the first graph, the raw anomaly data as it was published this month by all the above listed sources:
Here is the source data file for this plot and subsequent plots.
I also plotted a magnified view to show the detail of the lat 12 months with notations added to illustrate the depth of the anomaly over the past 12 months.
March 2005 to January 2008, magnified view – click for larger image
I was particularly impressed with the agreement of the 4 metrics during the 1998 El Niño year as well as our current 2008 La Niña year.
I also ran a smoothed plot to eliminate some of the noise and to make the trends a bit more visible. For this I used a 1 year (12 month) average.
Again there is good agreement in 1998 and in 2008. Suggesting that all 4 metrics picked up the ENSO event quite well.
The difference between these metrics is of course the source data, but more importantly, two are measured by satellite (UAH, RSS) and two are land-ocean surface temperature measurements (GISS, HadCRUT). I have been critical of the surface temperature measurements due to the number of non-compliant weather stations I’ve discovered in the United States Historical Climatology Network (USHCN) from my www.surfacestations.org project.
One of the first comments from my last post on the 4 global temperature metrics came from Jeff in Seattle who said:
Seems like GISS is the odd man out and should be discarded as an “adjustment”.
Looking at the difference in the 4 times series graphs, differences were apparent, but I didn’t have time to study it more right then. This post today is my follow up to that examination.
Over on Climate Audit, there’s been quite a bit of discussion about the global representivity of the GISS data-set due to all of the adjustments that seem to have been applied to the data at locations that don’t seem to need any adjustments to compensate for things like urban heat islands. Places like Cedarville, CA and Tingo Maria, Peru both illustrate some of the oddities with the adjustment methodology used by NASA GISS. One of the issues being discussed is the application of city nightlights (used as a measure of urbanization near the station) as a proxy for UHI adjustments to be applied to cities in the USA. Some investigation has suggested that the method may not work as well as one might expect. There’s also been the issue of whether of not stations classified as rural are truly rural.
So with all of this discussion, and with this newly collated data-set handed to me today, it gave me an idea. I had never seen a histogram comparison done on all four data-sets simultaneously.
Doing so would show how well the cool and warm anomalies are distributed within the data. If there is a good balance to the distribution, one would expect that the measurement system is doing a good job of capturing the natural variance. If the distribution of the histogram is skewed significantly in either the negative or positive, it would provide clues into what bias issues might remain in the data.
Of course since we have a rising temperature trend since 1979, I would expect all 4 metrics to be more distributed on the positive side of the histogram as a given. But the real test is how well they match. All four metrics correlate well in the time series graphs above, so I would expect some correlation to be present in the histogram as well. The histograms you see below were created from the raw data from 1979-2008. No smoothing or adjustments of any kind were made to the data. The “unadjusted” data in this source data file were used: 4metrics_temp_anomalies.txt
First we have the satellite data-set from UAH:
University of Alabama, Huntsville (UAH) Microwave Sounder Data 1979-2008 – click for larger image
The UAH data above looks well distributed between cool and warm anomaly. A slight warm bias, but to be expected with the positive trend since 1979.
Next we have the satellite data-set from RSS:
Remote Sensing Systems (RSS) Microwave Sounder Data 1979-2008 – click for larger image
At first I was surprised at the agreement between UAH and RSS in the percentages of warm and cool, but then I realized that these data-sets both came from the same instrument on the spacecraft and the only difference is methodology in preparation by the two groups UAH and RSS. So it makes sense that there would be some agreement in the histograms.
Here we have the land-ocean surface data-set from HadCRUT:
Hadley Climate Research Unit Temperature data 1979-2008 – click for larger image
Here, we see a much more lopsided distribution in the histogram. Part of this has to do with the positive trend, but other things like UHI, microsite issues with weather station placement, and adjustments to the temperature records all figure in.
Finally we have the GISS land-ocean surface data-set:
NASA Goddard Institute for Space Studies data 1979-2008 – click for larger image
I was surprised to learn that only 5% of the GISS data-set was on the cool side of zero, while a whopping 95% was on the warm side. Even with a rising temperature trend, this seems excessive.
When the distribution of data is so lopsided, it suggests that there may be problems with it, especially since there appears to be a 50% greater distribution on the cooler side in the HadCRUT data-set.
Interestingly, like with the satellite data sets that use the same sensor on the spacecraft, both GISS and HadCRUT use many of the same temperature stations around the world. There is quite a bit of data source overlap between the two. But, to see such a difference suggests to me that in this case (unlike the satellite data) differences in preparation lead to significant differences in the final data-set.
It also suggests to me that satellite temperature data is a more representative global temperature metric than manually measured land-ocean temperature data-sets because there is a more unified and homogeneous measurement system, less potential bias, no urban heat island issues, no need of maintaining individual temperature stations, fewer final adjustments, and a much faster acquisition of the data.
One of the things that has been pointed out to me by Joe D’Aleo of ICECAP is that GISS uses a different base period than the other data-sets, The next task is to plot these with data adjusted to the same base period. That should come in a day or two.
UPDATE1: I’ve decided to make this a 3 part series, as additional interest has been generated by commenters in looking at the data in more ways. Stay tuned for parts 2 and 3 and we’ll examine this is more detail.
UPDATE2: I had mentioned that I’d be looking at this in more detail in parts 2, and 3. However it appears many have missed seeing that portion of the original post and are saying that I’ve done an incomplete job of presenting all the information. I would agree for part1, but that is what parts 2 and 3 were to be about.
Since I’m currently unable to spend more time to put parts 2 and 3 together due to travel and other obligations, I’m putting the post back on the shelf (archived) to revisit again later when I can do more work on it, including show plots for adjusted base periods.
The post will be restored then along with the next part so that people have the benefit of seeing plots and histograms done on both ways. In part 3 I’ll summarize 1 and 2.
In the meantime, poster Basil has done some work on this of interest which you can see here.
UPDATE3: Part 2 is now online, please see it here.
[…] A look at temperature anomalies for all 4 global metrics […]
I am not a scientist, but the explanation for the above seems very obvious to me.
The basic principle that James Hansen and GISS work on is that there is a strong correlation in trend between neighbouring weather stations. Neighbouring stations are defined as any within 1200km (or 745 miles). Therefore any station is going to be under suspicion if its trend does not correlate well with two “neighbouring” stations in a radius of 745 miles.
If we assume that there is a general small warming trend as shown in the RSS and UAH data this will mean that most stations will show a positive trend. The minority that show a genuine cooling trend will therefore come under suspicion and when compared to their neighbours will need to be adjusted upwards so that the “error” is removed. Of course this upward adjustment of stations creates its own positive feedback, because these adjusted stations can then be used to justify making upward corrections in other stations. And so it goes on.
But the satellite data was “adjusted” to match the surface station data, because the sat data diverged, and was thought to be wrong?
Once again, when the issue of basic climate data is examined in depth, the data pumped out by our own government, generated by our tax money, is heavily biased toward a global warming conclusion. Hansen’s NASA data is clearly out of line with these other temperature sets. Hansen needs to be constantly vetted and called into account for his data sets. As a goverment employee, he should be held to the highest standard to examine and remove any biases from his work. If he continues to stonewall requests for all his data sources AND adjustments, he should be removes and replaced with a real scientist, not an agenda driven self-promoter.
Obviously the satellite data needs to be “adjusted” more! Where’s Hansen?
Facetiousness aside, this is very interesting, great idea to use the histogram to look at this data in another way. Often looking at data using a different technique will yield interesting insights, and this appears to be one of those cases. It’s hard to imagine that much of a shift without a significant warm bias in both the raw ground data and the adjustments made to allegedly correct it for UHI effects. As is being shown with your surface stations effort, and Steve McIntyre’s efforts looking at the code for the adjustments, we’re finally seeing all the warts on the ground data, and why it should not be relied upon.
Excellent work.
Anthony:
Can you spell out for those of us who are slow on the uptake, which anomaly you are referring to. Also doesn’t the difference in the positions of the satellite data series compared to the ground data essentially anticipate what you see in the histograms?
So the task now is to identify the “good” CRU and GISS sites and see if they will give a distribution similar to UAH/RSS. That is, will the most pristine sites eliminate what certainly looks like a systematic warm bias? I know folks are well on the way to doing this, but how would one or two particularly well known good sites compare to the satellite histograms?
Perhaps one of the agencies is using new math in its calculations and 2+2 might = 5. I find it hard to understand how the basicly the same instruments
(raw data) can have such divergance with out there being some sort of adjustment/bias being introduced into the data. I would wonder how the satalites have been calibrated over the years that the one agency with it wwould seeem more control would have come more close to the median of the satalite data.
just a few thoughts (probably not coherent)
Bill
I have to admit the first comparison GISS data doesn’t look quite as bad as in your earlier post. It’s also interesting that the more smoothing or filtering done it looks progressively less an outlier from the shape of the curve.
The histogram, however, is a telling comparison. Good analysis!
Anthony,
You should make it clear what the base period is for each of the datasets. You may be comparing apples and oranges if the base periods are different.
Hey! I got my name in lights! hehe.
And of course, as has been pointed out before, choosing 1979 as an anomaly starting point is extremely arbitrary. No fault of yours, Anthony, I understand that, but really, who gets to decide what the anomaly starting point is? Choosing a cold year is cherry-picking, choosing a warm year is cherry-picking. Therefore a mean of the entire century should be taken and that used as the measuring stick for any anomaly. Still, such a thing would have very little
value, in my opinion.
REPLY: The choice was not arbitrary, it was so that all four sets macthed in time scale. Satellite data starts in 1979.
On the anomaly charts, there is a clear difference between the satellite and GISS data to the right of the 1998 El Nino event. The satellite plots feature a head in 1998 and clearly defined shoulders on either side. The shoulders on the right are a little higher than those on the left but not by much, and El Nino is clearly a head (peak). In contrast, the GISS plot also shows the distinct rise in 1998, but the shoulders to the right are sloping upward and are level with the 1998 peak, forming what I call a hunchback.
Satellite plot: head and shoulders
GISS plot: hunchback
In summary, it appears that GISS is exploiting weaknesses in the SST measuring system and that you guys (especially CA) are incrementally revealing the tricks of their trade.
That’s raw GISS data. I have the GISS-adjusted, homogenized (and probably sissified) data, and it puts 2005 as the warmest year.
1998: .71
1999: .46
2000: .42
2001: .57
2002: .69
2003: .67
2004: .60
2005: .76
2006: .66
2007: .73
Those are the last 10 years of GISS “anomolies”. Cooked.
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
Note that 1998 clocks in at #3 on the hit parade. (2007 is #2.) They didn’t even “lose” El Nino. Just dumbed ‘im down.
On closer inspection, I see that GISS 2005 and 2007 are higher than 1998, even on your graph. (That’s also the case for NOAA data, come to think of it.)
Watt’s up with that?
REPLY: This data set is more “current” than the one from last summer.
And so it goes on.
Can’t you just see them adjusting UHI by comparing them with those CRN-4 and CRN-5 rural stations? (Thus concluding that UHI isn’t really all that much of a factor.)
fascinating. long time lurker (I read your stuff at Climate Audit) first time poster.
putting up all 4 data sources just proves to me that something weird happened over the last year.
Too bad the science is settled. I’d love to find out what it was.
(I’m being sarcastic about the science being settled.)
I mentioned that, or tried to. But is 1979 the anomaly point?
REPLY: Ah ok I see what you are getting at. I’ll put that up.
“When the distribution of data [GISS and HadCRUT] is so lopsided [compared to RSS and UAH data], it suggests that there may be problems with it”
They’re actually measuring two very different things, as I’m sure you know. And it’s possible for them both to be correct. The temperature at ~2m [GISS, HadCRUT] is likely going to be related to the integrated brightness temperature from the ground to 10km (with appropriate weightings) [RSS, UAH]. The fact that the 4 temperature measurements are so similar, in my opinion, gives weight to the fact that the rise in temperatures is not an artifact of measuring technique.
“But, to see such a difference [in the surface temperatures] suggests to me that in this case (unlike the satellite data) differences in preparation lead to significant differences in the final data-set.”
GISS has a method, right or wrong, that estimates the poles. I don’t think HadCRUT does, which could explain the difference.
Raven: “You may be comparing apples and oranges if the base periods are different.”
The base periods are different. GISS is always above HadCRUT, which is nearly always above the other two. This means that GISS has a base period where the temperature is the lowest, HadCRUT has a slightly higher temperature in its base period, and the satellite records have an even higher (but about equal) temperature during their base periods.
I forgot to mention, if you didn’t adjust the data in the 4 time series so that they had the same base period any conclusions you draw from the histogram analysis are likely to be wrong. I only mention this because it doesn’t appear that this occured. The easiest way to do this is to set 1979-2008(Jan) as the base period, and subtract the mean out of all the time series above. I suspect you’ll find the histograms in much better agreement.
REPLY: I’ve been corresponding with Joe D’Aleo at ICECAP on the very subject of the difference in base period. I have the adjusted base period data in hand, courtesy of Earle Williams, and I’ll be plotting that next and doing the same analysis. It will be interesting to see if the distribution in the histograms changes significantly. Stay tuned.
Here is a comment from Dr. Christy on CA:
Now, I have one misrepresentation to point out on Steve M.’s charts. The temperature comparisons shown are not apples to apples. All climate models indicate the global tropospheric temperature should warm at a rate of 1.2 times that of the surface (1.4 times that of the surface for the tropics – see CCSP SAP 1.1. or Douglass et al. 2007). So, to put surface temperature projections from models on a chart with observed tropospheric temperatures, one must reduce the tropospheric temperature trend by a factor of 1.2 for the comparison to be legitimate. I think the result would be of interest to the readers, and it is entirely defensible as shown in numerous publications.Adding this factor would compensate for the differences between satellite and surface measurements which Atmoz noted.
REPLY: Thanks Raven, good catch. This is all an ongoing learning experience, and I welcome this kind of input! Thanks to Earle Williams, I have the datasets adjusted to a common baseline, I’ll plot those next, then I’ll attempt what Dr. Christy suggests.
Looks like this will end up being a 3 parter then.
But they forgot the burping cows. http://www.foxnews.com/story/0,2933,332956,00.html
Atmoz beat me to the punch. Since GISS uses a cooler base period than the others, more of it’s anomalies will be positive rather than negative. They need to be compared using the same “normal” period or else it is not a valid comparison. Also, just so I am clear, I believe you indicated you were using the GISS data prior to homogenization. Is this correct? It would be especially interesting to see a comparison of both the un-homogenized and homogenized data sets to the other metrics to see just how much of a difference the homogenization protocols really make.
Thanks
Bob
Is this dataset available for download anywhere?
I had the same thought as D’Aleo about the difference in base period, so it will be interesting to adjust for that and see what we have.
Either way, I’ve some regressions I’d love to run on the data if it is available.
REPLY: Thanks See this original post which started it all. There are links to the datasets under each graph. I’ll be happy to post whatever your analysis brings, as I am interested too.
http://wattsupwiththat.wordpress.com/2008/02/19/january-2008-4-sources-say-globally-cooler-in-the-past-12-months/
I took the data provided in the original post and subtracted the mean from each time series, and plotted the 4 new time series and histograms. I don’t have a quick way to make the cool histogram plots that Anthony does, but they are much more similar. I look forward to seeing your analysis.
http://atmoz.org/blog/2008/02/27/4-global-temperature-anomalies-say-the-same-thing/
REPLY: Thanks for doing that, the real question though is why does GISS use a different base period than the other data sets?
More on this later as I promised.
Anthony: In the 12-month Smoothed 1979 to 2008 graph, a major difference between sets shows up in the HADCRUT data after the 97-98 El Nino. For each of the others, their respective minimum temperatures in the troughs preceding and following the El Nino are approximately the same value. Not the HADCRUT. The minimum temperature in the trough that follows is about 0.15 deg C higher than the minimum temperature of the preceding trough. I’ve noticed it in their data for years, but I didn’t know it didn’t exist in the others. What did Hadley change around then?
REPLY: Don’t know he answer to that? Anyone?
Thanks for the shout out Anthony. The data I used to collate all four indices were from the links provided under each graph in Anthony’s post here:
http://wattsupwiththat.wordpress.com/2008/02/19/january-2008-4-sources-say-globally-cooler-in-the-past-12-months/
The GISS data was gathered on Feb 14, 2008. The other three were downloaded on February 25, 2008. If you go to the GISS data here ( http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt ) you will see the GISS in table form, not conducive to plotting by month in Excel. I manually rearranged the GISS data into a single column with each monthly value per row, then joined that with the other three temperature indices, which are nicely organized into columns.
I agree that for direct comparison of all four it will be helpful to see them centered around the 1979-2008 mean. I disagree with Atmoz in that any histogram analysis are likely to be wrong. Conclusions based upon the histogram midpoint are not wrong, per se, as that is what the data present because two of the series have a much longer history. Conclusions regarding the shape of the histograms are entirely appropriate regardless of where the histograms are centered. Such analysis is facilitated when they histograms are centered about a common point.
I don’t find it surprising at all that the histrograms look as they do, because the surface readings are indeed shifted upwards relative to the satellite data. That’s what you would expect of two normally distributed random variables with different means. However, that doesn’t necessarily say that there is a data quality issue. That just says that one data set is biased upwards relative to another.
The question is not whether the surface temperature readings are higher than those of the sats (they obviously are), the question is whether the difference between the sats and surface temps are changing over time….and it appears to me that they are not (at least not significantly).
I compared the average satellite and average surface numbers, then took the 12 month moving average of both the satellite and surface readings. Although MA for surface is certainly above that of the satellite, the delta between the surface and satellite MA’s does not show any kind of significant trend (the slope of the regression line for the delta of the MA’s is 6.5E-05)
Although I would tend to have more faith in the satellite data, I don’t think that the histogram views says anything significant about the data quality.
Anthony, as Raven has said, you should make it clear that the reference period for all the four metrics is the same.
Since you are plotting anomalies, the reference period on which anomalies are computed, needs to be the same.
For istance, HadCRUT reference period is usually 61-90, for GISS is 51-80, whereas for satellite based estimation is more recent.
REPLY: Yes, agreed and all coming in part two in detail, I’ll make some notations to this post though.
Excellent work here Anthony. The data seems to support our feeling that GISS is nothing but loaded data.
Looking forward to parts 2 and 3…..
REPLY: It is premature to conclude that, a further examination is needed to be know what the differences are. Hence, parts 2 and 3.
BTW, I know it’s impossible for the RSS and UAH data, but I’d like to see “normal” be the entire 20th century. 1901-2000. That should get at least one complete PDO cycle in the “average”.
Matt
At my blog I have created a graph with all the histograms combined – I think it makes the relationships rather clearer.
REPLY: This is worth a read, folks. Thanks -Anthony
“It is premature to conclude that, a further examination is needed to be know what the differences are”
Fair enough. I suspect the GISS data is using a time period for the “Average” that has more of the negative phase of the PDO in it. Hence, all measurements based on that average are warmer. All the numbers seem to track each other well, with the difference being only where they are above or below “normal”.
Anthony:
I emailed NASA/GISS about their choice of reporting period, and the short answer was: “We use it because we’ve always used it”.
Adding this factor would compensate for the differences between satellite and surface measurements which Atmoz noted.
Hold it. How is that going to compensate for the differences? If surface temps are higher and we are supposed to reduce [sic] troposphere temps by a factor of 1.2, isn’t that just going to drive the differences even further apart?
OTOH, that would sure as heck confirm the degree of microsite error and its impact on the delta!
I get the impression I may have this backwards, but I can’t see how.
By the way, 1928 and 1969 are zero-anomaly years for GISS.
For zero-anomaly, GISS, for some reason unexplained, uses the mean of 1951-1980.
That figure is: 14C (or 57.2F )
Anthony, on a somewhat related issue, a month or so back, someone at CA did histograms of the amount of warming in USHCN sites for each of the CRN ratings.
Two things jumped out at me.
One was how skewed the distribution for sites with poor ratings was to the warming side. Which clearly showed the bulk of the warming was coming from problem sites.
The other was that the good sites showed a normal (or much less skewed) distribution with the mean and mode showing only slight warming. If I recall correctly, less than 0.2C.
So we’re basing entire economies off an arbitrary anomaly value during a notoriously cool period in the 20th century. Wonderful.
Look at it this way. No matter which base period one chooses, GISTEMP shows a series of ascending peaks post-1998. Each peak is higher than the last and higher than the El Nino peak. The higher highs (to borrow stock chart terminology) enable a certain someone to make certain claims about changes in global temperature, claims that are not supported by the other three studies.
While these differences in anomalies may not appear to be significant from a scientific or statistical perspective, it strikes me that they will be very significant when viewed from public relations and public policy perspectives.
What’s up with GISTEMP?
Harold Vance, you hit the nail on the head. I’m so sick of hearing that science says that x year was the nth warmest EVER, when the claim is clearly not scientific, becuase it isn’t independently verified! HadCrut falsifies GISS, GISS falsifies HadCrut, one or both must be totally wrong, but the media act as if GISS is the only surface network out there (and the scarcely even mention the sattelites!).
By the way, might be interesting to include balloons. But any idea why they end in 2005?
http://cdiac.ornl.gov/ftp/trends/temp/angell/global.dat
http://cdiac.ornl.gov/ftp/trends/temp/sterin/global.dat
Actually, there’s HadAT, to:
http://hadobs.metoffice.com/hadat/hadat2/hadat2_monthly_global_mean.txt
I realize part of the point is the recent drop, but it would be interesting to see, anyway.
The smoothed 12 month average graph makes this dip look a lot like 1998. Do you think this might be relevant, or is it an artifact of massaging the data?
Thanks.
William R says “The question is not whether the surface temperature readings are higher than those of the sats (they obviously are), the question is whether the difference between the sats and surface temps are changing over time….and it appears to me that they are not (at least not significantly).”
However, it seems obvious from the graph that the difference in readings is larger post-1998 than pre-1998.
This difference requires data analysis, which it seems to me has been accurately done by the surface stations project.
What’s up with GISTEMP?
Well . . . you mean besides 2005 and 2007? #B^1
I downloaded GISS and HADCRUT3GL data to do a comparison. I originally started with the Hadley data. Then I took the GISS data; divided by 100 and subtracted 0.1 and it sort of tracked Hadley data OK, until the late 1990’s. I saw the same thing that Harold Vance saw.
To go further, I originally plotted a 12 month moving boxcar. Both Hadley and GISS showed a “sawtooth” pattern from about 2001 onwards. I wondered if there was anything special about 12 month moving averages. I went back to square 1, and plotted a “1 month moving average”, 2 month moving average, etc, etc, until I got to the 25 month moving average, by which time I was getting bleary eyed. Anyhow, I noticed that with a 19 or 20 month moving average, the “sawtooth” pattern almost entirely disappeared on the Hadley graph, reappearing as I went on to 25 months. Does the Hadley graph indicate a 19 or 20 month periodicity somewhere? I know of one natural event with that approximate time length, but I really want to stay away from Velikovsky with a 10-foot-pole.
The 19 and 20 month moving averages really emphasized the difference between the 2 datasets. GISS showed a “checkmark”. I.e. a small decline, followed by a larger rise, while Hadley showed a “round top”. Both graphs started declining in mid/late 2007. But the differences do make me suspicious that one of them has to be badly broken.
So Anthony, I have this obsession that the satellite data shows no significant warming between Jan 1979 and Dec 1997. So I took your data and compared GISS and UAH data over two time periods using histograms – Jan1979 to Dec 1997 and Jan 1998 to present. The results:
GISS Jan 1979 to Dec 1997 Cumulative % of -ve values to 0 anomaly, was 8%
UAH Jan 1979 to Dec 1997 Cumulative % of -ve values to 0 anomaly, was 54%
GISS from Jan 1998 to Jan 2007 0.55 positive anomaly Cumulative % =63%
UAH from Jan 1998 to Jan 2007 0.55 positive anomaly cumulative % = 93%
1 The satellite data shows no significant warming or cooling for 19 years – explain that with the CO2 hypothesis.
2 The satellite data does not show the same amount of warming post 1998 as GISS.
I personally like any hypothesis other than that of CO2. The Canadian government has just allotted 254 Million dollars for CO2 geo-sequestration in Alberta. Looks like a colossal waste of money!
GISS seems to have this prejudice towards excess warmth. (Hadcrut, if you do the same analysis, not so much)
I think you already proved, no chiseled in concrete, that the GISS data, and some other data, is wrong and even deliberately falsified (though you would never make such an accusation!) It may already be too late to mitigate the humanitarian disaster which could arise if temperature is really going down!
Our politically correct prognisticators have made ignorance of history a virtue; you’ve shown they also ignore the present. It surprises me you address them as colleagues.
Can you please overlay CO2 concentrations onto the temperature graphs. Seems to me the essence of the Global Warming issue is the relationship between CO2 in the atmosphere and global temperature. Personally, I would like the warmists to stare at a credible display of verifiable global temperatures vrs a display of verifiable CO2 concentrations so that I can posit the question: How is it possible that there is an inverse relationship? See, I only have about 30 seconds of their attention before they flit off to wherever they go so I need something simple.
Thanks,
Larry
There’s a scenario that I seldom see mentioned, since the debate on global climate change is so polarized:
What if greenhouse gases are indeed heating up the climate, while at the same time, particulate matter (aka global dimming) and decreased sun activity are cooling down the climate. We could be in a period where global warming is being masked by other factors…
http://newsprism.wordpress.com/2008/02/14/faith-and-skepticism-in-environmental-politics/
Rev, To be clear, the GISS data you have in that 4-way link is unadjusted, the way you labelled it, right? (It does NOT match the adjusted data I linked to earlier, which continues to Jan 2008).
But isn’t unadjusted GISS data the same thing as adjusted NOAA data? (I.e, bumped up 0.29C with the 2005 and 2007 leading the charge.)
REPLY: 4 way link? I’m confused by this entire question.
I like your article on newsprism, it was interesting. What’s fun is requiring the gorebal warming skeptics(with whom I agree) to now apply first principles of scientific data, evidence and the scientific method itself, to the claim of the existence of god, per Rush, Sean, Anne and their ilk.
Suddenly the scientific method is flawed, there’s no logic, no reason just the “faith” card which only represents an individual’s lack of full commitment to their religion – it’s no different than the gorebal warming religion – just in different clothing.
LOL! It’s laughter at its best; entertaining and amusing.
There are 3 major differences between hadcru and giss.
1. giss estimate the polar region, hadcru do not.
2. hadcru use different stations
3. hadcru use a different method for grids that are a mix of land and sea
and the base peroid is different
I now see where Anthony linked to his original dataset.
When others get around to normalizing all four series on the series’ means, I think they will be intrigued by the results. Superficially, the satellite series appear more volatile, or extreme, than the land-sea series. Would there be a physical basis in climate science for this, or is it an artifact of the methodologies employed?
I’ll have more, later. If not before this thread gets old, then maybe we could take it up in a dedicated discussion on the CA forum.
REPLY: Our master of heat sinks, Evan should be able to tell you.
Evan Jones,
The 4-way link, ie. the text file with all four metrics, is captured directly from the spreadsheet I sent Anthony. The ‘Unadjusted’ label was mine, meaning those are the data exactly as distributed by the respective agencies. Whatever GISS or the others did to get their data into that form you will need to follow the original links to find out.
My notion of adjusted was what everyone else is describing with respect to subtracting out each series’ mean so that they are all centered around zero for the 1979 – 2008 time period. That is the adjustment I had in mind.
By the way, I tried not to use the word normalized, because in my mind normalizing implies scaling a series to fit within a defined range. The only thing I would consider doing to these data are to slide them up and down the Y axis, since as ‘temp anomaly’ profiles they reference an arbitrarily defined temp, in this case defined by the mean temperature over arbitrary time periods. The differences in those time periods is why the base temperatures differ.
I observed the same thing in the data as Basil, that the sat data appears to have a wider range. Given that these are completely differing methodologies not even measuring the same physical phenomena, I am more surprised at the degree of similarity.
Obsessive Ponderer says ” 1 The satellite data shows no significant warming or cooling for 19 years – explain that with the CO2 hypothesis.”
I’d venture to say that the sat data shows no SIGNIFICANT warming at all, ever, at least not in the realm where one can attribute what there is strictly to CO2. If you were to plot the change in population and the change in land use (forest now being farms, growing cities, etc.) and assume that said changes also have an influence, I don’t see how there’s enough signal to claim that the CO2 is even detectable. Problem is that I don’t know enough of the physics of this to know how the land use affects what the sats measure, but surely this is being measured as well.
I always thought that global warming meant more extreme weather, rather than specifically a warmer average temperature across large regions.
4 way link? I’m confused by this entire question.
Sorry. I meant the link in your above post to the 4 data series. It says the data is unadjusted.
Mr. Williams: Ah. Thanks for the clarification.
ANTHONY:
Blogger CCE at another site applied a twelve month “moving” average to the 4 global temperature metrics claiming that approach superior when examining anomalies. Assuming he used the same datasets, his results posted here:
http://cce.890m.com/temp-compare.jpg
show that GISS is in the mainstream of reporting temperature change and not at the high end.
When you write again, would you please comment on the efficacy, if any, of a “moving” average.
CCE also wrote that “[he keeps] hearing about how GISS is trending away from RSS. If anyone would bother to actually check, RSS and GISS have been converging over time.” Given your writings here and at surfacestations.org, and those at CA about the data difficulties, is there any basis for CCE’s claim?
EVAN CHECK YOUR EMAIL – Anthony
Brian Flynn,
The data are available above for you to look at and judge for yourself. If you don’t have software for displaying graphs and performing calculations on data, such as Excel or Microsoft Office, you can download a free software package called OpenOffice, which includes a spreadsheet application that will permit you to view the data yourself. I don’t mean to put you off in this regard, just mentioning that some simple but powerful software is available to anyone with a computer at http://www.openoffice.org .
If you plot the monthly data and calculate the difference between the GISS and RSS data you will see that they vary generally in the range of +/- 2 tenths of a degree Celsius over the 1979 – 2008 time period. One thing about smoothing the data is you may disregard the underlying variance in the data. There is no statistically significant trend in the monthly difference between GISS and RSS.
Please be aware that the GISS temperature calculations involve adjusting historical temperatures up or down to account for resumed biases and urban impacts in the observational data. Most recent temperatures are not adusted in calculating the GISS temperature anomaly. Any perceived recent convergence between the two measures could be an effect of the GISS method of adjusting the past temperatures.
Re Brian Flynn,
I quickly ran Anthony’s data through Excel 2007 Moving average using 12 months (putting 12 in the interval box). My initial reaction from looking at the link given was HUH?
Now I am far from an expert on this data manipulation stuff, but my chart (don’t know how to post it from Excel) looks absolutely nothing like the graph from the link(ie the exact opposite). I’m I doing something wrong, or are we looking a data manipulation par excellence?
I’ve normalized the data series, and fit a series of trend and step functions to the data. Rather than post a spaghetti graph, here’s a link to a plot of just the trend lines:
http://i31.tinypic.com/vzagj9.jpg
At least since 2001, it doesn’t look like RSS and GISS are converging. All four data sets show a downward trend since 2001, but the downward trend for GISS is much less than the downward trend for RSS.
Some quick observations:
From 1993 through 2001, all four series show a remarkably similar trend apart from the 1998 El Nino. Prior to 1993, the UAH series stands out from the other three. Since 2001, RSS and HadCRUT show almost identical downward trends, all the more remarkable for one being satellite, and the other a land-sea set.
When I get the time, I will try to collate the actual numbers into some kind of readable table.
To Obsessive Ponderer, all the data sets show some warming from 1979 to 1997, just as they all show cooling since 2001. But the warming was much less than is inferred from a simple trend line through the data for the entire time frame, as is common.
Brian: Anthony’s 12-month moving averages have been smoothed and CCE’s moving averages have not been smoothed. That’s the difference between the two charts. The idea of smoothing is to reduce the noise level.
I’m still struck by the headless El Nino (1998) of GISTEMP, including the one on CCE’s chart. This is a feature that is unique to GISTEMP, and it does not square up with HADCRUT, RSS or UAH.
In stock trading, the headless El Nino would be called a divergence. In our case, the equity GISTEMP is posting higher highs whereas the other three equities in the industry group are trending downward or sideways and are therefore failing to confirm GISTEMP’s uptrend. The obvious play for the trader here is to sell shares of GISTEMP short, meaning betting against the uptrend. (The assumption behind this trade is that part of a stock’s movements can be attributed to the overall movement of the peers in the industry group, especially if the moves are confirmed by hard data, such as news of declining sales, etc.)
Basil,
I beg to differ on the trend shown by UAH and RSS data from 1979 to approx Dec 1997.
I first noticed this on a bar graph of the satellite global temp. Line graphs seem to indicate an upward trend. If you look at a bar graph it does not.
If you put a simple trend line on a graph of satellite data from 1979 to Dec 1997 it will give you a very slight upward trend.
I asked W Briggs about this. Look at his website especially the wavelet analysis and his emphasis on going beyond simple trend lines. http://wmbriggs.com/blog/category/climatology/.
He doesn’t say it (being a causcious scientist) but I will – there is no discernable satellite temperature trend until the middle of the 1990s.
Then something happens – he indicates in 1996 or 1998.
If you do a histogram of the satellite data from the same dates (I did it from Jan 1979 to Dec 1997) the data is symmetrically centered on 0 with the the 0 mark’s cumulative %age = 54%. If I am not mistaken, this is about as perfect as you can get with real data. No warming, no cooling.
This is 19 years, surely a significant amount of time if CO2 was the most significant driver of global temperatures. It indicates to me that there is a more important influence or influences on climate than CO2. It concerns me that my government (Canadian) is spending 240 million dollars on sequestering CO2 (not 254 million as stated above-my bad) and all three US presidential candidates believe in CO2 catastrophic warming and one of them can’t wait to stop climate change.
Also, notice that the increase in global temperatures (if the sat data is more representative of the real situation) was just in time to give a impetus to the CO2 hypothesis (I believe the Rio conference was in 1992).
Simple trend lines will not convince me CO2 is the big culprit here.
There is something amiss in the GISS temperatures. We need to heed what the data is telling us.
Harold Vance
(The assumption behind this trade is that part of a stock’s movements can be attributed to the overall movement of the peers in the industry group, especially if the moves are confirmed by hard data, such as news of declining sales, etc.)
By the time the hard data becomes public (“MSM”) the damage is almost always done.
It is always good to check with the company’s suppliers before jumping on the “old bandwagon”. Several companies in an industry group could be “cooken the books”.
Obsessive Ponderer,
I looked at the Briggs web site. Do you know which version of the satellite data he used? I ask, because since last posting, I recalculated the trend lines using Cochrane-Orcutt to handle the serial correlation (what Briggs is probably alluding to when he talks about examining the residuals). I don’t have the results in front of me (they are at work, I am at home for the evening), but the UAH “trend” is nearly flat in the first period (given the way I broke the data up, that’s 1979-1992), and not significantly different than zero. I don’t recall the significance of the other data sets. I’ll look at it tomorrow.
I’m certainly not trying to infer anything about the influence of CO2 from simple trends. My interest is in discerning what the real trends are, regardless of the cause. And whatever they are, they are not uniform over very long periods of time, like is often inferred. While we might differ on how much, I don’t see a lot of increase from 1979 to 1992, a pretty obvious increase from 1992 through 2001, and then all the data sets show decreases since 2001. In fact, in the trends corrected for serial correlation, the trends since 2001 are even more sharply downward than in the image posted earlier today.
I’ll post more tomorrow, including statistical significance of the “trends” — whatever they are.
How does Jan 2008 show up on your graph of 12-month average temperature anomalies? Don’t you need 6 months of data AFTER Jan 2008 to calculate a 12-month average centered on January? Or is it a trailing average (average of the previous 12 months)?
REPLY: The data was supplied by the four agencies already in anomaly form, I did not calculate it.
GISS and RSS have been converging over time. The “divergence” of GISS from RSS over recent years is no different than any number of past “divergences.”
Other than the obvious satellite/instrument difference, the satellite coverage extends from 82.5 North to 70 South. All but the tip of Antarctica is excluded, as is the small area around the north pole (and the high altitude areas of the Himalayas and Andes). GISTEMP interpolates areas of poor coverage which gives it more polar coverage than the other analyses. GISTEMP and HadCRU show virtually identical warming over the past 30 years despite their differences. You will get periods where they are different and we expect differences.
This plot shows RSS subtracted from GISS, centered around 0.
http://cce.890m.com/gissvsrss.jpg
Over the entire Satellite era, RSS and GISS are converging. Over the most recent years, GISTEMP has trended upwards slightly in relation to RSS and the others, but this is no different than any number of times they have diverged (in either direction), and the magnitude of the divergence is not large compared to the differences with the early satellite data.
Slightly O.T. for the direction the thread has taken, but on the television network The C.W.’s morning news show “The Daily Buzz”, they reported this cooling trend. While their news coverage is a mile wide and an inch deep, the host said that all four temp records show a downward turn and have dropped so much in the last year that they have erased the rise of the past century.
This is where the war will be won or lost. It is the popular media, defined somewhat differently than the Main Stream Media, that influence the Dumb Masses and determine the duration of the hoax.
I find it interesting that Gavin hasn’t found the “spare time” in his day to post on this or any other topic in over a week.
Bob…L
As much as I appreciate and applaud Anthony’s work, he’s fueled a misleading view of the data when people make statements like “[temps] have dropped so much in the last year that they have erased the rise of the past century.” The drop from January 07 to January 08 was certainly of that order of magnitude, but January 07 was unusually warm. If you look at the trend lines I posted here:
http://i31.tinypic.com/vzagj9.jpg
Even with the downturn since 2001, we haven’t entirely erased the trend since 1979, let alone the past century. I’m with those who think we may be entering a cooling phase, and that the recent warming was more attributable to natural factors, than human, but I don’t think we should overstate what the data actually shows.
REPLY: Well the blame for the “misleading view” lies with Michael Asher, of Daily Tech. I complained within a few minutes of its posting about the use of the word “erase” and other issues, he was slow to react. It took three tries to get him to move, and by then hours had passed.
But, the same thing happens sometimes when NOAA or GISS puts out a press release. Unfortunately, our media is far from perfect, and they often don’t understand science well enough to translate it for the layman. I try to do that here, but even then, such terrible mistakes occur. I regret that it has happened but I can’t do much to put that genie back in the bottle.
Basil, Anthony obviously can’t control what people think, or prevent them from misinterpreting his presentations, happens constantly on both sides of the issue.
What he DOES show is that we just don’t know enough about global climate to make any major decisions about anything.
Please correct me if I’VE misinterpreted your data, Anthony.
Anthony,
I know you were not responsible. And you are right that the “warmists” do the same thing on the other side whenever they can get away with it.
FWIW, here’s the latest plot of “trends” I’ve fit through the four series:
http://i29.tinypic.com/302oxli.jpg
These are more “robust” than the previous trend lines, since I used Cochrane-Orcutt estimation to handle serial correlation. The resulting trends, in a tabular format (I hope this turns out readable):
1979:01-1992:12
---------------------------------------------
GISS 0.000783764** (0.094C/decade)
HadCRUT 0.000460122** (0.055C/decade)
RSS_MSU 0.000498964 (0.060C/decade)
UAH_MSU 1.71035E-05 (0.002C/decade)
1993:01-2001:12
---------------------------------------------
GISS 0.00174741** (0.210C/decade)
HadCRUT 0.00147990** (0.178C/decade)
RSS_MSU 0.00221135** (0.265C/decade)
UAH_MSU 0.00217023** (0.260C/decade)
2002:01-2008:1
---------------------------------------------
GISS -0.00091450 (-0.110C/decade)
HadCRUT -0.00270338** (-0.324C/decade)
RSS_MSU -0.00208111 (-0.250C/decade)
UAH_MSU -0.00130882 (-0.157C/decade)
The double asterisks indicate a 95% CI level of significance. No asterisks indicate that the numbers are not significantly different than zero. At some point, and in some venue, I’ll have more to say about what I’m doing here. But next up will be a composite trend weighted by inverse variance, and tests of each series against that composite.
REPLY: Thanks, I’ll put this up in part 2
Basil,
I appreciate what you say and agree. My intended point was my surprise at seeing these findings in that setting and to reiterate a point I have made repeatedly here. It is interesting to debate the fine minutia of the science of the AGW argument. The economy / freedom wrecking policies being discussed will be driven by politicians whose main interest is power and reelection. It is opinion of the “Dumb Masses”, who avoid most news programming, that will influence the politicians much more than any group of climate scientists.
The information presented by the host is typical of the treatment of most scientific information. Just rare to see the skeptic side shown.
cce,
Trying to detect fluctions in a trend that are an order of magnitude smaller than the standard deviation in your data is an exercise in futility.
The trend (LS linear fit) over the satellite era of the centered difference between GISS – RSS is minute, and of a negative slope at -0.0012 deg C per year with a mean of 0.
The trend over the last ten years is positive at 0.0145 deg C per year with a mean of -0.0216.
The trend over the last five years is positive at 0.0187 deg C per year with a mean of 0.0045. Above zero and climbing is not convergence.
However, the standard deviation in the GISS – RSS data is 0.1329 deg C.
The data do not support a conclusion that the two metrics are converging. There is simply no significant change over time. So if you wish to refute the notion that the two metrics are diverging, the data will back you up. But they won’t go so far as to support the conclusion that they are in fact converging.
Have a look at the main page, I just posted Basil’s graph and table.
Basil
I think Briggs just used the info available on the internet, but you could ask him. I sent him data in Excel format which I got from the UAH and RSS sites which I could sent to you if you like. Might save you some time. I’ll comment later as I got to do some work.
Here’s my graphing effort. The file both.xls has both Hadley and GISS data plotted. I’ve divided GISS by 100 and subtracted 0.1 to get an apples-to-apples comparison. Download the file both.zip and unzip. Read the notes on the “README” tab for a short summary of what I’ve done. The file will be temporarily available at my webspace by clicking this link.
Since about 2002, GISS has trended higher than RSS. The standard deviation of the residuals of the last 5 years is 0.085. The standard deviation from 1979 to 2001 is 0.142. In the last 5 years, RSS and GISS more closely agree than at any comparable time since the satellites went online.
This graph shows the 5 year moving standard deviation of the residuals.
http://cce.890m.com/gissvsrssstdev.jpg
The increasing agreement that you see starting around 1998 coincides with both the end of the ’98 el nino, which probably enhances the difference between the two methods, and the AMSU instruments going online. One of the AMSU channels is beginning to malfunction, so this probably won’t last forever.
[…] Antony Watts: So with all of this discussion, and with this newly collated data-set handed to me today, it gave me an idea. I had never seen a histogram comparison done on all four data-sets simultaneously. […]
A little disappointed that you haven’t picked up on the fairly basic observation that all these data series are displayed as anomalies …. but against means from different reference time periods (eg.GISS is referenced 1951-1980 whilst HADCRU is referenced to 1961-1990). So any direct comparison is meaningless. ie. “I was particularly impressed with the agreement of the 4 metrics during the 1998 El Niño year as well as our current 2008 La Niña year.”
Its a shame because there are clearly some good points … but you need to get the basics right.
For the Reply to Atmoz– “REPLY: Thanks for doing that, the real question though is why does GISS use a different base period than the other data sets?”
There are some reasons for GISS to use 1951-80 as a base period. First, Hansen and Lebedeff (http://pubs.giss.nasa.gov/abstracts/1987/Hansen_Lebedeff.html ), the first paper of GISS surface temperature analysis, used it, and they don’t want to change the base period at the end of every decade. Also 1951-80 was a time of good global coverage of the observations. 1951-80 was also the period that the global temperature didn’t change much ( http://data.giss.nasa.gov/gistemp/graphs/ ). They don’t want to take a period with a rapid change as a base period.
Basil–
I looked up Cochrane-Orcutt estimation. How do you determine the correlation coefficient for the noise when you do this? (Could you point me to a clear reference? I’m tempted to just do a mindless estimate using data since 1880, but I’d like to know how to do this the correct, official way.)
cce,
You are confusing a stabilizing trend with a converging one. Your graphic of the moving standard deviation isn’t measuring convergence. It is measuring noise in the residuals. if you don’t believe me try padding another 24 months of data onto your residual: 8 months at .04, then 8 months at .05, then 8 months at .06, a syntheic diverging trend of .01 deg C per year. Look at your moving standard deviation and tell me what you conclude about divergence from your metric.
Kind regards,
Earle
REPLY: Thanks Earle, I appreciate the positive input.
Anthony,
The baselines are arbitrary. I wouldn’t worry too much about this. When Gavin did his validation of Hansen, the code anomaly baseline as “perpetual 1958”; the GISS baseline is some particular set of 30 years. He didn’t rebaseline. (The only way to do it is to pick a string of years sometime after computations began and set them together.)
Steve Mc and I did set things to the same baseline (and in comments on some blogs, a few pro-AGW people sniped at us for doing so.)
I thought the flatness with a hint of bimodal of one of those GISS histograms was interesting. Though that may have meant nothing other than there is not enough data to get a nice smooth histogram.
> The real question here though is: why does GISS uses
> a different baseline period than the other 3 metrics?
It doesn’t matter. So please stop wasting your time on a cosmetic distraction and spend it doing some real work. Put it this way, does it matter if one of the datasets is in Fahrenheit, another in Celsius, and another in Kelvin? Kelvin is Celsius + 273.12. GISS is HADCRU + 0.1. Big deal. As long as we can convert back and forth, it doesn’t matter. As noted in my post on Feb 29, I subtracted 0.1 from GISS and it tracks HADCRU reasonably well until the late 1990’s when plotted.
However, it seems obvious from the graph that the difference in readings is larger post-1998 than pre-1998.
This difference requires data analysis, which it seems to me has been accurately done by the surface stations project
I confess that I am highly suspicious of the GISS data, which – like Dr. Hansen, seems to have fallen off the trolley. My suspicion is that the data has been deliberately manipulated into a higher-than-reasonable upward “creep” across the board.
I have no proof, this is only a hunch. Hansen’s ravings have become so irrational, even his own staff at NASA is starting to shake their heads in wonder.
I wonder if there are others who share my suspicions, or have I simply fallen victim to the dark side of the force.
Just asking.
Are there now enough good rural sites to do a study, surely this would indicate whether UHI has a significant effect on US average temps, that would be a start.
[…] UAH, RSS) data, and data set anomalies, including why GISS (NASA) is so different from the rest. A look at temperature anomalies for all 4 global metrics: Part 1 « Watts Up With That? It’s starting to look like some deliberate falsification has been going […]
[…] is another site.. A look at temperature anomalies for all 4 global metrics: Part 1 « Watts Up With That? Thus we play the fools with the time, and the spirits of the wise sit in the clouds and mock […]
[…] is another site.. A look at temperature anomalies for all 4 global metrics: Part 1 « Watts Up With That? Note the rather sharp drop depicted in this. Thus we play the fools with the time, and the […]
Hi Folks!
Windows vista is also causing lots of boot problems, so I often get questions like this:
I have a Dell Dimension, which won’t boot to Windows (Vista), and none the repair options work:
Startup repair: Reports repair fail due to problem with registry
System Restore: Reports no restore points available
Windows Complete PC Restore: Reports no backups available
Windows Memory Diagnostic Tool: No memory problems
Command Prompt.
Can’t think of any appropriate command to use here.
So I booted with the system DVD (as one would with XP) but the upgrade
option has been greyed don’t want to do a new install. I want to restore existing
installation.
What should I do?
————————————————————
So here is the answer:
You can’t do a ‘repair install’ because you need to launch the Vista DVD
from within Windows, not, as you have been doing, booting straight from the
DVD; that is why the ‘upgrade’ is greyed out.
If you cannot launch Vista and none of the repair options will work a new
install is the only other option.
To save problems in future it is actually a good idea to image the hard
drive, using something like True Image. What I do is install operating system, download all updates, check system I working okay for a day or two, activate system, then image the drive/partition. Any time I get a problem I can re-image the drive/partition quickly and be up and running without much trouble. And minor fixes are done by using any registry fix tool, there are plenty of them on the market today.
Regards,
Carl