What We Do in the GLODAP Show Case

GLobal Ocean Data Analysis Project

The GLobal Ocean Data Analysis Project (GLODAP, Olsen et al. 2016; www.glodap.info) is an ongoing synthesis activity for ocean surface to bottom biogeochemical data. The overarching goal is to provide one product, i.e. one access point, with consistent quality controlled data of carbon relevant ship-based interior ocean observations. The related core variables (salinity, oxygen, phosphate, nitrate, silicate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113 and CCl4) are subjected to extensive quality control and are biased corrected. As of now, more than 1.2 million samples from almost 1000 different research cruises dating back to 1972 have been included in GLODAPv2.2020 (Olsen et al. 2020). This enables, among other things, the quantification of the ocean carbon sink, ocean acidification and evaluation of ocean biogeochemical models.

A living data product

Regular and frequent data updates have become inevitable given the urgency and complexity of climate change. Thus, starting with GLODAPv2.2019 we aim at yearly updates and decadal version releases in concert with the repeated hydrography observation program GO-SHIP. The decadal version releases encompass a full consistency analysis, including the usage of inversion models, of all present cruise data. As this is very time demanding the yearly updates are restricted to the addition- and bias-correction of new cruise data using the most recent GLODAP update as reference. Towards these more regular and frequent releases, the automation of the corresponding workflow is crucial. Presently however, the workflow of GLODAP still includes a fair bit of manual labor with some bottlenecks present.

High quality and intercomparable data – The workflow

Data Ingestion

As a seamless data ingestion system with automated data formatting, unit conversion and sanity checks is still under development, the workflow starts with manual acquisition of individual cruise data. Once data is ingested and formatted, the scientific QC (quality control), the most important part of the workflow, has to be performed.

Scientific QC

The 1st QC (precision checks) is supported through the extensive plotting tool AtlantOS QC (Velo et al. 2020, http://doi.org/10.5281/zenodo.2603121) which enables faster and more consistent decision making. For the 2nd QC (accuracy checks) the crossover toolbox (Lauvset and Tanhua 2015; https://github.com/sivlauvset/2ndQCtool) executes a systematic evaluation of bias. Important to note is that the QC is performed in strong collaboration with the data generator and evaluated with external expert groups. The traceability of the QC decisions is further guaranteed through an online adjustment table.

Make Ocean routine

The actual merging routine Make Ocean (https://git.geomar.de/patrick-michaelis/python-for-glodap), implemented through Digital Earth, consequently, executes a large variety of tasks towards the final product automatically. These tasks include for example vertical interpolation, calculation of missing vales using the neural network CANYON-B (Bittig et al. 2018) and the carbon calculation software CO2SYS (van Heuven et al. 2011), a consistent bottom depth calculation, calculation of density surfaces and applying defined adjustments. In the last step of the routine, the actual merge of all data is performed.

Final product

The resultant consistent data product consists of four regional files and a global file, all available in exchange format and as mat-files. For each new full version release an additional mapping exercise will interpolate the single point data of the core variables on 33 depth-levels horizontally. Eventually, the end-user can then easily get an overview and analyze all data of GLODAP using the Digital Earth Viewer here.

References

Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., Körtzinger, A., and Gattuso, J.-P.: An alternative to static climatologies: Robust estimation of open ocean CO2 variables and nutrient concentrations from T, S, and O2 data using Bayesian Neural Networks, Frontiers in Marine Science, 5, 2018.

Lauvset, S. K. and Tanhua, T.: A toolbox for secondary quality control on ocean chemistry and hydrographic data, Limnol. Oceanogr.-Meth., 13, 601-608, 2015.

Olsen, A., Key, R. M., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X. H., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterstrom, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., and Suzuki, T.: The Global Ocean Data Analysis Project version 2 (GLODAPv2) - an internally consistent data product for the world ocean, Earth Syst. Sci. Data, 8, 297-323, 2016.

Olsen, A., Lange, N., Key, R. M., Tanhua, T., Bittig, H. C., Kozyr, A., Àlvarez, M., Azetsu-Scott, K., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterström, S., Landa, C. S., Lauvset, S. K., Michaelis, P., Murata, A., Pérez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R. and Woosley, R. J.: GLODAPv2.2020 – the second update of GLODAPv2, Under review 2020.

Van Heuven, S., Pierrot, D., Rae, J. W. B., Lewis, E., and Wallace, D. W. R.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, U.S.A., 2011.

Velo, A., Cacabelos, J., Pérez F. F., Tanhua T. and Lange N.: AtlantOS Ocean Data QC: Software packages and best practice manuals and knowledge transfer for sustained quality control of hydrographic sections, 2020, Zenodo. http://doi.org/10.5281/zenodo.2603121