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

Atmospheric Ar/N2  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)

LDEO11/05/03

Funding from:NSF

NOAA GCRPFord Res. Labs

NDSEGFP

GRL Vol 30, #15 (2003)

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

My perspective on transport modeling

Inferring fluxes

But…

How do we assess our understanding of transport?

Choose a computer model

Run a tracer with known sources through the model

Compare with model predictionswith the real world

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

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

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

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

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

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…

The Ar/N2 source/sink

Atmosphere

Ar: 1.2O2: 26.8N2: 100

The Ar/N2 source/sink

Heat Fluxes

Ar/N2

Atmosphere

Ar: 1.2O2: 26.8N2: 100

The Ar/N2 source/sink

Atmosphere

Ar: 1.2O2: 26.8N2: 100

Heat Fluxes

Ar/N2

Ar/N2

O2/N2

(thermal)

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

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

Princeton’s custom inlet system

Princeton Ar/N2 cooperative flask sampling network

Climatology ofAr/N2 seasonal

cycle

Monthly average

values shown

Multiple years (~3) stacked

Testing models with observations

Observed & modeled heat fluxes

Solubility equations

Atmospheric transport

model

Predicted Ar/N2

ECMWFor

MIT OGCM (NCEP/COADS)

TM2or

GCTMor

MATCH

Data-Model comparison

•Overall agreement

Data-Model comparison

•Overall agreement

•Phase problems

Syowa

TransportMatters

(tough to get rightover Ant-arctica)

MacQuarie

Heat fluxesMatter

(probably ECMWF-NCEP

difference)

SST relaxation term in MIT OGCM

Cape Grim

Transportand

heat fluxesmatter

Barrow

Modelgrid-cellselectionmatters

Data-Model comparison

•Overall agreement

•Phase problems

•SYO: Transport matters

•MAC: Heat fluxes matter

•CGT: Both terms matter

•BRW: Gridsize matters

Climatology ofAr/N2 seasonal

cycle

Monthly average

values shown

Multiple years (~3) stacked

What about that nasty scatter?

• Problems with analysis

• Problems with collection

• Real atmospheric variability

What about that nasty scatter?

• Problems with analysisIRMS 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.

What about that nasty scatter?

• Problems with collectionDoes bottle air = ambient air?From one bottle to next: Yes! ( = 2.6)From one site to next: No!

Improving collections

New samplinghardware at Cape

Grim

(and elsewhere)

What about that nasty scatter?

• Real atmospheric variabilityOceanic ( = 0.6 – 1.2)

Atmospheric ( = 0.8 – 2.1)

Interannual vs. Synoptic

InterannualVariability

Ocean+

Atmosphere

In summary…

• Problems with analysisNot negligible ( = 5.1 on a “collection”)

• Problems with collectionBig deal site-to-siteNew hardware helps!

• Real atmospheric variabilityDoesn’t look too big, but…Synoptic?

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

Correlated variability in Ar/N2 and O2/N2