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Bruno Sanso' : A Climatology for North Atlantic Sea Surface Temperatures

Authors: Ricardo Lemos and Bruno Sanso'


We consider the problem of fitting a statistical model to historical
records of sea surface temperatures collected sparsely in space and
time. The records span the whole of the last century and include the
Atlantic Ocean north of the Equator. The purpose of the model is to
produce and atlas of sea surface temperatures. This consists of
climatological mean fields, estimates of historical trends and a
spatio-temporal reconstruction of the anomalies, i.e., the transient
deviations from the climatological mean. Our model improves upon the
current tools used by oceanographers in that we account for all
estimation uncertainties, include parameters associated
with spatial anisotropy and non-stationarity, transient and long-term
trends, and location-dependent seasonal curves. Additionally, since the
data set is composed of four types of measurements, our model
also includes four different observational variances. The model is based
on discrete process convolutions and Markov random fields. Particular
attention is given to the problem of handling a massive data set. This
is achieved by considering compact support kernels that allow an
efficient parallelization of the Markov chain Monte Carlo method used in
the estimation of the model parameters. The model is based on a
hierarchical structure that is physically sound, it is easily
parallelizable and provides information about the quantities that are
relevant to the oceanographers together with uncertainty bounds.
The data set is sufficiently large and the area sufficiently
complex and extended to serve as a good testbed for global applications.