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Surrogate gas prediction model as a proxy for Δ 14 C‐based measurements of fossil fuel CO 2
Author(s) -
Coakley Kevin J,
Miller John B,
Montzka Stephen A,
Sweeney Colm,
Miller Ben R
Publication year - 2016
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2015jd024715
Subject(s) - trace gas , fossil fuel , environmental science , sampling (signal processing) , greenhouse gas , meteorology , atmospheric sciences , chemistry , geology , physics , oceanography , organic chemistry , detector , optics
The measured 14 C: 12 C isotopic ratio of atmospheric CO 2 (and its associated derived Δ 14 C value) is an ideal tracer for determination of the fossil fuel derived CO 2 enhancement contributing to any atmospheric CO 2 measurement ( C ff ). Given enough such measurements, independent top‐down estimation of U.S. fossil fuel CO 2 emissions should be possible. However, the number of Δ 14 C measurements is presently constrained by cost, available sample volume, and availability of mass spectrometer measurement facilities. Δ 14 C is therefore measured in just a small fraction of samples obtained by flask air sampling networks around the world. Here we develop a projection pursuit regression (PPR) model to predict C ff as a function of multiple surrogate gases acquired within the NOAA/Earth System Research Laboratory (ESRL) Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF 6 , and halocarbon and hydrocarbon acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 to 2010. Model performance for these sites is quantified based on predicted values corresponding to test data excluded from the model building process. Chi‐square hypothesis test analysis indicates that these predictions and corresponding observations are consistent given our uncertainty budget which accounts for random effects and one particular systematic effect. However, quantification of the combined uncertainty of the prediction due to all relevant systematic effects is difficult because of the limited range of the observations and their relatively high fractional uncertainties at the sampling sites considered here. To account for the possibility of additional systematic effects, we incorporate another component of uncertainty into our budget. Expanding the number of Δ 14 C measurements in the NOAA GGGRN and building new PPR models at additional sites would improve our understanding of uncertainties and potentially increase the number of C ff estimates by approximately a factor of 3. Provided that these estimates are of comparable quality to Δ 14 C‐based estimates, we expect an improved determination of fossil fuel CO 2 emissions.