Zuchuan Li, Nicolas Cassar

Division of Earth and Ocean SciencesNicholas School of the Environment

Duke University

Estimation of Net Community Production (NCP) Using O2/Ar Measurements and Satellite

Observations

Overall objective

• Develop an independent estimate of global Net Community Production (NCP)

1. A large independent training dataset : O2/Ar-derived NCP

2. Satellite observations

3. Statistical methods:

Support Vector Regression

Genetic Programming

• Compare to current algorithms of export production

Examples of current export production algorithms

• Laws et al. (2000)

• Dunne et al. (2005 & 2007)

0.04 < pe-ratio < 0.72

SSTNPP

ef-Ratio

Export production ~ NPP * Export ratio

Base of the mixed layer

Atmosphere

O2/Ar-derived NCP

NCP ~ Δ[O2]biosat*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 O2/Ar measurements

• See Reuer et al. 2007, Cassar et al. 2011, Jonsson et al.

2013

Photosynthesis (GPP)

Auto- & hetero- trophic respirationNCP

CO2Organic matter + O2

Total O2/Ar ObservationsN = 14795

(9km)

Satellite match observations

N = 3874

1. SeaWiFS1) NPP (from VGPM)2) POC3) Chl-a4) phytoplankton size

structure (Li et al. 2013)

5) Rrs(λ)6) PAR

2. Others1) SST2) Mixed-layer depth

(Hosoda et al. 2010)

Filter with Rossby Radius

N = 722

NCP vs. satellite observations• Increases with

productivity and biomass:– NPP– POC– Chl-a

• Decreases trend with:– SST

• Displays nonlinearity and scatter

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

Model validation• Equation from genetic

programming:

Observed NCP

Pred

icte

d N

CP

𝑁𝐶𝑃=𝑁𝑃𝑃

12.6+1.5∗𝑆𝑆𝑇Genetic Programming

Observed NCP

Pred

icte

d N

CP

Support Vector Regression

Observed NCP

Pred

icte

d N

CP

NCP has units of (mmol O2 m-2 day-1)

ComparisonA. 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 programmingH. This study (SVR): support vector

regression

Differences between algorithms• Consistent regions:

– North Atlantic– North Pacific– Region around 45o 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)

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

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

Acknowledgements

• All of our O2/Ar collaborators for providing the field observations

Thank you!

Dissolved O2/Ar-based NCP

• O2/Ar measurement

• [O2] contributed to biological process

• NCP

Base of the mixed layer

NCP = D[O2]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

O2/Ar-based NCP measurement

Validation• Genetic programming

– A: – B: – C: