final_program_abstracts[1]

11 IMSC Session Program
Assessing the relative importance of bias uncertainty,
structural uncertainty and random errors in SST data sets
Monday - Parallel Session 4
John Kennedy, Robert Smith and Nick Rayner
Met Office Hadley Centre, UK
Recent improvements in the analysis of SST data – including the development of bias
adjustments, more sophisticated error models and the free availability of a range of
SST analyses – mean that it is now possible to assess the relative importance of
uncertainties arising from each of these components.
The uncertainties in the bias adjustments are largest when there are large changes in
the database of observations in the way the recorded measurements were made
because relevant metadata are lacking. Two periods stand out in this respect: 1939-
1960, and the period from 1990 to the present. During the former period, there was a
poorly documented change from making SST measurements using canvas buckets to
making measurements using insulated rubber buckets and engine room thermometers.
Since 1990, the SST observing system has come to be dominated by observations
from drifting buoys.
Structural uncertainties arise when different, but equally valid methods are used to
derive a particular quantity such as the global average SST. The spread in such
quantities derived from different data sets is a measure of this uncertainty. Structural
uncertainties are largest at times when there are few observations but they also project
strongly onto long-term trends.
Since 1979, bias uncertainty is a larger contributor to the uncertainty in globalaverage
SST trends than structural uncertainty. However the uncertainty in the trends
at longer time scales show that structural uncertainty is perhaps the larger component.
Abstracts 59