Quote:
Originally Posted by
Taskiss 
I think you need to speak to the NOAA, not to me. It's pretty clear what they've written.
It certainly is, if you can understand it. There were areas where they had only very sparse data and that data was not used, because they considered it to be too inaccurate. So yes you could say they removed "some" data-
"In Smith et al. (2005) the global average was modified to exclude data from regions with sparse sampling to minimize
damping of global-average anomalies.☛ Other improvements were also considered and tested. Here a set of improvements and their effect on the reconstruction are evaluated. Of the improvements, the two that have
the GREATEST INFLUENCES on global averages are BETTER TUNING of the reconstruction method and inclusion of BIAS-ADJUSTED SATELLITE DATA since 1985."
~
http://www.ncdc.noaa.gov/oa/climate/...EA.temps08.pdf
So in other words they "removed" data that was considered inaccurate from areas that were poorly monitored. Hardly the basis of a great conspiracy story! Not only that but other factors had a major role in effecting the update that are established peer reviewed science.
From the same NOAA, Smith et al. paper-
"Changes in the Niño-3.4 SST anomalies between
ERSST.v2 and ERSST.v3 ARE VERY SMALL AFTER 1950.
Earlier in the record the two are also highly correlated,
but there are times when the ERSST.v2 anomaly is
greatly damped from a lack of sampling (Fig. 8). These
times include years before 1880 and around 1918,
Simulated data from models and observations are
used to improve the tuning of the National Oceanic and
Atmospheric Administration (NOAA) operational sur-
face temperature analysis. Errors from excessive damp-
ing are reduced in the improved analysis (merged.v3).
This is especially important in the ocean component of
the analysis (ERSST.v3). Compared to SR05, the great-
est improvements occur in the nineteenth century.
However, there are some sparsely sampled regions in
all periods that are improved by the new tuning. In
addition, global averaging of the analysis is optimally
tuned to exclude undersampled regions responsible for
excessive damping of global averages."