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Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC)

Potential Improvements to Primary Productivity Estimates through Subsurface Chlorophyll and Light Measurement. Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC) Mati Kahru , Daniel Rudnick (UCSD). 45 th International Liége Colloquium

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Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC)

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  1. Potential Improvements to Primary Productivity Estimates through Subsurface Chlorophyll and Light Measurement Michael Jacox University of California, Santa Cruz Raphael Kudela, Christopher Edwards (UCSC) MatiKahru, Daniel Rudnick (UCSD) 45th International Liége Colloquium May 17, 2013

  2. Study Data: Primary Productivity and Ancillary Measurements Shipboard: 1985-2011 CalCOFIPrimary Productivity Casts ~100 cruises >1500 stations Satellite: Data starting in 1997 SeaWiFS chlorophyll SeaWiFS/MODIS PAR AVHRR Pathfinder SST Match-ups for 723 CalCOFI stations Autonomous Profiling: Data starting in 2005 Scripps Spray gliders CTD, Fluorescence Regular coverage of lines 80 and 90

  3. The Roots of Satellite PP Models Globally: SC Bight: 22 cruises, ~270 stations (1974-1983) Correlated NPP with surface environmental variables Most of the variability explained is due to variability in surface chlorophyll Some explained by temperature and day length, which may reflect seasonality Shortcoming: All information on vertical structure is lost

  4. 28 Years Later, The Simplest Model is Often Among the Best Friedrichs et al. 2009 Saba et al. 2011

  5. PP Model Performance for the CalCOFI dataset VGPM ESQRT log (modeledproductivity) (mg C m-2 d-1) r2=0.55 Bias=0.11 RMSD=0.25 r2=0.64 Bias=0.20 RMSD=0.28 MARRA VGPM-KI r2=0.62 Bias=0.13 RMSD=0.25 r2=0.64 Bias=0.08 RMSD=0.24 ESQRT: Eppley square root model (Eppley et al. 1985) VGPM: Vertically Generalized Production Model (Behrenfeld and Falkowski 1997) VGPM-KI: VGPM variant with two phytoplankton size classes (Kameda and Ishizaka 2005) MARRA: Vertically resolved model based on chl-specific absorption (Marra et al. 2003) log (in situ productivity) (mg C m-2 d-1)

  6. Goal: Create an Improved PP Model for the Southern CCS r2=0.33 r2=0.00 r2=0.21 r2=0.12

  7. Goal: Create an Improved PP Model for the Southern CCS

  8. Start with VGPM: Goal: Create an Improved PP Model for the Southern CCS Model Statistics for 2005-2010 Behrenfeld and Falkowski 1997

  9. Revised Goal: Understand What Limits Model Performance Fall 2002 Surface chlorophyll poorly correlated with chl at depth Summer 2000 Surface chlorophyll well correlated with chl at depth Depth (m) Depth (m) log(chlorophyll) (mg m-3) log(chlorophyll) (mg m-3)

  10. Revised Goal: Understand What Limits Model Performance r2 (NPPMODEL, NPPIN SITU) r2 (NPPMODEL, NPPIN SITU) Model performance is strongly dependent on chl0 being representative of NPP …but not on accurate estimation of the photosynthetic parameter r2 (chl0, NPPIN SITU) r2 (PBOPT,MODEL, PBOPT,CALC) N = 14 years, 56 quarterly cruises

  11. Performance of a Simple Vertically Resolved Production Model r2 (NPPMODEL, NPPIN SITU) In situ surface chlorophyll SeaWiFS chlorophyll log (modeledproductivity) (mg C m-2 d-1) r2=0.59 Bias=0.02 RMSD=0.21 r2=0.64 Bias=0.03 RMSD=0.20 r2 (chl0, NPPINSITU) In situ chlorophyll and light profiles In situ chlorophyll profile r2=0.74 Bias=0.02 RMSD=0.17 r2=0.81 Bias=0.02 RMSD=0.14 log (in situ productivity) (mg C m-2 d-1) Jacox et al., submitted

  12. Several CalCOFI Lines are Regularly Sampled by Spray Gliders Pt. Arena Monterey Bay

  13. Los Angeles Jul 2007 San Diego Jan 2009 Correct profile amplitude based on surface chlorophyll Lavigne et al. (2012) Converting Glider Fluorescence to Chlorophyll Depth Depth Chlorophyll (mg m-3) 3*fluorescence

  14. Gliders Fluorescence Improves Productivity Estimates Potential for satellite alone r2 Potential for satellite/glider with fluorescence Potential for satellite/glider with fluorescence and PAR Data forCalCOFI/glider match-ups within 10km and 10 days (N=39)

  15. Conclusions Satellite model performance in the SCCS is largely determined by correlations between surface chlorophyll and NPP Knowledge of in situ vertical chlorophyll and light profiles raises model performance well above the variability between existing models The combination of surface satellite data and subsurface profiler data is a powerful one and a growing database of autonomous profiler data can now be used to refine PP estimates “In view of these prospects and challenges we urge our colleagues to examine their own data on primary production and chlorophyll. There is much yet to be done.” -Eppley et al. 1985, J. Plankton Res.

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