By Steve Goddard
We are all familiar with the GISS graph below, showing how the world has warmed since 1880.
http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.lrg.gif
The GISS map below shows the geographic details of how they believe the planet has warmed. It uses 1200 km smoothing, a technique which allows them to generate data where they have none – based on the idea that temperatures don’t vary much over 1200 km. It seems “reasonable enough” to use the Monaco weather forecast to make picnic plans in Birmingham, England. Similarly we could assume that the weather and climate in Portland, Oregon can be inferred from that of Death Valley.
The map below uses 250 km smoothing, which allows us to see a little better where they actually have trend data from 1880-2009.
I took the two maps above, projected them on to a sphere representing the earth, and made them blink back and forth between 250 km and 1200 km smoothing. The Arctic is particularly impressive. GISS has determined that the Arctic is warming rapidly across vast distances where they have no 250 km data (pink.)
A way to prove there’s no data in the region for yourself is by using the GISTEMP Map locator at http://data.giss.nasa.gov/gistemp/station_data/
If we choose 90N 0E (North Pole) as the center point for finding nearby stations:
We find that the closest station from the North Pole is Alert, NWT, 834 km (518 miles) away. That’s about the distance from Montreal to Washington DC. Is the temperature data in Montreal valid for applying to Washington DC.?
Even worse, there’s no data in GISTEMP for Alert NWT since 1991. Funny though, you can get current data right now, today, from Weather Underground, right here. WUWT?
Here’s the METAR report for Alert, NWT from today
METAR CYLT 261900Z 31007KT 10SM OVC020 01/M00 A2967 RMK ST8 LAST OBS/NEXT 270600 UTC SLP051
The next closest GISTEMP station is Nord, ADS at 935 km (580 miles) away.
Most Arctic stations used in GISTEMP are 1000 km (621 miles) or more away from the North Pole. That is about the distance from Chicago to Atlanta. Again would you use climate records from Atlanta to gauge what is happening in Chicago?
Note the area between Svalbard and the North Pole in the globe below. There is no data in the 250 km 1880-2009 trend map indicating that region has warmed significantly, yet GISS 1200 km 1880-2009 has it warming 2-4° C. Same story for northern Greenland, the Beaufort Sea, etc. There’s a lot of holes in the polar data that has been interpolated.
The GISS Arctic (non) data has been widely misinterpreted. Below is a good example:
Monitoring Greenland’s melting
The ten warmest years since 1880 have all taken place within the 12-year period of 1997–2008, according to the NASA Goddard Institute for Space Studies (GISS) surface temperature analysis. The Arctic has been subject to exceptionally warm conditions and is showing an extraordinary response to increasing temperatures. The changes in polar ice have the potential to profoundly affect Earth’s climate; in 2007, sea-ice extent reached a historical minimum, as a consequence of warm and clear sky conditions.
If we look at the only two long-term stations which GISS does have in Greenland, it becomes clear that there has been nothing extraordinary or record breaking about the last 12 years (other than one probably errant data point.) The 1930s were warmer in Greenland.
Similarly, GISS has essentially no 250 km 1880-2009 data in the interior of Africa, yet has managed to generate a detailed profile across the entire continent for that same time period. In the process of doing this, they “disappeared” a cold spot in what is now Zimbabwe.
Same story for Asia.
Same story for South America. Note how they moved a cold area from Argentina to Bolivia, and created an imaginary hot spot in Brazil.
Pay no attention to that man behind the curtain.
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Here in California, if you move 10 miles the temperature may be radically different. This is due to both the effects of elevation changes as well as our myriad microclimates.
“Of course, this isn’t a sensible thing to do for a single day. What GISS do is calculate it for monthly or annual means which will result in a far more robust correlation.”
One of the GISS graphs I posted shows a temperature change of around +1.5C from the 1940s to the 1970s. The other shows around -1C from the 1940s to the 1970s. That’s two temperature stations only a short distance apart, yet for an entire decade their ‘temperature anomaly’ is around 2.5C different.
How can anyone claim ‘robust correlation’ between widely separated areas when two stations only a short distance apart show a difference of 2.5C for pretty much an entire decade?
Either the data from one of those stations is garbage or the idea of correlation between stations is garbage. Neither option is good for the Warmers.
I have read everything on this post today and there is only one conclusion I can come to. It’s the same as that given by Sir Arthur Stanley Eddington, the astrophysicist, as his explanation of the universe: “Something unknown is doing we don’t know what.” Most of the arguments and explanations can be sulled up in three words: “We don’t know.”
MarkG
“One of the GISS graphs I posted shows a temperature change of around +1.5C from the 1940s to the 1970s. The other shows around -1C from the 1940s to the 1970s. That’s two temperature stations only a short distance apart, yet for an entire decade their ‘temperature anomaly’ is around 2.5C different.
How can anyone claim ‘robust correlation’ between widely separated areas when two stations only a short distance apart show a difference of 2.5C for pretty much an entire decade?”
The claim is supported by actually DOING the correlation study for a large number of stations ( thousands). See the literature I cited above. if you want to discuss actual studies, then that would be great. To be sure in a hundred station sample you may find many with correlated positive trends and few with a negative trend. The approach that methods use is to average all the stations within a given geographical area.
you have a station at position X,Y. Its trend is +1C. You have another at X2,Y2. Its trend is -1C. Estimate the trend at a point in between them? Well, when well allocate stations to grid cells, what we do is average all those within the cell. If the two points in question were the only ones in a grid, the grid would get an average of ZERO.
depending on your gridding you average stations from varying distances. ( size varies with latitude) Giss uses equal area grids.
SteveSadlov says:
July 27, 2010 at 3:15 pm (Edit)
Here in California, if you move 10 miles the temperature may be radically different. This is due to both the effects of elevation changes as well as our myriad microclimates.
temperature doesnt matter. trend matters. if its 100F in a place forever the anomaly is zero. If its -100F 5 miles away and that temperature is also steady, ITS ANOMALY is zero as well. We dont work in temperature. We work in anomaly. departure from the mean. the mean for that location over time. TREND differences matter. temperature difference doesnt.
Stephen Skinner says:
July 27, 2010 at 3:10 pm (Edit)
“…based on the idea that temperatures don’t vary much over 1200 km. ”
Where does this idea come from? I was in Comogli, Italy one Winter and the temp was +3. In Milan, 120km away it was -5, and in Como, a further 50km away it was -20.
The “idea” is the result of comparing ANOMALIES. changes in temp from the mean for that location.
if your house was 0C in 1900 and 1C now, thats a 1C change.
if myhouse was 14C in 1900 and 15C now, thats a 1C change.
Anomaly measures the change AT A LOCATION from its own local mean.
The desert goes from 50C to 51C– anomaly 1C
the Artic goes from -30C to -29C, 1C change.
its not temperature that we deal with. NOT. its the change from the local mean. Anomaly.
its NOT TEMPERATURE Changes over 1200km. Its the change in temperatures VERSUS their local mean. You can think of this as a trend change.
Steve Mosher: Would you comment on my post from 1:44 PM (above)?
dT
bemused
Let’s do the math properly for your example. The Mojave Desert is 90% of the way from Denver to San Diego, so if we weight the interpolation correctly it should have been seven degrees below normal in Death Valley. not two degrees above normal. So you missed by nine degrees. That is an error 50 times greater than the precision which GISS reports.
MarkG:
“How can anyone claim ‘robust correlation’ between widely separated areas when two stations only a short distance apart show a difference of 2.5C for pretty much an entire decade?”
http://wep.fi/pic/1987_Hansen_Lebedeff.pdf
Look at Figure 3.
For stations separated by around 500km the correlations between anomalies in the northern mid-latitudes are generally above around 0.8. Sure, there are a few station pairs which have low correlations even though they are close together, but the vast majority of stations show a good correlation.
Everyday experience also tells you this -if the summer was colder than average in Boston and Atlanta, then it was probably colder than average in Washington DC as well.
Steven Mosher,
If you look at a typical temperature anomaly map, you usually see variations of many degrees over relatively small distances. It is nonsense to assume that the variations are linear.
In this Australian BOM map, you can see anomaly variations of 12C over a few hundred miles
ftp://ftp.bom.gov.au/anon/home/ncc/www/temperature/maxanom/daily/colour/latest.gif
GISS will most likely claim a record this year by one or two hundredths of a degree -using anomalies which may be off by several degrees in many locations.
Steven Goddard:
“In this Australian BOM map, you can see anomaly variations of 12C over a few hundred miles
ftp://ftp.bom.gov.au/anon/home/ncc/www/temperature/maxanom/daily/colour/latest.gif
That is a daily anomaly map. When you average them over a full month, the variations become much smoother. e.g. see here:
ftp://ftp.bom.gov.au/anon/home/ncc/www/temperature/maxanom/month/colour/latest.gif
-in this monthly anomaly map the maximum change in anomaly over 1000km is more like 2-3K.
In fact, this looks very similar to the GISS map over Australia (which uses the 1200km smoothing):
http://data.giss.nasa.gov/work/gistemp/NMAPS/tmp_GHCN_GISS_HR2SST_1200km_Anom06_2010_2010_1951_1980/GHCN_GISS_HR2SST_1200km_Anom06_2010_2010_1951_1980.gif
Steven Goddard replied, “This video shows the GISS coverage holes for May, 2010 Pink represents missing data. GISS has huge holes in Africa.”
Are you attempting to redirect the discussion from trends in annual data to a snapshot of monthly data? That tactic doesn’t work with me, Steven.
You wrote, “When Hansen claims in December that 2010 was the warmest year on record – by 0.01 degrees, are you going to rush to his defense?”
Steven, this is another failed attempt at redirection. All the regular bloggers here at WUWT understand my position on AGW, and I replied to a similar accusation from you in my July 27, 2010 at 5:54 am reply above. Here’s a link:
http://wattsupwiththat.com/2010/07/26/giss-swiss-cheese/#comment-440841
In your next comment, you wrote, “The GISS 250km 1880-2009 trend map shows ‘missing data’ for almost the entire African interior.”
That’s correct. And I explained the reason for this numerous times in this thread.
But then you wrote, “The fact that they added a few stations 70 years later does not change the fact that they do not have any 1880-2009 trend data for most of Africa.”
Have you researched this dataset? They didn’t simply add a few stations 70 years later. As I showed in my July 27, 2010 at 12:35 pm comment, the GISTEMP African coverage was almost complete in 1980 with the 250km radius smoothing:
http://i26.tinypic.com/2iizfc3.jpg
It was nearly the same in 1970:
http://i30.tinypic.com/34ynvid.jpg
And in 1960:
http://i31.tinypic.com/2rpe0z6.jpg
So that’s a 20 year span with almost complete coverage of the African interior–and Asia, and South America.
And here’s a gif animation of the annual coverage in 1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, and 1960:
http://i27.tinypic.com/2pyoxo1.jpg
Sure does look like a gradual increase to me and not a simple addition of “a few stations 70 years later,” as you claimed.
You concluded with, “This article is about long term trend claims by GISS and their lack of data to support it.”
And as I discussed in my earlier reply to you…
http://wattsupwiththat.com/2010/07/26/giss-swiss-cheese/#comment-441123
…GISS does have data to support the trends they present. You may not agree with how they create it, but the data exists.
What part of this reply or the one that preceded it (July 27, 2010 at 12:35 pm) do you disagree with, Steven?
A guy, during his diligent tracking of temperature, finally configures the numbers and realizes his temps are getting warmer. So much so, he can ascertain it has been warming to a trend of 1 degree C over the last 40 years. From that, he knows the trend 500 miles to the east of him the trend has been 0.5 degrees warmer than average………..nope, not a chance, no way in hell one can come to that conclusion.
Steven Mosher says:
July 27, 2010 at 4:09 pm
“you have a station at position X,Y. Its trend is +1C. You have another at X2,Y2. Its trend is -1C. Estimate the trend at a point in between them?”
Yes, but the heat doesn’t distribute evenly, so, it is thus, only an estimate. Or a guess, if you will. One can correlate some parts of the world with other parts of the world, but it has been done by a process called observation. Does it not seem strange to you that the “observed” warming is in places we don’t “observe” anything? Given the way GISS presents its graphs, one has to really look at the fine print to see that these are just guesses. All the while, people take this to be literal. Our friends at GISS know this. PEOPLE IN CONGRESS ARE CONSIDERING LEGISLATION BASED ON THESE GUESSES WHICH THEY ARE TAKING FOR PROVABLE FACTS!. Even if these guesses are correct, it is wrong to present them in the manner in which is being done.
Moreover, each area of the globe is unique. I can show you from various temp gathering sources the trend where I live is different than the trends of places much closer than 500 miles away, but then we’d get into sighting issues and the like. But, I’ll grant that if it is warmer here(in SE Kansas), it is typically warmer in Wichita and Topeka. That being said, I know this is a unique place in the world of which there is no other. I know this because I’ve traveled much of the globe and tried to learn as much as I can about the rest. About 500 miles to the west of me sits the Rocky Mountains. There, the weather and currents do some strange things. You can probably talk to Anthony about this phenomena and be much better informed than reading this, but it is real just the same. Weather currents and patterns die or develop there. Sometimes they move in a seemingly tangential manner. The fact is, sometimes I can gauge what’s coming to us from weather in Washington state, other times I can’t. I lived in Fairbanks, Alaska (Ft. Wainwright) for 4 years. As you can imagine, weather and climate dominated much of my thought and concerns. My experience was that we got weather from all directions in no discernible pattern. I’m not saying there wasn’t one, I just couldn’t discern it. My recollection of Germany(BK) is that it was fairly mild there, constantly. Yeh, we got snow, but not too much. Yeh we had warm days, but they weren’t hot like Kansas. Now go any direction 500 miles from BK, Germany and tell me you can extrapolate the anomaly, not a chance! One can’t sit in Germany and say it’s cold here, so it’ll be real pleasant in Rome. Or, it’s warm here, I’ll go to Edinburgh to cool off. You can do that sometimes but I would pack my suitcase betting that to be true. Now, given the differences in the way each location I described operates, what model are they using for Brazil? Africa? Central Asia? And why? How did they come to the conclusion because climate behaves in a certain manner in a couple of similar locations that they could come to an understanding of how climate operates in totally different locations? Especially seeing that we don’t measure the temps? Steven, you’re a bright guy and I respect your opinions, but this isn’t defensible. If we don’t have accurate weather readings from these remote places, then we have no way of knowing how the climate acts in those locations. I suspect the plains of Asia west of the Urals will act differently than the jungles of the Amazon as to the sierras of Africa(as to how they effect surrounding areas). And I didn’t even go into the Antarctic. A one size fits all 1200Km radius probably isn’t the best way to extrapolate temp anomalies for the globe, unless one wants to purposefully create alarmist scenarios.
http://wep.fi/pic/1987_Hansen_Lebedeff.pdf
Look at Figure 3.
“For stations separated by around 500km the correlations between anomalies in the northern mid-latitudes are generally above around 0.8. Sure, there are a few station pairs which have low correlations even though they are close together, but the vast majority of stations show a good correlation.”
I have studied this paper in the past, particularly Figure 3, and the ONLY region where the correlation is any good in 44.4 N and above. Everywhere else, especially the Southern Hemisphere, it’s marginal or complete crud! But hey, it’s climate science – anything goes…
Bob Tisdale,
You do realize that a trend involves data at both ends, as well as the middle.
GISS is lacking data in Africa from the start of the period. They are also lacking data at the end of the period (present.)
I am quite surprised that you don’t seem to understand this rather basic concept.
Bemused:
Would you comment on my post of 1:44 PM (above)? I can’t understand why everyone thinks temperature, alone, tells us anything useful about the planet’s energy budget.
– dT
Coming back to this I do still think that most of this debate would go away if rigorous error terms were reported for the statistical estimates and relationships being bandied about.
A simple example is the nonsense about the number of points of measurement. If global surface temperature is defined as the integral of temperature at each point on the globe across the globe divided by the area of the globe, then a sample of one gives you very large error, and these reduce as the number of measurements increase. So more thermometers is obviously better.
Also interpolating data points using no more than actual measurements gives you no more information than you had to begin with. It is a waste of time.
Interpolation only helps if new information is being added through deriving the additional temperature estimates from observations of other phenomena related to temperature and/or relationships between temperatures. However the uncertainty in those derivative measurements need to be reported and are in addition to the uncertainty in the actual temperature observations. Quite a bit of the discussion has been generated because of a lack of transparency over what those models are (and in my case the errors implicit in them).
In these circumstances it is quite likely that diminishing returns set in – adding more temperature estimates reduces the certainty of the estimate of global temperature.
I therefore worry that I don’t see much that is easily available that actually addresses these latter errors. In a quick squiz through this thread I see the odd comment about the appropriate statistics to use, but the only reference to error calculations in the literature is from Steven Mosher’s reference to Caesar et al (2006) “Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set”. If I read this correctly it describes a method for calculating errors at points where the temperature has been measured, but is silent on the errors elsewhere (although perhaps carries the implication that the report errors can be extended to points where there is no data).
A final point on the importance of errors. The poor person in the desert will happily follow your estimates if you have a reputation for good estimates and ignore you if you don’t.
Some people in this discussion seem satisfied with the idea that interpolation sorta, kinda maybe works sometimes within a few degrees.
We are talking about a global temperature measurement reported within one one hundredth of a degree. It is ludicrous.
The original Hansen and Lebedeff 1987 paper showed that the correlation drops to 0.5 at 1,200 kms for all locations and there are a few latitude bands that are even worse than that.
For those of you using “annual” data or some smoothed annual data routine, the GISS 1,200 km smooth happens at the “Monthly anomaly” stage so you are not measuring what is actually ocurring here.
The problem is two-fold: in earlier times, there were not enough stations so a longer smooth is probably appropriate. In the most recent periods, GISS, NCDC, and Hadley/CRU are just being lazy/picking warming stations so that there is not adequate enough coverage.
One would have to assume there is not a weather station in all of Africa or all of central South America. I’m can guarantee you there are well-run, dedicated, and model weather stations that are available. “Nobody in Africa cares about the weather?” Sorry, it is fundamental human nature and people want to know and 5% of people are obsessed with it. There are thermometres everywhere.
In regards to the Alert weather station, well, there is a state-of-the-art weather station at Eureka (not far away) that is staffed by as many as 5 professional scientists at any one time (and has been since 1947) so there is no need for an Alert station too. The air base is not really used any more as well.
I think the obvious solution to this problem is to have more automated stations all over the world. Perhaps special designs should be developed for installation on top of sea-ice that may melt and land-locked glacial ice. Of course, it may not be possible to have unattended stations in areas where they might be easily be stolen or vandalized.
I think that all the data points for these global averages should all be given the same weight so that extrapolation over unknown regions does not give some readings an undue weight. I do not think we can reliably project the boundary conditions of an unmeasured region into the interior unless we really do know all factors forcing the interior region weather. This should be obvious.
Are the three diagrams at the top of this post created using the same GISS data?
If they are, everybody can forget about all the models and your theories and everything else climate-related that they are doing. Because the graph must be false, and that alone is enough to demolish the AGW myth.
In the world maps labelled 250 km smoothing and 1200 km smoothing, Change 1880-2009, most change around the world is under 1 degree. The maximum change anywhere is 4 degrees, and there are few places like that. (e.g. Canada and Siberia, I’ll bet they are delighted!)
How does that reconcile with the graph labelled Global Land-Ocean Temperature Index which shows an average rise around the world of 8 degrees C over 100 years?
This is the most important topic that sceptics should concentrate on.
jaymam wrote, “How does that reconcile with the graph labelled Global Land-Ocean Temperature Index which shows an average rise around the world of 8 degrees C over 100 years?”
Please look at the graph again, jaymann. The rise is approximately 0.8 deg C, not 8 deg C.
Regards
And this is why I plan to climb Kilimanjaro this winter! Gotta see the glaciers up there before they melt! http://www.offtrackbackpacking.com
It makes sense to me to use Death Valley and Palm Springs as my GISS stations of preference for California. Kind of keeps the trends running in the right direction.
I’ll trust John Christy’s research and others who actually do field work on the effects of land use change that affect not only urban but “rural” thermometers as well.
http://climateclips.com/archives/212
Also, as Roy Spencer has pointed out there is an amplification of LT temperatures during El Nino events in relation to the surface. In 2010, GISS appears to have completely wiped out that relationship and then some.
Unless and until this mess is sorted out, I find it difficult for anyone to legitimately defend not only GISS but the entire surface station network. Replication of error is still error nonetheless.