GISS Swiss Cheese

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!

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.

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

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.

“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.

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.

Nigel Harris says:
July 26, 2010 at 9:58 pm

Deliberately misleading and snarky. We’re talking about trends in anomolies here, not absolute temperatures. And we’re talking about climate, not weather.

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.

“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?

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.

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…

I still don’t understand the complete ocean coverage, even at 250km smoothing.
We certainly don’t have data available at 250km intervals across the world’s oceans since 1880. Highly unlikely that we have it any time before satellite data became available.

Yet another post from Goddard that should be titled “I don’t believe”.
Yet another post from Goddard that allows him to wallow in his personal incredulity and provide his readers fix of “daily hate”.
Yet another post from Goddard that takes us not one iota towards appreciating the magnitude of the problem.
It would not be hard to write a useful post, to test if the interpolation of anomalies to 1200km have any skill. It would require only a modicum of coding and statistical nous.
But Goddard would have to be brave enough to face the risk that the analysis shows that the 1200km interpolation is useful, and that it is most useful in the Arctic.

“Monaco might be a reasonable basis for estimating the change in temperature over the same timescale in Birmingham, England”
Good examples, since they demonstrate just the dangers of taking such an approach. Birmingham temperatures come from the airport, which is at the intersection of two motorways. It shows appreciable UHI. Monaco doesn’t have an airport at all. Birmingham is on flat land about as far away from the coast as you can get in the UK, but is still affected by the Gulf Stream. Monaco is between the Alps and the Mediterranean Sea.
Basically you couldn’t have two measurement sites that were much more different. Unless you head 1200km north from Birmingham, in which case you end up in the Shetland Isles. On the other hand you could move a 60 kilometers south-west from Birmingham to Ross-on-Wye – but Ross-on-Wye doesn’t show any warming (but then it doesn’t suffer from UHI)
So much for the idea that an anomaly in one site will replicated in a site 1200km away.

To me the elephant in the room is that we’ve warmed up since the Little Ice Age.
We don’t know why we went into that period and we haven’t a clue how we came out of it.
Now we have a field of science that claims that because we can’t explain recent warming with known natural mechanisms, it must be because of CO2.
That’s a pretty shaky foundation for any hypothesis especially one that demands endless investment and sacrifice if we are to avoid catastrophe.
When that investment flows into the coffers of the High Priesthood and the sacrices are those of others then a modicum of cynicism is not surprising.

Actually there is a much more obvious way of showing that the 1200km smoothing is unreasonable. If you look at the 250km gridding then you will see that the cool spots are very close to dark orange and red spots. This indicates that contrary to what has been suggested by proponents of AGW, two sites less than 500km apart can show anomalies of opposite (and extreme) polarities. It therefore indicates the fallacy of extrapolating warm anomalies into areas with no data. In fact, I would theorise further that regions that tend to produce more frequent areas of high pressure will tend to produce low pressure in adjacent regions such that hot regions will often be found adjacent to very cool regions.

The Alert NWT is based at an airfield:
http://en.wikipedia.org/wiki/CFS_Alert#Weather_Station
GISS lists it as “rural”. However, the airfield has been much the same as it is today for 60 years, so whilst UHI is probably a factor it is probably stable. GISS/GHCN drops it after 1990 although according to Wikipedia it is still recording temperatures today.
However, the GISS/GHCN data for Alert shows no warming over the period 1950-1990. Makes it even more curious as to how they have come to the conclusion that the Artic is warming rapidly when the few reliable stations nearest to the pole don’t show any warming at all.

Nick Stokes: July 27, 2010 at 1:27 am
“Is the temperature data in Montreal valid for applying to Washington DC.? “
Yes. it is. Here’s the plot. Lots of correlation.
You forgot the sarcasm font.

Do GISS run academic degrees (UK)? I’d like to do a law course and would love to be able to get a good pass mark in an exam and smooth this across the rest of my exams, I could be a straight A student even in classes i’ve never been to, cool. Oh wait the real world does not work like that, i must have results to show for all the areas i’m studying, silly me.
/Sarc

richard telford says: “But Goddard would have to be brave enough to face the risk that the analysis shows that the 1200km interpolation is useful, and that it is most useful in the Arctic.”
Actually, the 1200km smoothing in the Arctic is only useful for exaggerating Arctic temperature anomalies. GISS deletes SST data in areas where there is seasonal sea ice and extends land surface data out over the Arctic Ocean during seasons without sea ice. By deleting the SST data, GISS biases the Arctic Ocean with the land surface data which has a significantly higher trend. Discussed it in this post:
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html
And that post also ran here at WUWT:
http://wattsupwiththat.com/2010/05/31/giss-deletes-arctic-and-southern-ocean-sea-surface-temperature-data/

I have been checking correlations in WA looking for steps .
Here is a sample of the two Kalgoorlie stations compared with stations in a less than 600km radius.
http://members.westnet.com.au/rippersc/kalannualmaxr.jpg
http://members.westnet.com.au/rippersc/kalannualminr.jpg
Note the Kalgoorlie Airport record includes Kanowa in the early years.
The pearson R correlation is in 11 year segments which when done by month makes any outliers stick out like dogs balls.

The only thing you learn in college, in the academic sense, is how to find an answer. There are no guarantees in life. The answer you find may be right, or may not be. But at least you found something. It’s all yours. that is, it is until you come across more information, data, what not, and then you can update ‘your’ answer. Or not! Ain’t life a beach?

No wonder it’s warming so much at the Poles. There’s that big hole at each Pole extending all the way through the earth. Heat from the core of the earth, which is at several million degrees, is pouring out. 😉

Nick Stokes says: July 27, 2010 at 1:27 am
“Yes. it is.. Here’s the plot. Lots of correlation.”
Uhmm, Nick, looking at the graph, I see lots of correlation from about 1910-1950. But beyond that, no, not so much. In fact, from about 1950-1970, the anomaly looks inverted. According to your graph, it looks like Montreal gave up measuring their own temp about 1980. It looks like the graph starts about 1870 and ends with both about 1980 for a total of 110 years. Mont. and D.C. correlate very well for 40 yrs. Somewhat for 50 years and not at all for 20. Remember this is an anomaly graph not a temp graph, so similar shapes(bumps) aren’t correlation unless they are very close to each other. So, no, it isn’t correct to say Montreal and D.C. correlate. Sorry

“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”
That’s incorrect. It is observed that there is a correlation between temperature anomalies at widely spaced locations. In fact, the correlation coefficient is 0.5 or higher out to distances of 1200km at temperate latitudes. The GISS methodology for calculating the temperature anomaly at a given point includes all data from within 1200km, with a weighting that varies linearly from 1 at 0km to 0 at 1200km.
“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.?”
I got hold of weather station data from Montreal and Washington, choosing the station from each which had the longest record. I calculated the mean January temperature, and then subtracted it from the series, to convert it from absolute temperature to temperature anomaly. I calculated the correlation between the anomalies at the two locations. I found a Pearson coefficient of 0.75, which implies a significant correlation. So yes, if one had no data for Washington, one could make a pretty good guess at its temperature anomaly using Montreal data, and vice versa – at least for January. You may be interested in extending this analysis to the rest of the year.
It is always better to analyse the actual data, rather than argue from disbelief. Also, you should report correctly what the methodology actually is.

“Note: Gray areas signify missing data.”

Steve Goddard: You wrote, “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. ”
Actually, they do have data for the interior of Africa:
http://i25.tinypic.com/2z7lrty.jpg
They simply do not meet the 66% record threshold GISS uses for creating trends.

Bob,
If they “do not meet the 66% record threshold GISS uses for creating trends” then they shouldn’t be in their trend maps.
I’m surprised that you are defending them.

It’s very disappointing that many of you don’t understand some basic principles of meteorology and climate.
OVER LAND, regional temperature trends must correlate! I repeat: “REGIONAL” trends.
Coastal stations, instead, must not be used to infer trends over the sea.
All that means that, as a climate land region is identified, almost all the land stations over there must have a similar trend. If a station doesn’t correlate, it’s because of some non climatic influence.
Different reasoning is needed for coastal stations. It is not garanteed, in fact, that sst and air temperature have always the same trend or a trend of similar magnitude.
As an example, think at the Arctic Ocean. Over there, air temperature at a 2m elevation is bounded by the presence of ice or a mixture of ice and water. In coastal Siberia, instead, Summer temperature are free to climbe to +30° under some meteorological conditions. An anomaly of, say, +10 °C, over Siberia can’t be found over the Arctic Ocean, wathever Hansen thinks.
The only problem I see, is to identify climate regions. In some areas, 1200 km could also be a good approximation, in others is not. It depends on latitudine, geography and climate.

BBk replied: “But it’s not ‘data.'”
The numerical values created by 1200km radius smoothing is data. It might be data derived through methods that you disagree with, but it is data.

stevengoddard replied, “If they ‘do not meet the 66% record threshold GISS uses for creating trends’ then they shouldn’t be in their trend maps.”
They aren’t in the trend maps. That’s the point. The trends for Central Africa, and Asia, and South America do not appear in the trend maps with 250km radius smoothing because they don’t meet the data availability threshold for the maps you’re creating on the GISS webpage. But with the 1200km radius smoothing, more data exists, and because of the increase in data availability, the trend maps for the GISTEMP product with 1200km radius smoothing are more complete. You may disagree with how GISS creates the data with their 1200km radius smoothing, but the increase in data is the reason for the differences in trends and their spatial completeness.
You wrote, “I’m surprised that you are defending them.”
I’m not defending anyone or anything. I pointing out errors to you that exist in your post.

stevengoddard replied, “I don’t find the fact that ‘the linear trends of the three global land surface temperature anomaly products from 1880 to 2009 are remarkably similar between the latitudes of 60S-60N’ to be particularly interesting.”
Others might find it interesting, Steve, which is why it was a general comment and not addressed to anyone in particular.
You continued, “Hansen says that the reason GISS has diverged from HadCrut over the last decade is due to the Arctic.”
If the only difference of any value is the Arctic, then why does your post include trend maps for Africa, Asia, and South America? You introduced the lower latitudes, not me.
You wrote, “And all three suffer from UHI and other issues.”
“UHI and other issues” are not the subject of your post.

“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,”
Arr, well that would be because the Alert NWT weather station was originally run by the Royal Canadian Airforce and in the 1990’s control was handed over to the Canadian Environmen ministry. Sadly it seems nobody in team AGW could bother themselves to find out why 20 years of the most recent data was missing from this fine site, but then again it doesn’t show warming in the period 1951 to 1990 so why bother??? If they had gone to the Canadian Environment ministry I suspect they would have got the complete record.

Bob,
Your explanation is unpalatable.
The GISS maps say that grey areas represent missing data. If they can’t calculate an accurate 250 km trend for a limited area, then they certainly can’t calculate an accurate 1200 km trend over a larger area.

John F. Hultquist
According to Hansen’s theories, the world should already be heating up out of control.
The fact that they are having to do all these manipulations and artificially bump temperatures up a few tenths of degree, is an indication of how badly the catastrophists are failing.

I know it is just another cherry from the lot, but it looks like as rotten cherries are taken from the lot, there aren’t many of them left…
Portugal is one spot that seems to have some heating influence in Morocco and Spain, but the data for the Lisbon weather station shows cooling in the last series starting in the 80’s, I don’t know why there are so many series for this station but it seems like worth taking a look at it:

Series: <a href="http://data.giss.nasa.gov/cgi-bin/gistemp/findstation.py?datatype=gistemp&data_set=0&name=lisboa&quot; title="GISS series"
Station Location:
Station Photos:

EUREKA!
I want to emphasise again that there can not have been any man-made CO2 induced warming before 1950. This is quite simply because the Mauna Loa record shows that the concentration of CO2 in the atmosphere was about 310ppm in 1950 compared to a long term trend assumed to be 285ppm. Hence in 1950 the level of CO2 was only 9% higher than the long-term trend and not sufficient to cause significant warming, whereas today it is 390ppm (37% higher than the long-term trend). It follows from this that any AGW from 1950 -2010 must be bigger than any warming from 1880 – 1950 for the AGW theory to hold, because the added C02 in the latter half of the last hundred years or so is 4x bigger than for the first half.
Now take a look at the graphs that Steve Goddard has included above. They don’t show any warming 1950 to 2010. However they do show warming 1880 to 1940. A hell of a lot of warming. In fact it appears to be warming at maybe as much as 6Celsius per century! That’s a high rate of change – and not one part of it attributable to AGW!
So I took a look at all the GISTEMP raw data for the stations north of 65degrees. Almost all of them show the same trend. Only Alaska shows warming in recent times. Russia, Scandinavia and Greenland sites either show no warming or warming during the years 1880 to 1940, followed by cooling/flat-lining in the years 1950 – 2010. Those sites showing this high degree of warming in the years 1880 to 1940 cannot be showing AGW induced climate change – they are showing the power of the planet to alter its OWN climate all without our help.
All those red dots you see in the northern latitudes in the 250km smoothed map are due to sites that don’t show AGW at all. They show a massive amount of NATURAL warming in the years 1880 – 1940. These have then been used to suggest that the entire Arctic circle is warming up in the 1200km smoothed map, due entirely to AGW. This would be a gross misrepresentation of the truth.
Why did team-AGW want to show the years 1880-1940 in their anomaly maps? Could it be that by roping in the limited, unreliable data for years where AGW was not relevant, that they were able to show spurious warming in parts of the world that have actually cooled since 1950? You decide…..

stevengoddard,
If GISTemp (and others) are doing “all these manipulations and artificially bump temperatures up a few tenths of degree”, please suggest a way to calculate global temperature using the various raw station datasets available that you think would be ideal. I’d be happy go generate a record of, say, raw GHCN + GSOD stations that have no lights visible to satellites, are away from the coast, are not at airports, and have low population density. Do you think the results will differ that much from, say, NCDC land temps?

Spellbound says:
July 27, 2010 at 7:36 am
Correlation is not the ideal statistic to use here. There are other statistics, for example Reduction in Error (RE) that would be sensitive to correlated trends with different magnitudes.
See http://www.nap.edu/openbook.php?record_id=11676&page=93#p200108c09960093001 for the formula.

To Tisdale et. all above, regarding the 1880 data etc.
If we provide a plot of data from Point A to Point B, using a broad spectrum of data accuracies between, any trend observed from point A to point B is a direct result of data. The graph shown at the beginning obviously makes a clear case for a net increase in temperature from beginning to end. If 1880-? data is shown inaccurate, the conclusions drawn from (or implied by) the data are as well.
The true contention that can be made against this graph is thus quite simple: The error bar, vs. time, does not change significantly vs. the amount of data interpolated. Interpolated data for non-continuous systems is not real, and should be considered 100% statistical error for the purpose of this graph. However, given the magnitude of interpolation shown in Steve’s representation… I question that proper mathematical logic is applied, and thus the graph, by presenting data and error bars (to inspire confidence in the implication,) is not representative of the data.
Make the documentation of error correct in a strict sense, and I suspect this graph will look quite heavy to the left, and present a much smaller and more confusing trend (or possibly lack thereof, which probably the most valid conclusion as well, given the variability in the underlying data set across the period of this graph.)

@Stephen Mosher “The AGW theory does not predict a monotonic increase. There are natural cycles of warming and cooling. ups and downs. ”
Then what exactly DOES it predict? What is the theory ACTUALLY stating? You see, when I was at university, a scientific theory had to make firm PREDICTIONs which could then be TESTED by OBSERVATION.
What we now have is a theory which makes certain predictions which are so elastic as to permit fitting to any observation that may be made.
Therefore we can see in the graphs above a trend from 1880 to 1940 that suggests a staggering 3Celsius rise. We are told that this is due to AGW. Hence the maps show red dots in the areas where this AGW is said to have occured. We can then predict that the rise from 1940 to 2010 should have been a further 12 degrees, to account for the continuing rise in CO2 from 1940 point on. Except that the graphs above show that this didn’t happen. It refused to rise any more.
Ah yes, but we have NATURAL changes in climate you see. Natural changes so big they totally compensate for the AGW from 1940 to 2010 in this case, one presumes? Or was the natural change big enough to cause the warming from 1880 to 1940? Do we know? Do you care? One wonders why we worry about AGW at all when we have such large NATURAL changes occurring at unexpected moments in Earths climate cycle.
Whatever. There are RED dots all over Canada and Greenland where the climate change occuring could be natural or AGW. No way of telling really. What matters is the red dot. Not the observation, which could be said to contradict the red dot. And having put a spurious red dot where no data supports its existence, why not splurge it out over the upper 30degrees of the globe.
Its the new science. Its not based on using observations to challenge theories. It based on belief.

Hansen claims that reason he sees warming during the past decade (while HadCrut doesn’t) is because of his better Arctic coverage.
GISS doesn’t have better Arctic coverage and talk of how everything comes out in the wash does not impress me.
Hansen forecast rapidly warming temperatures for the 21st century – which are not happening.

Ok Steve Mosher, let’s discuss:
http://wattsupwiththat.com/2009/07/18/out-of-africa-a-new-paper-by-christy-on-surface-temperature-issues/

Ok let me see if I can paraphrase what Bob Tisdale has been alluding to:
250km smoothing:
Grid cell X in the middle of Africa has 50% data recorded. This does not meet the GISS Standard of 66%, so the algorithm looks for stations within 250km to “fill in” the missing data. It can’t find any, so it is displayed as gray “no data available”
1200km smoothing:
Same grid cell, looks for other stations with in 1200km and finds some, so is able to “fill in” the missing data using trends from other stations, displays an anomaly color on the map.
Am I close Bob?
This brings a question to mind: Can “filled in” data propogate across multiple grid squares? Or does the algorithm use only “raw” data?

“Which direction do you walk and why?
Which way is the wind blowing?

Mike says:
July 26, 2010 at 7:32 pm
So there is uncertainty and gaps in the data. Maybe that is why the first GISS graph has error bars on it. If you can demonstrate that their error bars are smaller than they should be you might have something worth talking about.
__________________________________________
I suggest you read AJStrata’s analysis of the error in the temperature data:
http://strata-sphere.com/blog/index.php/archives/11420

Steven Mosher: July 27, 2010 at 9:13 am
2. I tell you that 1200km north of you is a spot where the temperature is 100F. East of you at 1200KM is spot that is 110F. South of you is a spot that is 80F. West of you at 1200km it is 115F. Which direction do you walk and why?
Southeast.
If it gets warm as I head north and cool as I head south, then I must be in the Southern Hemisphere. If the mystery desert is 2,400km wide, then I must be in Australia.
Therefore, I head southeast because I know some *great* bars in Sydney…

So it begs the question what % of all the warming in the last 100 years comes from within these GISS 1200 km smoothing areas.

Unbelievable conversation. Steve Goddard, keep plugging away at it. Eventually, the rest will see the truth in what you are stating.
To the GISS rationalizers, we can’t extrapolate or assume things in which we have no experience. For instance, I know fire is hot, no because each time I see a fire I experience its heat, but rather I’ve experienced fires heat on many occasions in many different manners, so I assume all fires operate in a similar fashion because of my experience. No one really knows if we can extrapolate 1200 km in Africa or not. Why? Because we’ve never observed, or experienced the temperature anomalies there compared to where we do have thermometers. For instance, we don’t know how to figure the temp anomaly in Tanzania from a thermometer anomaly data in Capetown. Maybe one can, maybe one can’t. We don’t know because we’re not currently measuring the temp anomaly in Tanzania. Let’s work that out in a math formula. Let’s say just for argument sake the current average yearly temp in Capetown is 16C and that the anomaly is +2. So, the equation would look like 16(x)=2. The currently average temp in Tanzania is ?, well we don’t know, so we’ll call it Y. So, the anomaly is ……….well, we don’t know because we couldn’t possibly know how it relates to the anomaly in Capetown. I’m glad you guys aren’t accountants. Someone show me the formula for knowing something never measured specific to the composition, time and proximity.
One thing we do know is that while we can average temperatures for the globe, the heat isn’t evenly distributed, we just don’t know how or why.(We know somethings, but we don’t know many things.) But we(the GISS) are basically performing an average distribution function to determine specifically where the distribution of the heat is going. I don’t understand how you guys and gals can’t see that. Someone send me the extrapolating formula and I’ll destroy it in less than a day.
“Its getting hotter where we don’t measure temps.” Brilliant and beautiful. With apologies to the late Pierre Bosquet, “It is magnificent, but it is not war science: it is madness”

“When you compute a global average, no explicit interpolation is necessary. You can interpolate points and then add them if you want but the summed result is still just a combination of the data points just with different weighting. Where points are sparse, youre just regarding them as representing a larger area. That increases the error range.”
I suppose this is true if you don’t care what the final average is. If you’re interested in the numerical value of average itself, then the “weighting” becomes quite important…
In any case, the GISS “reference station” method is an ad hoc approach to deriving a thermodynamically meaningless anomaly index. Given the large uncertainty associated with the spatial correlation of the anomalies (see the original Hansen paper), it is amusing to see people talk about the “highest index value ever!” as if we really know these values within +/- 1 C.
By the way, since the anomalies are calculated relative to a local temporal average (using a predefined reference period), has anyone generated a spatial map of these averages? I’d be interested to see what the “ideal” reference temperatures are for different locations. For example, if the anomaly for 2009 for Chicago was +0.5 C, what is the underlying average that this anomaly is referenced to? 15 C? I wonder how different (or not) this is to, say, Detroit or Indianapolis.

RW says:
July 27, 2010 at 4:24 am
“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”
That’s incorrect. It is observed that there is a correlation between temperature anomalies at widely spaced locations….
….I got hold of weather station data from Montreal and Washington, choosing the station from each which had the longest record. I calculated the mean January temperature, and then subtracted it from the series, to convert it from absolute temperature to temperature anomaly. I calculated the correlation between the anomalies at the two locations. I found a Pearson coefficient of 0.75, which implies a significant correlation.
_____________________________________________________________
“Pearson correlation coefficient is largely used in economics and social sciences…..” Pearson Coefficient in Analyzing Social and Economical phenomena.
Assumptions in calculating the Pearson’s correlation coefficient:
“1. Independent of case: In Pearson’s correlation of coefficient, cases should be independent to each other.
2. Distribution: In Pearson’s correlation coefficient, variables of the correlation should be normally distributed.
3. Cause and effect relationship: In Pearson’s correlation coefficient, there should be a cause and effect relationship between the correlation variables.
4. Linear relationship: In Pearson’s correlation coefficient, two variables should be linearly related to each other, or if we plot the value of variables on a scatter diagram, it should yield a straight line.”
I do not see why anyone would be using Pearson’s correlation coefficient on temperature data. I do not think the data meets the criteria.
Independent of case:
I am taking it that “cases should be independent to each other” means “Two events are independent if the occurrence of one of the events gives us no information about whether or not the other event will occur; that is, the events have no influence on each other.” http://www.stats.gla.ac.uk/steps/glossary/probability.html#indepevents
Since storm systems sweep whole continents how could the two data sets (cases) be considered independent of each other?
Distribution:
I can not see how temperature data over a year could be considered normally distributed. I would think it is bimodal (winter /summer)
Cause and effect relationship:
How does the weather in Washington DC cause the weather in Montreal?
Untrained people keep plugging numbers into “statistical programs” without understanding the underlying principles. It is the main reason I hate the flavor of the month programs that supposedly teach managers and scientists statistics and actually muck things up instead.

richard telford says:
July 27, 2010 at 2:01 am
‘It would not be hard to write a useful post, to test if the interpolation of anomalies to 1200km have any skill. It would require only a modicum of coding and statistical nous.’
I lok forward to reading your post.

Scott Ramsdell says:
July 26, 2010 at 9:39 pm
How do such systemic problems with data manipulation/insertion persist across so many administrations? The heads of NOAA and NASA are political appointees….It’s incredible to me that the status quo has existed for so long.,..
BINGO
because there is no difference between administrations. people need to wake up and realise there is only one party in this country. the debt and war party.
as long as folks continue to vote repuglicrat there will by only the continuing slide towards totalitarianism in this country.

I will duck as is appropriate.
When you deal with anomalies, they are anomalous to a given baseline (normal, average, median) over a SELECTED time period. (As nice as they may be for modelling and other considerations.)
At least temperatures are absolutes that do not depend on a specific period of reference.

frank says:
July 27, 2010 at 10:45 am
David Jay says: July 26, 2010 at 6:45 pm
I understand the loss of the cool spot in Africa, averaging (smearing?) should move temperatures away from extremes. However, the hot spot in Brazil is a winner. I want to hear an explanation of that methodology!
…. The question is will Steve Goddard present data for any period besides 1880-2010 so that we can see if the mysterious red color arises from real data or extrapolated data.
______________________________________________________________________
Steve does not have to because CHIEFfIO already has
http://chiefio.wordpress.com/2009/11/16/ghcn-south-america-andes-what-andes/
http://chiefio.wordpress.com/2010/01/08/ghcn-gistemp-interactions-the-bolivia-effect/

Gail Combs says: “It is observed that there is a correlation between temperature anomalies at widely spaced locations….”
The existence of correlation does not suggest that extrapolation over 1200 km or over 250 km is legitimate. If trend A is increasing @ 10% and trend B is increasing @ 1%, you will get an extremely high correlation, but that does imply that we can toss out trend B, and say that the overall trend is 10%.
Numerous studies have shown that temperature trends in urban areas are increasing faster than temperature trends in rural settings. (I would understand an argument that Arctic stations are not urban. However, we have seen some Arctic station experience local siting issues leading to warming bias.) Moreover, temperature trends on land are increasing faster than trends over oceans, and GISS is using land stations to project temperature trends over oceans.
And back to the question of positive correlation — it is not always positive. My hometown has had anomalies decrease by .033 from the 1930s to the 1990s while Minneapolis (150 km away) has had anomalies increase by .013 over the same time.
(BTW, I noted that you used January temperatures in your Montreal / Washington correlation. Perhaps January was the easiest to compute, but it is curious that you used January rather than the year.)

stevengoddard replied, “ Your explanation is unpalatable. “
Not sure why. GISS presents how they prepare the trend maps on their mapmaking webpage. GISS writes, “Trends: Temperature change of a specified mean period over a specified time interval based on local linear trends.” And they further qualify it with, “’Trends’ are not reported unless >66% of the needed records are available.” Refer to:
http://i32.tinypic.com/jtkvpy.jpg
And that’s a screen cap from this page:
http://data.giss.nasa.gov/gistemp/maps/
You wrote, “The GISS maps say that grey areas represent missing data.”
Correct. They note this at the top of the map output page:
http://i25.tinypic.com/ngaky8.jpg
But for the trend maps you’ve presented in this post, it does not mean that monthly data does not exist in Africa, or Asia, or South America. It means those grey areas did not pass the requirement of 66% of the needed records in order to create a trend.
If you’re not aware, there is little 250km radius smoothing data in Africa, Asia, and South America for the year 1880:
http://i28.tinypic.com/o00wg1.jpg
But there’s considerably more on those continents in 1930:
http://i27.tinypic.com/70xogn.jpg
And there’s even more in 1980:
http://i26.tinypic.com/2iizfc3.jpg
In fact, in 1980, GISS doesn’t really need the 1200km smoothing to infill data in Africa, Asia, and South America.
And it’s well known that the number of stations have decreased in recent decades so that there less data available for the 250km radius smoothing in Africa, Asia, and South America in 2009, for example:
http://i29.tinypic.com/2vbn689.jpg
So your statement in the post, “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,” is incorrect. 250km radius smoothing data does exist on a monthly basis between 1880 and 2009 in Africa, Asia, and South America.
In an earlier comment, you replied, “Why don’t you write up a separate article about the issues you find interesting? “
I do and sometimes I link them here at WUWT to posts where they are relevant, as my comments on this thread have been. Do you find this comment irrelevant, Steve?
http://wattsupwiththat.com/2010/07/26/giss-swiss-cheese/#comment-440748
You wrote, “The point of this article was to show that the GISS 1200 km smoothing is inconsistent with their 250 km smoothing…”
The GISS trends you presented in this post for 1200km radius smoothing are not inconsistent the 250km radius smoothing. The trend maps present the available data in agreement with the GISS qualifications on their map making webpage, which I have discussed previously.
You continued, “– and that they are claiming to know long term trends in places where there is little or no data.”
No. They aren’t claiming any such thing. They are presenting trends for areas that pass a specified threshold for data availability and the data availability varies depending on the smoothing radius. This is why you’re seeing different trends with the different smoothing.
Steven, in summary, I have shown that monthly data is available in Africa (and Asia and South America) in growing amounts until the mid-to-late 20th century, which contradicts your statement, “…GISS has essentially no 250 km 1880-2009 data in the interior of Africa…”
And I presented the threshold GISS uses to determine trends for the maps, which explains why “…they ‘disappeared’ a cold spot in what is now Zimbabwe…” when you presented the map with the greater smoothing radius.

Steven Mosher says:
July 27, 2010 at 8:28 am
‘If it warms by 1C over a century at location lat X, Lon Y, then the available data shows the following: It will also warm by 1C at position lat X2, Lon Y2. Go figure, heat moves.’
Gibberish.
Reductio ad absurdum – one thermometer would be enough. Where shall we put it?

Bob Tisdalr,
This video shows the GISS coverage holes for May, 2010 Pink represents missing data. GISS has huge holes in Africa.
[youtube=http://www.youtube.com/watch?v=NDm4_NwRzVU]
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?

Here are a couple of station records from my home state North Carolina
Norfolk NC
Fayetteville NC
North – Raleigh NC
Or better yet look at some of the Stations in the Tennessee area all within less than 200 km
Hendersonville
Copperhill
Knoxville
Dahlonga
Walhalla
Rome
Middlesboro
Valleyhead
Rogersville
Greenville
Scottsboro
Pennington Gap
My Statistics teacher always said to PLOT the data. Well here are the plots and I do not see any patterns that justify saying the data from one area can be used to determine the trend in another area in this general location. The patterns of these station records are all over the place: lines up, lines down, broken lines at two different levels, sine waves trending up sine waves flat. I do not care what type of fancy statistics you use. These plots of the actual data prove the hypothesis that you can use temperature records from one area to predict the temperature data from another area can be falsified.
I do not have the computer skills to do the plotting on one graph. I already addresses the use of Pearson’s correlation coefficient in another comment.

Stephen Goddard wrote:
“It was almost 100 degrees in Colorado yesterday. San Diego was 65 degrees.
Therefore, Death Valley must have been about 70 degrees yesterday afternoon, and Chicago must have been about 125 degrees.”
No Stephen, you are still confused about the difference between temperatures and temperature anomalies.
According to Accuweather (not sure how accurate their figures are), Denver had a high of 97F yesterday, the climatological mean maximum is about 89F. That is an anomaly of +8F. San Diego was 68F yesterday, with the climatological mean maximum about 77F = -9F anomaly. Death Valley is located between the two and so you might expect the anomaly to sit somewhere in the middle.
In fact, Death Valley had a max of 118F which is an anomaly of about +2F (climate mean max = 116F), i.e. +2F is in between -9F and +8F.
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.

This is an interesting comment thread. I want to see more like it!
On weather in the San Francisco, it can be foggy, cloudy and 55F in San Francisco, and with a 40 minute drive east to a city called Walnut Creek, (on the other side of the Oakland Hills from San Francisco) it can be bright, sunny and 97F. This litteraly has happened.

“…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.

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.

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:
“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 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