Preprints
https://doi.org/10.5194/essd-2022-129
https://doi.org/10.5194/essd-2022-129
 
27 Jul 2022
27 Jul 2022
Status: this preprint is currently under review for the journal ESSD.

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

Peter Edward Land1, Helen S. Findlay1, Jamie D. Shutler2, Jean-Francois Piolle3, Richard Sims2, Hannah Green2,1, Vassilis Kitidis1, Alexander Polukhin4, and Irina I. Pipko5 Peter Edward Land et al.
  • 1Plymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, PL1 3DH, UK
  • 2University of Exeter, Centre for Geography and Environmental Science, Penryn, Cornwall. TR10 9FE
  • 3IFREMER
  • 4Shirshov Institute of Oceanology, 36, Nakhimovskiy prospect, Moscow, 117997, Russia
  • 5V.I. Il’ichev Pacific Oceanological Institute FEB RAS, Vladivostok, 690041, Russia

Abstract. 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 re-analysis 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 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. 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 multiparameter 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).

Peter Edward Land et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Citation of R packages', Jean-Pierre Gattuso, 12 Aug 2022
  • RC1: 'Comment on essd-2022-129', Anonymous Referee #1, 20 Sep 2022
  • RC2: 'Comment on essd-2022-129', Anonymous Referee #2, 27 Sep 2022
    • RC3: 'Reply on RC2', Anonymous Referee #3, 05 Oct 2022
  • RC4: 'Comment on essd-2022-129', Anonymous Referee #3, 05 Oct 2022
  • AC1: 'Comment on essd-2022-129', Peter Land, 23 Nov 2022

Peter Edward Land et al.

Data sets

OceanSODA standardised surface ocean carbonate system matchup dataset Peter Land, Jean-François Piollé https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931

Peter Edward Land et al.

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Short summary
Measurements of the ocean’s carbonate system (e. g. CO2 and pH) have increased greatly in recent years, resulting in a need to combine these data, along with satellite measurements and model results, so they can be used to test predictions of how the ocean reacts to changes such as absorption of the CO2 emitted by humans. We show a method of combining data into regions of interest (100 km circles over a 10-day period) and apply it globally to produce a harmonised and easy to use data archive.