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:
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