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<title>3.3.4 Refinements to Estimates of Pervasive Systematic Errors</title>
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<a href="3.3.3.html">Previous - 3.3.3 Estimating Uncertainty in Bias Adjustments</a> <a href="index.html">Index</a> <a href="3.3.5.html">Next - 3.3.5 Assessing the Efficacy of Bias Adjustments</a>
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<h1>3.3.4 Refinements to Estimates of Pervasive Systematic Errors</h1>
<p>
There are some factors that have not been explicitly considered in estimates of biases.
Refinements to the models of pervasive systematic errors will address with factors that
are implicitly included in uncorrelated and systematic measurement uncertainties. If it
is possible to estimate the bias on a ship-by-ship, or observation-by-observation basis,
taking account of the conditions peculiar to that observation, then it might be expected
that uncertainties associated with uncorrelated and systematic observational error will
decrease.
</p><p>
Both Kennedy et al. [2011c] and Hirahara et al. [2013] make simplifying assumptions about
the systematic errors associated with modern insulated buckets. Various bucket designs
have been used since the end of the Second World War, which are likely to have
different bias characteristics. Physical models could be developed for each type of
bucket similar to those used by Folland and Parker [1995], or statistical methods
could be used to estimate the biases as was done in Kent and Kaplan [2006]. Carella et
al. [2017b] studied heat loss from wooden and canvas buckets in a laboratory setting and
suggested that heat loss is closely linked to the wet bulb depression, pointing the way
to a simplified statistical approach to estimating bucket biases. Chan and Huybers [2019]
showed significant biases between bucket measurements made by ships from different nations
and from different ICOADS decks.
</p><p>
Other simplifying assumptions used in all analyses include such things as assuming that
changes in the observing system happened linearly. Evidence suggests that changes in
measurement method were not always monotonic and sometimes happened abruptly (see
Figure 6). Improved metadata or more sophisticated statistical techniques could help
assess these uncertainties. In particular, the changeover between canvas and wooden
buckets and the speed of ships, are unlikely to be linear as was assumed in Folland and
Parker [1995], Rayner et al. [2006], Kennedy et al. [2011c] and Smith and Reynolds [2002].
Huang et al. [2015, 2017] relaxed this assumption. Carella et al. [2017] suggests there
was a step change in ship speeds in ICOADS close to the start of the 20th Century.
</p><p>
An uncertainty associated with pervasive systematic biases, which is not explicitly resolved by
current analyses, arises when the conditions at the time of the measurement deviate from the
climatological values assumed by the bias correction scheme. If, for instance, the air sea
temperature difference is larger than that assumed by the Folland and Parker [1995] scheme,
then there will be an additional locally-correlated error with a potential long-term component
where differences persist for months or years. Likewise conditions vary during the day. Such
discrepancies could be assessed by evaluating the systematic error using local conditions.
Such information could be taken from reanalyses, or an appropriate bucket model could be
explicitly included when SST observations are assimilated into ocean-only and coupled reanalyses.
</p>
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<a href="3.3.3.html">Previous - 3.3.3 Estimating Uncertainty in Bias Adjustments</a> <a href="index.html">Index</a> <a href="3.3.5.html">Next - 3.3.5 Assessing the Efficacy of Bias Adjustments</a>
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