Verification of Emissions by Inverse Modelling

摘要

A wide range of scientific questions regarding the green-house gases necessitate determinations of their sources and sinks at local to global scales. A powerful method for such determinations involves solution of an inverse problem in which the observed concentrations are effectively Lagrangian line integrals and the unknown sources or sinks are contained in the integrands. The inverse problem consists of calculating optimal estimates of the unknowns in the Bayesian sense using an atmospheric transport model and trace gas measurements gathered over space and time. Great care is necessary to include the effects of both measurement and transport model errors in calculating the uncertainty in the optimal estimates. These methods have recently been applied to estimations of the emissions of several greenhouse gases including the chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, methane, and nitrous oxide. For the hydrogen-containing gases these emission estimates require accurate specification of the concentrations of the hydroxyl radical which constitute their major sink. Hydroxyl radical levels can be optimally estimated in a separate problem using measurements of a few special gases whose global emissions are already very well known. We briefly review the results of studies which use Eulerian or Lagrangian transport models and Kalman filter and other optimization methods to compute emissions of non-CO_2 greenhouse gases. These emission estimates can be compared to those obtained from industry production, sales, and end-use data or from aggregation of local emissions measurements where available. The results show that the inverse approach is a powerful complement to these other methods and that it has sometimes served to illuminate important systematic errors in the industry-based estimates. At the same time, the inverse approach has its own limitations associated especially with transport model errors and/or inadequate atmospheric measurements.