The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

• Published in: Sensors
• Main Authors: Brewin, Robert J. W., Ciavatta, Stefano, Sathyendranath, Shubha, Skákala, Jozef, Bruggeman, Jorn, Ford, David, Platt, Trevor
• Format: Text
• Language: English
• Published: MDPI 2019
• Subjects: Article; North Atlantic
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806171/; http://www.ncbi.nlm.nih.gov/pubmed/31561600; https://doi.org/10.3390/s19194182

We present a model that estimates the spectral phytoplankton absorption coefficient ([Formula: see text]) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on [Formula: see text] (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total [Formula: see text] with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine [Formula: see text] for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific [Formula: see text] using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.