Surrogate Gas Prediction Model

DESCRIPTION:

Since fossil fuels are devoid of ¹⁴C, the measured ¹⁴C ratio of atmospheric CO₂ (expressed as Delta-C) is the gold standard measurement to determine the fossil fuel enhancement, C_ff, to any atmospheric CO₂ measurement. With enough Delta-C measurements, independent, "top-down", estimation of US fossil fuel CO₂ emissions should be possible. However, the number of such measurements is severely constrained by cost, accessibility to accelerator mass spectrometers and the volume of air required for a sufficiently high precision measurement. Thus, Delta-C is currently measured in only a small subset of NOAA/ESRL Global Monitoring Division (GMD) tall-tower and aircraft air samples. We predict C_ff as a function of inexpensive and more frequently measured anthropogenic traces gases with a Projection Pursuit Regression (PPR) model. The surrogates gases include CO, SF₆, and halo- and hydro-carbons. These gases were measured at various altitudes near Cape May, NJ,and Portsmouth, NH, with airplane-based instruments from 2005 to 2011. In our study, we fit one PPR model to observations from both of these sites.

In the PPR model, the multivariate surrogate gases data vector is projected along particular direction vectors. Each direction vector is determined when fitting the PPR model to the data. The projection along each direction vector is smoothed. The form of the smoother is adjustable.We select the complexity and form of the Projection Pursuit Regression model with a statistical learning method called cross-validation. For each particular from of the PPR model, we fit the model to training data. We select the optimal model by minimizing the mean-square difference between observed and validation data. After selecting the model by this cross-validation procedure, we then predict C_ff values for test data excluded from the modeling building process.