Atmospheric Aerosol Measurements at the Pierre Auger Observatory
Measurements and Models of the Atmospheric Ar/N 2 ratio
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Transcript of Measurements and Models of the Atmospheric Ar/N 2 ratio
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Measurements and Models of the Atmospheric Ar/N2 ratio
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)
2002 Fall AGU
12/09/02
Funding from:NSF
NOAA GCRPFord Res. Labs
NDSEGFP
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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
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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
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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
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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
![Page 6: Measurements and Models of the Atmospheric Ar/N 2 ratio](https://reader033.fdocuments.us/reader033/viewer/2022052702/56814759550346895db498aa/html5/thumbnails/6.jpg)
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
![Page 7: Measurements and Models of the Atmospheric Ar/N 2 ratio](https://reader033.fdocuments.us/reader033/viewer/2022052702/56814759550346895db498aa/html5/thumbnails/7.jpg)
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…
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The Ar/N2 source/sink
Atmosphere
Ar: 1O2: 22.5N2: 84
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The Ar/N2 source/sink
Atmosphere
Ar: 1O2: 22.5N2: 84
Heat Fluxes
Ar/N2
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The Ar/N2 source/sink
Atmosphere
Ar: 1O2: 22.5N2: 84
Heat Fluxes
Ar/N2
Ar/N2
O2/N2
(thermal)
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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
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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
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Princeton Ar/N2 cooperative flask sampling network
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Climatology ofAr/N2 seasonal
cycle
Monthly average
values shown
Multiple years (~3) stacked
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Testing models with observations
Observed & modeled heat fluxes
Solubility equations
Atmospheric transport
model
Predicted Ar/N2
ECMWFor
MIT OGCM (NCEP/COADS)
TM2or
GCTM
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Data-Model comparison
•Overall agreement
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Data-Model comparison
•Overall agreement
•Phase problems
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Syowa
Transportmatters
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MacQuarie
Heat fluxesmatter
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Cape Grim
Transportand
heat fluxesmatter
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Data-Model comparison
•Overall agreement
•Phase problems
•SYO: Transport matters
•MAC: Heat fluxes matter
•CGT: Both terms matter
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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
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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
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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 (?)
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Correlated variability in Ar/N2 and O2/N2
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Improving collection protocols
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SST relaxation term in MIT OGCM