1. Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University Estimation of Net Community Production (NCP) Using O 2 /Ar Measurements and Satellite Observations

2. Overall objective Develop an independent estimate of global Net Community Production (NCP) 1.A large independent training dataset : O 2 /Ar-derived NCP 2.Satellite observations 3.Statistical methods:  Support Vector Regression  Genetic Programming Compare to current algorithms of export production

3. Examples of current export production algorithms Laws et al. (2000) Dunne et al. (2005 & 2007) SST NPP ef-Ratio Export production ~ NPP * Export ratio

4. Base of the mixed layer Atmosphere O 2 /Ar-derived NCP NCP ~ Δ [O 2 ]bio sat *gas exchange coefficient 1.NCP Gross Primary Production (GPP) – Community respiration Net Primary Production (NPP) – Heterotrophic respiration 2.NCP estimation O2/Ar measurements Satellite observations (e.g. NPP and SST) 3.Uncertainties in O 2 /Ar measurements See Reuer et al. 2007, Cassar et al. 2011, Jonsson et al. 2013 Photosynthesis (GPP) Auto- & hetero- trophic respiration NCP CO 2 Organic matter + O 2

5. Total O 2 /Ar Observations N = 14795 (9km) Satellite match observations N = 3874 1.SeaWiFS 1)NPP (from VGPM) 2)POC 3)Chl-a 4)phytoplankton size structure (Li et al. 2013) 5)Rrs( λ ) 6)PAR 2.Others 1)SST 2)Mixed-layer depth (Hosoda et al. 2010) Filter with Rossby Radius N = 722

6. NCP vs. satellite observations Increases with productivity and biomass: – NPP – POC – Chl-a Decreases trend with: – SST Displays nonlinearity and scatter

7. Statistical algorithms Genetic programming (Schmidt and Lipson 2009) Theory: Search for the form of equations and their coefficients Input: NPP, Chl-a, POC, SST … Output: Equations Support vector regression (Vapnik 2000) Theory: Search for a nonlinear model within an error and as flat as possible Input: NPP, Chl-a, POC, SST Output: Implicit model

8. Model validation Equation from genetic programming : Observed NCP Predicted NCP Genetic Programming Observed NCP Predicted NCP Support Vector Regression Observed NCP Predicted NCP NCP has units of (mmol O 2 m -2 day -1 )

9. Comparison A.Eppley: Eppley and Peterson (1979) B.Betzer: Betzer et al. (1984) C.Baines: Baines et al. (1994) D.Laws: Laws et al. (2000) E.Dunne: Dunne et al. (2005 & 2007) F.Westberry: Westberry et al. (2012) G.This study (GP): genetic programming H.This study (SVR): support vector regression

10. Differences between algorithms Consistent regions: – North Atlantic – North Pacific – Region around 45 o S Regions with large discrepancy: – Oligotrophic gyres – Southern Ocean – Arctic Ocean Possible reasons: – Limited observations – Different Field methods Measured properties – Uncertainties in satellite products ([Chla], NPP (VGPM), etc.) (CV: coefficient of variation)

11. Comparison with Laws et al. 2000 GP(this study)/Laws – Consistent in most regions – Our algorithm predicts higher NCP in: Southern Ocean Transitional regions GP(this study)/Laws

12. Conclusions Our method shows a relatively good agreement to other models – With a completely independent training dataset and scaling methods However: – Our algorithms predict more uniform carbon fluxes in the world’s oceans – Discrepancies are observed in some regions, such as Southern Ocean where our algorithms generally predict higher NCP Work in progress… – Develop region specific algorithms – Test consistency of the genetic programming solutions and transferability – Test with additional datasets

13. Acknowledgements All of our O 2 /Ar collaborators for providing the field observations Thank you!

14. Dissolved O 2 /Ar-based NCP O 2 /Ar measurement [O 2 ] contributed to biological process NCP

15. Base of the mixed layer NCP =  [O 2 ] sat *gas exchange coefficient NCP = Net (POC + DOC) change Atmosphere NCP=Photosynthesis-Respiration Assumptions, Limitations, Uncertainties: –No mixing across base of mixed layer –Steady-state (see Hamme et al. 2012) –Restricted to the whole mixed layer –Gas exchange parameterized in terms of windspeed Argon: Inert gas which has similar solubility properties as oxygen O 2 /Ar-based NCP measurement

16. Validation