Readers may recall yesterday where I posted this stunning image of cold for Europe and Russia for mid December 2009 from the NASA NEO MODIS satellite imager.
Deadly Cold Across Europe and Russia
Click image above to enlarge or download large image (3 MB, JPEG) acquired December 11 – 18, 2009
In that story were links to additional images, and I’d planned to return to them for a comparison. Inspired by my posting, METSUL’s Alexandre Aguiar saved me the trouble. There’s an interesting comparison here between the surface anomaly done by weather stations (NASA GISS) and that of satellite measurement (NASA NEO MODIS) – Anthony
Guest post by Alexandre Aguiar, METSUL, Brazil
COMPARE THE TWO MAPS
NASA GISS on the left, NASA MODIS on the right
Here’s the same images but larger – click either image for full size:
South America: The vast majority of the continent is near average or below average in the NEO map, but according to GISS only the southern tip of the region is colder. The most striking difference is Northeast Brazil: colder in the NEO map and warmer at the GISS.
Africa: Most of the continent is colder than average in the NEO map, but in the GISS most of Africa is warmer than average.
Australia: The Western part of the country is colder than average in the NEO map, but the entire country is warmer in the GISS map.
Russia: Most of the country is colder than average in the NEO map, a much larger area of colder anomalies that presented in the GISS map.
India: Colder than average at NASA’s NEO website and warmer at NASA’s GISS map.
Middle East: Huge areas of the region (Israel, Jordan, Turkey, Iraq, Syria) are colder than average in the NEO map and average/warmer in the GISS map.
Europe: Near average or slightly above average in the NEO map and much above average in the GISS map.
Greenland: Entire region colder than average at NEO and much of the area warmer at GISS.
Same source (NASA), but very different maps !!!
Why:
At NEO, land surface maps show where Earth’s surface was warmer or cooler in the daytime than the average temperatures for the same week or month from 2000-2008. So, a land surface temperature anomaly map for November 2009 shows how that month’s average temperature was different from the average temperature for all Novembers between 2000 and 2008.
Conclusion
Despite being very warm compared to the long term averages (GISS, UAH, etc), November 2009 was colder in large areas of the planet if compared to this decade average.
See PDF here. December should be very interesting in the northern hemisphere.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
What about these two?
http://weather.unisys.com/surface/sst_anom.html
http://www.osdpd.noaa.gov/data/sst/anomaly/anomnight.current.gif
E.M.Smith (12:50:56) : They simply removed cold sites!. When will you remove the removers?.
And the same happens in almost every other field of the rightfully adjectivized (by prof.Abdussamatov) “Hollywood Science”.
It´s the new “Nomenklatura” which you suffer there. You are brave in combating it for the sake of truth, your descendants and nobility of heart.
Wonder why the guest poster didn’t use the same baseline years (2000-2008) when he pulled up the GISS map?(the map maker does let you do that) It would have been a much closer comparison. Maybe a modification to the post is in order here.
wayne (18:32:23) : Does anyone know where, or how, to obtain either the grid data of the “1951-1980 mean” the anomaly grid is compared to or the single temperature these are differenced against. Also the “2000-2008 mean” would be helpful. That data might be in grid form also.
You get to ‘roll your own’. I’m running GIStemp and the way it fabricates that baseline is a bit, er, bizantine. Unless you run a copy of GIStemp, you can’t get the same baseline values…
“Why? Don’t ask why. Down that path lies insanity and ruin. -E.M.Smith”
Exploring why…
For the USA they do a ‘quasi merger’ of USHCN adjusted and GHCN unadjusted data. Which ever one you have, you keep unchanged, unless you have them both, then you do a bizzare ‘un-adjust’ on the USHCN and then a blend of it with GHCN. Sane? Why does it make sense to sometimes use one and sometimes the other?
Then it does a ‘homgenizing’ step on the data. Missing values can be ‘made up’ based on other ‘nearby’ stations up to 1000 km away. (What does Reno have to do with San Francisco?…) and “Rural” stations can be major airports (like the largest Marine base in the world at Quantico, Virginia …) This step uses some of the data to make a comparison baseline used for some things. But it is only one of the baselines used…
After that, an equally bizarre, and IMHO slightly buggy (but I’ve not had time to prove or disprove the bug) Urban Heat Island adjustment is done (that does things like move the past of Pisa by 1.4C in the wrong direction…)
THEN you get to the anomaly production step that uses ANOTHER calculation of the baseline (same time period, but different input) for making the anomaly maps.
So good luck even just figuring out what ‘the baseline’ data are…
If you really want to persue this, hit the chiefio.worpress links above and click on “GIStemp” in one of the top tabs. You will find directions there about how to make it ‘go’…
I’m like Syl as the top post, this cannot be correct even with the different base time periods. All but a few points, being conservative, are showing greater than 2 degrees and that’s conservative.
IMHO it isn’t right, but most of the issue comes from the GHCN data set thermometer changes. Only after that does the bizarre GIStemp process get a shot. It does tend to make crazy changes, but some of them improve things and some of them make things worse. Sorting out the net impact would be (or, since I’ve been working on it for a while… “has been”) a bit of a nightmare…
I want to investigate further, to be more accurate and check if this thought is correct, but don’t know where the data exists, if it’s public at all.
The ‘base’ data ( I can’t call it ‘raw’ since it has been pre-processed) is in the links in comments above. To get the GIStemp view of the baseline, you need GIStemp running (as stated in this comment). Best approach, IMHO, is to make the baseline from the unmollested GHCN data and then reach your own conclusions.
If someone is already doing that work, I don’t want to duplicate. Let me know if so.
It would be very good to have it duplicated since it needs that for a cross check in any case. It’s likely to be a year before I’m done…
As pointed out, this is really comparing apples and organges. One anomaly calculated froma cooler base period of 1951-1980 average, another based on a warmer base period of 2000-2008 average.
The GISS map from NASA using an anamoly of 2000-2008 for November (December is not available) shows a similar profile as NEO.
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=0&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=2000&base2=2008&radius=1200&pol=reg
“I’m in the red blob in the GISS map (+2 to 4C) in South Eastern Australia. It’s 22C here now, well below normal for this time of year.”
Speak for yourself. I’m in the north eastern part of that red blob, sitting in jeans & a jumper in the middle of an outback summer… Been like it for weeks now. The graph is clearly a case where one of the scientists has left the picture on a low bench and their child has taken to it with textas. My kids do it all the time.
Alex (00:07:07) :
> I understand that it is not a true mercator map, but it is -ish.
A Mercator map the reaches the poles is infinitely tall. That’s a far, far cry from a map that uses a linear, untransformed N-S scale.
> It does distort the data, visually.
All 2-D representations of an almost sphere will have geometric distortions. All that are used have some benefits, all have some drawbacks.
> I suggest we try to educate others to reality and present things in the way they understand, otherwise we become just as guilty as the warmers and their elitist attitude. I am not the enemy, neither a fool, but I understand human nature
> A computer generated globe is easy to produce.
What do you mean by this? Are you talking about something where you can only see no more than half the surface? (That horribly distorts the area around the horizon.) Replacing .jpgs and .gifs with Java applets or Shockwave movies? What happens if someone tries to print the globe?
Do you propose having the map producers start providing less misleading presentations? If so, perhaps you should contact them. You could ask Anthony to boycott their products, but I suspect a lot of providers would be quite happy to be shunned by WUWT.
We could provide a web service that takes the URL for a lat/long map and returns one for a better projection, but that’s a bit tricky because most such maps have borders with titles, scales, and other decoration, it would be a bit tricky to recognize the actual data. Probably not too bad, but I haven’t tried. I’m not going to try a globe.
Spencer (00:35:52) :
E.M.Smith (16:02:48) “Why does it make sense to sometimes use one and sometimes the other?”
It doesn’t. Since I wrote the post above last night I have downloaded the source code generating GISTEMP and have found the area I was blindly, hypothetically speaking of above. Here is some comments from the interpolation code file:
The software from “GISTEMP_sources.tar.gz/to.SBBXgrid.f “ Fortran source code file.
I’m going to give Dr. Hansen and his programmers the benefit of a doubt here. See the statement above about setting the mean to zero in a certain case, this may be correct but, what should I call it, a discontinuity in the program’s logic. Whenever I have written science related code, you must write your functions so that exactly the same logic is applied to each and every point or cell without exception. These discontinuities are what can wreck havoc on your results in certain rare cases. That is the only way you can clearly and in common English language that every one can understand describe your process because it is exactly the same for each and every point or cell.
The gist is: I used to write code in this manner years ago and through years of experience have learned never to write code in this manner, especially in science programs because math, science and mother-nature will always apply the same laws to each point or cell. You never see IF, OR, and WHEN statements in true science no matter how complex the subject is, one complex case, general relativity.
I might totally disagree with Dr. Hansen in certain points but I’m not here to belittle, be rude, or call names of him or his staff. That has no place in science. We should all be gentlepersons and pop ourselves in the head when we are not and let our emotions overtake our logic.
That’s all I’m going to say until I have the chance to absorb the code in this file. My last Fortran compiler was a 1984 version in MS-DOS 2.51. Yea, ancient. I can read Fortran, was my first language learned, but that was years ago. Will probably be faster for me to convert this code to C++ or C# so I can diagnose it line by line in a familiar language.
FWIW, here are a couple of links to GISS global temperature records:
The figures for the GISS Land-Ocean monthly, seasonal, and annual average global temperatures are provided in tabular form here:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
Here’s the link for the land stations (Meteorological) Stations only: http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
The 12 monthly figures are on the left in each row.
The four seasonal figures are on the right. These seasons are composed of three months, with meteorological winter considered to start in the December of the previous year. Winter is designated DJF (December / January / February); spring is MAM, etc.
The two annual averages are in the center, under the heading Annual Mean. The leftmost column, headed J-D, is for the calendar year and is the average of the 12 monthly figures to its left. The rightmost column, headed D-N, is for the “meteorological year,” and averages the four seasonal figures to its right. (Thus it includes data from the December of the prior year.)
Roger Knights (18:30:03) :
Thanks for the links. The statement at the bottom being: “Best estimate for absolute global mean for 1951-1980 is 14.0 deg-C” leads you to believe that all of the values in those files are differenced against a singular 14.0C value, not a station-by-station or cell-by-cell base value. Will have to totally install the GISTEMP software to get the answer to that question. Thanks again.
Ric Werme (17:42:35) :
2D representations of 3D objects are going to be awkward. Its probably not possible to see all of the Earth in a single glance. Do we need to? There are links to jpegs on this site that show satellite views. Several views centred over different areas should cover the entire globe. Some temperature points may need to be shown on several images due to overlap. Whatever distortions introduced by this method would be far less than the current way of displaying this data.
Earlier posts refer to smearing over 1000 kms from station to station, add to this the further smearing of the image and it could appear as if it is 4000 kms.
As to boycotting the current maps-that is out of the question. We need more information, not less, even if it is inaccurate to some degree. We need to see the data so that we can question it.
Map makers will produce what is requested, they won’t change until there is a need. I’m not suggesting that there is a conspiracy to misrepresent data on particular maps. It’s just that it has always been done that way. No-one has complained.
Global warming /global cooling and why. I am interested but not passionate.
Truth, lies and misrepresentation I am very passionate about.
Love this website- its my daily addiction
I can tell you precisely why the GISS differs from the MODIS…..
James Hansen had a hand in preparing the GISS
> I understand that it is not a true mercator map, but it is -ish
There’s an interesting comparison here between the surface anomaly done by weather stations