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Reenvisioning the Ocean: The View from Space
A RESPONSE
Dave SiegelUniversity of California, Santa
Barbara
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6 12.011
CCarbon
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Remote Sensing Carbon
• Stocks
– pCO2, DIC, DOC, POC & PIC
• Components
– Community structure, calcifiers, N2 fixers, etc.
• Fluxes
– Air-sea, export, net community production, etc.
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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
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Some Examples…
• Stocks
– POC
– pCO2
– CDOM (This is a shameless plug for our CDOM poster!!)
• Components
– Calcifiers - coccolithophorid bloom occurrence
• Fluxes
– Air-sea CO2 fluxes
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Remote Sensing of POC
Relate POC to estimates of optical backscattering by particle
Loisel et al. [2002] GRL
April 1998
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Remote Sensing of POC
SeaWiFS chlorophyll
concentration
April 1998
SeaWiFS POC
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Remote Sensing of POC
Average for the North Atlantic (0 to 60oN)
“POC” Chlorophyll
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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 (± 0.37) @ BATS & 0.94
(±0.27) @ 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
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Remote Sensing of Calcifiers
Coccolithophorid bloom classification
Iglesias-Rodríguez et al. [2002] GBC
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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
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Air-Sea CO2 Fluxes
• Relate observations of
pCO2 to SST
• Satellite SST to map
pCO2
• Highly variable relationship
Lee et al. [1998] Nature
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Air-Sea CO2 Fluxes
Incorporating winds enables air-sea CO2 fluxes to be estimated
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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)??
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6 12.011
CCarbon
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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
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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 …
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Thank you!!
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Remote Sensing of DOC
Colored Detrital & Dissolved Organic Material Absorption
Siegel et al. [2002] JGR
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Remote Sensing of DOC
NH Winter DOC Distribution
Siegel et al. [2002] JGR
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Remote Sensing of Trichodesmium
Index for Trichodesmium occurrence
Toby Westberry [work in progress]
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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
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Remote Sensing of DIC
& pCO2
• Average from 5oS to 10oN
• 1982 to 1994
• SST, SSS & wind products used
pCO2 GasEx
Flux
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Remote Sensing of DIC & pCO2
Anomalous evasion is well related to
SOI
SOI
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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