DOI: 10.5281/zenodo.15689007
title: Monthly gap filled Ocean Colour Climate Change Initiative (OC-CCI) chlorophyll-a using BGC-Argo as an observational constraint

description: Changelog

v0-1 – Preliminary release of the dataset before manuscript submission

 

Introduction

Chlorophyll-a (chl-a), the dominant photosynthetic pigment in phytoplankton, has been identified as an essential climate variable by the Global Climate Observing System for assessing current and future changes to oceanic global bio-geochemical cycles (GCOS, 2021). Satellite-ocean-colour-based synoptic chl-a fields of the surface and near-surface ocean (varying from a few millimetres depth to tens of metres dependent upon the water constituents) have routinely been produced since the launch of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in September 1997 and followed subsequently by more advanced satellite ocean colour sensors. The ocean colour signal at different wavelengths of light can be related to in situ chl-a concentrations and used to estimate synoptic scale chl-a (e.g. Gohin et al., 2002; Hu et al., 2012; O’Reilly & Werdell, 2019). These observations from multiple satellites that cover different time periods and often with different sensor characteristics, are now routinely robustly merged into continuous climate data records, the main effort of which results in the Ocean Colour Climate Change Initiative (OC-CCI) (Sathyendranath et al., 2019). These records are essential for assessing changes globally and regionally, and the data are used as input to assess changes in phytoplankton abundance and primary production (Kulk et al., 2020). Additionally they are routinely used for ecosystem monitoring, understanding biogeochemistry, supporting fisheries management, water quality monitoring and operational ocean forecasting (Sathyendranath et al., 2023). However, ocean-colour observations of chl-a have the limitation of data gaps due to cloud cover, and high sun zenith and viewing angles which routinely occur during polar winter, that reduce coverage.

These missing high-latitude polar winter data often make the exploitation of the overall chl-a record more challenging or result in assumptions being made about the missing wintertime chl-a concentrations. For example, within efforts to reconstruct the global ocean carbon dioxide (CO2) sink, these missing data are often manually filled with a fixed value (e.g., Chau et al., 2022; Gregor & Gruber, 2021), or chl-a is excluded from the input variables used to interpolate other data which means that any explicit biological control within the interpolation is omitted (e.g. Ford et al., 2024). For example, Gregor and Gruber (2021) use a fixed value of 0.3 mg m-3 for all missing polar wintertime data, whereas Chau et al. (2022) use a value of 0.0 mg m-3. These practical choices likely influence the underlying interpolation and reconstructions of the data (in this case the ocean CO2 sink) and are unlikely to be scientifically applicable across all times and geographic locations as they ignore regional and temporal variations and create unnatural boundaries (e.g., for the Arctic and Southern Ocean, which have different bio-geochemical characteristics). The expanding availability of autonomous BGC-Argo profilers with chl-a sensors (Roemmich et al., 2019) that can provide observations within the polar winter could be exploited to generate a data-driven reconstruction of these missing wintertime chl-a. Whilst able to provide data during polar winter, the BGC-Argo chl-a data have reduced accuracy with respect to the satellite observations, so using them to directly gap-fill the higher accuracy climate data record presents some challenges.

This dataset provides a spatially complete monthly OC-CCI (Sathyendranath et al., 2019) based chl-a record between October 1997 and December 2023 at a spatial resolution of 0.25º with spatially resolved uncertainties. Additionally, we provide a secondary dataset produced at 1º spatial resolution. The methodology is described in Ford et al. (in prep). In brief, clouds gaps were filled using a spatial kriging approach. Polar wintertime chl-a were reconstructed using relative changes between the wintertime BGC-Argo chl-a, and the previous autumntime or next springtime satellite observations, for the Northern and Southern Hemispheres separately. Under ice regions were filled with a fixed value.

 

Data records

The data are provided as single netCDF files for the corresponding spatial resolution (0.25º and 1º).

Within each netCDF file the original regridded OC-CCI observations (OC-CCI_chlor_a) are provided with their uncertainty fields (OC-CCI_chlor_a_log10_rmsd) unaltered, along with the sea ice concentration dataset (OSI-SAF; v3; (OSISAF_ice_conc).

The gap filled chl-a (chl_filled) and the uncertainty (chl_filled_unc) are provided as separate variables. The ‘chl_flag’ variable provides the information on how each pixel within ‘chl_filled’ has been filled. Flag numbers and there corresponding filling method are presented in Table 1, but also as metadata on the ‘chl_flag’ variable.

For further information the ‘flag_l’ variable provides the month lag number for the BGC-Argo gap filling approach. For example, a +2 would indicate the forward looking (springtime) relationship was used, at the month lag of 2. Finally, ‘chl_process’ is an internal processing variable and can be ignored for the purposes of using the dataset.

 

Table 1: The ‘chl_flag’ variable number converted to the filling methodology.






‘chl_flag’ number



Filling method





1



Land





2



OC-CCI observation





3



Cloud Kriging





4



Under sea ice





5



Southern Hemisphere backwards Argo relationship





6



Southern Hemisphere forwards Argo relationship





7



Northern Hemisphere backwards Argo relationship





8



Northern Hemisphere forwards Argo relationship





9



Final Kriging pass





 

Quick Start Guide

The gap filled chl-a can be retrieved from the ‘chl_filled’ variable along with the ‘chl_filled_unc’ variable. These are provided as a array with dimensions (longitude, latitude, time).

 

Additional Information

Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions.

 

This dataset contains two files contained within a single zip file:

Ford_et_al_oc-cci_chlor_a_gap_filled_1deg_v0-1.nc

Ford_et_al_oc-cci_chlor_a_gap_filled_0.25deg_v0-1.nc

 

How to cite these data

Please cite the DOI of this dataset (with the version), the manuscript describing the methodology (Ford et al., in prep), and the underlying OC-CCI record (v6) (Sathyendranath et al., 2019).

 

Acknowledgements

This work was funded by the European Space Agency under the projects ‘Satellite-based observations of Carbon in the Ocean: Pools, Fluxes and Exchanges’ (SCOPE; 4000142532/23/I-DT) and ‘Ocean Carbon for Climate’ (OC4C; 3-18399/24/I-NB). This work was also partially funded by OceanICU which was funded by the European Union under grant agreement no. 101083922 and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 1006367, 10064020, 10059241, 10079684, 10059012, 10048179]. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This work is a contribution to the activities of the National Centre of Earth Observation (NCEO) of the United Kingdom. We also acknowledge additional support from the Simons Collaboration on Computational Biogeochemical Modelling of Marine Ecosystems/CBIOMES (Grant ID: 549947, SS). 

 

References

Chau, T.-T.-T., Gehlen, M., & Chevallier, F. (2022). A seamless ensemble-based reconstruction of surface ocean CO2 and air–sea CO2 fluxes over the global coastal and open oceans. Biogeosciences, 19(4), 1087–1109. https://doi.org/10.5194/bg-19-1087-2022

Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschützer, P., Jersild, A., & Shutler, J. D. (2024). A Comprehensive Analysis of Air‐Sea CO2 Flux Uncertainties Constructed From Surface Ocean Data Products. Global Biogeochemical Cycles, 38(11), e2024GB008188. https://doi.org/10.1029/2024GB008188

Ford, D. J., Kulk, G., Sathyendranath, S., & Shutler, J. D. (in prep). Decadal and spatially complete global surface chlorophyll-a data record from satellite and BGC-Argo observations.

GCOS. (2021). The Status of the Global Climate Observing System 2021: The GCOS Status Report (GCOS-240). Geneva: World Meteorological Organisation.

Gohin, F., Druon, J. N., & Lampert, L. (2002). A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International Journal of Remote Sensing, 23(8), 1639–1661. https://doi.org/10.1080/01431160110071879

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Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(1), 1–25. https://doi.org/10.1029/2011JC007395

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