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<!DOCTYPE html>
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<link rel="stylesheet" type="text/css" href="sst_review_style.css">
<title>7 Concluding Remarks and Future Directions</title>
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<body>
<div class="row">
<div class="column side">
<p></p>
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<div class="column middle">
<p><a href="6.html">Previous - 6 Satellites</a> <a href="index.html">Index</a> <a href="8.html">Next - References</a></p>
<h1>7 Concluding Remarks and Future Directions</h1>
<p>
One of the chief difficulties in assessing the uncertainties in SST data sets is the
impossibility of tracing individual observations back via an unbroken chain to international
measurement standards. The creation of a global array of reference stations or buoys each making
simultaneous redundant measurements (such as the triple measurements made at CRN stations,
Diamond et al. 2013) of a variety of marine variables could solve some of the
problems of SST analysis that have bedeviled the understanding of historical SST change and
would provide a gold standard against which the future wider observing system, incorporating
observations from ships, buoys, profiling floats and satellites can be assessed. Even without
such traceability a climate record could be more easily maintained by stricter adherence to
the Global Climate Observing System [GCOS 2003] climate monitoring principles.
</p><p>
In the absence of such a network the estimation of uncertainties has depended heavily on
redundancies in measurement systems and in analysis techniques. Full use of the redundancies
is now being made in the modern period via comparisons of the many available satellite sources
with each other and with in situ sources [O'Carroll et al., 2008; Merchant et al., 2012,
Hausfather et al. 2017] and sub-surface data [Gille, 2012, Huang et al. 2018]. Analyses that
ingest a variety of data sources can produce bias
statistics for each of the inputs [Brasnett, 2008; Xu and Ignatov, 2010, 2014]. Such information can
be exploited to assess their relative quality and, as the analyses are pushed further back in
time [Roberts-Jones et al., 2012], they will help assess uncertainties through a larger part
of the record.
</p><p>
SSTs are physically related to other measurements including surface pressures and winds,
salinity, air temperatures, sub-surface temperatures and ocean biology amongst others. Information
from SST can be supplemented by analyses based on physical understanding of the climate system.
It has already been shown that by combining information from night marine air temperatures with
SST it was possible to greatly reduce uncertainties in early 20th and late 19th century SST. Yu
et al. [2004] used a joint estimation method to minimize uncertainties in flux estimates based
on a range of different variables mostly based on satellite data. Other studies [Tung and Zhou,
2010; Deser et al., 2010] have used physical reasoning based on a host of variables to explore
uncertainties in the long-term trends of tropical Pacific SSTs first raised by Vecchi et al. [2008].
It has even been suggested that proxy records such as isotope ratios from corals and ice cores could
be used, with appropriate care, to understand uncertainties in the longest-term changes in SST
[Anderson et al., 2013]. The most advanced exemplars of physical and statistical synthesis are ocean
and coupled reanalyses which will play an increasingly important role in understanding observational
uncertainty and long-term climate change.
</p><p>
A key barrier to understanding SST uncertainty is a lack of appropriate metadata. Better information
is needed concerning how measurements were made, which method was used to make a particular
observation, calibration information, the depths at which observations were made, and even basic
information such as the call sign or name of the ship that made a particular observation.
</p><p>
Some of this information can be inferred from data already contained in marine reports. Where
reports in ICOADS cannot be associated with a particular ship, either because they have a missing
ID, or a generic ID, there is much to be gained by grouping observations to give plausible ship
tracks, or voyages (Carella et al. 2017a). By using data association techniques to infer such
metadata from the location information and other clues such as how frequently observations were
made and which variables were observed, it should be possible to assess systematic and uncorrelated
errors on a ship-by-ship basis going back to the start of the record and even infer likely
measurement methods based on characteristic variations of the measurements with the meteorological
conditions (Carella et al. 2018).
</p><p>
A more systematic approach to the assessment of analysis techniques is needed to elucidate the
reasons for the differences between analyses and to assess the verisimilitude of analysis uncertainty
estimates. Approaches could include theoretical inter-comparisons of statistical methods, comparisons
based on well-defined sets of common input observations, and benchmarks built from datasets (such as
model output) where the truth is known a piori. Benchmark tests like those planned by the International
Surface Temperature Initiative [Thorne et al. 2011b] provide an objective measure against which analysis
techniques can be evaluated. Both analysis techniques and benchmarks will have to be tailored
appropriately for the particular problems affecting SST measurements and the latest understanding
of measurement uncertainties.
</p><p>
A key weakness of historical SST data sets is the lack of attention paid to evaluating the effects
of data biases particularly in the post-1941 records. Further independent estimates of the biases
produced need to be undertaken using as diverse a range of means as possible and the robust
critique of existing methods must continue. Ideally, these would be complemented by
carefully-designed field tests of buckets and other measurement methods.
</p><p>
A community review paper on SST biases (Kent et al. 2017) made a number of recommendations:
</p>
<ol>
<li>Add more data and metadata to ICOADS.</li>
<li>Reprocess existing ICOADS records.</li>
<li>Improve information on observational methods.</li>
<li>Improve physical models of SST bias.</li>
<li>Improve statistical models of SST bias.</li>
<li>Maintain and extend the range of different estimates of SST bias.</li>
<li>Expand data sources for validation and extend use of measures of internal consistency in validation.</li>
<li>Ensure adequacy and continuity of the observing system.</li>
<li>Improve openness and access to information.</li>
</ol>
<p>
Combining new analysis techniques that have been appropriately benchmarked with novel approaches
to assessing uncertainty arising from systematic errors, pervasive systematic errors and their
adjustments will give new end-to-end analyses that will help to explore the uncertainties in
historical SSTs in a more systematic manner.
</p><p>
For long-term historical analyses, there is no substitute for actual observations and relevant
metadata. Efforts to identify archives of marine observations and digitize them are ongoing
[Brohan et al., 2009; Wilkinson et al., 2011]. Such programs are labor intensive, first in
identifying and cataloguing the holdings in archives around the world, then in creating and
storing digital images of the paper books and finally in keying the observations. The difficulty
of decoding hand written entries in a variety of languages, formats and scripts means that
optical character recognition technologies are of limited use. A number of popular crowd-sourcing
projects have been started to key information from ships logs that have historical as well
meteorological interest. OldWeather.org has keyed data from Royal Navy logs from the First World
War [Brohan et al., 2009] and is now working on logs from polar expeditions. Digitization of data
also holds the possibility of extending instrumental records further back in time
[Brohan et al., 2010]. New observations, with reliable metadata, can be used not only to reduce
uncertainty in SST analyses, but also to test the reliability of existing interpolated products
and their uncertainties.
</p><p>
The ultimate destination of newly digitized observations is the International Comprehensive Ocean
Atmosphere Data Set (ICOADS) [Woodruff et al., 2011]. The ICOADS repository of marine meteorological
data has long been the focus of advances in the understanding of marine climatology. It provides a
consistent baseline for a wide range of studies, providing a solid basis for traceability and
reproducibility. The continued existence, maintenance and improvement of ICOADS are essential to
the future understanding of the global climate.
</p><p>
Finally, the work of identifying and quantifying uncertainties will be pointless, if those
uncertainties are not used. Uncertainty estimates provided with data sets have sometimes been
difficult to use or easy to use inappropriately. As pointed out by Rayner et al. [2009], "more
reliable and user-friendly representations of uncertainty should be provided" in order to encourage
their widespread and effective use.
</p>
<p><a href="6.html">Previous - 6 Satellites</a> <a href="index.html">Index</a> <a href="8.html">Next - References</a></p>
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