Inverse modeling using the Bayesian least squares method is applied to better constrain the sources and sinks of atmospheric methyl chloride (CH3Cl) using observations from seven surface stations and eight aircraft field experiments. We use a three-dimensional global chemical transport model, the GEOS-Chem, as the forward model. Up to 39 parameters describing the continental/hemispheric and seasonal dependence of the major sources of CH3Cl are used in the inversion. We find that the available surface and aircraft observations cannot constrain all the parameters, resulting in relatively large uncertainties in the inversion results. By examining the degrees of freedom in the inversion Jacobian matrix, we choose a reduced set of parameters that can be constrained by the observations while providing valuable information on the sources and sinks. In particular, we resolve the seasonal dependence of the biogenic and biomass-burning sources for each hemisphere. The in situ aircraft measurements are found to provide better constraints on the emission sources than surface measurements. The a posteriori emissions result in better agreement with the observations, particularly at southern high latitudes. The a posteriori biogenic and biomass-burning sources decrease by 13 and 11% to 2500 and 545 Gg yr^-1, respectively, while the a posteriori net ocean source increases by about a factor of 2 to 761 Gg yr^-1. The decrease in biomass-burning emissions is largely due to the reduction in the emissions in seasons other than spring in the Northern Hemisphere. The inversion results indicate that the biogenic source has a clear winter minimum in both hemispheres, likely reflecting the decrease of biogenic activity during that season. Copyright 2006 by the American Geophysical Union.

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from theMODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (~1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value fromthe potentialmaximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ~140 PgCyear-1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.