There are a numerous blogosphere posts about the global coverage, or lack thereof, of the GISS and Hadley Centre land plus sea surface temperature datasets. Few include the NCDC product. This post provides sample maps from 1880 to 2010 comparing NCDC merged land+sea surface temperature data to those of GISS (GISTEMP with 1200km radius smoothing--LOTI) and Hadley Centre (HADCRUT). It also shows GHCN and ERSST.v3b maps, which are the sources for the NCDC merged product. And this post illustrates how NCDC deletes infilled data in early years, discusses why they delete the data, and shows the very limited impact of the NCDC’s deletion of that data in early years.
As illustrated, the NCDC does not present data over sea ice. Also, there is a sizeable area of east-central Africa without data during July 2010. And the NCDC does not present Antarctic data. The infilling methods employed by the NCDC provide greater land surface coverage than the Hadley Centre product but less coverage than GISS. The methods used by NCDC are discussed in Smith et al (2008) Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), and in Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997).
In Smith et al (2008), Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), the NCDC describes why they delete data from their merged product. On page 6, under the heading of “Sampling cutoffs for large-scale averaging”, they write, “The above results show that the reconstructions can be improved in periods with sparse sampling. However, there can still be damping errors in periods with sparse sampling. Damping of large-scale averages may be reduced by eliminating poorly sampled regions because anomalies in those regions may be greatly damped. In Smith et al. (2005) error estimates were used to show that most Arctic and Antarctic anomalies are unreliable and those regions were removed from the global average computation. Here testing using the simulated data is done to find objectively when regions should be eliminated from the global average to minimize the MSE [global mean-squared error] of the average compared to the full data.” Smith et al then go on to describe the criteria for deleting the data in poorly sampled regions.
d. The difference between the MEI and NINO3.4 will make a good post. Give me a few days. First impression is that he's trying to take the 1976 Pacific Climate Shift into accout with the MEI. Interesting.
AGW is responsible for bigger, badder El Nino's, eh? I'm betting you disagree.
Thanks for checking it out. No mention of Modoki 2010.
I know what your views are on Modoki, but recently there have been several comments on it's "newness".