Development of an optimal calibration strategy for trace gas measurements Mark Battle (Bowdoin College) Zane Davis, Ryan Hart, Jayme Woogerd, Jacob Scheckman,

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Presentation transcript:

Development of an optimal calibration strategy for trace gas measurements Mark Battle (Bowdoin College) Zane Davis, Ryan Hart, Jayme Woogerd, Jacob Scheckman, Eric Sofen, Becca Perry, John Carpenter. Special thanks: Britt Stephens (NCAR), Ralph Keeling (SIO), Bill Munger (Harvard) Mary Lou Zeeman (Cornell/Bowdoin) CompSust09 June 11, 2009 Funding from: DOE, Bowdoin College

Outline Structure of a measurement program What measurements might tell us Example of one such program Call for help

Measuring the composition of air Precision vs. Accuracy

Measuring the composition of air Precision vs. Accuracy Differential measurements

Benefits of differential measurements Initial Group 1001 Women Final group 1002 Women

Benefits of differential measurements Initial Group 1001 Women Final group 1002 Women Absolute changes Initial # women: 1001 Final # women: 1002 Change in women: 0.1%

Benefits of differential measurements Initial Group 999 Men 1001 Women Final group 999 Men 1002 Women

Benefits of differential measurements Initial Group 999 Men 1001 Women Final group 999 Men 1002 Women Differential changes Initial gender diff: 2 Final gender diff: 3 Change in gender diff: 33%

Benefits of differential measurements Initial Group 999 Men 1001 Women Final group 999 Men 1002 Women Absolute changes Initial # women: 1001 Final # women: 1002 Change in women: 0.1% Differential changes Initial gender diff: 2 Final gender diff: 3 Change in gender diff: 33%

Measuring the composition of air Precision vs. Accuracy Differential measurements Measure samples relative to “standards”

Challenges of differential measurements

Measuring the composition of air Precision vs. Accuracy Differential measurements Measure samples relative to “standards” Instrumental response

Impact of instrumental non-linearity

Metric Precision & Accuracy Constraints Instrument time is precious Standard air is precious

In summary: Optimally combine many analyses of many standards to create a virtual standard against which all samples are measured.

Connecting to the real world: Measuring O 2 and CO 2 to constrain the carbon cycle

Where does anthropogenic CO 2 end up? Values for Canadell et al. PNAS 2007

How do we know these numbers?

Record CO 2 emissions Measure CO 2 in the atmosphere

How do we know these numbers? Record CO 2 emissions Measure CO 2 in the atmosphere Measure CO 2 in the oceans Estimate from small-scale land measurements Infer from spatial pattern and isotopes of atmospheric CO 2

How do we know these numbers? Record CO 2 emissions Measure CO 2 in the atmosphere Measure CO 2 in the oceans Estimate from small-scale land measurements Infer from spatial pattern and isotopes of atmospheric CO 2 Measure atmospheric O 2

The link between O 2 and CO 2  CO 2 = Land biota + Industry + Ocean  O 2 = Land biota + Industry

The link between O 2 and CO 2  CO 2 = Land biota + Industry + Ocean  O 2 = Land biota + Industry

The link between O 2 and CO 2  CO 2 = Land biota + Industry + Ocean  O 2 = Land biota + Industry

Google maps

The equipment

Real data

Summary Important questions require excellent atmospheric measurements

Summary Important questions require excellent atmospheric measurements Excellent measurements require intelligent weighting of experimental evidence

Summary Important questions require excellent atmospheric measurements Excellent measurements require intelligent weighting of experimental evidence I have abundant data. Intelligence, on the other hand…