Thursday, June 18, 2015

NCEP/NCAR matched to GISS, NOAA

In my previous post, I noted that comparing data on different anomaly bases brought in discrepancies because of vagaries of monthly averages over the base period. These are actually significant enough to detract from the use of the NCEP/NCAR index (base 1994-2013) in predicting GISS (1951-80) and NOAA (1961-90) indices.

So I have added to that table, below the NCEP monthly averages, recent monthly values adjusted to the earlier basis periods by adding the 1994-2013 month averages of GISS and NOAA anomalies. This expresses NCEP on those scales, although strictly, it is unique to those indices. Adding HADCRUT averages would give slightly different 1961-90 values.

I have also shown the actual GISS/NOAA values in the right column. The current tables are here:

GISS adj
NCEPGISS
Jun0.835NA
May0.7730.71
Apr0.7020.71
Mar0.8550.84
Feb0.8180.82
NOAA adj
NCEPNOAA
Jun0.618NA
May0.558NA
Apr0.4610.469
Mar0.5730.564
Feb0.5450.575


Except for GISS May, the correspondence is very good.

Update. I calculated a whole lot more values, in a table below the fold. It looks as if the last few months aren't typical. The correspondence is still reasonable, but discrepancies as observed April-May are not unusual.

HadCRUT adj
NCEPHadCRUT
Jun0.743NA
May0.671NA
Apr0.6080.655
Mar0.7260.68
Feb0.7190.66
Jan0.6160.69
Dec0.5950.63
Nov0.5460.487
Oct0.7210.62
Sep0.6550.592
Aug0.6690.666
Jul0.5810.544
Jun0.5530.62
May0.7110.596
Apr0.6690.658
Mar0.6520.548
Feb0.4380.305
GISSlo adj
NCEPGISSlo
Jun0.835NA
May0.7730.71
Apr0.7020.71
Mar0.8550.84
Feb0.8180.82
Jan0.7160.75
Dec0.7150.73
Nov0.6860.63
Oct0.8290.78
Sep0.7690.82
Aug0.7450.74
Jul0.6490.5
Jun0.6450.61
May0.8130.79
Apr0.7630.72
Mar0.7810.71
Feb0.5370.44
NOAAlo adj
NCEPNOAAlo
Jun0.618NA
May0.558NA
Apr0.4610.469
Mar0.5730.564
Feb0.5450.575
Jan0.4540.5
Dec0.4510.5
Nov0.4420.424
Oct0.6110.514
Sep0.5460.495
Aug0.5340.511
Jul0.4490.416
Jun0.4280.475
May0.5980.491
Apr0.5220.497
Mar0.4990.447
Feb0.2640.149
TempLSgrid adj
NCEPTempLSgrid
Jun0.715NA
May0.6550.667
Apr0.5780.618
Mar0.6810.669
Feb0.6750.668
Jan0.5650.641
Dec0.5760.643
Nov0.5350.512
Oct0.7030.627
Sep0.6280.598
Aug0.6230.606
Jul0.5310.51
Jun0.5250.581
May0.6950.61
Apr0.6390.606
Mar0.6070.546
Feb0.3940.318
TempLSmesh adj
NCEPTempLSmesh
Jun0.713NA
May0.6550.614
Apr0.6050.628
Mar0.70.709
Feb0.6980.699
Jan0.5920.663
Dec0.6050.658
Nov0.5820.574
Oct0.7250.659
Sep0.640.669
Aug0.6340.62
Jul0.5270.464
Jun0.5230.58
May0.6950.67
Apr0.6660.643
Mar0.6260.582
Feb0.4170.368




6 comments:

  1. I also compared NCEP and GISS temperatures and I think now there can be no doubt : each time you have cold anomalies in polar regions, the anomaly is lower for GISS that NCEP cfsv2. The opposite is true : look at may and september 2014 : these two months were warmer for GISS that for NCEP. Both months were quite hot in polar regions. July 2014 is quite extraordinary with low anomaly for GISS : it was cold in the Arctic an Antarctica. GISS is covering those regions and any discrepancy shows that something is going on there.

    ReplyDelete
    Replies
    1. Fortunately, GISS posts 2x2 grids of temperature anomaliess, and NCEP also posts 2x2 grids. I'll see if I can post difference maps (with global averages).

      Delete
    2. Oops - memory lapse. NCEP is 2.5x2.5. Not so easy.

      Delete
  2. Ok, different base periods explain some of the discrepancy. An other source of error could be that reanalyses calculate 2 m air temperatures, but the global observational datasets use 71% SST, and that is not always the same..
    And then we have the polar regions that are poorly covered:
    I encountered "strange" values when I ran NCEP/NCAR 2m for the Antarctic area 70S-90S at KNMI Climate Explorer. The May anomaly was +1.42 (1981-2010 base). That is not what the Gistemp map tells, the zonal anomaly for 70S-90 S is more like -1.7 for May (1981-2010 base).

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  3. I observed the difference over Antarctica too. The difference may be due the use of the sig995 level instead of 2m surface temperatures so missing inversions in the Antarctic winter?! See
    http://www.moyhu.blogspot.com.au/2014/11/a-new-surface-temperature-index.html

    ReplyDelete
  4. Olof, Anonymous, you might be right about the causes of discrepancy. When I check NCEP cfsv2 anomaly, it is to have an estimate of futur GISS data. Usually, you can have an estimate. But sometimes it doesn't work so I tried to understand why. Anomalies in polar regions (that you can see on NCEP month-to-date) seems to be associated with low giss global anomaly. So my question would be : can you use NCEP cfsv2 data to estimate Gistemp ? I would say it is possible to have an estimate if you make a correction, depending on anomalies in polar regions.

    ReplyDelete