Presentation is loading. Please wait.

Presentation is loading. Please wait.

Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University Estimation of Net Community Production.

Similar presentations


Presentation on theme: "Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University Estimation of Net Community Production."— Presentation transcript:

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 Photosynthesis (GPP) Auto- & hetero- trophic respiration NCP CO 2 Organic matter + O 2

5 Total O 2 /Ar Observations N = (9km) Satellite match observations N = 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 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


Download ppt "Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University Estimation of Net Community Production."

Similar presentations


Ads by Google