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.
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This is the GISTEMP anomaly vs 2000-2008 (the same baseline) and it still sucks compared to MODIS:
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
For example check India, Europe, Australia, South America.
The two maps show a number of things.
First, the Modis map is amazing – it is the best, highest resolution temperature map we have ever seen. It also correlates with the measured temperatures so we can assume it is reasonably accurate besides being better.
Second, the GISS smoothing algorithm using 1,200 kms needs to be damped down to a much smaller distance. The Modis map shows that inconsistencies can occur – the Arctic in this month for example as well as other areas.
Hadcrut3 and the NCDC seem to be much farther off than even GISS so who knows what they do with their data. But the smoothing algorithms need to be damped down.
http://hadobs.metoffice.com/hadcrut3/index.html
http://www.ncdc.noaa.gov/sotc/get-file.php?report=global&file=map-land-sfc-mntp&year=2009&month=11&ext=gif
Third, I think the Modis data can possibly be used to develop a new temperature series. The Modis instruments have been up since 1999 and 2002 so it would give us another check and another data source. Some mentioned above keeping GISS honest. We’ve seen this before when other newer systems come along, like UAH and RSS and the Argo buoys.
I am as puzzled by these results as many of you. I decided to dig in deeper to how these scientists adjust their data. Apologies if this is old news to you, but it has caused me some additional concern. I am referring to the latest document cited by NASA as affecting their temperature analysis (A closer look at United States and global surface temperature change – Hansen et al. (2001).
In this document they make great play of adjusting for UHI effects but the major adjustment seem to be as a consequence of Time of Observation Bias and Station History Adjustment – both positive by a significant amout.
Taking the adjustment for Time of Observation Bias – this claims to adjust for the fact that maximum and minimum temperatures are taken ,say, in the afternoon instead of at midnight, and is based on hourly records at stations in the US. Now my understanding of maximum and minimum temperatures is that one of each occurs at a random time in any 24 hour period.
It is simple to envisage with a traditional max/min thermometer – at some time in the day the max marker is pushed up as far as it will go by the mercury and there it stays until it is pushed down by the observer). The time of the observation will not affect the magnitude of the maximum, thus any adjustment to this temperature would seem to be inappropriate.
Assuming that the resetting of the max and min markers was carried out art consistent times then the reading should stand. The only possible effect a change in observation time could have is that may taken either before or after the maximum for the day that would have been recorderd at the old time – the consequence of this being a particular max value being assigned the the wrong day, resulting in either a double recording in effect in on day or a missed recording, depending on whether the new observation time is later or earlier than the previous.
But this will only affect one or two days’ values. Once a new observational routine is established the need for adjustment disappears.
In all cases an adjustment to the magnitude of a maximum would appear to me invalid, yet if I understand NASA correctly, the maximum recorded temperatures for all stations that are deemed to have changed the time of observation at some point in history are adjusted according to a positive value currently at around 0.2 degrees.
Moving on to Station History, the Hanson et al paper shows an example of Station History adjustment, where a station is moved from an urban to a peri-urban location. This causes a discontinuity in the readings, a drop in their example – so they apply a blanket increase to all stations that have been relocated. This of course ignores the fact that relocations are often to airports (with the problems associated as discussed here on many occasions).
The irony is that the example shows the “undisturbed temperature” ascending at a much lower rate (but still ascending – just another indication of biased thinking), but the total UHI correction they make is significantly less than the Station History adjustment introduced for moving from an urbanjust to a peri-urban location, not even a fully rural location.
Not being a climate scientist (or scientist of any sort!) I may have misunderstood, but I think I am applying logic to this analysis, for which a scientific qualification is not required.
For those of you interested, the graphs showing adjustments are shown in Plate 3 of the above-mentioned document. I should add that it is claimed that these adjustments are applied only to US station data. I can’t find any evidence as to what adjustments are applied to the rest of the world, but I can’t imagine they would be significantly different.
So I think if you want to get a real comparison between the two graphics at the top of this thread, maybe you should subtract approximately 0.2 degrees (the total adjustment shown from 2000 – the latest figure given in the paper) from the GISS anomalies for starters.
I regularly compare:
GISS Temp
CRU Surface (when it was available)
Hadley SST
MSU MT – RSS
MSU MT – UAH
MSU LT – RSS
MSU LT – UAH
Since 1979, the GISS is the clear high outlier.
The 1200km smoothing radius is pretty clearly the reason.
That smoothing exaggerates trends for areas in which
there are few samples, notably the Arctic, and interior Africa, based on the used peripheral stations.
For the decades of the early twentieth century,
it’s worthy to note that
the GISS trends were lower than CRU,
probably because the peripheral stations to the
’empty’ areas were similarly exaggerated.
The UAH MT data set exhibits the low outlier.
The remaining data sets exhibit relatively consistent trends.
(With deference to all hobgoblins).
Gash (22:09:44) :
Happy New Year to All Skeptics,
Hmmm…
As a Canadian living in the coldest captial city in the world, I would like to make a few observations from the layman’s perspective.
In Ottawa in the winter, it is generally much colder on clear days than on cloudy days.
MODIS does not see through cloud. MODIS imagery aggregates data from a number of passes to put together a full image. The data that is aggregated is based on the readings on clear days when cloud is not present.
Therefore a MODIS land temperature image for colder areas might be biased towards colder readings?
I would believe that to be true for higher latitudes.
But the converse is also probably true for lower latitudes,
where clearer areas are associated with higher temperatures.
Of course that bias is for the absolute values and not
necessarily for the anomalies. There are probably still
biases, but the comparison of anomlies against a baseline
would be the comparison of this November’s clear days
against all the November clear days of the record,
(which could vary in many inter-related ways).
If it has NASA on it, can it be trusted?
Here is GISS data with the correct base period 2000-2008, 250km radius:
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=250&pol=reg
Comparing this to the satellite data there is perhaps just one anomaly – western australia
Lucy Skywalker (03:44:30) :
If you want a comprehensive look at GISTemp visit EM Smiths site. He went throught he code and has how it works step by step. Also keep in mind that GISS doesn’t really use pure “raw” data. What they get is the GHCN+USHCNv2 added. What EM Smith found was that one of the first things Gistemp trys to do is take out all the adjustments NCDC made before they got it.
What he also found is that of the over 13,000 stations in the set only 3,000 have records over 64 years and those 3,000 long lived stations do not have the warming trend in them. It is in the 10,000 short lived station records. Also the 3,000 long lived stations supply roughly half of all the data in the set. So what you get is the a warming trend caused by adding stations in warm locations (like the tropics) and then shortly there after dropping stations in cooler areas like in the mountains. What it looks like to EM Smith was that a UN committee wanted a new network setup and NCDC dropped the cooler stations in response.
He has indepth analysis on stations around the world as well as how GIStemp works with a good dose of humor!
http://chiefio.wordpress.com/2009/08/05/agw-is-a-thermometer-count-artifact/
http://chiefio.wordpress.com/2009/10/22/thermometer-langoliers-lunch-2005-vs-2008/
01.01.2010, 18:00 GMT+1 : Looks like a new arctic blast here in Germany. Snow and North wind. Creeps southward. About the only place still above freezing is Munich which is in the deep south of germany.
Possibly a bit OT but maybe a few cracks at the BBC?
http://www.bbc.co.uk/blogs/dailypolitics/andrewneil/2009/12/its_going_to_be_a_cold_2010.html
Looking at the comments that have been allowed, most seem either luke warm or anti AGW with only one or two apologists.
So are the vast expanses of red at the two poles due to just one or two measurement stations (as we have recently seen in the south). The map is less alarming if the poles are not shown.
If this is a duplicate post, I apologize. (That’s what WordPress is saying. But I didn’t see it get posted yet.)
Syl (16:17:47) :
I understand the base periods are different but we can’t let it go at that. Look at western Australia, for example. We’re talking almost a 4C difference in anomaly between the two maps.
Am I nuts in saying that doesn’t make sense to me?
wayne (18:32:23) :
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.
The difference is not just due to a different base period. One is a monthly average, and the other is a weekly average. I suspect that it is the latter that accounts for the cognitive dissonance. Weather patterns are much more extreme week to week, than month to month. For a good idea of what I mean, just view the following:
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091216_e.png
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091209_e.png
The first is for the week ending December 16, so will be closest in time to “non-GISS” NASA image we’re looking at in this post. Look particularly at the light blue in Western Europe. Then look at the second, which is for a week earlier; Western Europe is now “warm.” Just a week’s difference in time.
But the GISS map is a monthly map for November. So now take a look at these images, which are weekly, going back through November, last week first:
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091202_e.png
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091125_e.png
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091118_e.png
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091111_e.png
http://ds.data.jma.go.jp/tcc/tcc/products/climate/synop/td20091202_e.png
Just the last week of November is dramatically different than the second week of December. That is “weather” for you, folks.
Incidentally, I believe the base period for the images linked above is a “standard” climatology of 1971-2000. I wish NASA would get with the program, and use the same 30 year climatology that most everyone else uses. (I know that the current “official” WMO climatology is 1951-1980, and only updates every 30 years, but most countries update their “normals” every decade, and following that convention, 1971-2000 is the current “standard” climatology. Of course, at the end of 2010, even the WMO climatology will update to 1981-2010.)
I’ve tried to post something here a couple of times, with some odd results (and nothing showing up yet). Without duplicating everything — I had a bunch of links to demonstrate what I’m about to say — one thing I think many are missing is that the GISS map is a monthly, while the NASA image based on 2000-2008 is for a single week. This likely explains the anomaly differences that cannot be accounted for by the different baselines. The second week of December was much cooler than November. If my previous post doesn’t show up, with the links I had to illustrate, I’ll try reposting it later.
I understand the base periods are different but we can’t let it go at that. Look at western Australia, for example. We’re talking almost a 4C difference in anomaly between the two maps.
December should show a very warm anomaly in Western Australia for December, based on the averages at 32 BoM ground stations across the state:
Albany Min Max
100y av 13.9 21.9
Dec 08 14.5 20.7
Dec 09 14.9 20.8
Balladonia
100y av 13.2 30.2
Dec 08 13.7 29.1
Dec 09 14.1 32.0
Bridgetown
100y av 10.6 27.5
Dec 08 10.1 26.1
Dec 09 10.2 28.9
Broome
100y av 26.4 33.8
Dec 08 26.4 33.8
Dec 09 26.6 33.9
Bunbury
100y av 13.9 25.6
Dec 08 12.5 25.8
Dec 09 13.1 29.1
Busselton
100y av 12.5 26.5
Dec 08 12.4 26.7
Dec 09 12.4 28.3
Cape Leeuwin
100y av 15.7 21.8
Dec 08 15.8 21.5
Dec 09 16.2 22
Cape Naturaliste
100y av 13.9 23.5
Dec 08 13.3 23.8
Dec 09 13.5 25.4
Carnarvon
100y av 20.1 29.3
Dec 08 20.1 29.5
Dec 09 21.4 31.8
Derby
100y av 26.4 36.2
Dec 08 26.0 36.4
Dec 09 25.8 36.5
Donnybrook
100y av 12.3 28.2
Dec 08 12.3 27.8
Dec 09 12.4 30.2
Esperance
100y av 14.3 24.5
Dec 08 14.2 23.3
Dec 09 15.2 26.6
Eucla
100y av 15.0 24.7
Dec 08 15.1 25.1
Dec 09 15.5 27.7
Eyre
100y av 14.1 25.2
Dec 08 12.9 25.1
Dec 09 12.5 29.0
Geraldton
100y av 16.9 27.5
Dec 08 15.6 28.2
Dec 09 16.6 33.0
Halls Creek
100y av 24.2 37.6
Dec 08 24.6 36.5
Dec 09 25.7 38.5
Kalgoorlie
100y av 16.8 32.8
Dec 08 17.0 31.8
Dec 09 18.4 33.5
Katanning
100y av 12.1 28.4
Dec 08 11.4 27.9
Dec 09 10.8 29.8
Kellerberrin
100y av 15.0 32.0
Dec 08 13.6 32.6
Dec 09 14.7 34.4
Laverton
100y av 19.3 34.9
Dec 08 19.8 34.1
Dec 09 21.4 35.5
Marble Bar
100y av 25.5 41.6
Dec 08 25.7 40.9
Dec 09 26.5 42.1
Merredin
100y av 15.7 31.9
Dec 08 15.0 32.5
Dec 09 16.1 34.1
Mt Barker
100y av 11.4 24.2
Dec 08 10.5 22.6
Dec 09 9.6 23.4
Northam
100y av 15.3 32.1
Dec 08 14.8 31.9
Dec 09 15.3 34.4
Onslow
100y av 21.6 35.2
Dec 08 21.8 35.0
Dec 09 23.1 37.1
Perth
100y av 16.3 27.4
Dec 08 15.6 27.8
Dec 09 16.5 30.8
Rottnest Island
100y av 16.9 24.3
Dec 08 17.2 23.3
Dec 09 17.9 25.4
Southern Cross
100y av 15.5 33.0
Dec 08 15.3 32.6
Dec 09 15.9 34.3
Wandering
100y av 12.0 29.6
Dec 08 11.5 29.6
Dec 09 11.0 32.5
Wiluna
100y av 21.0 36.8
Dec 08 20.9 37.0
Dec 09 23.1 38.3
Wyndham
100y av 27.2 37.1
Dec 08 26.3 35.6
Dec 09 27.2 38.1
York
100y av 14.9 31.5
Dec 08 13.8 31.7
Dec 09 13.4 34.0
The maxima in particular soared during December across Western Australia, which I suspect is due to the ongoing trend of below average rainfall in the heavily populated southern half of the state – i.e. less rain = less cloud cover = hotter days and cooler nights.
Removal of the below average Dec 08 figures and replacement with the above average Dec 09 figures has seen the average min and max temperatures at all 32 locations combined for the 12 months to December (i.e. 2009) at .59 and 1.09 degrees C higher than the averages for the early 1900s.
In the 12 months to Nov 09, the min was .53 and the max .92 above the early 1900s average, and in the 12 months to Oct 09 the min was .39 and .6 above the early 1900s average. i.e. the northern hemisphere seems to have been frigid but Western Australia has hit a hot spot over the past couple of months.
Sam the Sceptic
according to the monthly average temp report on my utility bill, the average Dec temp this year was 25 deg, compared to 13 deg last year. what are others recording ?
This post has the details:
Basil (09:31:22) :
I really think anyone interested in understanding the differences between the two images needs to read it. It is not just the difference in baseline (“climatology”). There is also the difference between one being monthly, and the other weekly, and that makes all the difference in the world, so to speak.
What we have here is an issue about taking issue with the old saying about enjoying “art for art’s sake” and making it a personal experience that will enlighten you, refresh your life, touch your soul, blow your mind, and give meaning and effect to color, form, contrast, etc., for the benefit of your senses. So the NASA Modis mob likes oil on black velvet and the NASA Giss guys like watercolors on cheap wet paper. The data is the same they say, so I guess the only real difference is amount they pay their inhouse art student-in-training do do the work each month/week. Looks like the budget at Modis can really be cut by quite a large sum. Oil on velvet? Really? Remember, “art for art’s sake”. Enjoy!
For Africa, GISTemp, via the GHCN dataset, shows Morocco as very warm. I looked into this. It is, IMHO, an artifact of GHCN deleting the thermometers near the cool ocean currents /shore and getting more temperature readings from the Atlas Mountains on the edge of the Sahara. (They leave the cooler thermometers in the baseline, though…).
http://chiefio.wordpress.com/2009/12/01/ncdc-ghcn-africa-by-altitude/
For Canada, GHCN deletes (only from the recent part of the record…) all thermometers in Yukon, Norwest Territories, and all but ONE in Nunavut (and that one is in a place called “the garden spot of the Arctic” due to the unusual plants and animals that survive in it’s unusal warmth). That big blob of excess “warmth” in the middle of Canada just reflects the GIStemp “smearing” of thermometer records from warmer (more coastal and more southernly) thermometer data inland to where it (now) has none, then comparing that to a baseline that actually WAS measured in the real cold. It does this in a couple of stages (first two are 1000 km ‘smears’, the final one in the anomaly map creation is a 1200 km smear. More than enough to move Vancouver temps to Northern Alberta…)
http://chiefio.wordpress.com/2009/11/13/ghcn-oh-canada-rockies-we-dont-need-no-rockies/
The GIStemp anomaly map is just a fantasy. It is based on horridly “cooked” GHCN data that have had hugh deletions of cold thermometers after about 1989 and then smears the remaining warm ones into the (now empty) cold places and “Surprise” finds them warmer when compared to the older real temperatures.
Oh, and I’m particularly fond of that South American map. Along with killing off all the thermometers in the Andies, we have that wonderful Big Red Blob over Bolivia. One small problem: GHCN has exactly NO temperature data for Boliva in recent years… From:
http://chiefio.wordpress.com/2009/11/16/ghcn-south-america-andes-what-andes/
We find that Bolivia ‘cuts off” in 1990:
The GHCN “By Altitude” report for Bolivia, Country Code 302: [chiefio@tubularbells Alts]$ cat Therm.by.Alt302.Dec.ALT Year -MSL 20 50 100 200 300 400 500 1000 2000 Space DAltPct: 1919 0.0 0.0 0.0 0.0 0.0 0.0 0.0 75.0 0.0 25.0 0.0 DAltPct: 1929 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 50.0 0.0 DAltPct: 1939 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.1 0.0 90.9 0.0 DAltPct: 1949 0.0 0.0 0.0 0.0 0.0 0.0 27.6 0.0 0.0 72.4 0.0 DAltPct: 1959 0.0 0.0 0.0 18.5 23.9 0.0 20.8 10.0 0.0 26.6 0.0 DAltPct: 1969 0.0 0.0 0.0 20.5 21.8 0.0 16.2 13.0 2.6 26.0 0.0 DAltPct: 1979 0.0 0.0 0.0 23.1 15.7 0.0 14.0 12.5 6.2 28.4 0.0 DAltPct: 1989 0.0 0.0 0.0 23.3 17.3 0.0 16.4 12.4 3.6 27.0 0.0 DAltPct: 1990 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 For COUNTRY CODE: 302 [chiefio@tubularbells Alts]$The thermometers migrate lower over time, but then in 1990 drops to a single thermometer that does not survive the year. (Each decade ends with a “9′ unless the data runs out, then it ends with that year).
Just how do you measure Bolivia with no data?
For those who don’t know, Boliva is mostly high cold mountains. So we are comparing real historical cold snowy mountain baselines with temperatures imported from, oh, coastal Peru and the Amazon …
I could go on, but it’s too painfull… If you want to read more, see:
http://chiefio.wordpress.com/category/ncdc-ghcn-issues/
for a list of articles. The “start here” page is:
http://chiefio.wordpress.com/2009/11/03/ghcn-the-global-analysis/
New NASA motto:
‘Making Life More Complicated With Each Passing Day”
or
“NASA – ASAN Spelled Backwords”
sagi (16:32:21) : Look how much of the ocean the GISS graphic incorporates!
Yup. When making the “anomaly map” GIStemp smears temperatures up to 1200 km from the nearest land station. That includes Islands. Think about it…
Yes, all those island airports with thermometers near the tarmac can “warm” an area of ocean up to 2400 km diameter.
http://chiefio.wordpress.com/2009/09/08/gistemp-islands-in-the-sun/
and from:
http://chiefio.wordpress.com/2009/12/08/ncdc-ghcn-airports-by-year-by-latitude/
we see that the percentage of thermometers at places that are airports is rising. This table is for Australia and may help explain that warm bubble out to sea near Adelaide:
Australia Airports percentage by lattitude band, Total on far right. Year SP -50 -45 -40 -35 -30 -25 -20 -15 -10 -NP [...] DArPct: 1929 0.0 0.0 0.0 1.6 2.4 1.2 2.0 1.3 0.4 0.0 8.9 DArPct: 1939 0.0 0.0 0.0 1.5 2.8 1.2 1.9 1.5 0.4 0.0 9.2 DArPct: 1949 0.0 0.0 0.3 2.6 4.4 2.4 2.4 1.8 0.9 0.0 14.9 DArPct: 1959 0.0 0.0 0.8 3.6 6.5 4.0 3.5 2.4 1.0 0.0 21.9 DArPct: 1969 0.0 0.0 1.2 4.4 8.3 4.4 4.1 2.8 1.9 0.0 27.0 DArPct: 1979 0.0 0.0 1.2 4.1 8.2 3.8 3.6 3.2 2.2 0.0 26.3 DArPct: 1989 0.0 0.0 0.9 4.8 9.1 4.2 4.0 3.8 2.7 0.0 29.4 DArPct: 1999 0.0 0.0 1.8 5.9 11.5 5.5 5.6 4.4 3.0 0.0 37.7 DArPct: 2009 0.0 0.0 3.8 11.0 21.9 11.4 11.4 7.6 3.8 0.0 71.0 For COUNTRY CODE: 501 From source ./vetted/v2.inv.id.withlatNotice that in the decade ending in 2009, we have 71% of total thermometers are at airports? And notice that when we distribute them by lattitude a disproportionate number end up in that 35 S up to 30 S band? So we’re basically saying that a lot of hot black tarmac is being measured around South Australia and Victoria, then used to make up fantasy temperatures out to sea to the south…
Yes, it’s that bad.
IMHO, the highest and best use of the deep red blobs on the GIStemp map is that it tells me exactly where to look for excess thermometer location data cooking in GHCN.
If you want to know where NOAA / NCDC have most damaged the temperature recording system by selective cold thermometer deletions (since 1989 ) just take a look at the GIStemp map. It’s the deep red blobs…
E.M.Smith (10:58:31) :
This has me puzzled. Either your data is plumb wrong (which I don’t for one minute believe) or the GHCN figures are being massaged and stupidly massaged at that since even I know that Bolivia tends towards the chilly (!) and if you’re claiming that it is showing warm based on readings from outwith that country then you are going to get very seriously found out and very soon.
Add it to the spate of UK stories in the last couple of weeks about the police solving burglaries by following the burglars’ footprints in the snow! I mean, it’s that stupid.
If you’re right about the Africa and Canada figures, which again I assume you are, then the same thing applies. Is there no-one at GHCN with the balls to post on here and explain exactly why their data are worth even having let alone being used for anything meaningful?
Sam the Skeptic (12:07:34) :
E.M.Smith (10:58:31) :
This has me puzzled. Either your data is plumb wrong (which I don’t for one minute believe) or the GHCN figures are being massaged and stupidly massaged at that since even I know that Bolivia tends towards the chilly (!)
Well, the “good news” is that it’s not my data, it’s the base GHCN data and anyone can download a copy and take a look. (It isn’t that big a dataset – the Dec 2009 that I just downloaded is 11.7 MB compressed download, for example). Just suck it into Excel or whatever you like and take a look. It is a flat text file, so anyone can check my work (and I’ve published the code, methods, etc. that I’ve used and I’ve had several folks cross check parts of it. I’m pretty sure it’s “rock solid”. But hey, I STRONGLY encourage anyone and everyone to do this for yourselves. It’s an easy process and it will make this a very real truth to you.)
The data set can be downloaded from:
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2
and I describe the data format here:
http://chiefio.wordpress.com/2009/02/24/ghcn-global-historical-climate-network/
use the v2.mean.Z file (that is what GIStemp uses) and the station detail information (like altitude, airstation flag, population, etc.) is in the v2.temperature.inv file. For some of the fancier reports (like “by altitude” or “by latitude”) you need to match these two on “country code : Station ID” but it’s easy for anyone with any programming or data analysis experience to do. For this example, you just need to search v2.mean on country code.
Country code is the first 3 digits ( the very first digit gives continent sized regions) so for example “3” gives you South America and “302” gives you Bolivia. The next 5 digits are the “station ID”, then you get 3 digits for the particular sub-location (I.E. LaPaz airport, then “at the tower” vs “near the runway”. Finally their is one digit of “modification flag” for the same temperature reading, but with a different “correction” history. (Tower with TOBS of noon vs TOBS of midnight, for example).
So to find Bolivia, just download v2.mean.Z and unzip it. Then fish out any record starting with “302”. Just after that 12 digit composite Country/Station number will be the year of the data. Look for the last year…
In Unix / Linx it would be done with this one line command:
That I just ran on the “Dec 2009” copy I downloaded about Dec 28th.
You can see that the command finds those lines that begin with 302, then sorts them on the first field, characters 13 to 16, then gives the last 10 lines (the “tail” command). Any unix / linux guy on the planet can repeat this test.
Notice that the data ends in 1990 and it a bit dodgy before that (the -9999 is the ‘missing data’ flag in GHCN).
But looking at a single theremometer record shows it was not so flaky prior to the GHCN Great Dying of Thermometers:
So again: Please do not believe me at all and just go look for yourself. I’m sure a “PC” guy can post the directions for how to do a text search on a PC for those folks not on Macs (which have Unix like tools under the skin in a terminal window) or on Linux / Unix.
ANYONE can test this.
and if you’re claiming that it is showing warm based on readings from outwith that country then you are going to get very seriously found out and very soon.
Well, I’ve been hollering about this for the better part of a year now and so far it is met with supreme silence from NOAA / NCDC and NASA / GISS. BTW, Hansen et.al have published that “The Reference Station Method” can be used to fill in missing data up to 1200km away on anomaly maps, so they do not deny it… they endorse it.
Still waiting to be “found out”… Frankly, I’d love to be “found out”. Any and all reporting of the cooking of the books by NCDC via GHCN deletions would be a Very Good Thing.
Add it to the spate of UK stories in the last couple of weeks about the police solving burglaries by following the burglars’ footprints in the snow! I mean, it’s that stupid.
Yet most folks never look upstream. They ASSUME that the data are clean and complete. Heck, I was banging my head on GIStemp for about a year before I realized that the real issue was the input data; and I’ve done forensics work before! If it took me that long, how long would it take the average researcher who just wants to look at something like “Temperature correlation with fish numbers”?
If you’re right about the Africa and Canada figures, which again I assume you are, then the same thing applies.
No need to assume. Just go look for yourself. It isn’t hard.
Is there no-one at GHCN with the balls to post on here and explain exactly why their data are worth even having let alone being used for anything meaningful?
Not as near as I can tell. A few months back I published the name and contact information for the “Dataset manager” on my web site. Nothing. (It’s a bit hard to find, but IS up on a NOAA / NCDC web page, so I was not publishing anything they did not already have published…)
It looks like “duck and cover” to me, but might just as easily be “If we ignore it, maybe it will go away”…
But again: DO NOT BELIEVE ME! Just go look for yourself…
You know, the way Science is supposed to be done…
ASSUME: It is not settled and I may have missed something and go take a whack at it. THAT is real Science.
Oh, and from v2.country.codes, this excerpt:
236 TAIWAN
301 ARGENTINA
302 BOLIVIA
303 BRAZIL
304 CHILE
305 COLOMBIA
306 ECUADOR
So you can see that 302 really is Bolivia…
The color choices used by NASA to indicate anomalies are used incorrectly in a way that visually over emphasizes warmth at the northern latitudes. NASA uses traditional color ordering for cooler temperatures – but reverses the standard order for warmer colors.
In the “color wheel” concept of colors used by artists, as well as a chart of Kelvin temperature colors, yellow is the warmest color (not the dark reddish brown shown on NASA maps).
In the NASA maps, the color choices range from “slightly warm” represented as yellow, to orange, then red, then dark red for the warmest temperatures.
This is backwards from how colors range from cooler to warmer coolers in a Kelvin temperature color chart. Slightly warmer should be darker red, then rising to red, orange and then yellow.
In the “color wheel” concept of colors, yellow is the warmest of hues. In the NASA maps, a little warmer is yellow, then transitions to orange, then bright red, and then dark red (almost a brown red) for the hottest areas. A simple chart showing this relationship is here:
http://www.mediacollege.com/lighting/colour/colour-temperature.html
An easy to read write up on the use of colors to represent “warm” or “cool” is located here
http://www.handprint.com/HP/WCL/color12.html#warmcool
The effect of NASA’s color choices is to visually over emphasize temperature anomalies in the higher latitude regions and to focus the eyes on the color red. As Wikipedia notes, “Studies show that red can have a physical effect, including increasing the rate of respiration, raising blood pressure and thus making the heart beat faster.” (See http://en.wikipedia.org/wiki/Color_symbolism_and_psychology#Red)
We do not know why NASA has selected this color scheme but it is very different than traditional color selections used everywhere else. It is a curious choice in that it defies common usage and might be used to bias the viewers’ interpretation of their chart, giving the charts an appearance of a sales pitch or marketing presentation.
kadaka (17:09:38) : And wow, the Arctic regions are predominantly running 4 to 9.9 degrees high! No wonder Dr. Al Gore, the noted climate scientist, thinks the ice will be gone by 2035, it’s obvious!
GIStemp “guesses” about the Arctic. The “data” are interpolated from “satellite ice estimates” if I understand Hansen’s paper correctly (via a Hadley / CRU SST annomaly map, so we just KNOW it has to be clean and right /sarcoff> )
That’s right. All those rosy reds in the Arctic are “optimal interpolation based on ice estimates“. No real temperatures need apply…
Tom T (17:32:06) : I can’t help thinking that Hansen is cherry picking when compares temps to 1951-1980. Why those years. I have often wondered about this.
I think it’s pretty easy to see that it’s a “Cherry Pick”. Look at this graph and notice that it’s right in that “blue dip”:
http://www.smhi.se/sgn0102/n0205/upps_www.pdf