1. Atmospheric Ar/N 2 A "New" Tracer of Oceanic and Atmospheric Circulation 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) LDEO 11/05/03 Funding from: NSF NOAA GCRP Ford Res. Labs NDSEGFP GRL Vol 30, #15 (2003)

2. On the agenda: What makes a good tracer Why Ar/N 2 How (and where) we measure Ar/N 2 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/N 2 is a good analog for CO 2

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/N 2 : 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/N 2 : 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/N 2 : 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/N 2 : 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/N 2 source/sink Atmosphere Ar: 1.2 O 2 : 26.8 N 2 : 100

15. The Ar/N 2 source/sink Heat Fluxes   Ar/N 2 Atmosphere Ar: 1.2 O 2 : 26.8 N 2 : 100

16. The Ar/N 2 source/sink Atmosphere Ar: 1.2 O 2 : 26.8 N 2 : 100 Heat Fluxes   Ar/N 2   O 2 /N 2 (thermal)

17. A quick word on units: Ar/N 2 changes are small  Ar/N 2 per meg  (Ar/N 2sa – Ar/N 2st )/(Ar/N 2st ) x10 6 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/N 2 cooperative flask sampling network

21. Climatology of Ar/N 2 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/N 2 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/N 2 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/N 2 a promising “new” tracer General data-model agreement Better observations to come Continental interior sites? Need Ar/N 2 as active tracer in OGCMs Working on variability with MATCH

40. Correlated variability in Ar/N 2 and O 2 /N 2