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We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to constrain global and North American inversions of methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. The GOSAT data are first evaluated with atmospheric methane observations from surface networks (NOAA, TCCON) and aircraft (NOAA/DOE, HIPPO), using the GEOSChem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOSChem that we correct via quadratic regression. The surface and aircraft data are subsequently used for independent evaluation of the methane source inversions. Our global adjoint-based inversion yields a total methane source of 539Tga-1 and points to a large East Asian overestimate in the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide full error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tga-1, as compared to 24.9-27.0 Tga-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tga-1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the South-Central US, the Central Valley of California, and Florida wetlands, large isolated point sources such as the US Four Corners also contribute. We attribute 29-44% of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20% to landfills/waste water, and 11-15% to coal with an additional 9.0-10.1 Tga-1 source from wetlands. [ABSTRACT FROM AUTHOR]

Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these timescales and how well transport models represent this covariation. Daily to seasonal covariation of surface fluxes and atmospheric transport was estimated in observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, and compared to an atmospheric transport model inversion (CarbonTracker). Covariation of transport and surface fluxes was stronger in Carbon-Tracker than in observations on synoptic (daily to weekly) timescales, with a wet year (2007) having significant covariation compared to a dry year (2006). Differences between observed and CarbonTracker synoptic covariation resulted in a 0.3 ppm CO2 enhancement in boundary layer concentrations during the growing season, and a corresponding enhancement in carbon uptake by 13% of the seasonal cycle amplitude in 2007, as estimated by an offline simplified transport model. This synoptic rectification of surface flux variability was of similar magnitude to the interannual variability in carbon sinks alone, and indicates that interannual variability in the inversions can be affected by biases in simulated synoptic rectifier effects. The most significant covariation of surface fluxes and transport had periodicities of 10 days and greater, suggesting that surface flux inversions would benefit from improved simulations of the effects of soil moisture on boundary layer heights and surface CO2 fluxes. Soil moisture remote sensing could be used along with CO2 concentration measurements to further constrain atmospheric transport model inversions. [ABSTRACT FROM AUTHOR]

Recent advances in transport model inversions could significantly reduce uncertainties in land carbon uptake through assimilation of high frequency CO2 concentration measurements. The impact of these measurements depends on the strength of covariation between surface fluxes and atmospheric transport and mixing at weekly and shorter time-scales, and on how well transport models represent this covariation. A stochastic boundary layer model was developed to quantify the effects of synoptic covariation on surface flux inversions at daily to season time-scales, and to compare covariation in transport model simulations to observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility. The most significant covariation of surface fluxes and transport occurred on weekly and longer time-scales, suggesting that surface flux inversions would benefit most from improved simulations of dynamics at the lower-frequency end of the synoptic spectrum. Biases in these rectifier effects contributed to surface flux biases of 13% of the seasonal cycle amplitude, estimated from differences between observations and a data assimilation system (CarbonTracker). Biases in simulated covariation of transport and surface fluxes resulted in overestimated boundary layer concentrations during the growing season over the Southern Great Plains, by up to 0.3 ppm CO2. Though small relative to the seasonal cycle, the strength of synoptic rectifier effects strongly varies on inter-annual time-scales, with some years having negligible and others having large vertical concentration gradients during the growing season, due only to differences in covariation of surface fluxes and transport. Inter-annual variability in vertical gradients due to synoptic rectifier effects is of similar magnitude to the inter-annual variability due to carbon sinks alone. [ABSTRACT FROM AUTHOR]

We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved from the Greenhouse gases Observing SATellite (GOSAT). We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land-sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a) reduces the estimated global land sink of CO2, and (b) shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source-sink inversions, small spatiotemporal biases -- beyond what can be detected using current validation techniques -- have serious consequences for optimized fluxes, even aggregated over continental scales. [ABSTRACT FROM AUTHOR]

Comparisons are made between mid-tropospheric Tropospheric Emission Spectrometer (TES) carbon dioxide (CO2) satellite measurements and ocean profiles from three Hiaper Pole-to-Pole Observations (HIPPO) campaigns and land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site over a 4-yr period. These comparisons are used to characterize the bias in the TES CO2 estimates and to assess whether calculated and actual uncertainties and sensitivities are consistent. The HIPPO dataset is one of the few datasets spanning the altitude range where TES CO2 estimates are sensitive, which is especially important for characterization of biases. We find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements; actual errors range from 0.8-1.2 ppm, depending on the campaign, and are approximately 1.4 times larger than the predicted errors. The bias of TES versus HIPPO is within 0.85 ppm for each of the 3 campaigns; however several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons of aircraft flask profiles, which are measured from the surface to 5 km, to TES CO2 at the SGP ARM site show good agreement with an overall bias of 0.1 ppm and rms of 1.0 ppm. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product. [ABSTRACT FROM AUTHOR]

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying the method to the Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) v2.8 data. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the correlation between free-tropospheric temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors. [ABSTRACT FROM AUTHOR]

This paper reexamines evidence for previously hypothesized errors in atmospheric transport models and CO2 flux inversions by evaluating the diagnostics used to infer vertical mixing rates from observations. Several conventional mixing diagnostics are compared to analyzed mixing using data from the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, the CarbonTracker data assimilation system based on Transport Model version 5 (TM5), and atmospheric reanalyses. The results demonstrate that previous diagnostics based on boundary layer depth and vertical concentration gradients are unreliable indicators of vertical mixing. Vertical mixing rates are anti-correlated with boundary layer depth at some sites, diminishing in summer when the boundary layer is deepest. Vertical CO2 gradients between the boundary layer and free-troposphere are strongly affected by seasonal surface fluxes and therefore do not accurately reflect vertical mixing rates. The infinite timescale over which vertical tracer gradients relax toward equilibrium is proposed as an improved mixing diagnostic, which can be applied to observations and model simulations of CO2 or other conserved boundary layer tracers with surface sources and sinks. This diagnostic does not require dynamical variables from the transport models, and is independent of possible systematic biases in prior- and post-inversion seasonal surface fluxes. Results indicate that observations frequently cited as evidence for systematic biases in atmospheric transport models are insufficient to prove that such biases exist. Some previously hypothesized transport model biases, if found and corrected, could cause inverse estimates to further diverge from land-based estimates. [ABSTRACT FROM AUTHOR]

We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ∼10 ppm for a single target and about 1.3 ppm for monthly aver ages on spatial scales of 20°×30°. Monthly spatially-averaged TES results from 2005-2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa observatory show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ∼2% low bias, which is subsequently corrected, and comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006-2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker with TES observation operator at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions. [ABSTRACT FROM AUTHOR]