Spencer: analysing alternate satellite data suggests July 2009 was not a record for sea temperature

TRMM Satellite Suggests July 2009 Not a Record for Sea Surface Temperatures

August 26th, 2009 By Dr. Roy Spencer

UPDATE ADDED: 8/26/09 13:30 CDT  see below

NOAA/NCDC recently announced that July 2009 set a new record high global sea surface temperature (SST) for the month of July, just edging out July 1998. This would be quite significant since July 1998 was very warm due to a strong El Nino, whereas last month (July, 2009) is just heading into an El Nino which has hardly gotten rolling yet.

If July was indeed a record, one might wonder if we are about to see a string of record warm months if a moderate or strong El Nino does sustain itself, with that natural warming being piled on top of the manmade global warming that the “scientific consensus” is so fond of.

Global-SST-NCDC-vs-AMSRE

I started out looking at the satellite microwave SSTs from the AMSR-E instrument on NASA’s Aqua satellite. Even though those data only extend back to 2002, I though it would provide a sanity check. My last post described a significant discrepancy I found between the NOAA/NCDC “ERSST” trend and the satellite microwave SST trend (from the AMSR-E instrument on Aqua) over the last 7 years…but with the AMSR-E giving a much warmer July 2009 anomaly than the NCDC claimed existed! The discrepancy was so large that my sanity-check turned into me going a little insane trying to figure it out.

So, since we have another satellite dataset with a longer record that would allow a direct comparison between 1998 and 2009, I decided to analyze the full record from the TRMM Microwave Imager (TMI). The TRMM satellite covers the latitudes between 40N and 40S, so a small amount of N. Hemisphere ocean is being missed, and a large chunk of the ocean around Antarctica will be missed as well. But since my analysis of the ERSST and AMSR-E SST data suggested the discrepancy between them was actually between these latitudes as well, I decided that the results should give a pretty good independent check on the NOAA numbers. All of the original data that went into the averaging came from the Remote Sensing Systems (RSS) website, SSMI.com. Anomalies were computed about the mean annual cycle from data over the whole period of record.

The results are shown in the following three panels. The first panel shows monthly SST anomalies since January 1998, and as can be seen July 2009 came in about 0.06 deg. C below July 1998. At face value, this suggests that July 2009 might not have been a record. And as you can see from the first 3 weeks of August data, it looks like this month will come in even cooler.

TMI-SST-comparisons-1998-2009

Now, if you are wondering how accurate these monthly anomalies are, the second panel shows the validation statistics that RSS archives in near-real time. Out of the 5 different classes of in situ validation data, I chose just the moored buoys due to their large volume of data (over 200,000 matchups between buoys and satellite observations), and a relatively fixed geographic coverage (unlike drifting buoys). As can be seen, the TMI SST record shows superb long-term stability. The 0.15 deg. C cool bias in the TMI measurements is from the “cool skin” effect, with water temperatures in the upper few millimeters being slightly cooler on average than the SSTs measured by the buoys, typically at a depth around 1 meter.

The third and final panel in the above figure shows that a substantial fraction of the monthly SST variability from year to year is due to the Southern Oscillation (El Nino/La Nina), and the Pacific Decadal Oscillation, PDO. Each of these indices have a correlation of 0.33 with SST for monthly averages over the 40N-40S latitude band, while their sum (taking the negative of the SOI first) is correlated at 0.39. I did not look at lag correlations, which might be higher, and it looks like some additional time averaging would increase the correlation.

I will post again when I have new information on my previously reported discrepancy between NOAA’s results and the AMSR-E results. That is still making me a little crazy.

8/26/09 13:30 CDT UPDATE

I computed the monthly global (60N to 60S latitudes) AMSR-E SST anomalies, adjusted them for the difference in annual cycles with the longer TMI record, and then plotted the AMSR-E and TMI SST anomalies together. Even though the TMI can not measure poleward of 40 deg. latitude (N or S), we see reasonable agreement between the two products.

TMI-AMSRE-SST-comparisons-1998-2009

None of this represents proof that July 2009 was not a record warm month in ocean surface temperatures, but it does cast significant doubt on the claim. But the focus on a single month misses the big picture: recent years have yet to reach the warmth of 1998. Only time will tell whether we get another year that approaches that unusual event.

86 thoughts on “Spencer: analysing alternate satellite data suggests July 2009 was not a record for sea temperature

  1. I’m highly suspicious of something which changed the absorption coef. of the ocean, (particularily Atlantic) for a month or two.
    I believe cloud cover was down during this time in the North Atlantic, and the next effect was a transient spike in the “surface” temp. (Which is what, 50′ of water.)
    Perhaps a circulation reverseal at the same time, kind of a “perfect storm” for surface temp increase. NOT NECESSARILY GOREBULL WARMING…but other circumstances.
    WHAT ARE THE TEMPERATURES NOW???

  2. Is there any historical event at the beginning of the year that could account for the divergence starting at that point in time? I would be inclined to suspect a software change, either uploaded to the instrument, or in software used in processing the incoming data stream.

  3. Glad to see some recent data on the difference between the temperature of the evaporating “skin” sensed by SMMRs and that measured ~1m below the surface by buoys. I presume that no “correction” is made for this variable difference in compiling global “SST anomalies.”

  4. Thanks Dr. Spencer,
    I’m assuming we can count the Jan 98 data as the overall record, (probably Dec 97 given that the 1997-98 El Nino peaked in Dec 97) since the anomalies should be corrected for seasonality.
    There is no point for the NOAA to say July 2009 is the record July when all the months are corrected for seasonality. The only reason to do so is to inappropriately sway opinion (or to use this line of reasoning for US monthly temperatures, for example, where removing seasonality is very difficult to do given the large variability).
    The July 09 sea surface temperature anomaly is not out of the ordinary for the current stage of the 2009 El Nino.

  5. This may be a silly question to ask Roy; but I have been known to do that. Is the raw data these satellite sensors gather “localised”, in the sense that you can say this SST was measured at this (surface)locatioon at this time ?
    What I am trying to get at, is if you have an older sensor with a longer history, but a reduced coverage range, can you extract just that same geographical range of data from the newer sensors with more coverage; and just leave off the extended coverage data for the purposes of matching up the older longer record with the newere but shorter record.
    The floating buoy system has some advantages, in that the one metre depth temperature shuld be more representative of the true water temps, being largely unaffected by surface evaporation, which skews the very surface measurement. Not that I am suggesting the SSTs are useless; I think they are extremely useful, but they do have that evaporation shift that is just another variable to get in your hair.
    A problem with the anchored bouy system is that of course ocean currents meander; so you can be in the exact same GPS location but still be in totally different water from time to time.
    Anyway, I’m glad you’re not getting any sleep Roy; somebody has to stay on top of this stuff.
    George

  6. since 1880, hmm?
    sorry, but this is nonsens. how can we compare modern messurements with datas between 1880 and 1950 and so on?
    we can have an almost objectiv look at the last 30 years, thats it and only this is not as easy as thought.

  7. I see no divergence at the start of the year… To me, it seems that for the common period, NCDC, TRMM and TMI are all in agreement, yet AMSR-E is showing a divergent trend since the start of that common period. (and this divergence is being investigated with refreshingly open disclosure)
    The trend between the 3 longer term series seems not to have been compared to date, not to the extent of being able to say if any is more likely to be accurate than another to guess the warmest june on record.

  8. This is a bit pathetic, no? When one’s data do not fit the picture one has in mind, oops! Switch to another dataset, and so on until you get a result you like.
    Bah!
    Does that mean we should not thrust the AMSR-E figures anymore? Adios UAH and RMSS?

  9. We aren’t anywhere near 1998 temps that was a general event.I still say this El Nino isn’t going anywhere..

  10. You didn’t go “a little insane” in 2003 (when the satellites showed a “cooler” SST.) Why go insane, now?
    In your tropospheric data for July it got hotter the farther South you went. Wouldn’t it be reasonable to assume that you were picking up a “warm patch” down there, somewhere, that the other sensors weren’t?

  11. I’d still like to know what’s up with the figure from AMSR-E, especially since we’re avidly following other data from the same satellite relating to sea ice extent.

  12. Good piece of detective work here. Now all that’s needed is to find out why results from the AMSR-E instrument on Aqua are have diverged and case closed Sherlock:-)

  13. What I see in the TRMM Monthly SST anomalies chart is natural variability of about 0.4C over a 30 month period. That says to me that a new record by just a few hundreds of a degree isn’t significant – assuming a new record was actually set.

  14. July 2009 is not the warmest July in one of the newest data setsmean [ NOAA NCDC ERSST version3b anom ]: Extended reconstructed SST anomalies data as seen below:
    Time Extended reconstructed SST anomalies
    months since 1960-01-01 degree_Celsius
    Jan 1960 -0.1895613
    Feb 1960 -0.1986694
    Mar 1960 -0.1693566
    Apr 1960 -0.2068793
    May 1960 -0.1641718
    Jun 1960 -0.1633955
    Jul 1960 -0.1435875
    Aug 1960 -0.143795
    Sep 1960 -0.1114525
    Oct 1960 -0.1112022
    Nov 1960 -0.1398526
    Dec 1960 -0.1490362
    Jan 1961 -0.1331744
    Feb 1961 -0.09308154
    Mar 1961 -0.09276848
    Apr 1961 -0.1017801
    May 1961 -0.08528394
    Jun 1961 -0.08774208
    Jul 1961 -0.1225637
    Aug 1961 -0.10004
    Sep 1961 -0.1546633
    Oct 1961 -0.1262313
    Nov 1961 -0.1496615
    Dec 1961 -0.1618083
    Jan 1962 -0.1424126
    Feb 1962 -0.1333045
    Mar 1962 -0.137601
    Apr 1962 -0.1311968
    May 1962 -0.1260047
    Jun 1962 -0.1210466
    Jul 1962 -0.08014925
    Aug 1962 -0.1000865
    Sep 1962 -0.09588733
    Oct 1962 -0.1179933
    Nov 1962 -0.1297825
    Dec 1962 -0.1563078
    Jan 1963 -0.1614998
    Feb 1963 -0.1498908
    Mar 1963 -0.1564853
    Apr 1963 -0.1393993
    May 1963 -0.1065444
    Jun 1963 -0.1195359
    Jul 1963 -0.09911085
    Aug 1963 -0.07933746
    Sep 1963 -0.1151092
    Oct 1963 -0.1244621
    Nov 1963 -0.09815708
    Dec 1963 -0.1173025
    Jan 1964 -0.152297
    Feb 1964 -0.1742728
    Mar 1964 -0.1950373
    Apr 1964 -0.229919
    May 1964 -0.2179496
    Jun 1964 -0.2103904
    Jul 1964 -0.2688888
    Aug 1964 -0.2980742
    Sep 1964 -0.2848862
    Oct 1964 -0.2709019
    Nov 1964 -0.2965808
    Dec 1964 -0.3089279
    Jan 1965 -0.2336067
    Feb 1965 -0.2280406
    Mar 1965 -0.2318629
    Apr 1965 -0.2385821
    May 1965 -0.2388369
    Jun 1965 -0.2351383
    Jul 1965 -0.255688
    Aug 1965 -0.2061422
    Sep 1965 -0.1902457
    Oct 1965 -0.1982044
    Nov 1965 -0.1901174
    Dec 1965 -0.1818056
    Jan 1966 -0.1594439
    Feb 1966 -0.1801292
    Mar 1966 -0.170122
    Apr 1966 -0.1900892
    May 1966 -0.2115872
    Jun 1966 -0.2021587
    Jul 1966 -0.1944913
    Aug 1966 -0.1803531
    Sep 1966 -0.1722898
    Oct 1966 -0.1571769
    Nov 1966 -0.1343429
    Dec 1966 -0.1429168
    Jan 1967 -0.170506
    Feb 1967 -0.164463
    Mar 1967 -0.1465881
    Apr 1967 -0.1457954
    May 1967 -0.1230988
    Jun 1967 -0.136538
    Jul 1967 -0.1518912
    Aug 1967 -0.1330451
    Sep 1967 -0.1910875
    Oct 1967 -0.1741909
    Nov 1967 -0.2173152
    Dec 1967 -0.2202712
    Jan 1968 -0.2352648
    Feb 1968 -0.2187596
    Mar 1968 -0.2103549
    Apr 1968 -0.1961094
    May 1968 -0.1732909
    Jun 1968 -0.1182799
    Jul 1968 -0.1325619
    Aug 1968 -0.1485457
    Sep 1968 -0.1464206
    Oct 1968 -0.08508737
    Nov 1968 -0.1106216
    Dec 1968 -0.06997634
    Jan 1969 -0.01449854
    Feb 1969 -0.007781216
    Mar 1969 0.009494903
    Apr 1969 -0.01508919
    May 1969 -0.04269476
    Jun 1969 -0.1128704
    Jul 1969 -0.1485584
    Aug 1969 -0.09911267
    Sep 1969 -0.0814807
    Oct 1969 -0.0894858
    Nov 1969 -0.06624499
    Dec 1969 -0.06979341
    Jan 1970 -0.05439935
    Feb 1970 -0.06860848
    Mar 1970 -0.09139698
    Apr 1970 -0.08460502
    May 1970 -0.1143256
    Jun 1970 -0.1808182
    Jul 1970 -0.2085502
    Aug 1970 -0.1706726
    Sep 1970 -0.1606662
    Oct 1970 -0.1281125
    Nov 1970 -0.1401001
    Dec 1970 -0.1621605
    Jan 1971 -0.1597588
    Feb 1971 -0.2170013
    Mar 1971 -0.2169112
    Apr 1971 -0.1939088
    May 1971 -0.1808045
    Jun 1971 -0.2029496
    Jul 1971 -0.1869121
    Aug 1971 -0.1820295
    Sep 1971 -0.21248
    Oct 1971 -0.1832071
    Nov 1971 -0.2018365
    Dec 1971 -0.2052867
    Jan 1972 -0.1390608
    Feb 1972 -0.1071651
    Mar 1972 -0.1328058
    Apr 1972 -0.1085266
    May 1972 -0.1124208
    Jun 1972 -0.09245814
    Jul 1972 -0.08774026
    Aug 1972 -0.1225737
    Sep 1972 -0.1359219
    Oct 1972 -0.07335912
    Nov 1972 -0.03459865
    Dec 1972 0.04247361
    Jan 1973 0.01902803
    Feb 1973 0.01357572
    Mar 1973 -0.02022024
    Apr 1973 -0.03316163
    May 1973 -0.05703403
    Jun 1973 -0.08701766
    Jul 1973 -0.1112486
    Aug 1973 -0.1091818
    Sep 1973 -0.1015171
    Oct 1973 -0.1250783
    Nov 1973 -0.1336567
    Dec 1973 -0.213218
    Jan 1974 -0.1955351
    Feb 1974 -0.1928076
    Mar 1974 -0.2232472
    Apr 1974 -0.2168302
    May 1974 -0.1838669
    Jun 1974 -0.2078522
    Jul 1974 -0.1822024
    Aug 1974 -0.148694
    Sep 1974 -0.1425228
    Oct 1974 -0.1228194
    Nov 1974 -0.1474727
    Dec 1974 -0.1324645
    Jan 1975 -0.1196705
    Feb 1975 -0.120678
    Mar 1975 -0.1349363
    Apr 1975 -0.1589225
    May 1975 -0.1789998
    Jun 1975 -0.194362
    Jul 1975 -0.190445
    Aug 1975 -0.1904032
    Sep 1975 -0.1969248
    Oct 1975 -0.2510666
    Nov 1975 -0.2559119
    Dec 1975 -0.2818493
    Jan 1976 -0.2514616
    Feb 1976 -0.2205151
    Mar 1976 -0.2030433
    Apr 1976 -0.2071878
    May 1976 -0.2179205
    Jun 1976 -0.1784956
    Jul 1976 -0.1845158
    Aug 1976 -0.1943311
    Sep 1976 -0.1873871
    Oct 1976 -0.1164825
    Nov 1976 -0.09172734
    Dec 1976 -0.03473334
    Jan 1977 -0.001030215
    Feb 1977 -0.02008282
    Mar 1977 -0.03789315
    Apr 1977 -0.06973971
    May 1977 -0.06666454
    Jun 1977 -0.03048234
    Jul 1977 -0.06786767
    Aug 1977 -0.06706953
    Sep 1977 -0.05810703
    Oct 1977 -0.06129323
    Nov 1977 -0.09105387
    Dec 1977 -0.0938451
    Jan 1978 -0.06824899
    Feb 1978 -0.08628321
    Mar 1978 -0.09033309
    Apr 1978 -0.1196687
    May 1978 -0.1346869
    Jun 1978 -0.08469785
    Jul 1978 -0.1164061
    Aug 1978 -0.1093957
    Sep 1978 -0.09765289
    Oct 1978 -0.09328813
    Nov 1978 -0.07577721
    Dec 1978 -0.07810702
    Jan 1979 -0.04812796
    Feb 1979 -0.04720695
    Mar 1979 -0.0356771
    Apr 1979 -0.04132417
    May 1979 -0.08239352
    Jun 1979 -0.03549691
    Jul 1979 -0.07050601
    Aug 1979 -0.03898162
    Sep 1979 -0.01650164
    Oct 1979 -0.00576629
    Nov 1979 0.01730069
    Dec 1979 0.03228886
    Jan 1980 0.04615945
    Feb 1980 0.03834729
    Mar 1980 0.05134874
    Apr 1980 0.01587641
    May 1980 -0.018249
    Jun 1980 -0.02254368
    Jul 1980 -0.05258737
    Aug 1980 -0.04405715
    Sep 1980 -0.03696396
    Oct 1980 -0.08959319
    Nov 1980 -0.06678558
    Dec 1980 -0.03743174
    Jan 1981 -0.05093648
    Feb 1981 -0.06358027
    Mar 1981 -0.04807972
    Apr 1981 -0.03944121
    May 1981 -0.01530943
    Jun 1981 -0.002850382
    Jul 1981 -0.03147706
    Aug 1981 -0.03351838
    Sep 1981 -0.06188842
    Oct 1981 -0.06847379
    Nov 1981 -0.07986803
    Dec 1981 -0.02918456
    Jan 1982 -0.0382581
    Feb 1982 -0.07787859
    Mar 1982 -0.08346377
    Apr 1982 -0.08139697
    May 1982 -0.07493083
    Jun 1982 -0.07806152
    Jul 1982 -0.1291937
    Aug 1982 -0.103532
    Sep 1982 -0.06015289
    Oct 1982 -0.02341736
    Nov 1982 -0.009364761
    Dec 1982 0.03185111
    Jan 1983 0.07858391
    Feb 1983 0.06627139
    Mar 1983 0.04726338
    Apr 1983 0.03512741
    May 1983 0.03732617
    Jun 1983 0.03018202
    Jul 1983 -0.02356116
    Aug 1983 -0.02925191
    Sep 1983 -0.02326083
    Oct 1983 -0.04989352
    Nov 1983 -0.04253549
    Dec 1983 -0.03780579
    Jan 1984 -0.02152985
    Feb 1984 -0.01661267
    Mar 1984 -0.03729159
    Apr 1984 -0.06353658
    May 1984 -0.06144521
    Jun 1984 -0.0990799
    Jul 1984 -0.0939079
    Aug 1984 -0.06482799
    Sep 1984 -0.04502639
    Oct 1984 -0.09551784
    Nov 1984 -0.07727247
    Dec 1984 -0.05335366
    Jan 1985 -0.0527885
    Feb 1985 -0.03435384
    Mar 1985 -0.06279123
    Apr 1985 -0.109018
    May 1985 -0.08739351
    Jun 1985 -0.1027248
    Jul 1985 -0.1102439
    Aug 1985 -0.1433673
    Sep 1985 -0.08961594
    Oct 1985 -0.08456134
    Nov 1985 -0.0846578
    Dec 1985 -0.09117765
    Jan 1986 -0.09936749
    Feb 1986 -0.0843147
    Mar 1986 -0.08042865
    Apr 1986 -0.07344194
    May 1986 -0.05531125
    Jun 1986 -0.04450036
    Jul 1986 -0.06781944
    Aug 1986 -0.0745213
    Sep 1986 -0.05241081
    Oct 1986 -0.02139971
    Nov 1986 -0.06777757
    Dec 1986 -0.05680925
    Jan 1987 -0.02492628
    Feb 1987 -0.02498544
    Mar 1987 0.01730979
    Apr 1987 0.0136012
    May 1987 0.01246087
    Jun 1987 -0.01615399
    Jul 1987 0.02462231
    Aug 1987 0.0594494
    Sep 1987 0.07321624
    Oct 1987 0.04536494
    Nov 1987 0.08621132
    Dec 1987 0.0347024
    Jan 1988 0.09297415
    Feb 1988 0.08401801
    Mar 1988 0.07056152
    Apr 1988 0.04866036
    May 1988 0.01541682
    Jun 1988 -0.002296141
    Jul 1988 -0.008674008
    Aug 1988 -0.0111103
    Sep 1988 -0.01405533
    Oct 1988 -0.009735165
    Nov 1988 -0.07476155
    Dec 1988 -0.102227
    Jan 1989 -0.05539953
    Feb 1989 -0.05561795
    Mar 1989 -0.05502457
    Apr 1989 -0.02513287
    May 1989 -0.02876046
    Jun 1989 -0.01258646
    Jul 1989 0.02765016
    Aug 1989 0.04654896
    Sep 1989 0.03290772
    Oct 1989 0.004623225
    Nov 1989 -0.002046778
    Dec 1989 -0.01889061
    Jan 1990 0.007325264
    Feb 1990 0.06892428
    Mar 1990 0.07378504
    Apr 1990 0.08839006
    May 1990 0.09609938
    Jun 1990 0.06150255
    Jul 1990 0.08545231
    Aug 1990 0.08767655
    Sep 1990 0.07707681
    Oct 1990 0.07249909
    Nov 1990 0.06295959
    Dec 1990 0.06808063
    Jan 1991 0.06738078
    Feb 1991 0.05919731
    Mar 1991 0.06561612
    Apr 1991 0.08618857
    May 1991 0.0563806
    Jun 1991 0.07904987
    Jul 1991 0.1072807
    Aug 1991 0.06684656
    Sep 1991 0.02330815
    Oct 1991 0.01121587
    Nov 1991 0.02117674
    Dec 1991 0.01924099
    Jan 1992 0.02399709
    Feb 1992 0.05118402
    Mar 1992 0.05677557
    Apr 1992 0.06302512
    May 1992 0.05880051
    Jun 1992 0.04993538
    Jul 1992 -0.02203222
    Aug 1992 -0.07009829
    Sep 1992 -0.07201401
    Oct 1992 -0.08923553
    Nov 1992 -0.08401347
    Dec 1992 -0.05437841
    Jan 1993 1.2650171E-04
    Feb 1993 0.04539952
    Mar 1993 0.01597106
    Apr 1993 0.006828358
    May 1993 0.02242992
    Jun 1993 0.008203494
    Jul 1993 -0.01287404
    Aug 1993 -0.0116336
    Sep 1993 -0.008084274
    Oct 1993 -0.01514379
    Nov 1993 -0.01379323
    Dec 1993 -0.03431198
    Jan 1994 -0.0362368
    Feb 1994 -0.03956043
    Mar 1994 0.003884237
    Apr 1994 0.01469694
    May 1994 0.03241627
    Jun 1994 -0.005961048
    Jul 1994 0.0263087
    Aug 1994 0.05016563
    Sep 1994 0.02128049
    Oct 1994 0.06779396
    Nov 1994 0.06396797
    Dec 1994 0.08485439
    Jan 1995 0.06169003
    Feb 1995 0.08299053
    Mar 1995 0.06904805
    Apr 1995 0.04908992
    May 1995 0.04376956
    Jun 1995 0.07697488
    Jul 1995 0.09507553
    Aug 1995 0.09952766
    Sep 1995 0.04295413
    Oct 1995 0.05149527
    Nov 1995 0.03683018
    Dec 1995 0.01103021
    Jan 1996 0.01029851
    Feb 1996 0.04403622
    Mar 1996 0.06895705
    Apr 1996 0.07295322
    May 1996 0.062338
    Jun 1996 0.05673826
    Jul 1996 0.03450309
    Aug 1996 0.0588806
    Sep 1996 0.05974973
    Oct 1996 0.03873589
    Nov 1996 0.05083546
    Dec 1996 0.04214325
    Jan 1997 0.03264288
    Feb 1997 0.08427921
    Mar 1997 0.1297252
    Apr 1997 0.1037841
    May 1997 0.1408473
    Jun 1997 0.1770131
    Jul 1997 0.1894003
    Aug 1997 0.2067947
    Sep 1997 0.2361813
    Oct 1997 0.2328476
    Nov 1997 0.2225246
    Dec 1997 0.246285
    Jan 1998 0.242905
    Feb 1998 0.2308254
    Mar 1998 0.2344667
    Apr 1998 0.2402739
    May 1998 0.2335693
    Jun 1998 0.2401256
    Jul 1998 0.2530087
    Aug 1998 0.2499254
    Sep 1998 0.1447415
    Oct 1998 0.1113551
    Nov 1998 0.1355679
    Dec 1998 0.08194575
    Jan 1999 0.07515016
    Feb 1999 0.07498635
    Mar 1999 0.1052093
    Apr 1999 0.08447852
    May 1999 0.04280397
    Jun 1999 0.08062977
    Jul 1999 0.07071169
    Aug 1999 0.09189206
    Sep 1999 0.06243083
    Oct 1999 0.03865217
    Nov 1999 0.02874499
    Dec 1999 0.04130688
    Jan 2000 0.03844831
    Feb 2000 0.06037404
    Mar 2000 0.05879232
    Apr 2000 0.08194394
    May 2000 0.09320349
    Jun 2000 0.06336458
    Jul 2000 0.08680925
    Aug 2000 0.1237887
    Sep 2000 0.1397861
    Oct 2000 0.1022588
    Nov 2000 0.0712823
    Dec 2000 0.07463779
    Jan 2001 0.08270568
    Feb 2001 0.1156816
    Mar 2001 0.1468302
    Apr 2001 0.1521924
    May 2001 0.147814
    Jun 2001 0.17751
    Jul 2001 0.19754
    Aug 2001 0.1959883
    Sep 2001 0.1791117
    Oct 2001 0.1720449
    Nov 2001 0.1469903
    Dec 2001 0.1485475
    Jan 2002 0.1395823
    Feb 2002 0.1580624
    Mar 2002 0.185709
    Apr 2002 0.1832927
    May 2002 0.2010266
    Jun 2002 0.2124081
    Jul 2002 0.174453
    Aug 2002 0.1907526
    Sep 2002 0.2072088
    Oct 2002 0.1969357
    Nov 2002 0.1676047
    Dec 2002 0.1814916
    Jan 2003 0.1687423
    Feb 2003 0.1825819
    Mar 2003 0.1823316
    Apr 2003 0.162945
    May 2003 0.1813169
    Jun 2003 0.1913233
    Jul 2003 0.2404132
    Aug 2003 0.2514006
    Sep 2003 0.2564752
    Oct 2003 0.2644121
    Nov 2003 0.2156461
    Dec 2003 0.1834547
    Jan 2004 0.1782636
    Feb 2004 0.1783864
    Mar 2004 0.1626329
    Apr 2004 0.1775255
    May 2004 0.160961
    Jun 2004 0.1562004
    Jul 2004 0.1796378
    Aug 2004 0.1923908
    Sep 2004 0.1961212
    Oct 2004 0.202955
    Nov 2004 0.2019148
    Dec 2004 0.1686895
    Jan 2005 0.163947
    Feb 2005 0.1619621
    Mar 2005 0.1731489
    Apr 2005 0.1857262
    May 2005 0.2070996
    Jun 2005 0.231862
    Jul 2005 0.2565544
    Aug 2005 0.2660439
    Sep 2005 0.2280342
    Oct 2005 0.2108345
    Nov 2005 0.1713751
    Dec 2005 0.1444722
    Jan 2006 0.1818948
    Feb 2006 0.1599053
    Mar 2006 0.156377
    Apr 2006 0.1853395
    May 2006 0.2148389
    Jun 2006 0.2241145
    Jul 2006 0.2375992
    Aug 2006 0.2526374
    Sep 2006 0.2565653
    Oct 2006 0.2488205
    Nov 2006 0.2536167
    Dec 2006 0.2293839
    Jan 2007 0.1737495
    Feb 2007 0.152247
    Mar 2007 0.1279241
    Apr 2007 0.1168311
    May 2007 0.1053258
    Jun 2007 0.1332335
    Jul 2007 0.1260821
    Aug 2007 0.1221715
    Sep 2007 0.1533127
    Oct 2007 0.1053941
    Nov 2007 0.05161904
    Dec 2007 0.01218784
    Jan 2008 0.02712232
    Feb 2008 0.03710502
    Mar 2008 0.04172916
    Apr 2008 0.0660921
    May 2008 0.08436203
    Jun 2008 0.125253
    Jul 2008 0.1472342
    Aug 2008 0.1710402
    Sep 2008 0.1727576
    Oct 2008 0.17164
    Nov 2008 0.1128185
    Dec 2008 0.1058309
    Jan 2009 0.09593465
    Feb 2009 0.08207044
    Mar 2009 0.1023098
    Apr 2009 0.1352293
    May 2009 0.1789652
    Jun 2009 0.232763
    Jul 2009 0.2471906

  15. George E. Smith (10:20:24) : The floating buoy system has some advantages, in that the one metre depth temperature shuld be more representative of the true water temps, being largely unaffected by surface evaporation, which skews the very surface measurement. Not that I am suggesting the SSTs are useless; I think they are extremely useful, but they do have that evaporation shift that is just another variable to get in your hair.
    I would again point out that this hurricane season has been a complete dud.
    There ought to be an “increase” in the SST anomaly just due to the lack of hurricanes that would normally pump the heat to the stratosphere AND mix the surface layers. Stagnant water gets very warm in the top inch or three. Stirred water, not so much…

  16. Roy, thank you for holding the fort of integrity. I’m glad you showed the difference between TMI data and moored-buoy data because therein lies another clue which may solve the mystery. The “cool skin” effect which your graph shows gives an average discrepancy of 0.15 degrees – the buoy readings at 1m depth being warmer. Now in the original chart which was driving you crazy, the discrepancy appears to be of about this order of magnitude.
    Here is the hypothesis. Suppose the “cool skin” effect is really a measure of average wind chill at the surface. This year has been remarkably short on hurricanes in the hurricane season. Does this mean that “wind chill” surface effect might be missing somewhat; that the TMI readings might be unexpectedly higher than seasonal average?

  17. If the purveyors of the AMSR-E SST data are unhappy with the Ice Cap parking at the North Pole, then perhaps they might be interested in renting out space in Canada for it.
    Hey buddy, where do you want this Ice Sheet?
    Sorry, Antarctica is full and Putin doesn’t take American Express.

  18. The July rise in SST is from the late June heatwave, higher land temperatures through August are just starting to show in SST readings and ENSO status.
    http://www.cpc.noaa.gov/products/analysis_monitoring/enso_update/ssta_c.gif
    As a solar based forecaster, I predicted this August uplift in temperature way back at the start of last year. There a few smaller uplifts on the way, from September 21st, the first week of October, and at the end of October. These, and S.Hemisphere summer on the way will ensure the El Nino continues through our N.H. winter.

  19. Paul J. Trimble (11:57:16) : Another way to see it
    August 1982, 4 months before 1983 big El Nino SOI Index= -23.6
    August 1997, ” 1998 ” ” = -19.8
    August 2009, ” = +0.5
    Any sure forecast?. El Nino or La Nina?

  20. Lucy Skywalker (11:58:39) : .. therein lies another clue which may solve the mystery. The “cool skin” effect which your graph shows gives an average discrepancy of 0.15 degrees – the buoy readings at 1m depth being warmer. Now in the original chart which was driving you crazy, the discrepancy appears to be of about this order of magnitude.
    Here is the hypothesis. Suppose the “cool skin” effect is really a measure of average wind chill at the surface. This year has been remarkably short on hurricanes in the hurricane season. Does this mean that “wind chill” surface effect might be missing somewhat; that the TMI readings might be unexpectedly higher than seasonal average?

    Interesting and plausible. But the difference as I understand it is not just in this year. Its in the trends that have been increasing over the years.

  21. Am I the only one bugged by the notion of a “passive microwave sensor” being used to measure “warming” in the context of ever higher levels of microwaves being used all over the place (in everything from jet radars to communications satellites to weather radars to..) at ever higher frequencies with lots of power?
    It would not take much of a “leakage” or “cross modulation” or any of a few other “issues” to raise the signal level in a highly sensitive antenna…
    (And yes, I know about filtering and adjacent channel rejection and such. This is just a “worry” about the potential for a very small impact from a very large change of environment for the sensor, that can reject a lot of the noise, but no filter is perfect. Do we know the db rejection figure? Do we know the average power level in frequencies of interest from human activity? I hope so, but hope is not a method and trust is not a measurement technique… )

  22. Just want to put my vote behind Lucy’s hypothesis. Low hurricanes/trade winds have caused less surface cooling than normal and the ‘normal adjustment’ is now causing over-reading.

  23. not even close to record in the Kaplan data set:
    IRI Data Library mean [ KAPLAN EXTENDED v2 ssta ] Jan 1997 – Jul 2009. I has a color graphic from the IRI site that really illustrated well that july 2009 was not a record. Since i cann’t include it I will again sendthe data.
    Table of
    T
    mean [ KAPLAN EXTENDED v2 ssta ]
    Additional Information
    Time SST anomaly
    months since 1960-01-01 degree_Celsius
    Jan 1997 0.1272288
    Feb 1997 0.1710906
    Mar 1997 0.258347
    Apr 1997 0.2350922
    May 1997 0.2644247
    Jun 1997 0.3325218
    Jul 1997 0.3703358
    Aug 1997 0.3858419
    Sep 1997 0.4382685
    Oct 1997 0.4237945
    Nov 1997 0.4362565
    Dec 1997 0.489069
    Jan 1998 0.5077713
    Feb 1998 0.5263865
    Mar 1998 0.5203856
    Apr 1998 0.5148227
    May 1998 0.5182164
    Jun 1998 0.4705538
    Jul 1998 0.5324815
    Aug 1998 0.4822871
    Sep 1998 0.3824461
    Oct 1998 0.3414868
    Nov 1998 0.3556817
    Dec 1998 0.2669311
    Jan 1999 0.2846394
    Feb 1999 0.2625872
    Mar 1999 0.3153403
    Apr 1999 0.2477425
    May 1999 0.1790639
    Jun 1999 0.213057
    Jul 1999 0.2161601
    Aug 1999 0.2019157
    Sep 1999 0.2111863
    Oct 1999 0.1880307
    Nov 1999 0.1771605
    Dec 1999 0.1571581
    Jan 2000 0.2236347
    Feb 2000 0.2280948
    Mar 2000 0.2543916
    Apr 2000 0.3019583
    May 2000 0.2706494
    Jun 2000 0.2163278
    Jul 2000 0.2378533
    Aug 2000 0.2798342
    Sep 2000 0.28018
    Oct 2000 0.2634172
    Nov 2000 0.2285683
    Dec 2000 0.2123592
    Jan 2001 0.2639228
    Feb 2001 0.270036
    Mar 2001 0.3644242
    Apr 2001 0.328829
    May 2001 0.2905475
    Jun 2001 0.3138832
    Jul 2001 0.3470206
    Aug 2001 0.3620696
    Sep 2001 0.3621167
    Oct 2001 0.3665281
    Nov 2001 0.3296509
    Dec 2001 0.3041613
    Jan 2002 0.3261148
    Feb 2002 0.3393204
    Mar 2002 0.398867
    Apr 2002 0.3961641
    May 2002 0.3576576
    Jun 2002 0.3196683
    Jul 2002 0.302704
    Aug 2002 0.2935557
    Sep 2002 0.3052749
    Oct 2002 0.3551796
    Nov 2002 0.3050871
    Dec 2002 0.3371052
    Jan 2003 0.3354701
    Feb 2003 0.3251238
    Mar 2003 0.3737465
    Apr 2003 0.3509815
    May 2003 0.3058659
    Jun 2003 0.3420707
    Jul 2003 0.4320515
    Aug 2003 0.4709901
    Sep 2003 0.4609617
    Oct 2003 0.4381919
    Nov 2003 0.3984548
    Dec 2003 0.3373342
    Jan 2004 0.375291
    Feb 2004 0.3799066
    Mar 2004 0.3407118
    Apr 2004 0.3771892
    May 2004 0.3022715
    Jun 2004 0.2923895
    Jul 2004 0.3667154
    Aug 2004 0.4104019
    Sep 2004 0.4081523
    Oct 2004 0.3867066
    Nov 2004 0.3935599
    Dec 2004 0.3896537
    Jan 2005 0.4079126
    Feb 2005 0.3731517
    Mar 2005 0.4024897
    Apr 2005 0.3799401
    May 2005 0.3810709
    Jun 2005 0.3863166
    Jul 2005 0.4667181
    Aug 2005 0.4779713
    Sep 2005 0.4488237
    Oct 2005 0.3905315
    Nov 2005 0.3212799
    Dec 2005 0.297783
    Jan 2006 0.2892483
    Feb 2006 0.3026505
    Mar 2006 0.298452
    Apr 2006 0.3390791
    May 2006 0.3501258
    Jun 2006 0.366066
    Jul 2006 0.4212319
    Aug 2006 0.4354265
    Sep 2006 0.4412307
    Oct 2006 0.4121148
    Nov 2006 0.4189708
    Dec 2006 0.4226654
    Jan 2007 0.3857343
    Feb 2007 0.3727368
    Mar 2007 0.3275784
    Apr 2007 0.3301101
    May 2007 0.3006511
    Jun 2007 0.3332646
    Jul 2007 0.2981873
    Aug 2007 0.2627462
    Sep 2007 0.2550932
    Oct 2007 0.2412905
    Nov 2007 0.1375479
    Dec 2007 0.1263361
    Jan 2008 0.1488565
    Feb 2008 0.1639627
    Mar 2008 0.2209785
    Apr 2008 0.2149105
    May 2008 0.2142945
    Jun 2008 0.2583461
    Jul 2008 0.3139726
    Aug 2008 0.3200661
    Sep 2008 0.3522811
    Oct 2008 0.3366898
    Nov 2008 0.2636707
    Dec 2008 0.2734931
    Jan 2009 0.2739146
    Feb 2009 0.2564478
    Mar 2009 0.2899732
    Apr 2009 0.3039182
    May 2009 0.3290615
    Jun 2009 0.431152
    Jul 2009 0.4391241

  24. Flanagan (11:17:49) :This is a bit pathetic, no? When one’s data do not fit the picture one has in mind, oops! Switch to another dataset, and so on until you get a result you like.
    No.
    It is right and proper to test and validate and Q.A. check any instrument when it has a divergent behaviour. You will, typically, either find an “issue” with the instrument or learn something really really interesting (either about the different things the two instruments are measuring and what that detail means, or about the subject itself.
    If my oral temperature and my skin temperature diverge, for example, I might “discover” that the body moves blood to the core when under cold stress. (Or I might discover that one of my thermometers needed a new battery…).
    The only “error” is to blindly trust the readings. That seems to be the choice you are advocating.
    Does that mean we should not thrust the AMSR-E figures anymore? Adios UAH and RMSS?
    “Trust, but verify!”
    Or perhaps you would say “Thrust, but verify”…

  25. Lucy Skywalker (11:58:39) :
    Roy, thank you for holding the fort of integrity. I’m glad you showed the difference between TMI data and moored-buoy data because therein lies another clue which may solve the mystery. The “cool skin” effect which your graph shows gives an average discrepancy of 0.15 degrees – the buoy readings at 1m depth being warmer. Now in the original chart which was driving you crazy, the discrepancy appears to be of about this order of magnitude.
    Here is the hypothesis. Suppose the “cool skin” effect is really a measure of average wind chill at the surface.

    There is no such thing as “wind chill”. In fact total ait temperature in moving air is warmer than the same statc air by (TAS/100)^2 °C, where TAS is speed in mph.

  26. E.M.Smith (12:19:30) :
    Am I the only one bugged by the notion of a “passive microwave sensor”

    You made me remember the contrary: active microwave generation weather experiments…

  27. E.M.Smith (12:19:30) : Am I the only one bugged by the notion of a “passive microwave sensor” being used to measure “warming” in the context of ever higher levels of microwaves being used all over the place (in everything from jet radars to communications satellites to weather radars to..) at ever higher frequencies with lots of power?
    It would not take much of a “leakage” or “cross modulation” or any of a few other “issues” to raise the signal level in a highly sensitive antenna…

    The satellite data is cross-checked with the buoys as I understand it.

  28. Sandy (12:24:11) :
    If there is no enough energy below, in the sea, there is no need of big transfer events from the sea to the atmosphere (tropical storms, hurricanes)…no “terra spots” (earth version of sunspots).

  29. What is strange (about the blue line) is the almost-elimination of the noise seen before 2007. The same noise leel is not seen after 2007 and it almost looks like an amplification of the signal with a filter applied.

  30. Having previously groused about comments going off-subject, I’m about to do just that with the following because those who can probably answer my question are in attendance.
    Roy Spencer’s original post got me looking at the ASMU-A plots of global average temperatures at various altitudes. In comparing them I noticed something peculiar
    I noted that the annual swings in global average temperature as recorded by satellite follow a curious pattern at differing altitudes. The annual positive and negative temperature swings measured at near-surface (and at 14,000 feet) are mirror images of the annual temperature swings at 102,000 feet. The lower-altitude global average atmospheric temperature invariably peaks in mid-July, within a month of the northern hemisphere’s summer solstice. At the exact same time, the temperature at 102,000 feet invariably reaches its minimum annual temperature. In both cases, that annual swing is roughly in the range of 3.5 to 4.5 degrees F. At the same time, the temperature half way in-between at 56,000 feet shows little annual variation.
    This raises the following questions:
    1) Why does global average temperature at 14,000 feet and below track northern hemisphere seasons and, conversely
    2) Why does the global average temperature at 102,000 feet track southern hemisphere seasons?
    I don’t imagine I’m the first to notice this oddity and I’d really like to hear the explanation.
    CH

  31. E.M.Smith (12:19:30) :
    Am I the only one bugged by the notion of a “passive microwave sensor” being used to measure “warming” in the context of ever higher levels of microwaves being used all over the place (in everything from jet radars to communications satellites to weather radars to..) at ever higher frequencies with lots of power?
    It would not take much of a “leakage” or “cross modulation” or any of a few other “issues” to raise the signal level in a highly sensitive antenna…

    Man-made microwave radiation would be utterly insignificant even if at 288K wavelength but typical MM microwave is about 4 orders of magnitude longer wavelength; it will not cause error.

  32. Richard (12:12:44) [commenting on Lucy Skywalker (11:58:39)] “Interesting and plausible. But the difference as I understand it is not just in this year. Its in the trends that have been increasing over the years.”
    You have to think about how anomalies for a given month are not independent of one another in conjunction with thinking about how x-values further from the x-mean exert relatively higher leverage. We have a climatology based on only n = 7 and we have something ‘unusual’ right at the end of the record (which drives the other end of the record in the opposite direction by definition, yielding a slope – just basic Stat 101 (chapters 1 & 2)). Unless Dr. Spencer & the remote sensing experts come up with something, the jury might have to be out for awhile on statistical grounds (n = 7 – i.e. unstable statistics).
    I will repeat here my request (from the related recent thread) for direct links to plain-text monthly-summaries of all anomaly & raw series under discussion. It is to be expected that the discussion might go a little off the rails if the majority of the people commenting have not independently looked at the series (particularly the raw series – pre-climatology & anomaly).

  33. I don’t think the suggestion of a link to lack of hurricane activity holds up, since the past years that were duds for TSs through July i e 02 and 04 don’t show similar divergence. In fact AMSR-E was negative to NCDC by 0.1degree at this point in 2002

  34. “”” E.M.Smith (12:19:30) :
    Am I the only one bugged by the notion of a “passive microwave sensor” being used to measure “warming” in the context of ever higher levels of microwaves being used all over the place (in everything from jet radars to communications satellites to weather radars to..) at ever higher frequencies with lots of power? “””
    Well E.M. , That is the reason I have asked for some input on the physics of this measurement technique. I have a basic distrust of any remote sensing method that relies on a signal amplitude; when I hear the word “calibrate” it sends shivers up my spine.
    The celebrated “microwave background” radiation for example is referred to as the 3K radiation because multi frequency measurments of it spectrally match a 3K black body radiation curve; and I had alwasy assumed that satellite measures of surface temps were likewise based on fitting a BB spectrum.
    For example if you can plot enough of the spectrum of a thermal emitter, without serious frequency differential errors, and locate the spectral peak, then Wien’s Displacement law is all you need to identify the radiation temperature , and that is basically what identifies the microwave background temperature.
    Off the top of my head, I don’t know if this is correct, but I suspect that the intensity ratio of two different frequencies in a fixed temperature BB spectrum is directly proportional to the Temperature (K). So now I am going to drive myself mad until I get out the formulas and solve that condition.
    But even then, I wonder about the contamination of the as recieved signals at the satellite from emissions at other than the surface.
    We don’t seem to have too many lurkers here who are familiar with these remote sensing stratgems.
    Hopefully Roy or John Christy will chip in with some insight on this; I know it drives me barmy not knowing what the sensing mechanism is. Or Frank Wentz at RSS for that matter. I don’t like data of somewhat doubtful or unknown parentage.
    But astronomers seem uniquely good at remote sensing; in my next life I plan to be an astronomer; maybe a Solar Physicist like Leif.

  35. “”” Flanagan (11:17:49) :
    This is a bit pathetic, no? When one’s data do not fit the picture one has in mind, oops! Switch to another dataset, and so on until you get a result you like.
    Bah! “””
    Well Flanagan, you do raise a legitimate issue; but if I understand Dr Spencer’s message the problem is not the accuracy of a data set, but the fact that two sets disagree. I think roy is man enough to accept the result; one it is determined what that result is.
    Nothing is more uselesss than two different answers to the same question. Absent additional evidence you can’t have ANY confidence in either of them.
    So no need to get your panties in a bunch; if the discrepancy gets resolved, then we’ll know what is going on.
    I have a Scientific American article by a well know author that shows clearly two Antarctic ice cores covering the exact same recent (1000 year) period, and they show exactly the opposite trend in the temperature/CO2 story; so neither one is believable.
    REPLY: I second that. Roy is definitely “man” enough to post results even if contrary to his project.. Flanagan is just a cowardly university wonk hiding behind net anonymity. When you are “man” enough to challenge Dr. Spencer in the open as he does his posts, then you’ll get some respect. Otherwise, slink off. – Anthony

  36. E.M.Smith,
    “test and validate and Q.A. check”
    Flanagan and his pals don’t need to bother with that sort of thing. If things look like they are not going according to plan they merely need to adjust!

  37. wattsupwiththat (13:48:51) :
    Flanagan, why not tell us what you do at Universite Libre de Bruxelles ?

    I was under the impression that ex cathedra had no place in science. Isn’t the truth of any scientific statement independent of the origin?
    acementhead (13:08:37) :
    E.M.Smith (12:19:30) :
    Am I the only one bugged by the notion of a “passive microwave sensor” being used to measure “warming” in the context of ever higher levels of microwaves being used all over the place (in everything from jet radars to communications satellites to weather radars to..) at ever higher frequencies with lots of power?
    It would not take much of a “leakage” or “cross modulation” or any of a few other “issues” to raise the signal level in a highly sensitive antenna…
    Man-made microwave radiation would be utterly insignificant even if at 288K wavelength but typical MM microwave is about 4 orders of magnitude longer wavelength; it will not cause error.
    I hold that my above assertion is correct; it depends on fact not credentials. Credentialism is a curse on the modern world; it gives the warmists their power. “I have credentials therefore I’m right.” Well, no.
    REPLY: My interest is putting the criticisms of Dr. Spencer on the same level as his discourse. In the open. I have a low tolerance for people like “Flanagan” who say Dr. Spencer’s approach to solving the problem is “pathetic” while hiding behind a mask of anonymity. In a court of law, the accused has the right to face the accuser. This net court of public opinion on AGW should be the same in my opinion. Besides, Flanagan won’t come clean anyway, he’ll deflect. – Anthony

  38. Bob Tisdale (12:29:07) “Paul J. Trimble: Please provide a link to the dataset you posted above. Thanks.”
    Bob, it’s not at all straight-forward to find, but I located the data Paul J. Trimble copied/pasted from here:
    http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/%5BX+Y+%5Daverage/T+exch+table-+text+text+skipanyNaN+-table+.html
    The pathway to there:
    1) Start here:
    “KAPLAN EXTENDED v2 ssta: SST anomaly data”
    http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/
    2) Click “Filters”.
    3) Choose “Average over _X_Y_” (that’s a line underscoring both X & Y together – i.e. the 4th option in the “Average over” line if you are very careful to note the pattern of hyperlink-underscoring).
    4) Click “Tables”.
    5) Click “columnar table”.
    I actually started further up the pathway at:
    “IRI/LDEO Climate Data Library”
    http://iridl.ldeo.columbia.edu/
    ….and then chose “Datasets by Source”, “Kaplan”, “overview”, “Extended v2”, “SST anomaly”.
    Very awkward website (but it appears to have a lot of capabilities that might be useful for anyone tolerant of the antiquated software).

  39. More thoughts: the top graph shows a steady increase in (satellite minus insitu) SST. Two possibilities (a) the satellite sensor is faulty and steadily hyping (b) the Smiths’ worst fears are realized: we are cumulatively microwaving our planet (did I understand the gist of that anything near correctly?) and the microwaves are getting into the sensors, so yes it’s AGW but no it’s not GHG…

  40. George E. Smith (13:23:34) :
    That is the reason I have asked for some input on the physics of this measurement technique. I have a basic distrust of any remote sensing method that relies on a signal amplitude; when I hear the word “calibrate” it sends shivers up my spine.
    I put up this link in a comment on another post where you asked this question but you may have missed it, so I’ll try again
    http://fermi.jhuapl.edu/avhrr/primer/primer_html.html
    It’s from 1996 so the software part is probably obsolete but it does cover the physics and algorithms pretty well, at least as far as I can tell. I hope it helps.

  41. They were in Perfect agreement in Jan-Feb, 2008.
    Comeon, a very short period of time (7 yrs.) On one end the in-situ renders higher temps, and on the other end the satellite does.
    All the rest of the time the agreement is perfect.

  42. Paul J. Trimble and Paul Vaughan: Thanks for the links. The reason I asked, the ERSST.v3b available directly from the NCDC contradicts the IRI data. Here’s one version from the NCDC, with the base years of 1971-2000:
    ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo/aravg.mon.ocean.90S.90N.asc
    And here’s another version, with 1901-2000 as base years:
    ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.ocean.90S.90N.df_1901-2000mean.dat
    The ERSST.v3b data available through the KNMI Climate Explorer also agrees with the above.
    http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
    I’ve plotted the KNMI version against the ERSST.v3b data available through the IRI website. It appears IR adds another level of filtering or smoothing that’s not present in the others:
    http://i31.tinypic.com/2hf51ft.png

  43. From the article: “Anomalies were computed about the mean annual cycle from data over the whole period of record.”
    Could someone who is a climate scientist please explain what is meant by “anomalies” with as much detail as possible? This seems to be either climate science terminology or statistical but it sure is everywhere and after asking a couple of times and searching The InterTubes with The Google I’ve not found out what it means (no pun intended).
    Anthony/moderators, maybe you could add the answer to glossary page along with other commonly used climate science jargon. Thanks very much.

  44. Re: Bob Tisdale (15:37:31)
    Thanks for the notes Bob.
    My level of agitation with with the various processing & anomaly conventions is going nuclear.
    What do we need to do?
    Hire an army of full-time auditors to keep track of all the nonsense?
    Note to data agencies:
    1.
    Post raw data (not anomalies) and log notes. This is ABSOLUTELY ESSENTIAL to responsible data analysts intent on drawing sensible conclusions.
    2.
    If you want to post anomalies:
    (a) BE ABSOLUTELY CLEAR & EXPLICIT about the climatology and any processing right at the top of the plain-text webpages.
    (b) ALSO post raw data and log notes. This is ABSOLUTELY ESSENTIAL to responsible data analysts intent on drawing sensible conclusions.
    Untangling the mysteries of natural climate is already a consuming task without the needless administrative hurdles impairing our every step like a string of traffic lights. (Administrators’ favorite tactic: Build in delays.)

  45. Claude Harvey wrote:
    “…Roy Spencer’s original post got me looking at the ASMU-A plots of global average temperatures at various altitudes. In comparing them I noticed something peculiar. I noted that the annual swings in global average temperature as recorded by satellite follow a curious pattern at differing altitudes…”
    …and likewise in the ocean depths, are there any fixed instruments to monitor the temperature trends of vertical currents?

  46. “anomaly
    1. The deviation of a property at a particular location from the long-term mean value. Anomalies, such as sea-surface temperature anomalies, are often an extremely significant factor in … ”
    http://www.encyclopedia.com/doc/1O16-anomaly.html
    It seems to me to be a very weird, if not anomalous, use of the word anomaly. Weird because it assumes that something is out of whack or not normal about the “deviation of the property” being examined. It suggests that “it should not be” as it’s anomalous or broken or out of the normal what ever that is. It assumes that the “long-term mean” value is the normal value that SHOULD BE! This term is clearly a form of BIAS in climate science (and possibly from statistical science as well). This doesn’t bode well for anyone who isn’t aware of this BIAS upon their way of thinking. A more neutral term would be preferable, is there one?

  47. pwl re anomaly
    I’m not an expert, but an anomaly is just a deviation from some average (e.g. 1979 – 2009) value. The controvercy lies in what period is chosen.

  48. Thanks Tom, that’s insightful and useful.
    I think it’s also clear from the discussions on climate science that what is considered normal is also controversial… which often rests on whether or not the “mean” has any meaning to Natural cyclic data. Sounds like man applying binary thinking (of a sorts) upon a fuzzy Nature. It seems to me that the statistical approaches being taken in climate science at their very core are the wrong ones or should be treated with deep suspicion.
    Please correct me if I’m wrong.

  49. Re: pwl
    The convention which developed a long time ago in Climatology was to define a “climatology” ….confused yet?
    The way this works is that a “base period” is established, commonly a 30 year period that slides by a decade at the end of each decade. The average for each month within the base interval is calculated. These 12 values are then accepted as defining “climate normals” for the period.
    Subtracting this “climatology” from the raw series yields the anomalies.
    A few important things to realize:
    1) The anomalies change when the base period changes. (The changes can be dramatic when the base periods are short.)
    2) Most people think of anomalies as being the raw data with the annual cycle removed, but this is a somewhat loose & misleading interpretation (based only on mainstream convention for those who have not thought it through carefully).
    Anyone wanting to understand more deeply is advised to role up their sleeves and get their hands into the raw data.
    I sincerely hope there is not a single climate-related policy-maker who could not sit down and quickly whip-up climatologies & anomalies with raw series.
    A very serious problem for a responsible data analyst who intends to draw sensible conclusions is that one cannot reconstruct the raw data from anomalies without the “climatology” (which is seldom provided on anomaly webpages …AND WHAT’S THE BIG DEAL ADMINISTRATORS??? IT’S ONLY 12 NUMBERS!!! Save us any lame/creative excuses and GET REAL!).
    A more fundamental question is: Why not just supply the raw data? It only takes seconds to whip-up a climatology & anomalies if one is operating from the natural starting-point (i.e. raw data).
    The problem seems to be that mainstream convention & tradition is overlooking/suppressing/obfuscating something that should be REALLY obvious to any good Stat 101 student who has understood chapters 1 & 2 (…& to be clear I am not suggesting this is being done maliciously).
    Everything should start with raw data.
    Anomalies, differencing, smoothing, etc. are useful in various data analysis contexts, but it is important to have the raw data on hand so that a responsible analyst KNOWS WITH ABSOLUTE CERTAINTY FIRST-HAND EXACTLY what processing has been done to the data — this is the way to ensure SENSIBLE CONCLUSIONS can be drawn.
    Any arguments otherwise are just hurdle-erection based on tradition, mainstream momentum, &/or misunderstanding of Stat 101 chapters 1 & 2. (I’m convinced [in part based on my experience teaching Stat 101] that a lot of otherwise very bright people overlook the latter.)
    A more complicated issue, which I will not go into in depth here, is the issue of shared variance. One cannot recover from a bad decomposition and we have to realize that just because diurnal & annual cycles are dominant, that does not mean they do not share variance with other factors. It is not reasonable for agencies to be precluding a wider range of responsible analyses for no reason other than stubborn tradition. It is foolish to try to guarantee that there is no confounding.
    To be completely honest: I abhor the tradition used in Climatology because of the flawed assumption that a series can be decomposed into 2 components, one of which is based on such a dominant mode that it does not share any variance with any other factor. Based on analyses I have done, my strong instinct is that this is one of the places where conventional climatology has gone seriously off the rails.
    We won’t be able to shake the convention of using anomalies …and we don’t need to do so — anomalies are useful, as I’ve noted – (I use them frequently). HOWEVER: We should be able to succeed in getting the following message across: PROVIDE RAW DATA TOO – WITHOUT EXCEPTION. This message needs to be hammered consistently from now until the day when it happens without exception.

  50. Thanks Dave for the link; I’ll be digesting that. Not too interested in the software, just the physical sensing process, but as a working stiff, I can’t dig much of this up myself.
    Thanks again.
    George

  51. pwl (16:34:34) :
    It seems to me to be a very weird, if not anomalous, use of the word anomaly. Weird because it assumes that something is out of whack or not normal about the “deviation of the property” being examined. It suggests that “it should not be” as it’s anomalous or broken or out of the normal what ever that is. It assumes that the “long-term mean” value is the normal value that SHOULD BE! This term is clearly a form of BIAS in climate science (and possibly from statistical science as well). This doesn’t bode well for anyone who isn’t aware of this BIAS upon their way of thinking. A more neutral term would be preferable, is there one?
    I agree wholeheartedly. The most anomalous result possible would be if any component of the climate never deviated from the long term mean of its record. But this is hardly a singular instance of linguistic bamboozling in the climate sciences. Aspects of the climate that have barely demonstrated an ability to influence the climate are routinely described as “forcings” implying not only that their influence is assured, but irresistible. Minor changes in the alkalinity of the oceans are always ominously characterized as “acidification”. Even warmth which has been a positive quality since the time when humanity developed language is a pejorative now. The damage that is being inflicted on all human discourse is profound. In my view the denizens of the climate science swamp could all use some force feeding in Korzybski’s “General Semantics”, although most probably wouldn’t get it. We could at least hope that some number of them could absorb and benefit from his most famous injunction i e “The map is not the territory”.

  52. pwl wrote:
    “anomaly
    1. The deviation of a property at a particular location from the long-term mean value…It seems to me to be a very weird, if not anomalous, use of the word anomaly. A more neutral term would be preferable, is there one?”
    Use the alternate term used in the definition, “deviation.” (Of course “deviants” doesn’t sound that good either!)

  53. Thank you Paul Vaughan (19:09:44) for your excellent post explaining the usage of “anomaly” in the context of statistics and climate science in particular. Obviously I need to learn more statistics. Which book(s) do you recommend or online materials (videos, pdf papers, web sites, audios, etc…)?
    Dave Wendt (19:25:12) your post is very interesting indeed. Thanks.
    noaaprogrammer (20:36:30), deviation sounds good unfortunately the climate scientists would need to change which they won’t likely do. Do you really work for NOAA programming their systems? Which systems? Model-E or others? On the Parallel Super computer? Systems and Computer Science are my specialties.
    Thanks again to those who’ve responded with eloquent and educational posts. I have learned from you. Now where are the statistics book in my collection.

  54. Oh, Dave Wendt (19:25:12), you’ll find the Korzybski quote, “The map is not the territory”, on my web site PathsToKnowledge dot NET (http://www.PathsToKnowledge.NET) where it’s been since I started the site. I have a background in Neuro-Linguistic Programming which has adopted the quote as one of the general principles of NLP. I have had the book “Science and Sanity” for a couple of decades now and recently ordered and received a number of additional books written by Korzybski.
    Korzybski, awesome is!
    [:|]

  55. Claude Harvey (12:52:29) : .. I noted that the annual swings in global average temperature as recorded by satellite follow a curious pattern at differing altitudes. The annual positive and negative temperature swings measured at near-surface (and at 14,000 feet) are mirror images of the annual temperature swings at 102,000 feet. The lower-altitude global average atmospheric temperature invariably peaks in mid-July, within a month of the northern hemisphere’s summer solstice. At the exact same time, the temperature at 102,000 feet invariably reaches its minimum annual temperature. In both cases, that annual swing is roughly in the range of 3.5 to 4.5 degrees F. At the same time, the temperature half way in-between at 56,000 feet shows little annual variation.
    1) Why does global average temperature at 14,000 feet and below track northern hemisphere seasons and, conversely
    2) Why does the global average temperature at 102,000 feet track southern hemisphere seasons?..

    Well I can answer your first question. As for the 102,000 ft I never looked at it till today, but I’m sure there is an explanation why its the opposite of the lower atmosphere. The temperatures peak and have their minimum very close to aphelion and perihelion day, which are out of sync with the solstices and will get more so as we plunge once more inevitably into another ice-age.
    Please look here http://wattsupwiththat.com/2009/08/21/soho-back-up-and-running-didnt-miss-anything-sun-still-blank/#more-10138 and the posts Richard (01:48:07) : Richard (01:52:25) : Richard (10:37:19) : Richard (11:05:32) : Richard (20:54:22) : John Finn (03:32:23) : Richard (11:53:47) : where I asked this very question and then tracked down the answer.
    In brief, in Dr Roy Spencers words, “it’s because most of the land is in the Northern Hemisphere, and land reaches a higher temperature than ocean under the same amount of sunlight”.

  56. Paul Vaughan (19:09:44), so the statement “The anomalies change when the base period changes. (The changes can be dramatic when the base periods are short.)” implies that with 30 year base periods that slide every ten years that the anomalies for the last twenty years (decades BC) in the first 30 year sample ABC COULD HAVE, and often WILL HAVE, different “anomalies” (deviations) after the 10 year slide to sample BCD? That’s spooky but understandable with the math involved. If I’m not mistaken it also implies that the anomaly data can’t be understood – it’s utterly useless – unless you have the full context of the original data used to compute the base periods! Is that right?
    So just like a high school math question is incomplete if you don’t show your work, climate science is incomplete if the scientists don’t show ALL the STEPS of their work? Didn’t these guys ever go to high school math class? How did climate science get so sloppy?

  57. Without all the steps shown in a science paper, which would include all the data being available along with all the programs, spreadsheets, etc…, used in the paper how could any scientist sign off on a peer review? It baffles the mind. It smacks of bad science and poor ethics.

  58. Re: Richard (22:26:49)
    “Well I can answer your first question….In brief, in Dr Roy Spencers words, ‘it’s because most of the land is in the Northern Hemisphere, and land reaches a higher temperature than ocean under the same amount of sunlight’”.
    Thanks for that straightforward explanation. As to the second question, I vaguely remember being told the earth is actually closer to the sun during the northern hemisphere’s winter than during its summer. Perhaps the global average temperature at 102,000 feet is driven by “closeness” whereas at 14,000 feet it is, as you indicate, dominated by axial angle and exposed landmass?
    CH

  59. noaaprogrammer (20:36:30) :
    pwl wrote:
    “anomaly
    1. The deviation of a property at a particular location from the long-term mean value…It seems to me to be a very weird, if not anomalous, use of the word anomaly. A more neutral term would be preferable, is there one?”
    Use the alternate term used in the definition, “deviation.” (Of course “deviants” doesn’t sound that good either!)

    “Variation” seems like a reasonably value judgement free word to use.

  60. Re-post (ripost?) for comments,
    from an earlier thread, Allan M R MacRae (17:27:29) :
    First, it has to be established that this ocean warming measurement is real.
    I think it is already safe to conclude that whatever is happening is almost entirely natural and not humanmade.
    Why? Because (I’m guessing that) humanity does not have nearly enough horsepower to effect such rapid warming and cooling changes.

  61. NOAA/NCDC recently announced that July 2009 set a new record high global sea surface temperature (SST) for the month of July, just edging out July 1998.

    While July 1998 may have been a record for July ocean temps it was not particularly warm given the extraordinary El Nino event. A month is only a thirty or so period of days in 365. You can have record 30 day temps spanning two months without any of the months setting a record. You can have a one month period of moderately hot temps that beat both the months that nested the record thirty days.
    July 1998 was well into the decline of the ENSO for that period and the tropical Atlantic temperatures had also dropped. A better candidate for a July record would have been 1997, if we only use the Pacific and Atlantic tropics.
    http://i599.photobucket.com/albums/tt74/MartinGAtkins/ATL-PAC.jpg
    Obviously other latitudes and oceans conspired to rob 1997 of the coveted prize of record July ocean temps. Candidates for the record month for that event would be anywhere between 97-11 and 98-05.
    UAH judges the contest.
    http://i599.photobucket.com/albums/tt74/MartinGAtkins/UAH-Ocean97.jpg

  62. Re: pwl
    People are buying too heavily into assumptions of cyclostationarity.
    Sensible interpretation can get tricky with functions of functions of functions of …
    The best IntroStats textbook on the market is by DeVeaux, Velleman, & Bock. I’d refer you to my 100s of webpages of online intro-stats notes had my access to them not been mysteriously & suddenly blocked. (Watch your back if you speak truth to power.)

  63. pwl
    Looks like your eyes are opening to the true state of official Climate Science. It can take a while to grasp the lot – but re. your interest in statistics and its importance, I’d highly recommend you read McKitrick’s original What is the Hockey Stick debate all about? and that article is a good lead-in to the Climate Audit website which, par excellence, is pushing for proper standards in IPCC science, particularly w.r.t. statistics and transparency – openness to inspection by any intelligent Jo Ordinary.
    If you really want to learn the relevant statistics, nose around the CA site and its linked sites, CA101 and the forum and when you’ve done all you can that way, ask for help.

  64. Claude Harvey (23:36:43) : .. As to the second question, I vaguely remember being told the earth is actually closer to the sun during the northern hemisphere’s winter than during its summer. Perhaps the global average temperature at 102,000 feet is driven by “closeness” whereas at 14,000 feet it is, as you indicate, dominated by axial angle and exposed landmass? Possibly but I’m not sure about that. The Earth is closest to the sun at perihelion which is around the 3rd of Jan and furthest at aphelion around the 5th of July. We get 6.9% more radiation from the Sun on the 3rd of Jan than the 5th of july. But in absolute terms the 5th of July is 21.55 % warmer than January.

  65. Ok, I’ll dive in here with a question that has been bugging me, although I suspect I’ll feel like an idiot once its answered…. with regard primarily to surface temperatures from surface met. stations – how in the world do they justify anomalies in the x.y range, when the original data has error ranges in the neighborhood of 1 to 5 degrees?
    Whatever happened to keeping the results and conclusions within the same order of magnitude as the data collected? In other words, how can they justify temps to the tenth of a degree or smaller, when that’s at least an order of magnitude more sensitive than the actual data collected??
    Then to really show some climatology/technology ignorance and confessing the fact that I haven’t looked at the actual satellite data – what is the sensitivity of satellite measurements of both the surface and higher layers?
    Ready with a dunce cap, stool, and handy corner for myself if necessary and looking forward to replies none-the-less.

  66. “None of this represents proof that July 2009 was not a record warm month in ocean surface temperatures, but it does cast significant doubt on the claim…..”
    Maybe not as yet but it has the potential of proving exactly such a thing and that, if indeed this is the case, that the warming has been overstated.
    As such the significance of such a finding is huge and deserves concerted further investigation.
    I think that if Dr Spencer is proved correct this would have far greater impact and consequence than the debunking of the “hockey stick”.
    How could the AGW alarmists justify their alarmism if in fact the oceans have heated far less than they have stated?

  67. Do we owe Karl a nod in apology following his dissing of the satellite data?
    I recoil at the thought. Thinking of skunky homebrew.

  68. If Flanagan finds this posting’s examination of two data sets distateful, he must abhor the IPCC approach: “Since observed data does match model outputs, either the model has errors or the observed data is incorrect. We tend to believe the latter is the case.”

  69. Inquirer :
    “Since observed data does match model outputs, either the model has errors or the observed data is incorrect. We tend to believe the latter is the case.”
    You surely meant “do not”?

  70. E.M.Smith I found your “March of the Thermometers” article quite fascinating.
    http://chiefio.wordpress.com/2009/08/17/thermometer-years-by-latitude-warm-globe/
    Now there seems to be some doubt about SST’s. The SST trend differences are also greatest at the low latitudes where you would expect the seas to be warmer.
    Take those warm waters and make them warmer still and viola you get warming when you do your global averages.
    This thought must have occurred to you. Could you possibly do a similar investigation with SST’s?

  71. Mire accurately – take warming trends over the warmer waters exaggerate those trends and you have exaggerated trends globally.

  72. “”” Dave Wendt (15:17:20) :
    George E. Smith (13:23:34) :
    That is the reason I have asked for some input on the physics of this measurement technique. I have a basic distrust of any remote sensing method that relies on a signal amplitude; when I hear the word “calibrate” it sends shivers up my spine.
    I put up this link in a comment on another post where you asked this question but you may have missed it, so I’ll try again
    http://fermi.jhuapl.edu/avhrr/primer/primer_html.html “””
    Well after reading the paper referenced here, I am not going to go gaga over the satellite radiometer measurments.
    First off I understand the BB radiation aspects very well; I should after using it all the time for now about 50 years.
    I notice they put the surface reflected soalr spectrum at about 1%.
    Well the Fresnel reflection at normal incidence is given by [(n-1)/(n+1)]^2 where n is the refractive index; 1.333 for water over most of the visible spectrum; call it 4/3, so R = [(1/3)/(7/3)]^2 =1/49 or 2% normal reflectance. At angles of incidence other than normal the total reflection coefficient goes up, although it is fairly constant up to the Brewtser angle (arctan (n) or 53 degrees from normal.
    Of course the satellite is not going to look at a specular reflection of the sun off the earth surface.
    In any case this measurment technique seems too dependent on corrections for atmospheric absorption for my liking

  73. Thanks Lucy, I read the Hockey Stick stuff a few years back. I’ll read it again.
    In computer science it’s really important to have the correct “representation” of information and methodologies for a program to be meaningful, applicable and useful for it’s task.
    In building engineering applications it takes quite a bit of work to have the math be impeccable. Don’t want buildings or bridges or airplanes crashing down due to math mistakes. It seems that climate science doesn’t have this real world seriousness to the correctness of programs since climate science is theoretical rather than being practical in the real world with dire consequences.
    It sounds like climate science needs a bit of work. Pun intended.

  74. Flanagan:
    “Since observed data does match model outputs, either the model has errors or the observed data is incorrect. We tend to believe the latter is the case.”
    You surely meant “do not”?
    You probably have been waiting a long time for this: somebody on this blog saying “you are right.” 🙂

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