Guest post by Bob Tisdale
Date: August 16, 2012
Subject: Southern Hemisphere GISS LOTI Land Surface Temperature Anomaly Data
From: Bob Tisdale
To: James Hansen – GISS
Dear James:
I discovered what appear to be an atypical upward step and a recent abnormal increase in variability in the land portion of the GISS Land-Ocean Temperature Index (LOTI) dataset for the Southern Hemisphere. I found this yesterday while preparing one of the final chapters of my book Who Turned on the Heat? The Unsuspected Global Warming Culprit, El Niño-Southern Oscillation.
I used the ocean mask feature of the KNMI Climate Explorer to isolate the Land Surface Temperature portion of the LOTI data for the Southern Hemisphere, then smoothed it with a 13-month running-average filter. Since I’m using the satellite-based Reynolds OI.v2 sea surface temperature dataset as the primary source of data for my book, the graph starts in November 1981, and the GISS LOTI data through the KNMI Climate Explorer was only available through March 2012, and that explains the end month. I used the base years of 1982 to 2011 for anomalies to try to minimize any seasonal components. As shown in Figure 1, there appears to be an upward shift in the data during the 1998/99/00/01 La Niña, around the year 2000. The timing of the shift does not agree with what would be an expected response to a major ENSO event.
Figure 1
Comparing the Southern Hemisphere LOTI data without the ocean data to scaled NINO3.4 sea surface temperature anomalies as a reference for the timing of ENSO events, and shifting the NINO data upwards by 0.3 deg C after January 2000, helps to highlight the upward step I was seeing. See Figure 2.
Figure 2
I found this odd, to say the least, so I started looking for explanations. I checked to see if there was a problem with the land-mask feature of the KNMI Climate Explorer. I had never encountered one before, but I checked anyway. Figure 3 compares the GISS land-only surface temperature anomaly data (with 250km smoothing) to the LOTI data with the ocean data masked. Both datasets show the unusual rise.
Figure 3
I checked NOAA’s GHCN and the UK Met Office’s CRUTEM3 land surface temperature anomalies for the Southern Hemisphere, and they did not display the shift, as shown in Figure 4.
Figure 4
When I compared the four versions of the Southern Hemisphere land surface temperature anomalies, Figure 5, a few other things stood out. It appears the two GISS datasets pick up an additional warming trend after 2000 that does not exist in the GHCN and CRUTEM3 data, and the two GISS datasets appear to have much greater year-to-year variations in recent years.
Figure 5
Regarding the trends, Figure 6 shows the GISS LOTI data for the Southern Hemisphere, with the ocean data masked, for two periods: November 1981 to December 1999 and January 2000 to March 2012. The trend for the GISS data starting in January 2000 is 3 times greater than the earlier period. But if we look at the average of the GHCN and CRUTEM3 data, Figure 7, the trend from January 2000 to present is considerably less than the earlier period.
Figure 6
HHHHHHHHHHHHH
Figure 7
The GHCN and CRUTEM3 datasets include less data in Antarctica, and of course, they don’t have the 1200km smoothing employed by GISS to infill areas with missing data. So for the next two graphs, Figures 8 and 9, to try to isolate the cause, I excluded all land surface temperature data south of 60S, to remove the Antarctic data. The GISS data still has a higher linear trend during the latter period, while the average of the GHCN and CRUTEM3 data continues to warm at a lesser rate after 2000.
Figure 8
HHHHHHHHHHHHH
Figure 9
Could the 1200km smoothing be the cause? I wouldn’t think so, but I checked. Figure 10 shows the linear trends for the two periods using the GISS land-only temperature data (250km smoothing) for the Southern Hemisphere, and, as you’ll note, I’ve excluded the Antarctic data. The period starting in January 2000 has a much higher trend than the earlier period. That’s really odd since the GHCN and CRUTEM3 data in Figure 9 should be using data similar to, if not the same as, GISS, but their trend is less during the later period. And as you’ll note, the GISS trend for the period starting in 2000 is about 2.8 times higher than the average of the other two datasets.
Figure 10
That made me wonder if the coverage of the GISS land-only temperature data (250km smoothing) was in fact similar to the GHCN and CRUTEM3 datasets, so I used the map-making feature of the KNMI Climate Explorer to run a quick comparison of spatial coverage. I set the map type so that it would display grids and areas where data existed, and I set the contours so that the grids and areas wouldn’t offer any distractions by changing colors. I plotted the Southern Hemisphere for each January starting in 1982, and animated the sequence of maps. This was just a cursory look. It appears that GISS excludes grids or stations in the Southern Hemisphere sooner than the other two and GISS seems to have less coverage than GHCN and CRUTEM3 as time progresses. You may need to click on the animation to view it.
Animation 1
James, you should look into this matter. I don’t have the time or the inclination to carry this investigation any further.
Some persons might think GISS has been manipulating data to acquire a higher land surface temperature anomaly trend in recent years. They also might assume GISS has been reducing coverage in recent years to create a little more variability, thereby increasing the chances for new record temperatures with each El Niño. And the way that all suppliers of temperature data appear to use data for a grid one year but not the next, and then have data for that grid reappear a year or two later, may lead some persons to think data is being cherry picked for use. We wouldn’t want people to think those things.
Final note: As you know, GISS, in effect, deletes sea surface temperature data in areas of seasonal sea ice and replaces it with much-more-variable land surface temperature data. This, of course, creates a warming bias at the poles in the GISS data. Refer to the zonal-mean graph in Figure 11 that compares the linear trends of the Reynolds OI.v2 data and the version of it with the GISS modifications, for the period of January 1982 to October 2011. It’s from my most recent post that discusses this subject: The Impact of GISS Replacing Sea Surface Temperature Data With Land Surface Temperature Data.
Figure 11
Because of that monumental bias, when I present GISS Land-Ocean Temperature Index data, I usually limit the latitudes to exclude polar data. Now, with this find in your land surface temperature data, I’ve had to switch to an average of the GHCN and CRUTEM3 data for that chapter of Who Turned on the Heat? The Unsuspected Global Warming Culprit, El Niño-Southern Oscillation.Sorry to say, but with all of the biases toward warming, your GISS LOTI data, in my eyes, is becoming more and more unsuitable for research.
Sincerely,
Bob Tisdale
SOURCE
The data and the maps used in this post are available through the KNMI Climate Explorer.
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“Ducks in a row” probably means you will never get a reply. GK
I hope you get a reply from Hansen Bob. BTW here is a report of a new study by the CSIRO about a rise in sea temp and tropical fish moving further south.
Just hope somebody has the time check out this study when it’s available online.
http://www.abc.net.au/am/content/2012/s3569893.htm
Wasn’t there a recent blog: Who is warming Columbia? That was “James” too?
Bob, this might be of interest to you. Its a graph of atmospheric water content and low, middle and high level cloud cover.
Note the step down in atmospheric water content in 1998, and the start of a trend of decreasing low level clouds and increasing middle and high level clouds in the same year.
Unfortunately I was unable to find a SH only version of this graph.
http://climate4you.com/images/CloudCoverAllLevel%20AndWaterColumnSince1983.gif
for me, the question isn’t whether Hansen will read Bob’s memo, but if Hansen will even utter the name ‘Bob Tisdale’ ? Hansen refuses to utter the name ‘Steve McIntyre’. Bob could be on that list?
I wonder who else is on Hansen’s ‘names that should never be uttered” list ?
Well I looked at Figure 11 above and thought to myself… I’ve seen that graph before. Or one an awful lot like it. And I have. IPCC AR4 fig 10.6
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/figure-10-6.html
which is in turn drawn from AR4 10.3.2
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch10s10-3-2.html
One can only be impressed that Hansen’s GISS has managed to produce a graph so similar to what was predicted back in 2007. Who helped write that chapter again? How come the keepers of the other termperature records were unable to produce the predicted profile? What skill does Hansen possess that his data matches predictions and their’s don’t?
Perhaps I misunderstand what Tisdale’s graphs show versus the IPCC predictions Dr Hansen, and if so, I’d appreciate you setting the record straight.
Bob, Thank you for that essay. There are many topics under this broad umbrella. For example, Australia sends CLIMAT monthly data overseas, where various agencies treat it in various ways. I have often wondered who was affected by this little gem from CG2 and whether it has been corrected by now.
It’s from Blair Trewin, of Australia’s Bureau of Meteorology.
“I’ve finally had a chance to have a look at this – it turned out to be
> more complicated than I thought because a change which I thought had
> been implemented several years ago wasn’t.
> Up until 1994 CLIMAT mean temperatures for Australia used (Tx+Tn)/2. In
> 1994, apparently as part of a shift to generating CLIMAT messages
> automatically from what was then the new database (previously they were
> calculated on-station), a change was made to calculating as the mean of
> all available three-hourly observations (apparently without regard to
> data completeness, which made for some interesting results in a couple
> of months when one station wasn’t staffed overnight).
>
> What was supposed to happen (once we noticed this problem in 2003 or
> thereabouts) was that we were going to revert to (tx+Tn)/2, for
> historical consistency, and resend values from the 1994-2003 period. I
> have, however, discovered that the reversion never happened.
>
> In a 2004 paper I found that using the mean of all three-hourly
> observations rather than (Tx+Tn)/2 produced a bias of approximately
> -0.15 C in mean temperatures averaged over Australia (at individual
> stations the bias is quite station-specific, being a function of the
> position of stations (and local sunrise/sunset times) within their time
> zone.”
As my colleague Chris noted to the BoM, “Is there a reason why the error was overlooked for nine years and why it then wasn’t corrected for six years? Does the correction suggest that international (GISS, NCDC) records of Australia’s temperatures from 1994 to 2009 underestimate (by about .15C) the actual temperatures recorded, or adjusted for the HQ series? Are Australian temps since 1994 accurate in current global climate records?”
This alone would not account for all of your postulated ‘shift’, but a combination of episodes from donor countries might.
BTW, I’ve been studying the 1998 hot year. Your graphs above simply increase my confusion about it. It’s already gathered enough conflict to be the topic of a book. Chapter one would be be about the correct estimation of errors.
Can you point me to a reference that shows large lake temperature changes spanning the 1998 year, preferably up to near-present? I’m trying to mask ocean current complications in water T measurements. I hate to ask because you do so much of value and your time must be at a premium.
http://www.australianclimatemadness.com/2012/08/lomborg-on-extreme-weather-myths/?utm_source=rss&utm_medium=rss&utm_campaign=lomborg-on-extreme-weather-myths australian climate madness and what the fruit cakes are saying about the climate
I personally believe that NASA is entirely corrupted in this field. And it will get far worse before the election. These people care not about the real data, they are simply far left ideologues that will miss Dr. Chu, a willing accomplice to theft of public funds to pay for their advisory roles in private business and their political goals re capitalism versus collectivism.
You have a very nice way of telling the world that Hansen and his cronies in the IPCC, GIS, NOAA, NASA are intellectually naked. I unfortunately lack such tact. They are incompetent at best and possibly liars pushing intellectual fraud.[snip] these fluff brained elitists with empty Ivy League degrees conferring undeserved power allowing idiots to push intellectual junk science for clowns like Brown and Obama to use to further “evil” as Hitler did with eugenics.
It is unfortunate that “good men” such as yourself are too few to stop this new evil as we’re those who saw Eugenics for the evil it was.
“Some persons might think GISS has been manipulating data …
We wouldn’t want people to think those things.”
No need to speculate. GISS have placed their code on line here. They use accessible data sources. You can see exactly what they do. Have you tried looking?
Thanks Bob, good work. Over at the talkshop we have started looking at SH land data to see how badly it has been Mullered by BEST. I won’t spoil the surprise (shock) now, but we hope to have a major post up soon.
I used the GISS map tool to compare 2005-2008 to 1994-1997: http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2012&month_last=7&sat=4&sst=0&type=anoms&mean_gen=0112&year1=2005&year2=2008&base1=1994&base2=1997&radius=250&pol=reg
It seems like the warming is highest in Antarctica and southern central Africa, followed by Tasmania and southern Australia. I used their station location tool to try to find out which stations contribute to the central southern Africa warming, but all stations in that area seem to have poor records… The Tasmanian warming, though, is probably just familiar airport warming: http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=501949750000&data_set=14&num_neighbors=1
Reading the wikipedia entry for Hobart International Airport, reveals that a LOT happened in that area from 1998 on: “On 11 June 1998, the airport was privatised on a 99-year lease, being purchased by Hobart International Airport Pty Ltd, a Tasmanian Government-owned company operated by the Hobart Ports Corporation.[10][13][16] In 2004, the domestic terminal was redeveloped for the first time in its 30-year history. This development involved modernising the terminal, moving the retail shops to within the security screening area,[17] realignment of the car park and moving the car rental facilities to new building in the car park. During 2005, Hobart Airport experienced record annual passenger numbers[14] and it was then decided to bring forward plans to upgrade the seating capacity of the airport. This work involved expanding the domestic terminal building over the tarmac by three metres to provide more departure lounge space.” (http://en.wikipedia.org/wiki/Hobart_International_Airport).
If you look further down the Wikipedia page, you’ll see that traffic has increased significantly. So I think that in Tasmania, Hansen just measures the growth of international air traffic 🙂
Geoff is on the right track. I think the GHCN – GISS divergence is due to the former relying more on fixed time observations, while the latter relies more on min/max temps. Not sure about HADCRUT.
As my graph above shows, low level clouds declined post 1998 and minimum temperatures are especially sensitive to changes in low level clouds, because minimum temperatures are generally set just after dawn when sunlight traverses the atmosphere at a low angle. Less low level clouds = increased solar insolation = earlier and higher Tmin.
And the BoM has got the source of their error the wrong way round. The -0.15C error is in the min/max derivation, and had they gone back in time they would have found the error was rather larger than -0.15C.
davidmhoffer: Please advise what similarities you find between the zonal-means graph in Figure 11 and the ones you linked from the IPCC’s AR4. I can find nothing in common.
Here’s the comparison you’re interested in. It shows the global sea surface temperature anomaly trend for the past 30 years on a zonal mean basis versus the IPCC AR4 (CMIP3) hindcast/projection for the same period:
http://i53.tinypic.com/wjt82o.jpg
The graph is from part 1 of a two part post. Refer to the comparisons on an individual ocean-basin basis. See here:
http://bobtisdale.wordpress.com/2011/04/10/part-1-%e2%80%93-satellite-era-sea-surface-temperature-versus-ipcc-hindcastprojections/
And here:
http://bobtisdale.wordpress.com/2011/04/19/492/
Good work Bob. I did some digging on this subject, and found a spurious hockey stick. By suppressing historical temperatures and bumping up more recent ones these naughty boys have created a warming trend from the comfort of their offices. What power – to change the climate without needing to go outdoors!
http://endisnighnot.blogspot.co.uk/2012/03/giss-strange-anomalies.html
Now, THAT’s what I call an Inconvenient Truth…
GISS LOTTO sounds more apt?
There seems to be a surge in junk climate science lately. Could it be a last desperate bid to keep a hoax alive and the grants flowing particularly at a time when nations around the globe are realising they have incurred serious debt and are unable to repay it. The world economy has slowed, the ever increasing GDP of the 90s has stalled, the slush fund that fed the AGW scam has diminished as has politicians enthusiasm for sponsoring a cause which the populace no longer believes in. The climate frauds will keep sending their press releases to a complicit media but no one is listening as the 100 year fear is replaced by the more immediate concerns of paying the mortgage and keeping one’s job.
Geoff Sherrington says: “Can you point me to a reference that shows large lake temperature changes spanning the 1998 year, preferably up to near-present?”
Sorry, Geoff. I haven’t looked into it.
Have you tried using the KNMI Climate Explorer for the Reynolds OI.v2 data. It’s presented in 1-degree grids and you may be able to eek out some data from very large lakes through it. Example for the Great Lakes:
http://i45.tinypic.com/xenz8l.jpg
An obvious upward shift in response to the 1997/98 El Niño shows up in that data.
Do you suppose that GISS has its own equivalent of Harry Read Me?
At last real proof that the global warming is man made(AGW)!
And it’s made by one man only, James Hansen?
To stop global warming we just have to stop James Hansen?
Deary, deary me! I must say Mr Tidsale you’ve really gone & done it now. I bet Dr Hansen has removed you from his Christmas card list after this!
Thanks Bob! Makes one wonder whether even Hansen has a clue what GISS is now *really* measuring.
Geoff Sherrington says (August 16, 2012 at 11:23 pm)
——
Geoff – would any of the work cited here help you?
http://thegwpf.org/the-observatory/6060-worlds-lakes-show-global-temperature-standstill.html
Tallbloke,
Theres nothing wrong with BEST….Mosher says the data is fine I believe.
“And the way that all suppliers of temperature data appear to use data for a grid one year but not the next, and then have data for that grid reappear a year or two later, may lead some persons to think data is being cherry picked for use.”
And that’s the most damning bit in my mind. If you could find the raw data and show that the values would have brought down the averages in those years that they vanish you’d have a smoking gun.