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Reenvisioning the Ocean: The View from Space A RESPONSE

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Presentation on theme: "Reenvisioning the Ocean: The View from Space A RESPONSE"— Presentation transcript:

1 Reenvisioning the Ocean: The View from Space A RESPONSE
Dave Siegel University of California, Santa Barbara

2 C Carbon

3 Remote Sensing Carbon Stocks Components Fluxes
pCO2, DIC, DOC, POC & PIC Components Community structure, calcifiers, N2 fixers, etc. Fluxes Air-sea, export, net community production, etc.

4 Remote Sensing Carbon Ocean color is not perfect for this task
Optical properties are not carbon species CDOM is not DOC, optical backscattering is not POC Need to think like paleo-oceanographers… Develop “quantitative proxies” But … test them using real observations

5 Some Examples… Stocks Components Fluxes POC pCO2
CDOM (This is a shameless plug for our CDOM poster!!) Components Calcifiers - coccolithophorid bloom occurrence Fluxes Air-sea CO2 fluxes

6 Remote Sensing of POC April 1998 Relate POC to estimates of optical backscattering by particle Loisel et al. [2002] GRL

7 Remote Sensing of POC SeaWiFS chlorophyll concentration SeaWiFS POC
April 1998 SeaWiFS chlorophyll concentration SeaWiFS POC

8 Remote Sensing of POC Average for the North Atlantic (0 to 60oN) “POC”
Chlorophyll Average for the North Atlantic (0 to 60oN)

9 Remote Sensing of POC POC patterns are very different from chlorophyll
Validation at BATS & HOT is OK, but not great Ratios of satellite to field POC are 1.22 (± BATS & 0.94 HOT Enables POC budgets to be assessed Mean POC = 54 mg C m-3 Large uncertainty in conversion from optics to POC Estimated to be ~40% - Improvements are underway

10 Remote Sensing of Calcifiers
Coccolithophorid bloom classification Iglesias-Rodríguez et al. [2002] GBC

11 Remotely Sensing of Calcifiers
Classification analysis based on observations of coccolithophorid blooms Enables space/time characteristics of coccolithophorid blooms to be assessed First (& probably easiest) step towards determining phytoplankton community structure remotely

12 Air-Sea CO2 Fluxes Relate observations of pCO2 to SST
Satellite SST to map pCO2 Highly variable relationship Lee et al. [1998] Nature

13 Air-Sea CO2 Fluxes Incorporating winds enables air-sea CO2 fluxes to be estimated

14 Air-Sea CO2 Fluxes Used regional relationships for pCO2 as f(SST)
Drive with remote estimates of wind & SST Approach has promise, but we need a better way to predict pCO2 What if climate change, alters pCO2 =f(SST)??

15 C Carbon

16 Remote Sensing Carbon Ocean color is not easily related to carbon
Chlorophyll is not carbon Key is using “real” observations to build simple models Global data are finally available – more in future The JGOFS legacy is its open data access We are really just at the beginning of this work

17 The Future is Remotely Sensible
There are many applications under consideration Primary production & export fluxes Photochemical rxn rates (CO, CO2, COS, etc.) DMSP/DMS cycling & air-sea DMS fluxes Trichodesmium distributions Physiological status from fluorescence and many more …

18 Thank you!!

19 Remote Sensing of DOC Colored Detrital & Dissolved Organic Material Absorption Siegel et al. [2002] JGR

20 Remote Sensing of DOC NH Winter DOC Distribution
Siegel et al. [2002] JGR

21 Remote Sensing of Trichodesmium
Index for Trichodesmium occurrence Toby Westberry [work in progress]

22 Remote Sensing of DIC & pCO2
Empirical approach for the Tropical Pacific Model … DIC = f(SST,SSS) TA = f(SST,SSS) Loukos et al [2000] GRL Fall ‘92 Spring ’92 DIC pCO2

23 Remote Sensing of DIC & pCO2
DpCO GasEx Flux Remote Sensing of DIC & pCO2 Average from 5oS to 10oN 1982 to 1994 SST, SSS & wind products used

24 Remote Sensing of DIC & pCO2
SOI Anomalous evasion is well related to SOI

25 Remote Sensing of DIC & pCO2
Empirically model DIC & TA for Tropical Pacific Drive this with remote sensing (& other) data Find relationship between CO2 evasion & SOI BUT, evasion flux uncertainties are ~50% More field observations should help So would the remote sensing of sea surface salinity

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