A B S T R A C TUsing an Observing System Simulation Experiment (OSSE), we investigate the impact of JAXA Greenhouse gases Observing SATellite 'IBUKI' (GOSAT) sampling on the estimation of terrestrial biospheric flux with the NASA Carbon Monitoring System Flux (CMS-Flux) estimation and attribution strategy. The simulated observations in the OSSE use the actual column carbon dioxide (X CO 2 ) b2.9 retrieval sensitivity and quality control for the year 2010 processed through the Atmospheric CO 2 Observations from Space algorithm. CMSFlux is a variational inversion system that uses the GEOS-Chem forward and adjoint model forced by a suite of observationally constrained fluxes from ocean, land and anthropogenic models. We investigate the impact of GOSAT sampling on flux estimation in two aspects: 1) random error uncertainty reduction and 2) the global and regional bias in posterior flux resulted from the spatiotemporally biased GOSAT sampling. Based on Monte Carlo calculations, we find that global average flux uncertainty reduction ranges from 25% in September to 60% in July. When aggregated to the 11 land regions designated by the phase 3 of the Atmospheric Tracer Transport Model Intercomparison Project, the annual mean uncertainty reduction ranges from 10% over North American boreal to 38% over South American temperate, which is driven by observational coverage and the magnitude of prior flux uncertainty. The uncertainty reduction over the South American tropical region is 30%, even with sparse observation coverage. We show that this reduction results from the large prior flux uncertainty and the impact of non-local observations. Given the assumed prior error statistics, the degree of freedom for signal is Â1132 for 1-yr of the 74 055 GOSAT X CO 2 observations, which indicates that GOSAT provides Â1132 independent pieces of information about surface fluxes. We quantify the impact of GOSAT's spatiotemporally sampling on the posterior flux, and find that a 0.7 gigatons of carbon bias in the global annual posterior flux resulted from the seasonally and diurnally biased sampling when using a diagonal prior flux error covariance.

Abstract.We have examined the utility of retrieved columnaveraged, dry-air mole fractions of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO 2 , using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO 2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO 2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO 2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO 2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO 2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO 2 inversions, the a posteriori CO 2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TC-CON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimatesPublished by Copernicus Publications on behalf of the European Geosciences Union. 3704F. Deng et al.: Inferring regional sources and sinks of atmospheric CO 2 from GOSAT XCO 2 data suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO 2 . The fact that the a posteriori CO 2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

[1] Intercontinental Transport of Ozone and Precursors (ITOP), part of International Consortium for Atmospheric Research on Transport and Transformation (ICARTT), was a large experimental campaign designed to improve our understanding of the chemical transformations within plumes during long-range transport (LRT) of pollution from North America to Europe. This campaign took place in July and August 2004, when a strong fire season occurred in North America. Burning by-products were transported over large distances, sometimes reaching Europe. A chemical transport model, Modélisation de la Chimie Atmosphérique Grande Echelle (MOCAGE), with a high grid resolution (0.5°Â 0.5°) over the North Atlantic area and a daily inventory of biomass burning emissions over the United States, has been used to simulate the period. By comparing our results with available aircraft in situ measurements and satellite data (MOPITT CO and SCIAMACHY NO 2 ), we show that MOCAGE is capable of representing the main characteristics of the tropospheric ozone-NO x -hydrocarbon chemistry during the ITOP experiment. In particular, high resolution allows the accurate representation of the pathway of exported pollution over the Atlantic, where plumes were transported preferentially at 6 km altitude. The model overestimates OH mixing ratios up to a factor of 2 in the lower troposphere, which results in a global overestimation of hydrocarbons oxidation by-products (PAN and ketones) and an excess of O 3 (30-50 ppbv) in the planetary boundary layer (PBL) over the continental United States. Sensitivity study revealed that lightning NO emissions contributed significantly to the NO x budget in the upper troposphere of northeast America during the summer 2004.

Variational methods are widely used to solve geophysical inverse problems. Although gradient-based minimization algorithms are available for high-dimensional problems (dimension > 10 6 ), they do not provide an estimate of the errors in the optimal solution. In this study, we assess the performance of several numerical methods to approximate the analysis-error covariance matrix, assuming reasonably linear models. The evaluation is performed for a CO 2 flux estimation problem using synthetic remote-sensing observations of CO 2 columns. A low-dimensional experiment is considered in order to compare the analysis error approximations to a full-rank finite-difference inverse Hessian estimate, followed by a realistic high-dimensional application. Two stochastic approaches, a MonteCarlo simulation and a method based on random gradients of the cost function, produced analysis error variances with a relative error < 10%. The long-distance error correlations due to sampling noise are significantly less pronounced for the gradient-based randomization, which is also particularly attractive when implemented in parallel. Deterministic evaluations of the inverse Hessian using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm are also tested. While existing BFGS preconditioning techniques yield poor approximations of the error variances (relative error > 120%), a new preconditioner that efficiently accumulates information on the diagonal of the inverse Hessian dramatically improves the results (relative error < 50%). Furthermore, performing several cycles of the BFGS algorithm using the same gradient and vector pairs enhances its performance (relative error < 30%) and is necessary to obtain convergence. Leveraging those findings, we proposed a BFGS hybrid approach which combines the new preconditioner with several BFGS cycles using information from a few (3-5) Monte-Carlo simulations. Its performance is comparable to the stochastic approximations for the low-dimensional case, while good scalability is obtained for the high-dimensional experiment. Potential applications of these new BFGS methods range from characterizing the information content of high-dimensional inverse problems to improving the convergence rate of current minimization algorithms.