MIT model and an atmospheric inversion

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MIT model and an atmospheric inversion

• Galen McKinley
• Program in Atmospheric and Oceanic Sciences
• Princeton University
• Department of Atmospheric and Oceanic Sciences
• University of Wisconsin - Madison

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Mean CO2 flux
Takahashi et al. 1999
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Global CO2 flux variability

• Flux variability
• 0.5 PgC/yr
• Equatorial Pacific (blue) is the dominant region
• Neither the Atlantic (red) or Indian (green) have
  significant global impact

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Equatorial Pacific

• EOF1 explains 18 of the year-to-year variability
  
• Only 15S-15N, EOF1 explains 50

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Regional comparison to Bouquet et al.
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Inversion Methodology of Rödenbeck et al. 2003

• Estimate fluxes at each grid point
• Add term to cost function that penalizes
  heterogeneity
• A softer constraint on spatial coherence of
  fluxes
• Comparison to model?

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Regional comparison to Rodenbeck et al. 2003
Model - black Inversion - colors
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Global and Pacific Comparison
Inversions without Seychelles
Model
Global (solid), Pacific (dash)

• Model Global and Inversion Global correlate at
  0.58 for 1987-1998
• For both model and inversion, Global correlates
  with Pacific at 0.9

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Conclusions

• Mechanisms
• ENSO drives global CO2 flux variability
• Inversions
• Requring spatial coherence over large regions may
  enhance error at high latitudes
• First signs of Model - Inversion agreement
• Pacific drives global air-sea CO2 flux
  variability
• Magnitude of variability 0.5 to 0.7 PgC/yr

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MITgcm offline biogeochemical model

• Physical fields from JPL ECCO group
• Model forced with 12-hourly winds, heat flux
• Fields saved 1980-1998 at 10 day resolution
• Space
• 1? longitude x 1/3?-1? latitude
• 47 vertical levels
• Gent-McWilliams, KPP vertical mixing

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Biological Cycling
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At surface DpCO2 and winds
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Dramatic air-sea flux change
Normal
El Nino
Chavez et al. 1999
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Equatorial Pacific annual anomaly (mol/m2/yr)
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North Atlantic annual anomaly (mol/m2/yr)
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CO2 fluxes at BATS and HOT