Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data
Guest post by BobTisdale
Over the past few days there has been some blogosphere buzz about the apparent ratio of Land Surface Temperature (LST) Data used in the GISTEMP combined land and sea surface temperature data with 1200km smoothing. I’ll provide a few comparison graphs to explain.
Figure 1 includes a time series graph of the Hadley Centre’s HADCRUT3 Combined Surface Temperature product, from January 1982 to April 2010. Also included is the weighted average of the two datasets that make up the HADCRUT3 data, with the weighting of 27% LST [CRUTEM3] and 73% Sea Surface Temperature (SST) [HADSST2]. Those weightings were required to match the linear trend of the weighted average to the linear trend of the HADCRUT3 combined product. The weighting makes sense, since the global oceans represent about 70% of the surface area of the globe.
http://i28.tinypic.com/2yoe59f.jpg
Figure 1
Figures 2 and 3 provide similar comparison graphs. Figure 2 shows the NCDC combined surface temperature product and the weighted average of its LST and SST components. To align the linear trends, the weighting required for the components of the NCDC product was also 27% LST data and 73% SST data. Figure 3 shows the GISTEMP product with 250km radius smoothing. For this GISTEMP product, the weighting required for the components was 28.5% LST data and 71.5% SST data. Again, the relationships of the SST and LST data make sense.
http://i26.tinypic.com/2qmi3yh.jpg
Figure 2
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http://i25.tinypic.com/1rx6v7.jpg
Figure 3
Here’s the curiosity. It appears in the GISTEMP Product with 1200km radius smoothing when we apply the same component weighting (28.5% LST data and 71.5% SST data) that we had used on the other GISTEMP combined product. The weighted average of the components of the GISTEMP combined product with 1200km radius smoothing has a significantly lower trend than the actual GISTEMP data. This can be seen in Figure 4.
http://i28.tinypic.com/9jot4x.jpg
Figure 4
In order to achieve the same linear trend as the GISTEMP combined product with 1200km radius smoothing, the components have to be weighted with 67% LST data and 33% SST data, almost reversing the ratio of the areas of global oceans and continental land masses.
http://i30.tinypic.com/p9g5d.jpg
Figure 5
Figure 6 is a map illustrating the GISTEMP LST data (trends) from 1982 to 2009. Note how the GISTEMP LST data extends out over the oceans. This is not the case for their combined product, because GISS masks the LST data over the oceans in its combined product. So in order to properly create a weighted average of GISTEMP land and sea surface temperature data with 1200km radius smoothing, the land surface data where it extends out over the oceans would first need to be masked.
http://i26.tinypic.com/4ieop2.jpg
Figure 6
A NOTE ON THE DIVERGENCE BETWEEN GISS AND THE OTHER DATASETS
Much of the divergence between GISTEMP and the Hadley Centre and NCDC combined surface temperature products is likely caused by the fact that GISS deletes SST data in the Southern and Arctic Oceans and replaces it with LST data, which has a significantly higher linear trend than the SST data it replaces. This was discussed in the post GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.
ANOTHER CURIOSITY
It can also appear that GISS extends LST data out over the oceans in areas other than those with seasonal sea ice. In fact, I made this mistake in a comment at Lucia’s The Blackboard this morning. Refer to my Comment#49191 at the bottom of her post NOAA: Hottest June in Record. This illusion can be seen in the following .gif animation of GISTEMP trend maps for the period of 1982 to 2009. The April trend is presented in Figure 7. Note how, in the highlighted area of the North Atlantic, there are differences between the SST trend and the trend of the GISTEMP combined product with 1200km radius smoothing. The Faroe Islands are located between Scotland and Iceland, and GISS uses station data there, so that explains the differences in that area. But what of the area of the North Atlantic west of Ireland and south of Iceland, with the approximate coordinates of 50N-60N, 20W-15W? There aren’t any islands there with weather stations.
http://i29.tinypic.com/2i0vhif.jpg
Figure 7
In its GISTEMP LST products, GISS also includes surface station data identified as Ship followed by a letter; that is, “Ship J”, “Ship R”, etc. Refer to Figure 8. These can be found using the station locator feature on the GISTEMP Station Data webpage.
http://i32.tinypic.com/2i09k7r.jpg
Figure 8
Here’s a link to the webpage presented in Figure 8.
I have found little to no information on these GHCN “ship stations”. Are they presenting SST or Nighttime Marine Air Temperature? Dunno. They may have served a purpose when GISS first prepared their GISTEMP product due to the sparseness of SST data in the early SST datasets, but now, these “ship stations” only add an unknown bias to well-documented optimally interpolated SST data. (And if they don’t add a bias either way, then there’s really no reason to have them. All they do is add confusion.)
yes. Not only was the discrepancy obvious, even with the attempt to pretend that oceanic areas that were cooling were ‘hot’, but the extension of land temperatures onto the oceans was retarded.
Bob, you’re a complete denier paid by Exxon Mobil and BP… go swim in that hot, boiling oil polluted hurricane basin!
Oh, and there was something on the Blackboard a while back about how GISS does not weight it’s land temps by hemisphere… not sure if that would answer your question… but it shows how incompetent the boys at GISS are if they publish numbers and they didn’t consider how there are different amounts of landmass in each hemisphere.
If the data shows warming, attack the data, and the methods used to collect and analyse them. If the data shows cooling, no problem then!
We must present scarry senarios since the summit in Mexico is coming up….must get an agreement…/sarc off.
Has anyone looked at the siting of the Faroe Islands station?
WRT ships.
In my code these ships recieve ZERO weighting, they are dropped.
Eek, Zeke!
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I wrote up the whole land/ocean ratio thing on my blog a month ago:
http://knowledgedrift.wordpress.com/2010/06/16/giss-global-temperatures-and-math/
I did that with every 5th year of annual data, but I have since done it with the monthlyy data from 1880 and got the same result. However, while doing that analysis, I discovered something even more interesting. In addition to the changing weighting between land and ocean which isn’t anywhere near proper math, I also discovered something very odd about the SST data they use. Is it NOAA or is it Hadl/Reyn?
Answer; It appears to be both. They slide the warmer Hadl/Reyn SST data into the places where it would go undetected if you didn’t break the grid points down to a very detailed level. But I did. I only looked at the 250 Km smoothing, not the 1200, but it seems pretty plain that gridpoints that report a merged land/ocean anomaly are calculated from the warmer Hadl/Reyn SST from 1982 on:
http://knowledgedrift.wordpress.com/2010/07/17/games-from-giss/
Has anybody found the warm CO2 greenhouse roofing placed over the equator by all the computer models? Isn’t the missing CO2 greenhouse roofing proof enough the whole theory is composed of falsehoods? In normal times it would disprove.
http://rankexploits.com/musings/2010/the-great-gistemp-mystery
In other words, the GISTEMP global gridded land anomaly is not weighted by the ‘land area fraction.’
———————–
The ship data does not come from GISTEMP but GHCN
(I read this out of the ghcn v2.mean but you can get the same info in GISS station data page)
80099906001 SHIP I 59.00 -19.00 -999 0 R -9
80099907001 SHIP J 52.50 -20.00 -999 0 R -9
80099909001 SHIP L 57.00 -20.00 -999 0 R -9
SHIP I has data in 1948-1975
SHIP J has data in 1948-1975
SHIP L has data in 1975-1989
SHIP L is dropped from the processing (noted on the GISTEMP station page and confirmed in the short.station.log output in GISTEMP step 3)
As to the Fig 7 flip chart, most of the NAtl bobble goes away with a smoothing of 250km.
And just a follow up –
If you do a ‘GISS’ only graph (“land” only) for 1970-75, you will see some spots in the mid North Atlantic south of Iceland and west of Ireland for Ships I and J.
Do it again for 1976-1980, and Ships I and J are gone.
Steven Mosher wrote: “WRT ships. In my code these ships recieve ZERO weighting, they are dropped.”
But they appear to influence the trends. Scroll up to Figure 7 and note the white dot that appears in the GISTEMP combined product in the extreme Northeast Pacific, about 50N & 145W, but is not there in the SST data. That should be “Ship P”.
http://data.giss.nasa.gov/cgi-bin/gistemp/findstation.py?lat=50.0&lon=-145.0&datatype=gistemp&data_set=1
Bob
Just look at Fig 4 – at the left you will see that the data shows anomalous cooling. But I know that’s not what you mean.
Perhaps you could enlighten us with all this data that shows cooling. I’m sure the experts here would be happy to comment on that too.
You see, what the vast majority of people here want to see is simply temperature data that can with stand scrutiny from questioning scientific minds. They want to see better recording, more realistic modelling and unbiassed reporting of the outcomes, with transparency throughout.
I suspect they also would like to see a little more humility from many of the leading proponents with regard to the uncertainties in their work.
It worries me immensely when leading scientists and advisors to governments say warming has to be causd by man’s activities because we can’t think of anything else that would cause it! If that view had prevailed throughout the history of science we would still be bleeding and leeching to induce a balance of humo(u)rs in the body, light would still be carried by the luminiferous ether and the earth would still be the centre of the universe.
I will remind you that the people who caused science to move forward were the questioners. The deniers were those refused to question the accepted truths of the time (aka the concensus view).
davidmhoffer says: “They slide the warmer Hadl/Reyn SST data into the places where it would go undetected if you didn’t break the grid points down to a very detailed level. But I did. I only looked at the 250 Km smoothing, not the 1200, but it seems pretty plain that gridpoints that report a merged land/ocean anomaly are calculated from the warmer Hadl/Reyn SST from 1982 on…”
David, first, “Hadl/Reyn SST” are two separate SST datasets. GISS notes this in numerous papers and webpages. The “Hadl” portion is HADISST, which they use from January 1880 to November 1981. The “Reyn” portion is Reynolds (OI.v2) SST data, which they use from December 1981 to present. Refer to the Ocean data source description under “Input Elements” here:
http://data.giss.nasa.gov/gistemp/maps/
Second, the Reynolds (OI.v2) SST dataset does have a higher trend than HADISST over the period they use the Reynolds SST data, but the difference in the trends is minimal, 0.01 deg C/decade:
http://i32.tinypic.com/2kocoy.jpg
With respect to your first linked post, a few questions? If, for example, land surface data does not exist for Antarctica prior to 1950, how is that an error on the part of GISS? Second, when you were analyzing your percentages of land to ocean data, did you mask where the land surface data extends out over the oceans?
Bob says: “If the data shows warming, attack the data, and the methods used to collect and analyse them. If the data shows cooling, no problem then!”
Hmm. Did you read the post? I don’t believe I attacked the data, methods or analysis.
Peter Plail says: “Just look at Fig 4 – at the left you will see that the data shows anomalous cooling. But I know that’s not what you mean.” And continued, “Perhaps you could enlighten us with all this data that shows cooling. I’m sure the experts here would be happy to comment on that too.”
Figure 4 doesn’t show cooling. It shows gradual increases in temperature from 1982 to present that is impacted by volcanic eruptions and ENSO events to create the year-to-year variability. The data is below zero in the early years due to the base years used for anomalies.
Weather ships : ‘fixed’ location floating weather stations for aviation
http://en.m.wikipedia.org/wiki/Weather_ship?
(a valid use of wikipeadia ! )
Some history here : http://iancoombe.tripod.com/id56.html – includes some detail of the OBS carried out
Ron Broberg wrote, “In other words, the GISTEMP global gridded land anomaly is not weighted by the ‘land area fraction.’ ”
Thanks for the clarification, but I didn’t write or imply that it was. This post was about the curiosity found by other bloggers. I attempted to explain why they were seeing the significant disparity in the ratio of land to ocean data.
You wrote, “As to the Fig 7 flip chart, most of the NAtl bobble goes away with a smoothing of 250km.”
I guess it depends on one’s definition of “most of”:
http://i30.tinypic.com/10pwtua.jpg
You wrote, “If you do a ‘GISS’ only graph (“land” only) for 1970-75, you will see some spots in the mid North Atlantic south of Iceland and west of Ireland for Ships I and J… Do it again for 1976-1980, and Ships I and J are gone.”
Thanks, that confirms the dates in your earlier comment, leaving only “Ship R” for the period we’ve been looking at. And it covers the period of 1975 to 1985.
Regards
Data Sources: Ocean
Bob Tisdale,
Yes, HadISST and Reyn are different data sets as specified in the Global Maps web site at GISS which reads:
None: NO ocean temperature data are used
Hadl/Reyn_v2:SST 1880-present
1880-11/1981: Hadley HadISST1, ship and buoy data (Rayner 2000),
12/1981-present: oisst v2, satellite data (Reynolds-Rayner-Smith 2001)
NOAA/ER_v3b:SST 1880-present
NOAA ERSST V3b, ship and buoy data (Smith,Reynolds,et al 2008)
So… If you derive your data from the Global Maps site, what you get is the adjusted version of the data based on Hadl/Reyn for their respective periods of time, OR you get NOAA V3b for the SST data But if you download the raw monthly anomaly data for Land and Combined, the period from 19450 to current clearly reflects NOAA in the combined data with the exception of those grid points that overlap between land and ocean since Dec 1981 which reflect Hadl/Reyn in months when there is no Land station data available.
As to your second question:
“If, for example, land surface data does not exist for Antarctica prior to 1950, how is that an error on the part of GISS? Second, when you were analyzing your percentages of land to ocean data, did you mask where the land surface data extends out over the oceans?”
The fact that land surface data for Antarctica didn’t exist prior to 1950 isn’t an error on the part of GISS. Adding the data since 1950 and calculating the combined global anomaly as if the planet’s land mass suddenly grew by the area of Antarctica while the oceans stayed the same size is. We don’t get more land on the planet by measuring it in more places. No, I didn’t mask where the land surface data extends into the ocean. I used the 250 km smoothing which reduces the problem in the first place, compared the number of land grid points to ocean grid points and concluded that Land was about 6% of the Combined data in 1880 and somewhere over 30% by now. Frank Lasner took a different approach in his post just before yours to calculating the numbers, but also conlcuded that Land data counted for almost nothing in the Combined data set in the early part of the graph and that it increases significantly over time as more land station reporting becomes available while ocean coverage is pretty much static. In brief, Land influence in the combined set increases as more land station reporting becomes available, which accelerates the warming trend in the Combined data. The fact (as you have pointed out) that GISS ignores or deletes SST data if it is reported within a certain distance of (100 km?) of a land station also means that as land station reporting increases over time, SST data decreases, hence biasing the global combined anomaly more heavily toward the generaly larger Land anomalies. I haven’t looked too much at the 1200 km smoothing but I imagine this effect would be more pronounced there than with the 250 km smoothing.
MikeC: You wrote, “Oh, and there was something on the Blackboard a while back about how GISS does not weight it’s land temps by hemisphere… not sure if that would answer your question… ”
I looked into it, and over the period I chose to work with, the differences in linear trends are minimal (0.001 deg C/decade) using Global Combined Land Plus Sea Surface Temperatures with 1200km radius smoothing.
Peter_dtm: Thanks for the link on weather ships. Now the question is, is GHCN presenting NMAT or SST? One would assume NMAT, but…
tarpon says:
July 17, 2010 at 11:06 pm
Has anybody found the warm CO2 greenhouse roofing placed over the equator by all the computer models? Isn’t the missing CO2 greenhouse roofing proof enough the whole theory is composed of falsehoods? In normal times it would disprove.
==========================================================
You would think, since that is a requirement for verifying the computer programs.
Without it, the computer programs are wrong
Last I heard, they said warming is there, because the computer programs say it is, they just can’t find it
thanks Bob!
davidmhoffer says: “But if you download the raw monthly anomaly data for Land and Combined, the period from 19450 to current clearly reflects NOAA in the combined data with the exception of those grid points that overlap between land and ocean since Dec 1981 which reflect Hadl/Reyn in months when there is no Land station data available.”
They do? You’ll need to clarify that, because it made little sense to me.
First, please provide a link to the “raw monthly anomaly data for Land and Combined” data you’re using? Is it this data, which is their standard combined product?
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
The standard GISTEMP SST dataset is the merger of HADISST and Reynolds data as noted in my earlier reply. This is further clarified in the header of the above link and further confirmed in step 4 of their Sources webpage:
http://data.giss.nasa.gov/gistemp/sources/gistemp.html
FYI. GISTEMP source code and documentation is available here:
http://data.giss.nasa.gov/gistemp/sources/
You wrote, “…since Dec 1981 which reflect Hadl/Reyn in months when there is no Land station data available.”
What months might those be? There’s land surface data available for all months since December 1981:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
You wrote, “If you derive your data from the Global Maps site…”
I don’t. I use the GISS map-making webpage to plot maps. If I’m looking into their global temperature product, I use their global mean product:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
Or their surface station only based data (land surface temperature data):
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
Then, for a post like this that only examines the period after 1982, I also use Reynolds OI.v2 SST data through the NOAA NOMADS website:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=
Or if I want to subdivide the globe, I use GISS data available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere%5B
You wrote, “The fact that land surface data for Antarctica didn’t exist prior to 1950 isn’t an error on the part of GISS. Adding the data since 1950 and calculating the combined global anomaly as if the planet’s land mass suddenly grew by the area of Antarctica while the oceans stayed the same size is.”
Huh? Anyone who analyses global temperature data understands, or should understand, when and where data is available, which parts of the data is manufactured by the producer through whatever infilling method they employ, and they should understand the methods used to infill the missing data.
You replied, “I didn’t mask where the land surface data extends into the ocean. I used the 250 km smoothing…”
Then you’re not evaluating the standard GISTEMP product, which uses the 1200km radius smoothing.
You wrote, “Frank Lasner [sic] took a different approach in his post just before yours to calculating the numbers, but also conlcuded that Land data counted for almost nothing in the Combined data set in the early part of the graph and that it increases significantly over time as more land station reporting becomes available while ocean coverage is pretty much static.”
If you’re talking about Frank’s post at Jo Nova’s “Did GISS discover 30% more land in the Northern Hemisphere?…
http://joannenova.com.au/2010/07/did-giss-discover-30-more-land-in-the-northern-hemisphere/
…Frank concluded that he should have masked the land surface data over the oceans in his evaluation. Refer to his July 18th, 2010 at 4:54 pm comment.
You wrote, “The fact (as you have pointed out) that GISS ignores or deletes SST data if it is reported within a certain distance of (100 km?) of a land station also means that as land station reporting increases over time…”
I did not write or imply what you’ve written.
Bob says:
July 17, 2010 at 8:36 pm
“If the data shows warming, attack the data, and the methods used to collect and analyse them. If the data shows cooling, no problem then!”
Attacking the data and the methods is in fact the scientific way of doing things. If the data is fine and the methods to process them are sound they will easily withstand the attack.
How about someone answering a more basic question –what is the legitimate scientific need to have two different products, 250km vs 1200km? To me, granularity is a very important topic, and you should drive to the highest degree of granularity you can reasonably produce. This is probably part of what’s wrong with Mann’s hockey stick, btw –even Briffa seems to recognize that.
If there are significant differences (as there appear to be) between the 250km smooth and the 1200km smooth, then to me that is merely evidence of the advisability of ash-canning the 1200km smooth.
Excellent analysis, but you missed something important. You cannot simply blow off as inconsequential the bleeding of LST data over large swaths of SST data. Everyone knows the oceans are the dominate temp regulator, not the land. All over the Earth you can see higher LST’s over riding lower SST’s due to the GISS sloppiness.
Everyone knows large bodies of water will attenuate large changes in temp. Regions next to oceans tend to be warmer in winter and cooler in the summer.
So I see no scientific (let alone logical) reason to bleed LST’s over water where the SST’s directly measure a lower temp. Is it a surprise the shifts on the GISS chart appear mainly towards hotter temps?
You nailed the problem when you showed the slight shift in percentage of LST verses SST.
What you should have also nailed was the error in measurements of both data sets going back in time. There is NO WAY SST data has any accuracy prior to satellite coverage. While I would guesstimate the LST to have errors of +/-3°C over 1200 km up until the age of satellites, the SST data prior before the 1980’s is garbage.
Which means 70% of the historical data from which we determine whether it is warm now or not is garbage.
These ‘scientists’ who claim they can confidently detect a sub °C increase in global temp since 1900 are rank amateurs, slavishly lost in statistical noise and seeing ghosts.
Peter_DTM has it exactly right. These are Ocean Station Vessels that were discontinued decades ago.
See: http://www.uscg.mil/history/webcutters/rpdinsmore_oceanstations.asp
For a map showing where these ships maintained stations and took weather observations including two radiosonde runs every day.
davidmhoffer: Sorry. I didn’t have time to finish my earlier reply.
The bottom line: Here’s a comparison graph of the GISTEMP combined land plus sea surface temperature (1200km radius smoothing) with the global data (90S-90N) and the global data minus the Antarctic and the Southern Ocean (60S-90N):
http://i30.tinypic.com/11awlqb.jpg
Note that there is no difference in the linear trends. So after all is said and done, all of your speculation that the addition of the Antarctic data after 1950 changes this and that is really meaningless.
AJStrata says: “You cannot simply blow off as inconsequential the bleeding of LST data over large swaths of SST data.”
I didn’t. The GISTEMP LST data extends out over the oceans in the GISTEMP land surface data only. They mask the LST data over the oceans when they combine it with SST, as I noted in the post.
Regards
Rod Smith: Thanks for the link to the Coast Guard Weather ships. The coding appears to be the same as those used by GHCN and, in turn, by GISS.
Bob Tisdale says:
July 18, 2010 at 1:16 am (Edit)
Steven Mosher wrote: “WRT ships. In my code these ships recieve ZERO weighting, they are dropped.”
But they appear to influence the trends.
***************************************************
I think you are missing the point. in MY CODE I handle ship reports differently than GISS. It’s a methodological choice.
I regard every record in GHCN as a point source. Those point sources are allocated
to an area cell. In the case of a ship it is allocated to a cell that has ZERO LAND.
To calculate the temperature for that cell I then do the following:
Multiply the temperature by the LAND FRACTION in that Cell. if there is no land
in the cell it doesnt get weighted. no value.
Then I test the sensitivity of this decision by Not using land fraction. In this case
the ship cell recieves a FULL weight. Based on area.
The difference between these is real but minor. Its a methological uncertainty.
According to World Weather Records, mean temperature computations for Ship Stations are “based on 8 observations per day, made at 3-hourly intervals”. So in GHCN, they are neither SST nor NMAT.
Hi Bob!
My article that Davidmhoffer refers to is this:
http://wattsupwiththat.com/2010/07/17/tipping-point-at-giss-land-and-sea-out-of-balance/
As Ron has pointed out and as I pointed out to someone who raised this issue to me a while back this phenomena is not the result of GISS throwing any data away or sneakily weighting data. Its a result of a METHOD. a method of creating a global average when you have land data missing. The SST datasets that everybody uses
are infilled so that they are complete. every 5 degree cell is present. For the land,
thats not so. There is another issue. Land FRACTION. CRU cover this in detail in their paper. We have noted this as a difference between CRU and GISS over TWO YEARS
ago. There is no “right” way to handle this issue in constructing an average. So what we do ( zeke, chad, myself) is explore the different ways of handling this issue. Yes, you get different answers. marginally different answers.
AJ
“So I see no scientific (let alone logical) reason to bleed LST’s over water where the SST’s directly measure a lower temp. ”
It depends. If what you are trying to do is capture the TREND, then what you want is a consistent method OVER TIME. For coastal cells there are several ways to approach the problem. GISS is just one. as a METHOD it is brittle WRT changing frequency of reports from coast stations.
That’s one reason why I prefer to use land fraction. I think land fraction is the “right” approach, but GISS approach does “work” to capture changes in trend. I dont think it works as well as others, however
Bob,
As other folks have mentioned, the GISTemp land record is not actually a land record; rather its an attempt to estimate global temps using only land stations. This means it ends up using a zonal weighting (and no land mask) such that it runs considerably colder than other land series. Linearly combining published GISTemp land record with the ocean record is not strictly meaningful.
As a quick experiment, try examining the land fraction implied by combining NCDC land with Reynolds instead of GISTemp land. You will see that interpolation plays a role, but the largest factor in the odd 70% land finding is simply that the GISTemp land record is being used inappropriately.
Thanks Bob, that looks like a lot of work. The bottom line wrt temperature measurement seems to be that lacking enough data points, the numbers can be fiddled with to look like anything. I have to agree with AJStrata that parsing a fraction of a degree of ‘climate’ change/century from this mess isn’t science.
Steven Mosher says: July 18, 2010 at 8:10 am: Thanks for the clarification. I had assumed you were discussing how GISS handled the Ship Stations.
Frank Lansner says: “My article that Davidmhoffer refers to is this”
Is it the same as the one at Jo Nova’s?
Going from Fig 1-3, 1998 goes from highest temp to being surpassed in 2007. I recall many months ago 1998 was heralded as the highest temp on record only to be corrected to second highest after the 1930s high. We certainly aren’t informed as to the rubbery nature of these curves. Would it not be best to stick to land temps if we are trying to guage warming – its where most of the disasters are supposed to occur. We could have separate trends for land and sea, especially since the SST is basically only a few millimetres deep.
One fine day Pea met
Thimble, Thymble, and Thuemble.
You pick the vowel.
============
Is it the same NASA´s GISS where J. Ali Mustafa Califa Ak Bhar Hansen, a.k.a. the famous death trains´Emir works?
Now, they must and should care to reach, ASAP, the Crescent Moon. LOL!
And….no amount of intelligent and elaborated statistic corrections will ever convince the followers of the most holy Global Warming religion, of changing their faith, but the chilling reality of shorter summer seasons and earlier and longer winters.
Steve Mosher, you wrote:
“As Ron has pointed out and as I pointed out to someone who raised this issue to me a while back this phenomena is not the result of GISS throwing any data away or sneakily weighting data. Its a result of a METHOD.”
Steve, do you think that if someone creates a “method” i cannot be .. well … rotten? A method cannot be developed with “so-so” intensions?
Steve, in CRU program code released in climate gate its a permanent method that temperature proxy graphs are cut of in 1960, and the same can be read in their mails. So they have “a method” to cut of data most often in 1960, in then the sceptics cannot complain?
Just because “its a method” then no critique? No?
In the present case for example we have a GISS LST+SST that is the same as the SST (HADISST) 1900-1920. The GISS LST is higher. So the weight of land data is zero. Obviously this is build into a method , a program etc. and then its suddenly ok??
If you think so, you are giving the Alarmist such an incredible easy time to get away with anything.
The argument: “Its a METHOD” changes absolutely nothing, zippo zilch nada.
A land% = zero in year 1900-20 appears rotten even though its obviously result of a GISS “method”.
This reminds me of H.C.Andersen, the emperor has no clothes on. Oh.. but its a “method”!!! Then its ok.
The Ships are unmanned buoys which measure air temperature. they use some from Canada too.
Bob says:
July 17, 2010 at 8:36 pm
“If the data shows warming, attack the data, and the methods used to collect and analyse them. If the data shows cooling, no problem then!”
And if the land data is suspect (Refer to the Surface Stations Project) and is applied to the oceans (read the post above), then publish it and scream “WE’RE ALL GONNA DIE!!!!!!”
(Thanks Mr. Tisdale. Good post.)
Gary Pearse says: “I recall many months ago 1998 was heralded as the highest temp on record only to be corrected to second highest after the 1930s high.”
Wasn’t that the U.S. surface temperature record?
Bob Tisdale;
The bottom line: Here’s a comparison graph of the GISTEMP combined land plus sea surface temperature (1200km radius smoothing) with the global data (90S-90N) and the global data minus the Antarctic and the Southern Ocean (60S-90N):
http://i30.tinypic.com/11awlqb.jpg Note that there is no difference in the linear trends. So after all is said and done, all of your speculation that the addition of the Antarctic data after 1950 changes this and that is really meaningless.>>
It isn’t the specific data that is added it is the manner in which it is handled in the analysis that matters. Sorry, but I am far more familiar with the 250 km smoothing than the 1200, so this is from the 250 km data |(downloaded in answer to your question from NOAA’s site because they provide it in NetCDF format). In 1880 there are just over 8000 grid points with data in the combined data set. There are about 1100 land and almost 500 of them overlap with ocean. The official GISS combined anomaly runs very close to the SST anomalies. By 2009 there are over 5000 land grid points and a total of over 12000 in the combined set. As you go from one end of the graph to the other, the combined anomaly is increasingly biased toward land and diverges from SST while in Hadcrut it stays about the same between SST and Land. (Thanks to Steven Mosher for advising that this was a known issue, wasn’t aware of that, only started looking at this stuff 6 mos ago).
Now I don’t know what the right ratio is, I think Frank Lansner suggested 30% land 70% ocean. For trending purposes, I just don’t see how you can change the weighting between land and ocean of the combined anomaly based on the number of places you have weather station data for. Suppose the number of land cells doubled, but their new ones had the exact same average anomaly as the original ones. In Hadcrut the combined anomaly would not change, but in GISS it would because a larger fraction of the data is now Land.
Measuring something in more places shouldn’t change the final result if the anomalies are the same.
Zeke “As other folks have mentioned, the GISTemp land record is not actually a land record; rather its an attempt to estimate global temps using only land stations.”
Very true, Hansen et al (1999) was still praising the use of Land Surface Tempertures only as a means of estimating global temperatures. They may also have done it in later papers.
You continued, “This means it ends up using a zonal weighting (and no land mask) such that it runs considerably colder than other land series.”
Some of this should also result from the use of smaller island (and ship) stations, which will track closer to the cooler SST anomalies. There are very few islands used in the CRUTEM and NCDC land surface data, and those islands are chunks (technical term) of land mass such as Madagascar, Great Britain and Iceland.
You wrote, “Linearly combining published GISTemp land record with the ocean record is not strictly meaningful.”
Which was why I wrote this post.
BTW, disregard the note I left for you over at Lucia’s. As noted in the post, I discovered my error. I had thought GISS extended land surface data over the oceans, but what I was seeing was the impacts of the Ship stations.
Regards
Frank Lansner: You wrote to Steven Mosher, “In the present case for example we have a GISS LST+SST that is the same as the SST (HADISST) 1900-1920.”
What’s the source of your data? A quick trip to the KNMI Climate Explorer shows that to be incorrect. There are significant differences between Global GISTEMP LST + SST (1200km radius smoothing) and Global HADISST from 1900 to 1920:
http://i25.tinypic.com/2mi14bm.jpg
And here’s the difference (GISTEMP MINUS HADISST):
http://i25.tinypic.com/2rwrbpg.jpg
Steve Keohane says:
July 18, 2010 at 8:34 am (Edit)
Thanks Bob, that looks like a lot of work. The bottom line wrt temperature measurement seems to be that lacking enough data points, the numbers can be fiddled with to look like anything. ”
******************************
actually quite the OPPOSITE. Its very hard to make the trend in the data disappear.
As a simple experiment ( RonB and others) have taken a simple
straight average of all the stations. That is, average the stations with NO area weighting whatsoever.
http://rhinohide.wordpress.com/2010/07/10/trb-0-01-ghcn-simple-mean-average/#more-832
There isnt much you can do to the numbers to get the basic facts to disappear.
davidmhoffer says:
“Now I don’t know what the right ratio is, I think Frank Lansner suggested 30% land 70% ocean. For trending purposes, I just don’t see how you can change the weighting between land and ocean of the combined anomaly based on the number of places you have weather station data for. Suppose the number of land cells doubled, but their new ones had the exact same average anomaly as the original ones.”
I’m not sure you understand how spatial averaging works or the temporal aspect.
Lets start with the spatial.
Box A: 5 degrees square: it has 50 stations.
Box B. 5 degrees square. it has one station.
The first step is to average the stations in box A. that gives you 1 time series for that
Box. Box B has 1 station. That station represents that box.
Assume an area of 25 for each. So Box A gets 25/50 (50%) weight and box B gets
50%. Now if Box B is 50% land, you have to scale for that
Box A would get 25/37.5 weight and box B would get 12.5/37.5 weight.
Adding stations to A will not change it much. Adding stations to B can change B..
but the leverage is not that great.
New stations also have to have a temporal overlap ( in RSM or CAM)
The best approach ( from a stats perspective) is the LSM approach (Nick Stokes,
RomanM) but they are not substantially different than other approaches.. they do use more data, but MORE DATA does NOT shift the curves. That’s because the field is already oversampled in certain areas,despite what people think about the sampling requirements. Take stations away or add them, the warming (TREND) does not change appreciably.
davidmhoffer: You replied, “It isn’t the specific data that is added it is the manner in which it is handled in the analysis that matters.”
Are you suggesting that GISS should standardize on only those Land and Sea Surface Temperature grids that are available over the entire 1880 to 2010 period? If not, how should they introduce new data as it becomes available or drop it when it is no longer available?
For GISTemp, either the Ocean SST number is wrong, the Land Temp is wrong or the Global Temp is wrong.
It is just basic math.
I would normally say “just fix the one that is wrong” but that would likely involve an increase in all of them.
Steve, do you think that if someone creates a “method” i cannot be .. well … rotten? A method cannot be developed with “so-so” intensions?
Frank:
“Steve, in CRU program code released in climate gate its a permanent method that temperature proxy graphs are cut of in 1960, and the same can be read in their mails. So they have “a method” to cut of data most often in 1960, in then the sceptics cannot complain?”
As I explained in my book on climategate there is nothing WRONG with truncating a proxy series when it fails to correlate. Given the lack of correlation the analyst has several choices:
1. Reject the underlying science that tree rings work to capture temperature.
2. Include the series and live with the uncertainty this creates.
3. Drop the WHOLE series
4. Truncate.
The issue is full disclosure of the method and the Sensitivity to the analyst choice.
each of the decisions above is a rational defensible choice. Each choice has an impact. In my experience I would aways back such choices up by doing the sensitivity analysis. So yes you can complain, but you ALSO have to acknowledge that some choices make no difference.
“In the present case for example we have a GISS LST+SST that is the same as the SST (HADISST) 1900-1920. The GISS LST is higher. So the weight of land data is zero. Obviously this is build into a method , a program etc. and then its suddenly ok??”
I wouldnt characterize the method as OK.
Every method has limitations. They are all approximations. All “wrong.” Giss has a method. Its weak points Are well know. CRU also has a method. Its weak points are known. Some of those weak points are addressed by Nick and Roman M. Some addressed by Zeke and me. The bottom line is the weaknesses do not make the warming disappear.
Here is another method to ESTIMATE the average temp of the world.
The highest temp ever recorded was 136F. The coldest -128F
Can you estimate the average temp of the globe today with those two facts?
Sure. the average would be 4F. Not a very good estimate. but given those two facts
its the best estimate you have. Thats always the question. whats the best estimate given the facts. every method of estimating has limitations. hence the word estimate.
Now lets look at the place that is consistently the warmest.. Dahli ethiopia about 94F and coldest antartctica.. about -70F.. that gives us an average of about 12F. better estimate it uses average temps and not extremes
Now Add england to that Average: CET is around 50F over time..
Now your average is about 25F
Now lets add a long american record orland CA.. about 62F and our average is
up above 30F.. keep adding stations and you will get to the numbers that Ron shows.
Thats one method. This method, however, is limited becuase we didnt ask how close the stations were to each other. that brings in the issue of area averaging
“Are you suggesting that GISS should standardize on only those Land and Sea Surface Temperature grids that are available over the entire 1880 to 2010 period? If not, how should they introduce new data as it becomes available or drop it when it is no longer available?”
one advantage of GISS method ( RSM) is you can add stations over time. With CAM thats less likely
Steven Mosher: And what do the acronyms CAM and RSM stand for?
Bob Tisdale says:
July 18, 2010 at 3:35 pm
davidmhoffer: You replied, “It isn’t the specific data that is added it is the manner in which it is handled in the analysis that matters.”
Are you suggesting that GISS should standardize on only those Land and Sea Surface Temperature grids that are available over the entire 1880 to 2010 period?>>
No, I’m not saying that. I’m saying that the current method can result in a slightly amplified trend based on the method used now. If you can increase the trend by adding more data points with the exact same anomaly as the ones you already have, then you have a problem. I think dispensing with ocean data reported from within a certain distance of a land station is a similar issue. As more land stations in proximity to ocean come on line, less ocean data is used. These things may be small, but we’re arguing about tenths of a degree per century and they do add up. Does it change the over all trend? No. We’re talking perhaps 0.6 degrees per century instead of 0.8 or in that neighbourhood. But if governments are going to take action at potentialy massive financial and social costs, do they (and we the public) not deserve that decisions be made on the most accurate analysis? Suppose, just to illustrate, that our globe had 50 ocean points and 50 land points. On day one all the ocean points have anomalies of 1 and there’s only one of the 50 land points that has data and it has an anomaly of 2. So someone averages out the 51 data points and gets an over all anomaly of 1.0196. The next morning the ocean anomalies are all 1 again, but there is data for all 50 land points and they are all 2, just like the single one from yesterday. So now we have 50 at 1 and 50 at 2. Despite the extra 49 land points being exactly the same as the first one, I’m supposed to be OK with the anomaly now being 1.5? I think not.
Continuing to present data that contains a known mathematical construct or two that artificially enhance the trend is not the best possible data, the fact that this problem has been known for two years and it hasn’t been addressed disturbs me, and if nothing else it leaves wide open the question of what other oddities not yet discovered are hiding in that data simply from poor treatment from a mathematical or other fundamental perspective.
As I said before, I don’t know the right answer, just that this approach is wrong. As Steve Mosher suggested, there may be no “right” approach possible, just wrong ones and less wrong ones. Mosher suggested a possible approach as did Lansner.
Interestingly, I once graphed every 5th year of land anomalies using only those grid points with continuous data in each of those years starting in 1880. I think there were around 800 or so. The match to GISS Land was surprisingly good. I may still have that graph around somewhere.
I’m an idiot, of course I have it.
http://knowledgedrift.files.wordpress.com/2010/06/pic41.png
Every 5th year and downloaded from Global Maps so not to be relied on in any way, but I do plan to do it again using monthly data on the same grid points or something similar. I may even do it with 1200 km smoothing to nobody can say I’m not using the “official” data. 🙂
latitude says:
July 18, 2010 at 5:22 am
tarpon says:
July 17, 2010 at 11:06 pm
Has anybody found the warm CO2 greenhouse roofing placed over the equator by all the computer models? Isn’t the missing CO2 greenhouse roofing proof enough the whole theory is composed of falsehoods? In normal times it would disprove.
==========================================================
You would think, since that is a requirement for verifying the computer programs.
Without it, the computer programs are wrong
Last I heard, they said warming is there, because the computer programs say it is, they just can’t find it
Last I heard, since actual temperature measurements didn’t agree with the models, they had to find a proxy for actual temperature: Winds.
All the graphs look as if they climb till sometime in 1997 and then hold steady or dip from 1997 till now. One regression line doesn’t always cut it.
Is it just me or is there a pattern here of Mosher and Zeke defending some pretty ridiculous work by hansen?
Lucy Skywalker
Could you send me John Daly data? The person managing the site now is obviously overwhelmed.
davidmhoffer: You wrote as an example. “Suppose, just to illustrate, that our globe had 50 ocean points and 50 land points. On day one all the ocean points have anomalies of 1 and there’s only one of the 50 land points that has data and it has an anomaly of 2. So someone averages out the 51 data points and gets an over all anomaly of 1.0196. The next morning the ocean anomalies are all 1 again, but there is data for all 50 land points and they are all 2, just like the single one from yesterday. So now we have 50 at 1 and 50 at 2. Despite the extra 49 land points being exactly the same as the first one, I’m supposed to be OK with the anomaly now being 1.5? I think not.”
It’s wrong for me to speculate and hopefully Steven Mosher will be back to explain, but I believe you’re missing something. In your example, aren’t you missing the point that the ocean and land points are treated independently, meaning that in your first example with only one land point that the anomaly would 1.5, not 1.0196? Refer to Steps 3 and 4 of the GISS current analysis discussion:
http://data.giss.nasa.gov/gistemp/sources/gistemp.html
The coastal cells are another matter.
You wrote, “As more land stations in proximity to ocean come on line, less ocean data is used.”
I assume this concern is based on the GISS Step 5 from…
http://data.giss.nasa.gov/gistemp/sources/gistemp.html
…in which they write, “The same method as in step3 is used, except that for a particular grid box the anomaly or trend is computed twice, first based on surface data, then based on ocean data. Depending on the location of the grid box, one or the other is used with priority given to the surface data, if available.”
But the opposite would be true when fewer land stations are used and isn’t that what has happened over the past decade globally?
(An observation)
Ahhhh Yes!
Peer (and Equal, and Colleague, and Contemporary, and Cohort, and Friend, and Not) Review at it’s finest. Limiting ‘Old World Print Media’ Peer Review to 2 or 3 select individuals is such a long and shallow exercise. Here at the Forum we are all truly naked and wrestling for the truth in the finest traditions of the Great Greek Masters of Science AND Philosophy. What a World we live in.
This is a perfect example of why WUWT is so popular.
This should help solve any remaining confusion on the land ratio issue: http://rankexploits.com/musings/2010/the-gistemp-land-fraction/
Steven Mosher:
You write: “As I explained in my book on climategate there is nothing WRONG with truncating a proxy series when it fails to correlate. ”
I disagree!
I have recently collected the most comprehensive overview of NH temperatures, mostly land.
http://hidethedecline.eu/pages/posts/part1-the-perplexing-temperature-data-published-1974-84-and-recent-temperature-data-181.php
Most of the proxies that where cut in 1960, but IPCC and others used tree ring data from (land !!) and from NH.
The temperature decline in NH land after 1960 was STRONG. So any idea of cutting tree ring data off in 1960 because it decline is extremely poor judgement, and I am surpriced how you can defend this??
See rough illustration:
http://hidethedecline.eu/media/PERPLEX/fig4.jpg
How about we discussed this in a seperate writing?
I have earlier gone through some “arguments” explaining why trees shows decline “when they shouldn” and its a poor reading to say the least.
K.R. Frank
And while all the attention is being focused on surface temperature measurements, who is going to remind us of the decreasing upper atmospheric temperatures measured by satellites, and the increased COOLING of the thermosphere by CO2? NASA GISS? NOAA? Anyone?
Steven Mosher says:
July 18, 2010 at 4:06 pm
As I explained in my book on climategate there is nothing WRONG with truncating a proxy series when it fails to correlate. Given the lack of correlation the analyst has several choices:
1. Reject the underlying science that tree rings work to capture temperature.
2. Include the series and live with the uncertainty this creates.
3. Drop the WHOLE series
4. Truncate.
The correct conclusion is 1. in my opinion.
2 I could live with, if the errors take it into account
Climate scientists are cavalier about errors , and par excellence, propagation of errors.
To make a thermometer of some proxy, one has to prove very good correlation with temperatures measured with another proxy, i.e. classical thermometers.
i.e. I would need a paper with 1000 tree measurements per point ( three percent error) in time that showed good correlation up to the present temperatures. Then I could start thinking of what might have happened for this particular series to show a decline and whether truncation would be rigorous science.
But if I had 1000 tree measurements, what would I need this single one for?
As I said, climate science is not rigorous science in any sense, but there is a limit to how much video gaming can be done with the data and can be accepted/swallowed by the rest of the scientific community.
As with the particular navel gazing over the GISS temperature anomalies. So yes, the temperatures have been rising since the little ice age. It is as trivial as saying winters are colder than summers. But what do anomalies really have to say about the price of tea in China? i.e about energy inputted outputted from the planet?
The number of convolutions that lead to anomalies is too large to be able to use them as a gauge for real energy numbers. It is as if one is given a map without a scale, so one cannot tell if it is a country one is looking at or a county. It is a small r that is missing but very crucial for driving. It is even worse, because it it a distorted map without rigorous notion of the proportions .
Even if one used average temperatures, that the GCMs model so very badly, there would be an almost inpossible task of getting real energy numbers: ground skin surface temperatures, gray constants and radiation spectra are needed over the whole map, in addition to a large number of samples to satisfy the Nyquist criterion of statistics.
It is ground surface skin temperatures ( including SSTs) that are doing the bulk of radiation, and here one is navel gazing over the air temperatures at 2 meters as if they are controlling bulk radiation.
This is an interesting plot that shows the huge variations in sea surface temperature with the time of day http://www.ghrsst.org/images/rubbish.jpg from
http://www.ghrsst.org/SST-Definitions.html .
Remember , radiation goes like T^4 . The average day night temperature does not give the average radiated energy.
Simple it ain’t even for sea. Imagine what happens for land.
Thanks, Anthony.