In recent years, large datasets of in situ marine carbonate
system parameters (partial pressure of CO2 (pCO2), total
alkalinity, dissolved inorganic carbon and pH) have been collated, quality-controlled and made publicly available. These carbonate system datasets have
highly variable data density in both space and time, especially in the case
of pCO2, which is routinely measured at high frequency using underway
measuring systems. This variation in data density can create biases when the
data are used, for example, for algorithm assessment, favouring datasets or
regions with high data density. A common way to overcome data density issues
is to bin the data into cells of equal latitude and longitude extent. This
leads to bins with spatial areas that are latitude- and projection-dependent
(e.g. become smaller and more elongated as the poles are approached).
Additionally, as bin boundaries are defined without reference to the spatial
distribution of the data or to geographical features, data clusters may be
divided sub-optimally (e.g. a bin covering a region with a strong
gradient). To overcome these problems and to provide a tool for matching
surface in situ data with satellite, model and climatological data, which often
have very different spatiotemporal scales both from the in situ data and from each
other, a methodology has been created to group in situ data into “regions of
interest”: spatiotemporal cylinders consisting of circles on the Earth's
surface extending over a period of time. These regions of interest are
optimally adjusted to contain as many in situ measurements as possible. All surface
in situ measurements of the same parameter contained in a region of interest are
collated, including estimated uncertainties and regional summary statistics.
The same grouping is applied to each of the non-in situ datasets in turn, producing
a dataset of coincident matchups that are consistent in space and time.
About 35 million in situ data points were matched with data from five satellite
sources and five model and reanalysis datasets to produce a global matchup
dataset of carbonate system data, consisting of ∼286 000 regions of interest spanning 54 years from 1957 to 2020. Each region of
interest is 100 km in diameter and 10 d in duration. An example
application, the reparameterisation of a global total alkalinity algorithm,
is presented. This matchup dataset can be updated as and when in situ and other
datasets are updated, and similar datasets at finer spatiotemporal scale can
be constructed, for example, to enable regional studies. The matchup dataset
provides users with a large multi-parameter carbonate system dataset
containing data from different sources, in one consistent, collated and
standardised format suitable for model–data intercomparisons and model
evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and
Piollé, 2022)....

OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons

10.5194/essd-15-921-2023