Differences in satellite CO2 data coverage and their influence on regional flux constraints

Abstract:

Inverse modeling of atmospheric transport is a technique that systematically searches for space-time distributions of trace gas fluxes that yield modeled atmospheric concentrations close to observations. This technique has been employed for the estimation of surface CO₂ flux distributions in better understanding the mechanisms of the global carbon cycle. As this inference relies on observations, several studies were conducted in the past to see the sensitivity of flux estimates to the expansion of surface monitoring networks over time and the choice of data-providing sites in the estimation. These studies showed that changes in the geographical distribution of the surface data have a large impact on regional-scale flux estimates.

With the advent of the Greenhouse gases Observing SATellite (GOSAT) in early 2009, the spatial coverage by the surface monitoring networks can now be widely expanded with the spaceborne soundings, from which column-averaged CO₂ concentrations (X_CO2) are retrieved. These GOSAT-based X_CO2 retrievals are made available by five research groups, and their precisions have been reported to be below 2 ppm level. Where they coincide, the five X_CO2 retrievals (all biases corrected) agree within one standard deviation of less than 1 ppm. On one hand, the extent that each of the X_CO2 retrieval data product covers the surface differs from one to another, owing to differences in the retrieval algorithms and data screening criteria, and the coverage differences were found to be dependent on geographical locations.

We investigated the extent to which these data-coverage differences alter constraints on individual regional CO₂ flux estimates. For this, we used a diagnostic known as the resolution kernel, which quantifies how well the regional flux estimates can be resolved by the observations. The inversion system used here is the same as what is used to generate the GOSAT Level 4 regional flux data product, and consists of NIES 08.1i transport model and the Kalman Smoother optimization scheme. Here, we will present the results for several representative regions including eastern Asia that are under-sampled by the surface networks but well-observed by the satellite. We will also touch briefly on the latest GOSAT Level 4 CO₂ data product.