Marine Primary Production: Model Parameters from Space

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1 Marine rimary roduction: Model arameters from Space T.Jackson, H.Bouman, S.Sathyendranath, T.latt, F.Melin, S. Saux-icart Earth Observation for Ocean-Atmosphere Interactions Science 2014

2 Outline The importance New Agenda of estimates of phytoplankton photophysiology. hotophysiological parameter measurements and existing algorithms for estimating parameters remotely. Aims of the MArine primary roduction: model arameters from Space (MAS) project and the approach we are taking. Initial results of algorithm comparison. Conclusions and Future work

3 Marine rimary roduction Marine phytoplankton are important players in the global carbon cycle. According to current estimates, they are responsible for fixing some 50 GT of carbon per year, globally. Observing variations in primary production, at a number of temporal and spatial scales provides an essential basis for a full understanding of the ocean carbon cycle. Algorithms exist for estimating primary production from satellite-derived chlorophyll data. These computations require input of photosynthesis-irradiance parameters.

4 Marine rimary roduction Modelled rimary roduction for April 2004 (TWA project).

hotosynthesis-irradiance arameters hotophysiological parameters are fundamental bio-optical New Agenda properties of phytoplankton and are at the heart of methods for estimating primary production by

5 hotosynthesis-irradiance arameters hotophysiological parameters are fundamental bio-optical New Agenda properties of phytoplankton and are at the heart of methods for estimating primary production by remote sensing. mb provides information on the maximum photosynthetic capacity in the absence of light limitation. αb provides information on the low-light efficiency of the phytoplankton production. We can also derive Ik, a measure of the light level to which the phytoplankton are acclimated.

6 -I arameter measurements -I incubation OR In-situ measurement Or Adapted from M.Babin and F.Jochem

7 Relevance of -I arameters -I incubations In-situ production measurement Short incubation time Long incubation time Controlled light conditions Natural light source (variable) Carries information on how production would change with varying light rovides information on local production at a given time Directly usable in primary production models Usable for validation of primary production models

8 Importance of Estimates of hotophysiology Global coverage of -I parameters measured directly at sea is relatively sparse. However, we must specify them at every pixel, for computation of primary production using satellite data. The paucity of data has necessitated that we develop extrapolation protocols for photosynthetic parameters. The MAS project explores satellite based methods for estimating -I parameters.

9 Existing Algorithms for arameter Estimation New Agenda Method mb αb References rovince-based Longhurst et al. (1995) Sathyendranath et al. (1995) Melin and Hoepffner (2011) Temperature-based O Eppley (1972) Antoine and Morel (1996) Behrenfeld & Falkowski (1997) hytoplankton Functional Type Uitz et al. (2010) Nearest Neighbour O latt et al. (2008) O Saux-icart et al (2014) Light, Temperature, Nutrient model Light, Temperature, Biomass model Saux-icart et al (2014)

10 Aims and approach of the MAS project New Agenda The MAS projects strives to improve our ability to estimate phytoplankton photophysiological parameters at the global scale. The project will involve three stages of work: Stage 1: Compare existing algorithms. Stage 2: Try to improve existing algorithms. Stage 3: Innovation of new approaches. The project relies on utilising a number of datasets. Remote sensing data (OC-CCI, SST-CCI, NASA AR) In situ data (-I parameters, SST, phytoplankton pigments, nutrients, AR)

11 Methods of the MAS project A large in situ database has been compiled, with data provided by: Bedford Institute of Oceanography (BIO) Villefranche Oceanographic Laboratory (LOV) Institute of Marine Research Norway (IMRN) Marine Research Institute Iceland (MRII) University of Oxford (OXF) Instituto Nacional de Investigación y Desarrollo esquero (INIDE) lymouth Marine Laboratory (ML) Duke University (DUKE) CSIRO Tasmania (CSIRO) National Oceanography Centre Southampton (NOCS) Spanish JGOFS (FRUELA programme) This will be used in the assessment and improvement of parameter estimation algorithms.

12 Methods of the MAS project JGOFS (FRUELA)

13 Initial Results of Algorithm Comparison New Agenda Assimilation number estimates for July rovince (top-left), NN (top-right), and LTB (bottom) methods.

Initial results - Nearest Neighbour method Density plot of in-situ data (old database) Density plot of July 2004 Remote Sensing data Nearest neighbour method was developed

14 Initial results - Nearest Neighbour method Density plot of in-situ data (old database) Density plot of July 2004 Remote Sensing data Nearest neighbour method was developed for implementation in a area with a large database of -I measurements. Extrapolation to poorly-sampled areas is the root of the anomalous patterns seen in the central gyres.

15 Initial Results of Algorithm Comparison LTB model shows very high values in the central gyres during summer. rovince-based methods are based on averages so no extremes observed. But is that the only reason that we see such differing patterns? LTB July 2004 rovince-based July 2004

16 Conclusions and Further Work Accurate estimation of photophysiological parameters is an essential part of modelling phytoplankton primary production. The MAS project aims to provide the best possible method for estimating -I parameters at the global scale. Initial results have highlighted the differences between current methods. Some methods may be limited by the availability of in situ data (a tractable problem). The next step is to improve the performance of existing methods and develop new algorithms.

17 Conclusions and Further Work Thanks for listening. Any questions?

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DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Integrating Environmental Optics into Multidisciplinary, Predictive Models of Ocean Dynamics John J. Cullen Department