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.
Sponsored IT training links:
No matter you have to pass 70-667 exam or looking for 642-165 training, our up to date 640-721 exam dumps are guaranteed to provide first hand success.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Excellent article. It just keeps getting worse.
I sure would love to hear a response on this from the GISS. But I’m guessing they wouldn’t dare even try.
Sad days for the credibility of the scientific establishment, to put it mildly.
Inspired by this article, tonite I spent some time doing some graphics work. Hope you like it:
It would be most interesting to take each years data, put it through an image restoration process to bring the psf down, and leave the non-data as NAN. The GISS images look smeared and lacking of resolution.
There are many things that could be done to present the data in a more realistic manner.
There is a fellow from my Church, who decided…after doing several “visits” to Kenya in the late ’80’s and early ’90’s, to move there and work with a Christian college/university outside of Niarobi.
When he was back in the Spring, I arranged (beauty of Email) to meet at a local coffee house, have breakfast, and talk about his “work” in Kenya.
The one thing I got COMPLETELY WRONG was my belief about the temperature profiles, and that it meant to live in Niarobi.
Although it get’s “hot” a two or three months in the summer, most of the year it’s pretty moderate. AND, he lives in a “compound” outside the city, which is located at about 6,800′ ASL.
He described the temperature flutuations as this: “Max, my outdoor thermometer broke a while back. I was going to take it down. But then I had a great idea..I took it down and used a red marking pen. I then drew a line from the lowest temperature position, where the ‘bulb’ would be, up to 80 F. I hung that outside, and it’s worked great ever since.”
I was rather perplexed, until he explained: “A variety of factors control the temperature at 7,000′! It’s an intersection of altitute, latitude, and weather factors, which moderates and controls everything. The temperature varies from about 75 at night to a max of 84 during the day. It feels like a nicely warm, 80 F day where you live…almost every hour and every day of every year, 24/7, 365…etc.”
I then asked the key question: “How long has it been this way?” Now it was his turn to be perplexed. Finally, after some give and take, he indicated to me that about as long as there are any records, (which for this originally British colony, dates back about 200 years), it has been “that way”.
It seems that with day to day struggles, life in a ‘3rd world’ country, etc, GOREBULL warming doesn’t really rank high on the list in that area of the world!
Sorry to post twice, but one other quick comment: ARCTIC temps going back to the ’50’s, ’60’s and ’70’s might be “unreliable” due to the fact that certain of the DEW Line and BMEWS (Distant Early Warning, for Aircraft attack, and Ballistic Missle Early Warning, for ICBM attacks) personnel just DIDN’T MAKE READINGS WHEN IT WAS TOO COLD, and they “made them up”!
Steven Mosher
If Bob believes that the GISS maps do not accurately represent GISS data, he should write an article about it. That is a different issue.
Looking at places which have actual data, there is no reason to assume that one geographic region has the same trend as a nearby region. The US southwest is supposedly warming, while the southeast is cooling.
Anthony writes:
“This post is not about disproving 1200 km correlation, it is about demonstrating vast areas of missing data that is being infilled by extrapolation/gridding/homogenization. ”
Sorry. but this post is primarily about inferring that the methods for doing the necessary interpolation are incorrect. Or was I imagining that The Goddard wrote: “Is the temperature data in Montreal valid for applying to Washington DC.? ”
Maybe Steve should answer his own question. The reason that interpolation works is because these are ANOMALIES that are being interpolated, not absolute temperatures. I’d be willing to wager that the anomalies in Montreal are pretty similar to those in Washington D.C. That’s why the approach is valid in data-poor areas.
jose
Anomalies vary tremendously over short distances
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=6&sat=4&sst=3&type=anoms&mean_gen=01&year1=2010&year2=2010&base1=1951&base2=1980&radius=250&pol=reg
This, particularly the South American map, elicited a one word response from me. It begins with “b” and ends with “astards!” It is unbelievable that the people creating this illusion of AGW regard themselves as scientists. Shameful. Truly appalling. That the politicians and media willfully takes their word for it is nothing short of criminal. But then, the people here at WUWT already know that. Sigh…
It is not intuitive to me that correlations 1200 k are valid. Very often one area has a high pressure system that keeps it warm and the low pressure area, often within four of five hundred miles, is causing cool weather there.
What’s Australia look like?
GISS does not plot data for most/all rural stations after the early 1990s. They only show urban data after this. Don’t ask me why.
Ken
“I’d be willing to wager that the anomalies in Montreal are pretty similar to those in Washington D.C.”
Even the two GISS station records in Montreal appear to be significantly different over the last seventy years, so why would anyone believe that Montreal’s weather will be anything like DC?
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=403716270030&data_set=1&num_neighbors=1
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=403716270007&data_set=1&num_neighbors=1
They are simply making data up for most of the world when even a quick look at the data which does exist shows that the correlation they’re claiming does not.
“Yes, it’s true, and well known, that there were not a lot of met stations operating in interior of Africa, or the Amazon jungle, in 1880. And that there is not a long history of measurements on the sea ice of the Arctic Ocean.”
Why do we need lots of stations?? To me the fact that the 2 Greenland stations correlate so well with each other convinces me that the thirties were significantly warmer there than now. Are there stations which show a significantly different profile there??
Don’t panic. All those great scientists we have in the U.S. congress are looking into the GISS data. I can’t wait to see the cover-up. The cover-up is always the best part.
– dT
(Are there any scientists in congress? Any engineers?)
From a naive empiricists point of view isn’t the issue here the error limits around the various estimates of temperature (and then of trends)?
I have been thinking that the use of anomalies does rather lead people into adding them together etc without thinking about whether the variance in the data should be normalized (and what this might mean in turn for the standard errors in the statistics derived from them).
Anyway in posting on some of this at The Blackboard, Carrot Eater drew my attention to what looks like the basis for the errors reported in the GISS series referenced at the beginning of this post – “Global Trends of Measured Surface Air Temperature” Hansen and Lebedeff (1987). As I posted over at The Blackboard I hoped that the approach used in this paper to develop error terms (comparing their estimates to the output of a GCM) wouldn’t pass muster anymore – but I can’t see references to more robust methodologies on the NASA site.
Because of this thread I went and had a closer look at the GCM used by Hansen and Lebedeff (GCM II in Hansen et al 1983 “Efficient 3-D global models for climate studies”?) and do find that GCM II is optimized against observations of surface temperatures i.e. it all looks interdependent. (Also I worry about the coarse resolutions that all this is happening at).
I presume the science has moved on from here but can’t spot it on the NASA site – any helpful suggestions?
In response to Bob Tisdale and others about the early 1880 data. Bob wrote:
“There are fewer stations used in 1880 than what you’ve presented in the trend maps but the numbers increase with time.”
See graphic:
This is true, as the US Weather Bureau did not exist as a civilian agency until 1891 when it was removed from control of the Army Signal Corps. Shortly after that, the Stevenson Screen design was contracted and deployed by the USWB as a standardization for thermometer exposure. Prior to this, thermometer exposure was rather haphazard, using many different schemes. See Nonstandard Thermometer Exposures at U.S. Cooperative Weather Stations during the Late Nineteenth Century by Michael Chenoweth.
My point is that until about 1900, when deployment of the Stevenson Screen was essentially complete, and more stations were added to the Cooperative Observer Network, not only did we have less stations, but their data quality suffered due to non standard exposure.
I generally don’t value the pre-1900 record due to these issues. For example, you can see how after 1900, the data extremes “quieted”. I believe this to be a function of exposure standardization. Moving from older methods to the new Stevenson Screen certainly improved the measurement of air temperature, and limited how direct solar and nighttime IR could affect measurements.
Interestingly, while GISS keeps pre-1900 data in their famous temperature graph at the top of this post, in individual station practice, they have removed it, see below:
Detailed explanation here
And it is not just Orland, many pre-1900 records have been removed. Visit here Source:
http://data.giss.nasa.gov/gistemp/station_data/
And try to find any pre-1900 data now, though it clearly existed before at GISS. They recognized what I know, that pre-1900 data isn’t all that good quality wise and pretty sparse. Yet they leave the global temperature graph showing pre-1900 data while removing it from individual station data sets.
This is incongruous.
“The 1930s were warmer in Greenland.”
Warming Arctic Climate Melting Glaciers Faster, Raising Ocean Level, Scientist Says – “A mysterious warming of the climate is slowly manifesting itself in the Arctic, engendering a “serious international problem,” Dr. Hans Ahlmann, noted Swedish geophysicist, said today. – New York Times, May 30, 1937
I’m not quite understanding the use of anomalies. If reference station A shows say, 14C average over the reference period, and soon to be discontinued station B shows an average of 16C over the same reference period, the difference between them is 2C. So when it is discontinued. and A shows temps of 20C (an anomaly of 6C from its reference), it can then be extrapolated from that that the temps at B are 22C. Why not use that as the basis for the calculations, instead of adding another layer of indirection by saying that both rose 2C. So much information is lost by doing this. If somebody goes to see if temperatures actually rose at B, they’ve got to do complicated calculations to see if it is in agreement, instead of just being able to say yes, it’s 22C(or not as the case may be). Further, if any sort of subsequent smoothing or averaging is done on these anomalies, then more errors are introduced because these are derived values rather than actual.
So mosher and co are saying that less than a century of fairly accurate anomaly readings are adequate?
They are also saying that on a planet where it can rain for a week and be cold in one place yet ten degrees celcius 200 miles away, the UK a few weeks ago, extrapolation is fine?
And finally. If its so unprecedented, explain the panic and the field work and anecdotal navigational tales of the arctic in the 1930s?
Nigel Harris says:
July 26, 2010 at 9:58 pm
I’m sick of hearing “it’s the anomaly”. Grab yourself a global geography map and a beer, and spend a couple of hours studying the topography of the planet.
Having done that, you will hopefully realise that 1200km or 250km or even 100km smoothing/gridding is not and can never be an accurate gauge of temperature anomalies.
What can be an accurate measure is if the globe was divided into “climatic regions”, with each region having it’s own station. The size of these individual regions would be irrelevant, some could be relatively small and yet others could be 100’s of square kms. But you wouldn’t have mountanious regions coupled together with valleys or coasts, or dry regions coupled with wet regions etc as it happens now with the current gridding method.
This gridding method was developed by Hansen. It’s wrong, it’s irrelevant, but he will stick with it to the end because it is so easy to manipulate as has been shown by many many people over the last 12 years.
paulhan :
July 27, 2010 at 12:17 am
You are right, anomalies are an extra level of complication. I have the analogy: analogies are like a map with no scale written on the side and in addition with unmapped distortions in relative distances.
The reason climatologists love them is because they cannot compute/model global average temperatures as Lucia has shown. . There is a spread of 3C between model outputs and if they publisized that how could they claim consistency of model solutions?
The logic of anomalies is the logic of the map I described above. In absence of the real map an anomaly map still carries information, just not information the world should gamble its future on.
“based on the idea that temperatures don’t vary much over 1200 km
It’s temperature anomalies which are an entirely different thing, and which do show more coherence over large distances [though whether 1200km is too far or not is another issue]. However, that difference alone makes your following sentences entirely pointless and they are a classic case of misdirection and distraction.
“In the process of doing this, they “disappeared” a cold spot in what is now Zimbabwe”
Look more closely at the whole picture. You will see that the smoothing also “disappears” orange warm anomalies along the west coast of central Africa. This is unsurprising when you consider that they are smoothing the data, a process which is inevitably going to reduce the size of anomalies, and spread out the signal from small-scale anomalies [such as the one in Zimbabwe].
So you can see that the blue over Zimbabwe contributes to an expanded white zone – effectively the blue has been used to cancel out some of the yellow and orange.
There’s no secret method here. Nothing dodgy going on to manipulate the data in one direction. It’s simply an attempt to make the best of the limited data that is available.
Also, if you really want to test whether what they are doing is reasonable, one thing you can do is to take spatially complete data [such as re-analysis data] and subsample it to replicate the holes in the observational network. Then you can compare the global means calculated with all the data and with only the subsampled smoothed data.
This would be more constructive than the “look dodgy!” accusations that you level here. Of course, it’s possible that this sort of analysis has been done already. Does anyone know?
Steve, what do the blinking pictures look like if you run against just rural data and then against just urban data for the 2 smoothing scenarios?
Due to a paucity of accurate data, poor understanding of atmospheric inhomogeneity and data massaging I don’t think any of the global temperature data-sets are able to produce anomaly information which is meaningful. The production errors are greater than the anomaly which they are trying to measure.
Even Dr. Jones, of the infamous CRU. stated that there has been no statistically significant global warming for the last 15y in an interview with the BBC. In the same period atmospheric CO2 continued to increase, so in the best case it makes only a small contribution to climate oscillation and thus the CAGW hypothesis is falsified.
The next few years are going to be interesting if the sun stays in quiet mode, as there seems to be a strong link between solar activity and changes in weather regime.
“Is the temperature data in Montreal valid for applying to Washington DC.? “
Yes. it is.. Here’s the plot. Lots of correlation.
pwl says: July 26, 2010 at 9:08 pm
“How can we have this 1987 paper, “Global Trends of Measured Surface Air Temperature”, by Hansen and Lebedeff falsified and rescinded?”
Hi pwl, you and anybody else are most welcome to try. GISTEMP justifies its 1200km smoothing based on this paper. It is over 20 years old. Have at it in the peer reviewed literature! Based on all the outrage it should surely be trivial to show where it all went wrong in a rigorous analysis. Lets see how far everybody gets…