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Measurements and Models of the Atmospheric Ar/N 2 ratio

Measurements and Models of the Atmospheric Ar/N 2 ratio. 2002 Fall AGU 12/09/02. Mark Battle (Bowdoin College) Michael Bender (Princeton) Melissa B. Hendricks (Princeton) David T. Ho (Princeton/ Columbia) Robert Mika (Princeton) Galen McKinley (MIT/INE Mexico)

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Measurements and Models of the Atmospheric Ar/N 2 ratio

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  1. Measurements and Models of the Atmospheric Ar/N2 ratio 2002 Fall AGU 12/09/02 Mark Battle (Bowdoin College) Michael Bender (Princeton) Melissa B. Hendricks (Princeton) David T. Ho (Princeton/ Columbia) Robert Mika (Princeton) Galen McKinley (MIT/INE Mexico) Song-Miao Fan (Princeton) Tegan Blaine (Scripps) Ralph Keeling (Scripps) Funding from: NSF NOAA GCRP Ford Res. Labs NDSEGFP

  2. On the agenda: • What makes a good tracer • Why Ar/N2 • How (and where) we measure Ar/N2 • What we observe • Comparison with models • Conclusions and future prospects

  3. The ideal tracer(one experimentalist’s perspective) • Conservative • Known sources and sinks, globally distributed • Seasonally varying over land and ocean • Measurable with great signal to noise

  4. Ar/N2: The almost ideal tracer(one experimentalist’s perspective) • Conservative • Known sources and sinks, globally distributed • Seasonally varying over land and ocean • Measurable with great signal to noise chemically and biologically inert

  5. Ar/N2: The almost ideal tracer(one experimentalist’s perspective) • Conservative • Known sources and sinks, globally distributed • Seasonally varying over land and ocean • Measurable with great signal to noise chemically and biologically inert oceanic sources driven by heat fluxes

  6. Ar/N2: The almost ideal tracer(one experimentalist’s perspective) • Conservative • Known sources and sinks, globally distributed • Seasonally varying over land and ocean • Measurable with great signal to noise chemically and biologically inert oceanic sources driven by heat fluxes seasonal, but ocean only

  7. Ar/N2: The almost ideal tracer(one experimentalist’s perspective) • Conservative • Known sources and sinks, globally distributed • Seasonally varying over land and ocean • Measurable with great signal to noise chemically and biologically inert oceanic sources driven by heat fluxes seasonal, but ocean only well, maybe not great…

  8. The Ar/N2 source/sink Atmosphere Ar: 1 O2: 22.5 N2: 84

  9. The Ar/N2 source/sink Atmosphere Ar: 1 O2: 22.5 N2: 84 Heat Fluxes  Ar/N2

  10. The Ar/N2 source/sink Atmosphere Ar: 1 O2: 22.5 N2: 84 Heat Fluxes  Ar/N2 Ar/N2  O2/N2 (thermal)

  11. A quick word on units: Ar/N2 changes are small Ar/N2 per meg  (Ar/N2sa – Ar/N2st)/(Ar/N2st) x106 1 per meg = 0.001 per mil

  12. Our measurement technique: • Paired 2-l glass flasks • IRMS (Finnigan Delta+XL) 40/28 and 32/28 • Custom dual-inlet system • Standards: High pressure Al cylinder For more details: Sunday afternoon poster Ho et al. GC72B-0230

  13. Princeton Ar/N2 cooperative flask sampling network

  14. Climatology of Ar/N2 seasonal cycle • Monthly average • values shown • Multiple years (~3) stacked

  15. Testing models with observations Observed & modeled heat fluxes  Solubility equations  Atmospheric transport model  Predicted Ar/N2 ECMWF or MIT OGCM (NCEP/COADS) TM2 or GCTM

  16. Data-Model comparison • Overall agreement

  17. Data-Model comparison • Overall agreement • Phase problems

  18. Syowa Transport matters

  19. MacQuarie Heat fluxes matter

  20. Cape Grim Transport and heat fluxes matter

  21. Data-Model comparison • Overall agreement • Phase problems • SYO: Transport matters • MAC: Heat fluxes matter • CGT: Both terms matter

  22. Conclusions and the future… • Ar/N2 a promising “new” tracer • General data-model agreement • Better observations to come • Need Ar/N2 as active tracer in OGCMs • Ready for Ar/N2 in more atmospheric models

  23. Odds and Ends • Interannual variability in the seasonal cycle (perhaps primarily atmospheric) • Secular trend: Tiny (~0.2 per meg/yr) • Size of O2/N2 thermal cycle: 13-34% of total • Intersite gradients: A problem

  24. Uncertainties • All fitting techniques equivalent • Std error on monthly avg. shown in plots • Std error reflects: • Limited IRMS precision (4.0) • Fractionation during transfer from flask to IRMS (8.6) • Uncorrelated fractionation of flasks during collection (2.6) • Correlated fractionation of flasks during collection (?) • Real variability within month (?)

  25. Correlated variability in Ar/N2 and O2/N2

  26. Improving collection protocols

  27. SST relaxation term in MIT OGCM

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