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.
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.
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.
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.
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???
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.
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.”
Yep, it seems for these guys the Devil is in the “adjustments.”
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.
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
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.
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.
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?
We aren’t anywhere near 1998 temps that was a general event.I still say this El Nino isn’t going anywhere..
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?
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.
According to this,
http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt
You showed an anamoly of 1.08 for South Pole – Ocean.
The second largest ocean anamoly was the north pole. Perhaps you did a better job of covering those areas.
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:-)
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.
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
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…
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?
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.
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.
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?
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.
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… )
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.
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
Paul J. Trimble: Please provide a link to the dataset you posted above.
Thanks.
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”…
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.
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…
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.
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).
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.
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
Sure a “funny-looking” El Nino:
http://www.osdpd.noaa.gov/data/sst/anomaly/anomwsc.gif
By the way, it’s not Just This Year’s lack of Hurricane activity. Remember, the ACE has been declining for a while now.
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.
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).
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
“”” 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.
“”” 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
Flanagan, why not tell us what you do at Universite Libre de Bruxelles ?
http://www.ulb.ac.be/
Then you’ll be on par with Dr. Spencer and your pot shots might actually mean something.
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!
acementhead (12:41:49) :
There is no such thing as “wind chill”.
Come to Saskatchewan on a nice -40 degree day with a stiff breeze blowing and say that. 😉
Hi Bob,
Below are the links:
http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/.ERSST/.version3b/.anom/
http://ingrid.ldgo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/
I used Data Selection (all data and data filter xy mean to compute global average time series
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
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).
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…
Not “cumulatively”, I meant “increasingly”
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.
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.
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
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.
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.)
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?
“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?
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.
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.
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.
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
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”.
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!)
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.
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!
[:|]
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”.
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?
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.
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
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.
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.
The farse continues. I don’t understand how can anyone make science with this kind of data.
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
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.)
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.
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.
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.
“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?
Do we owe Karl a nod in apology following his dissing of the satellite data?
I recoil at the thought. Thinking of skunky homebrew.
Here is an anomaly of anomaly graphics:
http://www.cdc.noaa.gov/map/images/rnl/sfctmpmer_01a.rnl.html
http://weather.unisys.com/surface/sst_anom.html
I don’t know where UNISYS gets their SST data, but the warm antarctic ocean seems to be peculiarity of NOAA.
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.”
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”?
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?
Mire accurately – take warming trends over the warmer waters exaggerate those trends and you have exaggerated trends globally.
More accurately….
“”” 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
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.
pochas (07:54:27)
http://www.cdc.noaa.gov/map/images/rnl/sfctmpmer_01a.rnl.html
Careful: This^ one is air temperature.
[Note the anomaly range of -15 to +15 within the Southern Ocean.]
Here is the SST map:
http://www.cdc.noaa.gov/map/images/sst/sst.anom.gif
(Note the much smaller anomaly range.)
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.” 🙂