Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Robert J W Brewin,Thomas Jackson,Emanuele Organelli,Vanda Brotas,Giorgio Dall'Olmo,Shubha Sathyendranath,Denise Cummings,Dionysios E Raitsos,Gavin Tilstone,Ruth L Airs,Kieran Curran,Stefano Ciavatta

doi:10.3389/fmars.2017.00104

Abstract

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-colour data. There is a growing demand from the ecosystem modelling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeller these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modellers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size (pico- (20μm)). The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterise the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.

Highlights

The size structure and taxonomic composition of phytoplankton influence many processes in phytoplankton biology, marine biogeochemistry and marine ecology (Chisholm, 1992; Raven, 1998; Le Quéré et al, 2005; Marañón, 2009, 2015; Finkel et al, 2010)
The SST-dependent parameterization (CiSST) has a similar statistical performance compared with that obtained when using a single set of parameters, the SST-dependent parameterization is not constrained by static asymptotes for Cp,n, concentration of nano-phytoplankton (Cn), and Cp (Figure 9 top-row, horizontal purple dashed lines) and captures better the variability in the size-fractionated chlorophyll at these higher concentrations
We re-tuned an abundance-based model (Brewin et al, 2010, 2015) for estimating the chlorophyll concentration of three phytoplankton size classes as a function of total chlorophyll in the North Atlantic region using a large dataset of size-fractionated chlorophyll measurements

Summary

Introduction

The size structure and taxonomic composition of phytoplankton influence many processes in phytoplankton biology, marine biogeochemistry and marine ecology (Chisholm, 1992; Raven, 1998; Le Quéré et al, 2005; Marañón, 2009, 2015; Finkel et al, 2010). Though it would appear more sensible to use a direct approach, issues with spectral-based techniques can arise when the signal-to-noise ratio in the oceancolor data is too low to detect the targeted signature (Garver et al, 1994; Wang et al, 2005), when the phytoplankton group being targeted has a similar optical signature to other groups, when the spectral signatures are not known sufficiently well, or when the spectral resolution is not adequate for detecting the target signature In such cases, an indirect method (e.g., ecological or abundance based) would be more suitable. Efforts have been made to combine abundance and ecologicalbased approaches, for instance, Brewin et al (2015) and Ward (2015) modified the relationship between the chlorophyll concentration of the phytoplankton groups and total chlorophyll (abundance-based) according to the environmental (ecologicalbased) conditions (e.g., temperature or light availability)

Methods

Results

Conclusion