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

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

3. Marine primary production plays an important role regulating atmospheric CO 2 Biological pump In principle ….. We can calculate global ocean Primary Production Earth observation data from satellites (Behrenfeld et al., 2001) Motivation

4. 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 Motivation

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

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

7. The main goal ASSESS the ACCURACY Remote-Sensed Chlorophyll ….This is of crucial importance in determining the MAGNITUDE at which the OCEANS ABSORB CO 2 Aim of this work

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

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

10. 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 Why in the Mediterranean Sea

11. Spectral shape R( ) 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 Rationale for a Bio-optical Algorithm 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. GLOBAL OCRS of the Mediterranean Sea: Three existing Algorithms REGIONAL algorithms improve the accuracy of satellite chlorophyll estimates (Garcia et al., 2005; Gitelson et al., 1996) REGIONAL DORMA (D’Ortenzio et al., 2002 ) R is log 10 (490/555) reflectance ratios. BRIC (Bricaud et al. 2002) R is 443/555 Reflectance ratios for Chl < 0.4 OC4v4 is used in the other cases OC4v4 (O’Reilly et al. 2000) R is log 10 of the maximum value between: 443/555 490/555 510/555

13. Mediterranean Ocean Colour CAL-VAL dataset 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 In situ chlorophyll-a data 944 chl profiles used to validate satellite chlorophyll products

14. Fluorescence Calibration Calibration performed cruise by cruise r 2 = 0.98 Clearly Log-normalMore Gaussian shape Log-transformation

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

16. Statistical Parameters for Algorithms’ Evaluation r 2 = coefficient of determination Alg = [Chl] estimated from different algorithms covariance between in situ observations and algorithm derived chlorophyll Error as function of the chlorophyll values

17. Algorithms’ Evaluation Analysis

18. Algorithms’ Evaluation

19. Need for a NEW Mediterranean Sea OC Algorithm

20. The New Mediterranean Algorithm: the MedOC4 MedOC4 R is log 10 of the maximum value between: 443/555 490/555 510/555

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

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

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

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

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

26. DORMA, BRIC and the New MedOC4 into the SeaDAS Code A Match-up dataset between concurrent SeaWiFS passes and in situ measurements was built SeaWiFS data validation 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 OC4v4 MedOC4 OC4v4 – MedOC4 0-0.1 0.2 0.01 5 5 2 July 2004 Same dynamical patterns Significant difference between the two algorithms

29. Positive difference for lower values Negative difference for higher values MedOC4 vs OC4v4: Meso-eutrophy 0-0.800.30.015 5 21 April 2004 OC4v4 MedOC4 OC4v4 – MedOC4

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

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

32. 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) Conclusions

33. 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 Future Work