Time-Filtered Inverse Modeling of Land-Atmosphere Carbon Exchange

Abstract:

The sources and sinks of biospheric carbon dioxide represent one of the least understood and most critical processes in carbon science. Since the 1990’s, carbon dioxide inversion models have estimated the magnitude, location, and uncertainty of carbon sources and sinks. These inversions are underconstrained estimation problems that employ aggressive statistical regularizations in both space and time to estimate quantities like net ecosystem exchange (NEE) on weekly timescales over fine spatial scales. We developed and tested a new method focusing observational constraints on estimation of corrections to slowly varying biospheric processes, which control time-averaged sources and sinks. Rather than estimate weekly additive corrections to NEE, we estimate persistent multiplicative biases to time mean and several seasonal harmonics of gross primary production (GPP) and total respiration (RESP). We tested the new method by estimating corrections to simulated component fluxes from the Simple Biosphere Model 4 (SiB4) using observations from 8 different eddy-covariance flux towers selected from the North American Carbon Program (NACP) site synthesis dataset. The time-filtering method correctly estimates of both the net and component fluxes and is more robust to observational uncertainty than a control experiment meant to represent current global inversions. Furthermore, the new method is flexible enough to separately estimate component fluxes (GPP and RESP) using additional observational constraints even with a high degree of uncertainty.