Atmospheric Ar/N 2 A "New" Tracer of Oceanic and Atmospheric Circulation

1. Atmospheric Ar/N2A "New" Tracer of Oceanic and Atmospheric Circulation LDEO 11/05/03 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) Natalie Mahowald (NCAR) GRL Vol 30, #15 (2003) 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 • Dirty laundry • Conclusions and future prospects

4. My perspective on transport modeling

5. Inferring fluxes

6. But…

7. How do we assess our understanding of transport? Choose a computer model Run a tracer with known sources through the model Compare with model predictions with the real world

8. Not all tests of transport are equal • Different aspects of atmospheric transport are important for different species • Ar/N2 is a good analog for CO2

9. 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

10. 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

11. 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

12. 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

13. 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…

14. The Ar/N2 source/sink Atmosphere Ar: 1.2 O2: 26.8 N2: 100

15. The Ar/N2 source/sink Atmosphere Ar: 1.2 O2: 26.8 N2: 100 Heat Fluxes  Ar/N2

16. The Ar/N2 source/sink Atmosphere Ar: 1.2 O2: 26.8 N2: 100 Heat Fluxes  Ar/N2 Ar/N2  O2/N2 (thermal)

17. 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

18. 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: GRL paper or David Ho

19. Princeton’s custom inlet system

20. Princeton Ar/N2 cooperative flask sampling network

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

22. 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 or MATCH

23. Data-Model comparison • Overall agreement

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

25. Syowa Transport Matters (tough to get right over Ant- arctica)

26. MacQuarie Heat fluxes Matter (probably ECMWF- NCEP difference)

27. SST relaxation term in MIT OGCM

28. Cape Grim Transport and heat fluxes matter

29. Barrow Model grid-cell selection matters

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

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

32. What about that nasty scatter? • Problems with analysis • Problems with collection • Real atmospheric variability

33. What about that nasty scatter? • Problems with analysis IRMS precision ( on one aliquot = 4.0) Transfer from flask to IRMS ( = 8.6) Total analytic uncertainty ( on a single flask = 6.7) Average two flasks.

34. What about that nasty scatter? • Problems with collection Does bottle air = ambient air? From one bottle to next: Yes! ( = 2.6) From one site to next: No!

35. Improving collections New sampling hardware at Cape Grim (and elsewhere)

36. What about that nasty scatter? • Real atmospheric variability Oceanic ( = 0.6 – 1.2) Atmospheric ( = 0.8 – 2.1) Interannual vs. Synoptic

37. Interannual Variability Ocean + Atmosphere

38. In summary… • Problems with analysis Not negligible ( = 5.1 on a “collection”) • Problems with collection Big deal site-to-site New hardware helps! • Real atmospheric variability Doesn’t look too big, but… Synoptic?

39. Conclusions and the future… • Ar/N2 a promising “new” tracer • General data-model agreement • Better observations to come • Continental interior sites? • Need Ar/N2 as active tracer in OGCMs • Working on variability with MATCH

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