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The seasonal pattern of the carbon isotope content (δ13C) of atmospheric CO2 depends on local and nonlocal land-atmosphere exchange and atmospheric transport. Previous studies suggested that the δ13C of the net land-atmosphere CO2 flux ( δsource) varies seasonally as stomatal conductance of plants responds to vapor pressure deficit of air (VPD). We studied the variation of δsource at seven sites across the United States representing forests, grasslands, and an urban center. Using a two-part mixing model, we calculated the seasonal δsource for each site after removing background influence and, when possible, removing δ13C variation of nonlocal sources. Compared to previous analyses, we found a reduced seasonal (March-September) variation in δsource at the forest sites (0.5‰ variation). We did not find a consistent seasonal relationship between VPD and δsource across forest (or other) sites, providing evidence that stomatal response to VPD was not the cause of the global, coherent seasonal pattern in δsource. In contrast to the forest sites, grassland and urban sites had a larger seasonal variation in δsource (5‰) dominated by seasonal transitions in C3/C4 grass productivity and in fossil fuel emissions, respectively. Our findings were sensitive to the location used to account for atmospheric background variation within the mixing model method that determined δsource . Special consideration should be given to background location depending on whether the intent is to understand site level dynamics or regional scale impacts of land-atmosphere exchange. The seasonal amplitude in δ13C of land-atmosphere CO2 exchange ( δsource ) varied across land cover types and was not driven by seasonal changes in vapor pressure deficit. The largest seasonal amplitudes of δsource were at grassland and urban sites, driven by changes in C3/C4 grass productivity and fossil fuel emissions, respectively. Mixing model approaches may incorrectly calculate δsource when background atmospheric observations are remote and/or prone to anthropogenic influence. [ABSTRACT FROM AUTHOR]

The interaction of land with the atmosphere is sensitive to soil moisture ( W). Evapotranspiration (ET) reacts to soil moisture in a nonlinear way, f( W), as soils dry from saturation to wilt point. This nonlinear behavior and the fact that soil moisture varies on scales as small as 1-10 m in nature, while numerical general circulation models (GCMs) have grid cell sizes on the order of 1 to 100s of kilometers, makes the calculation of grid cell-average ET problematic. It is impractical to simulate the land in GCMs on the small scales seen in nature, so techniques have been developed to represent subgrid scale heterogeneity, including: (1) statistical-dynamical representations of grid subelements of varying wetness, (2) relaxation of f( W), (3) moderating f( W) with approximations of catchment hydrology, (4) 'tiling ' the landscape into vegetation types, and (5) hyperresolution. Here we present an alternative method for representing subgrid variability in W, one proven in a conceptual framework where landscape-scale W is represented as a series of 'Bins ' of increasing wetness from dry to saturated. The grid cell-level f( W) is defined by the integral of the fractional area of the wetness bins and the value of f( W) associated with each. This approach accounts for the spatiotemporal dynamics of W. We implemented this approach in the SiB3 land surface parameterization and then evaluated its performance against a control, which assumes a horizontally uniform field of W. We demonstrate that the Bins method, with a physical basis, attenuates unrealistic jumps in model state and ET seen in the control runs. [ABSTRACT FROM AUTHOR]

We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem 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 GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a-1 with some important regional corrections to 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 error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a-1, as compared to 24.9-27.0 Tg a-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a-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 southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44% of US anthropogenic methane emissions to livestock, 22-31% to oil/gas, 20% to landfills/wastewater, and 11-15% to coal. Wetlands contribute an additional 9.0-10.1 Tg a-1. [ABSTRACT FROM AUTHOR]

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]

The Department of Energy Atmospheric Radiation Measurement (ARM) program is a climate research user facility operating stationary ground sites that provide long-term measurements of climate-relevant properties, mobile ground- and ship-based facilities to conduct shorter field campaigns (6-12 months), and the ARM Aerial Facility (AAF). The airborne observations acquired by the AAF enhance the surface-based ARM measurements by providing high-resolution in situ measurements for process understanding, retrieval-algorithm development, and model evaluation that are not possible using surface-or satellite-based techniques. Several ARM aerial efforts were consolidated to form AAF in 2006. With the exception of a small aircraft used for routine measurements of aerosols and carbon cycle gases, AAF at the time had no dedicated aircraft and only a small number of instruments at its disposal. AAF successfully carried out several missions contracting with organizations and investigators who provided their research aircraft and instrumentation. In 2009, AAF started managing operations of the Battelle-owned Gulfstream I (G-1) large twin-turboprop research aircraft. Furthermore, the American Recovery and Reinvestment Act of 2009 provided funding for the procurement of over twenty new instruments to be used aboard the G-1 and AAF contracted aircraft. Depending on the requested scope, AAF now executes campaigns using the G-1 or contracted aircraft, producing freely available datasets for studying gas, aerosol, cloud, and radiative processes and their interactions in the atmosphere. AAF is also engaged in the maturation and testing of newly developed airborne sensors to help foster the next generation of airborne instruments. [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 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]

Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 ìm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005-2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the Carbon Tracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8-1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6-2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of -0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to Carbon Tracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of -0.2 ppm yr-1. 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 report on 10 yr of airborne measurements of atmospheric CO2 mole fraction from continuous and flask systems, collected between 2002 and 2012 over the Atmospheric Radiation Measurement Program Climate Research Facility in the US Southern Great Plains (SGP). These observations were designed to quantify trends and variability in atmospheric mole fraction of CO2 and other greenhouse gases with the precision and accuracy needed to evaluate ground-based and satellite-based column CO2 estimates, test forward and inverse models, and help with the interpretation of ground-based CO2 mole-fraction measurements. During flights, we measured CO2 and meteorological data continuously and collected flasks for a rich suite of additional gases: CO2, CO, CH4, N2O, 13CO2, carbonyl sulfide (COS), and trace hydrocarbon species. These measurements were collected approximately twice per week by small aircraft (Cessna 172 initially, then Cessna 206) on a series of horizontal legs ranging in altitude from 460m to 5500ma.m.s.l. Since the beginning of the program, more than 400 continuous CO2 vertical profiles have been collected (2007-2012), along with about 330 profiles from NOAA/ESRL 12-flask (2006-2012) and 284 from NOAA/ESRL 2-flask (2002- 2006) packages for carbon cycle gases and isotopes. Averaged over the entire record, there were no systematic differences between the continuous and flask CO2 observations when they were sampling the same air, i.e., over the oneminute flask-sampling time. Using multiple technologies (a flask sampler and two continuous analyzers), we documented a mean difference of <0.2 ppm between instruments. However, flask data were not equivalent in all regards; horizontal variability in CO2 mole fraction within the 5-10 min legs sometimes resulted in significant differences between flask and continuous measurement values for those legs, and the information contained in fine-scale variability about atmospheric transport was not captured by flask-based observations. The CO2 mole fraction trend at 3000ma.m.s.l. was 1.91 ppm yr-1 between 2008 and 2010, very close to the concurrent trend at Mauna Loa of 1.95 ppm yr-1. The seasonal amplitude of CO2 mole fraction in the free troposphere (FT) was half that in the planetary boundary layer (PBL) (∼15 ppm vs. ∼30 ppm) and twice that at Mauna Loa (approximately 8 ppm). The CO2 horizontal variability was up to 10 ppm in the PBL and less than 1 ppm at the top of the vertical profiles in the FT. [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]

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from groundbased near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model.We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of -5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability. [ABSTRACT FROM AUTHOR]

The article presents a study on a 10-year airborne measurements of atmospheric carbon dioxide (CO2) concentrations from continuous and flask systems collected between 2002 and 2012 over the Atmospheric Radiation Measurement Program Climate Research Facility in the U.S. Southern Great Plains (SGP). The study uses estimation theory and inverse models. Results show there were no systematic differences between the continuous and flask observations during air sampling.

The article discusses a study on the use of Aura TES and TCCON instruments for profiling tropospheric carbon dioxide. The study aims to improve the capability of estimating regional fluxes and estimates near-surface carbon dioxide values from ground-based near infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. It reveals the consistency of calculated random uncertainties in total column and boundary layer with actual uncertainties as compared to aircraft data.

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 it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. 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 observed correlation between free-tropospheric potential 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, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed. [ABSTRACT FROM AUTHOR]

This paper reexamines evidence for systematic errors in atmospheric transport models, in terms of the diagnostics used to infer vertical mixing rates from models and observations. Different diagnostics support different conclusions about transport model errors that could imply either stronger or weaker northern terrestrial carbon sinks. Conventional mixing diagnostics are compared to analyzed vertical mixing rates 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 diagnostics based on boundary layer depth and vertical concentration gradients do not always indicate the vertical mixing strength. Vertical mixing rates are anti-correlated with boundary layer depth at some sites, diminishing in summer when the boundary layer is deepest. Boundary layer equilibrium concepts predict an inverse proportionality between CO2 vertical gradients and vertical mixing strength, such that previously reported discrepancies between observations and models most likely reflect overestimated as opposed to underestimated vertical mixing. However, errors in seasonal concentration gradients can also result from errors in modeled surface fluxes. This study proposes using the timescale for approach to boundary layer equilibrium to diagnose vertical mixing independently of seasonal surface fluxes, with applications to observations and model simulations of CO2 or other conserved boundary layer tracers with surface sources and sinks. Results indicate that frequently cited discrepancies between observations and inverse estimates do not provide sufficient proof of systematic errors in atmospheric transport models. Some previously hypothesized transport model biases, if found and corrected, could cause inverse estimates to further diverge from carbon inventory estimates of terrestrial sinks. [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]

The article discusses the calibration of the Total Carbon Column Observing Network (TCCON) with World Meteorological Organization (WMO)-scale aircraft profile data. It notes that the TCCON develops accurate measurements of the column average dry-air mole fractions of chemical elements and gases in different sites worldwide. It mentions the consistency of the calibration curves shown between the different TCCON sites which can be described by a single calibration factor for each molecule.

The article discusses the mixing ratios of carbon monoxide (CO) retrieved from an atmospheric emitted radiance interferometer (AERI) spectra in Oklahoma between 2002 and 2009. It presents CO mixing ratios for the atmospheric layer recovered from infrared (IR) radiance spectra by a celestial point AERI deployed at an observatory in Oklahoma. It offers a comparison of CO values measured by AERI between 2002-2009 with those measured from the Atmospheric IR Sounder (AIRS) over North America.

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 1.3-2.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES data 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 data 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. 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 Carbon-Tracker 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]

The article presents a study which evaluates the importance of Total Carbon Column Observing Network (TCCON) which was calibrated by World Meteorological Organization (WMO) in situ trace gas measurement scales. It states that the network is designed for aircraft campaigns for Stratosphere-Troposphere Analyses of Regional Transport 2008 (START-08) included a profile over the Lamont and Lauder sites. The global calibration factor of gas captures the TCCON data is also evaluated.

The article discusses on the retrieval of carbon monoxide (CO) mixing ratios from the Atmospheric Emitted Radiance Interferometer (AERI) spectra at the Atmospheric Radiation Measurements (ARM) Program observatory near Lamont, Oklahoma between 2002 and 2009. It cites that the retrieved CO consists of approximately 70% from the boundary layer and 30% from the lower part of the free troposphere. Moreover, the ARM program aims to improve understanding on clouds and atmospheric radiation interaction.

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]

An increase in photosynthetic activity of the northern hemisphere terrestrial vegetation, as derived from satellite observations, has been reported in previous studies. The amplitude of the seasonal cycle of the annually detrended atmospheric CO2 in the northern hemisphere (an indicator of biospheric activity) also increased during that period. We found, by analyzing the annually detrended CO2 record by season, that early summer (June) CO2 concentrations indeed decreased from 1985 to 1991, and they have continued to decrease from 1994 up to 2002. This decrease indicates accelerating springtime net CO2 uptake. However, the CO2 minimum concentration in late summer (an indicator of net growing-season uptake) showed no positive trend since 1994, indicating that lower net CO2 uptake during summer cancelled out the enhanced uptake during spring. Using a recent satellite normalized difference vegetation index data set and climate data, we show that this lower summer uptake is probably the result of hotter and drier summers in both mid and high latitudes, demonstrating that a warming climate does not necessarily lead to higher CO2 growing-season uptake, even in high-latitude ecosystems that are considered to be temperature limited. [ABSTRACT FROM AUTHOR]

Presents information on a study which measured the lower troposphere over a southern taiga forest area in Fyodorovskoye, western Russia. Site description; Methodology; Results and discussion; Conclusions.