Southern Ocean summer chlorophyll-a climatology
This dataset is a climatological summer chlorophyll-a layer for the Southern Ocean south of 40S, following the OC3M algorithm of Johnson et al. (2013). The climatology was calculated from level-3 binned MODISA RRS products spanning the 2002/03 to 2015/16 austral summer seasons (summer taken as day 3...
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| Format: | Dataset |
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Australian Ocean Data Network
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| Online Access: | https://researchdata.ands.org.au/southern-ocean-summer-a-climatology/934445 https://data.aad.gov.au/metadata/records/AAS_4343_so_chlorophyll http://data.aad.gov.au/eds/4423/download https://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=AAS_4343_so_chlorophyll https://secure3.aad.gov.au/proms/public/projects/report_project_public.cfm?project_no=AAS_4343 |
| Summary: | This dataset is a climatological summer chlorophyll-a layer for the Southern Ocean south of 40S, following the OC3M algorithm of Johnson et al. (2013). The climatology was calculated from level-3 binned MODISA RRS products spanning the 2002/03 to 2015/16 austral summer seasons (summer taken as day 355 to day 80). From the abstract of the published paper: Remote sensing of Southern Ocean chlorophyll concentrations is the most effective way to detect large-scale changes in phytoplankton biomass driven by seasonality and climate change. However, the current algorithms for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, algorithm OC4v6), the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua, algorithm OC3M), and GlobColour significantly underestimate chlorophyll concentrations at high latitudes. Here, we use a long-term data set from the Southern Ocean (20 degrees - 160 degrees E) to develop more accurate algorithms for all three of these products in southern high-latitude regions. These new algorithms improve in situ versus satellite chlorophyll coefficients of determination (r 2 ) from 0.27 to 0.46, 0.26 to 0.51, and 0.25 to 0.27, for OC4v6, OC3M, and GlobColour, respectively, while addressing the underestimation problem. This study also revealed that pigment composition, which reflects species composition and physiology, is key to understanding the reasons for satellite chlorophyll underestimation in this region. These significantly improved algorithms will permit more accurate estimates of standing stocks and more sensitive detection of spatial and temporal changes in those stocks, with consequences for derived products such as primary production and carbon cycling. |
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