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Motivations & Aims Ocean Colour Principle Development of a NEW MED algorithm

This research outlines the development of a new chlorophyll algorithm for the Mediterranean Sea region using satellite and in-situ data to evaluate primary productivity sensitivity. The aim is to assess the accuracy of remote-sensed chlorophyll in determining CO2 absorption by oceans. Factors like atmospheric correction and phytoplankton distribution influence chlorophyll retrieval. Different algorithms are evaluated to optimize primary production calculation. The newly proposed MedOC4 algorithm improves regional chlorophyll estimation accuracy.

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Motivations & Aims Ocean Colour Principle Development of a NEW MED algorithm

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  1. Definition and assessment of a regional Mediterranean Sea ocean colour algorithm for surface chlorophyll Gianluca VolpeNational Oceanography Centre, Southampton School of Ocean and Earth Science MPhil/PhD Transfer

  2. Outline • Motivations & Aims • Ocean Colour Principle • Development of a NEW MED algorithm • Regional vs Global datasets • SeaWiFS data Validation • Conclusions & Future work

  3. Motivation Marine primary production plays an important role regulatingatmospheric CO2 Biological pump In principle ….. We can calculate global ocean Primary Production Earth observation data from satellites (Behrenfeld et al., 2001)

  4. Motivation Gregg et al. (2003) blending CZCS & SeaWiFS data with in situ data primary production declined by 6% since 1980s Antoine et al. (2005) algorithm refinements chlorophyll concentration increased by 22% since 1980s

  5. Motivation DISCREPANCIES Different methodologies Related assumptions Highlight NEED ASSESS and QUANTIFY large UNCERTAINTIES satellite retrieved chlorophyll

  6. Aim of the PhD Evaluate SENSITIVITY Primary Productivity Calculation with respect to: • Quality of the input data • Primary production models • Integration length and time scales

  7. Aim of this work The main goal ASSESS the ACCURACY Remote-Sensed Chlorophyll ….This is of crucial importance in determining the MAGNITUDE at which the OCEANS ABSORB CO2

  8. Factors influencing the chlorophyll retrieval Atmospheric Correction Aerosol space-time variability Optical properties of seawater Phytoplankton species composition Vertical distribution CDOM

  9. PhD Conceptual Scheme Chlorophyll a & Fluorescence DATA Satellite data In situ Optical Measurements Fls calibration & Data quality Assess Atmospheric Correction error budget CHLOROPHYLL Compute OWP (chl seen from surface) Run best Chl algorithm over satellite data Algorithms’ Evaluation Run different PP models Are they significantly different? yes Select best algorithm PRIMARY PRODUCTION In situ PP data no Assess PP changes and their significance Compute ERROR budget Sensitivity analysis on input parameters Done Still to be done

  10. Why in the Mediterranean Sea Laboratory basin (Lacombe et al., 1981; Robinson & Golnaraghi, 1995) Processes controlling the global ocean general circulation in reduced temporal and spatial scales. … Large amount of data

  11. Rationale for a Bio-optical Algorithm • Spectral shape R(l) defines the so called “Ocean Colour” • OC is indexed by the Blue-to-Green reflectance ratio In case 1 waters is essentially due to phytoplankton chlorophyll content • B/G decreases with increasing pigment concentration Ocean colour algorithms relate surface chlorophyll concentration to B/G (O'Reilly et al., 1998; O'Reilly et al., 2000; Morel and Maritorena, 2001)

  12. OCRS of the Mediterranean Sea: Three existing Algorithms REGIONAL DORMA (D’Ortenzio et al., 2002 ) R is log10 (490/555) reflectance ratios. OC4v4 (O’Reilly et al. 2000) R is log10 of the maximum value between: 443/555490/555510/555 BRIC(Bricaud et al. 2002) R is 443/555 Reflectance ratios for Chl < 0.4 OC4v4 is used in the other cases REGIONAL algorithms improve the accuracy of satellite chlorophyll estimates (Garcia et al., 2005; Gitelson et al., 1996) GLOBAL

  13. Mediterranean Ocean Colour CAL-VAL dataset In situ chlorophyll-a data 944 chl profiles used to validate satellite chlorophyll products Bio-optical measurements 137 chl/opt measurements used to define the Mediterranean regional algorithm 10 Mediterranean cruises (1998-2003 ): Organized by GOS in the framework of Italian National Projects

  14. Fluorescence Calibration r2 = 0.98 More Gaussian shape Clearly Log-normal • Calibration performed cruise by cruise • Log-transformation

  15. Optical Weighted Pigment Concentration (OWP) • Chl = in situ chlorophyll concentration • Zpd = 1/k • k = attenuation coefficient of downwelling PAR irradiance Calibrated Fluorescence OWP “an accurate representation of the pigment concentration measured by a remote sensor viewing a stratified ocean” (Clark, 1997)

  16. Statistical Parameters for Algorithms’ Evaluation Alg = [Chl] estimated from different algorithms Error as function of the chlorophyll values covariance between in situ observations and algorithm derived chlorophyll r2 = coefficient of determination

  17. Algorithms’ Evaluation Analysis

  18. Algorithms’ Evaluation

  19. Algorithms’ Evaluation Need for a NEW Mediterranean Sea OC Algorithm

  20. The New Mediterranean Algorithm: the MedOC4 MedOC4 R is log10 of the maximum value between: 443/555490/555510/555

  21. The good, the ugly, the bad and the MedOC4

  22. Regional VS Global Datasets Mediterranean Maximum Band Ratio Maximum Band Ratio Global Ocean Chlorophyll Chlorophyll Max Ratio choice similar for MED and Global Low Chlorophyll: Med Band Ratio < Global Band Ratio Is the Mediterranean Greener or less Blue than the Global Ocean?

  23. Is the Mediterranean Greener and/or less Blue than the Global Ocean? Global Regional Global Regional 0.01 < Chl < 0.1 Global 30% BLUER than MED MED 15% GREENER than Global

  24. Is the Mediterranean Greener and/or less Blue than the Global Ocean? Global Regional Global Regional 0.1 < CHL < 0.3 Global 12% BLUER than MED MED 10% GREENER thanGlobal

  25. Is the Mediterranean Greener and/or less Blue than the Global Ocean? Global Regional CHL > 0.3 Global Regional Global and MED tend to overlap

  26. SeaWiFS data validation • DORMA, BRIC and the New MedOC4 into the SeaDAS Code • A Match-up dataset between concurrent SeaWiFS passes and in situ measurements was built Matchup criteria Clouds 944 profiles 290 data points

  27. SeaWiFS data validation • does not show significant changes from the one calculated using in situ data • MedOC4 is the most stable algorithm performing better than the other in all the chlorophyll ranges

  28. MedOC4 vs OC4v4: Oligotrophy 2 July 2004 5 5 0.2 0.01 0.01 -0.1 0 OC4v4 – MedOC4 OC4v4 MedOC4 Same dynamical patterns Significant difference between the two algorithms

  29. MedOC4 vs OC4v4: Meso-eutrophy OC4v4 – MedOC4 OC4v4 MedOC4 21 April 2004 0.01 5 0.01 5 -0.80 0 0.3 Positive difference for lower values Negative difference for higher values

  30. Impact on Primary Productivity Morel’s Primary Production Model Plots by courtesy of Simone Colella

  31. Impact on Primary Productivity Colella’s Primary Production Model Plots by courtesy of Simone Colella

  32. Conclusions • Mediterranean Sea DIFFERENT bio-optical properties as compared to the global ocean • Large Errors associated with Global algorithm • Need of a NEW Regional algorithm • MedOC4 IMPROVES chlorophyll retrieval (17 % error vs 110 % OC4v4)

  33. Future Work • Estimate ERROR Budget for MedOC4 evaluation and implementation • Assess the atmospheric correction impact • Understand WHY MED bio-optical properties are so DIFFERENT as compared to the GLOBAL ocean ones • Identify most suitable PP model for Mediterranean basin

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