Bror F. Jönsson, Gemma Kulk, Shubha Sathyendranath (2023), Review of algorithms estimating export production from satellite-derived properties, Front. Mar. Sci., doi: 10.3389/fmars.2023.1149938
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NEW CBIOMES PUBLICATION
Isabelle Cox, Robert J.W. Brewin, et al (2023), Distinct habitat and biogeochemical properties of low-oxygen-adapted tropical oceanic phytoplankton, Limnology and Oceanography, doi: 10.1002/lno.12404 [Shubha Sathyendranath]
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Shubha Sathyendranath appointed MBE in the King’s Birthday Honours
Warm congratulations to PML CBIOMES PI on this outstanding honor. Continue reading “Shubha Sathyendranath appointed MBE in the King’s Birthday Honours”
NEW CBIOMES PUBLICATION
Smyth, T., D. Moffat, G. Tarran, S. Sathyendranath, F. Ribalet, J. Casey (2023), Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale, Frontiers in Marine Science, doi: 10.3389/fmars.2023.
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Prochlorococcus in a Changing Climate
A new CBIOMES paper has important implications for our understanding of how plankton populations will change in a warming ocean – suggests caution when using statistical models to predict changes in plankton abundance. Continue reading “Prochlorococcus in a Changing Climate”
Ocean Color – a window on marine life
In this short European Space Agency (ESA) explainer video CBIOMES PI Shubha Sathyendranath talks about her work as part of ESA’s Climate Change Initiative is taking advantage of the observational power of satellites to understand ocean change.
Linking Transects and Eco-provinces
The CBIOMES Workshop on Transects and Eco-provinces was held on January 23-25, 2023, at MIT’s Samberg Conference Center. Continue reading “Linking Transects and Eco-provinces”
February 2023 CBIOMES e-meeting Žarko Kovač (University of Split, Croatia; the PML Group)
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CBIOMES Welcomes Mahasweta Saha
A warm welcome to PML Senior Scientist Mahasweta Saha who recently joined CBIOMES.
Capturing High Spatial and Temporal Variability in Phytoplankton Biomass from Space
A new paper from the PML Group and collaborators uses a moving standard deviation saturation approach to reveal dominant timescales of variability in global satellite data.
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