Surrogate gas prediction model as a proxy for Δ14C-based measurements of fossil fuel-CO2.
The measured14C:12C isotopic ratio of atmospheric CO2(and its associated derived Δ14C value) is an ideal tracer for determination of the fossil fuel derived CO2enhancement contributing to any atmospheric CO2measurement (Cff). Given enough such measurements, independent top-down estimation of US fossil fuel-CO2emissions should be possible. However, the number of Δ14C measurements is presently constrained by cost, available sample volume, and availability of mass spectrometer measurement facilities. Δ14C is therefore measured in just a small fraction of samples obtained by ask air sampling networks around the world. Here, we develop a Projection Pursuit Regression (PPR) model to predictCffas a function of multiple surrogate gases acquired within the NOAA/ESRL Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF6, and halo- and hydrocarbons acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 through 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 Δ14C measurements in the NOAA GGGRN and building new PPR models at additional sites would improve our understanding of uncertainties and potentially increase the number ofCffestimates by approximately a factor of three. Provided that these estimates are of comparable quality to Δ14C-based estimates, we expect an improved determination of fossil fuel-CO2emissions.
Statistical Engineering Division, National Institute of Standards and Technology, Boulder, CO, USA.
Kevin J
KJ
Coakley
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA; CIRES, University of Colorado, Boulder, CO.
John B
JB
Miller
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA.
Stephen A
SA
Montzka
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA; CIRES, University of Colorado, Boulder, CO.
Colm
C
Sweeney
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA; CIRES, University of Colorado, Boulder, CO.
Ben R
BR
Miller