1 / 16

Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences

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

regina
Télécharger la présentation

Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Estimation of Net Community Production (NCP) Using O2/Ar Measurements and Satellite Observations Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University

  2. Overall objective • Develop an independent estimate of global Net Community Production (NCP) • A large independent training dataset : O2/Ar-derived NCP • Satellite observations • 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) ef-Ratio NPP SST Export production ~ NPP * Export ratio 0.04 < pe-ratio < 0.72

  4. O2/Ar-derived NCP Atmosphere NCP ~ Δ[O2]biosat*gas exchange coefficient Photosynthesis (GPP) Organic matter + O2 CO2 Auto- & hetero- trophic respiration NCP Base of the mixed layer • NCP • Gross Primary Production (GPP) – Community respiration • Net Primary Production (NPP) – Heterotrophic respiration • NCP estimation • O2/Ar measurements • Satellite observations (e.g. NPP and SST) • Uncertainties in O2/Armeasurements • See Reuer et al. 2007, Cassar et al. 2011, Jonsson et al. 2013

  5. Total O2/Ar Observations N = 14795 (9km) Satellite match observations N = 3874 Filter with Rossby Radius N = 722 • SeaWiFS • NPP (from VGPM) • POC • Chl-a • phytoplankton size structure (Li et al. 2013) • Rrs(λ) • PAR • Others • SST • Mixed-layer depth (Hosoda et al. 2010)

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

  7. Statistical algorithms Genetic programming (Schmidt and Lipson 2009) 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 • Theory: Search for the form of equations and their coefficients • Input: NPP, Chl-a, POC, SST … • Output: Equations

  8. Model validation • Equation from genetic programming: Genetic Programming Support Vector Regression Predicted NCP Predicted NCP Predicted NCP Observed NCP Observed NCP Observed NCP NCP has units of (mmol O2 m-2 day-1)

  9. Comparison • Eppley: Eppley and Peterson (1979) • Betzer: Betzer et al. (1984) • Baines: Baines et al. (1994) • Laws: Laws et al. (2000) • Dunne: Dunne et al. (2005 & 2007) • Westberry: Westberry et al. (2012) • This study (GP): genetic programming • This study (SVR): support vector regression

  10. 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)

  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 O2/Ar collaborators for providing the field observations Thank you!

  14. Dissolved O2/Ar-based NCP • O2/Ar measurement • [O2] contributed to biological process • NCP

  15. O2/Ar-based NCP measurement Atmosphere NCP = D[O2]sat*gas exchange coefficient NCP = Net (POC + DOC) change NCP=Photosynthesis-Respiration Base of the mixed layer • 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

  16. Validation • Genetic programming • A: • B: • C:

More Related