zuchuan li, nicolas cassar division of earth and ocean sciences

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

CO2 Organic 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 windspeedArgon: Inert gas which has similar solubility properties as oxygen

O2/Ar-based NCP measurement

Validation• Genetic programming

– A: – B: – C: