We present preliminary results from hydrogen isotopic measurements in atmospheric methane obtained from the NOAA CCGG Cooperative Air Sampling Network. Recent developments at INSTAAR, University of Colorado have brought on line the capability to measure hydrogen deuterium ratios in methane using continuous flow mass spectrometry coupled with an extraction combustion sample preparation system. Preliminary results show reproducibility of cylinder air samples to less than ± 2 ‰. Data from several months of samples from six network sites are presented, including data from: Barrow and Cold Bay, Alaska, U.S.A; Tutuila American Samoa; Black Sea, Constanta, Romania; Park Falls Wisconsin, U.S.A.; and Baltic Sea, Poland.

Atmospheric CO₂ concentrations have been obtained from the Atmospheric Infrared Sounder (AIRS) radiance data within the European Centre for Medium-Range Weather Forecasts (ECMWF) data assimilation system. In a first explorative configuration, a subset of channels from the AIRS instrument has been assimilated providing estimates of tropospheric column-averaged CO₂ mixing ratios representative of a layer between the tropopause and about 700 hPa at observation locations only. Results show considerable geographical and temporal variability with values ranging between 370 and 382 ppmv. The 5-day mean estimated random error is about 1%, which is confirmed by comparisons with flask observations on board flights of Japanese airliners in the west-Pacific region. This study demonstrates the feasibility of global CO₂ estimation using high spectral resolution infrared satellite data in a numerical weather prediction data assimilation system. Currently, the system is being improved to treat CO₂ as a full three-dimensional atmospheric variable included in the forecast model. This allows more flexibility in the constraints on the CO₂ estimation as well as the possibility of assimilating other data sources (e.g., near-infrared satellite data and flasks). The CO₂ fields provided by the data assimilation system have great potential to assist the surface flask network in constraining current top-down carbon flux estimates.

We have employed a Multi-parameter Linear Regression (MLR) analysis procedure to determine the uptake of anthropogenic CO₂ between two east-west hydrographic surveys of the North Pacific that occurred in 1994 and 2004. The results revealed water column integrated uptake rates of anthropogenic CO₂ that ranged from 1.1 to 1.3 mol m^-2 yr^-1 depending on location. The combined effect of the tilted density surfaces and the younger waters with higher anthropogenic CO₂ concentrations leads to higher total column inventories in the western North Pacific.

Anthropogenic CO₂ releases to the atmosphere have changed the total inorganic carbon concentration of ocean by no more than 3-4% at any location. Main differences between three approaches [Poisson and Chen, 1987; Gruber et al., 1996; Friis, 2005] are presented that define marine anthropogenic CO₂ (C_T^ant) as deduced from total inorganic carbon. All definitions are based on a back-calculation technique that was independently proposed by Brewer [1978] and Chen and Millero [1979]. The overall importance of this presentation is in the comparability of anthropogenic CO₂ findings from described methods with these derived from global bookkeeping approaches or full carbon model results.

There are two basic approaches for inferring surface-atmosphere exchange for trace gases on regional scales: a bottom-up approach, in which local process knowledge is scaled up, and a top-down approach, in which the larger-scale constraint from atmospheric concentration measurements is applied in combination with transport models. Here we combine the two approaches, and assess the information content added by boundary layer concentration data. More specifically, we analyze the potential for inferring spatially resolved surface fluxes from atmospheric tracer observations within the mixed layer, such as from monitoring towers, using a receptor oriented transport model (Stochastic Time-Inverted Lagrangian Transport [STILT] model, [Lin et al., 2003]) coupled to a simple biosphere in which CO2 fluxes are represented as functional responses to environmental drivers (radiation and temperature, [Gerbig et al., 2003]). Transport and fluxes are coupled on a dynamic grid using a polar projection with high horizontal resolution (~20 km) in near field, and low resolution far away (as coarse as 2000 km), reducing the number of surface pixels without significant loss of information. To test the system, and to evaluate the errors associated with the retrieval of fluxes from atmospheric observations, a pseudo data experiment was performed. A large number of realizations of measurements (pseudo data) and a priori fluxes was generated, and for each case spatially resolved fluxes were retrieved. Results indicate strong potential for high resolution retrievals based on a network of tall towers, subject to the requirement of correctly specifying the a priori uncertainty covariance, especially the off diagonal elements that control spatial correlations.

Measurements of the radiocarbon content of atmospheric carbon dioxide are a potentially powerful, yet relatively unexplored method of improving the understanding of natural carbon dynamics and verifying fossil fuel emissions. Development of ¹⁴CO₂ as a tracer has been limited by measurement capabilities given that seasonal and spatial variation in D¹⁴C is currently of the same order as traditional instrument precision: 3-5 per mil. We have demonstrated 1-2 per mil reproducible measurement precision at the Center for Accelerator Mass Spectrometry of Lawrence Livermore National Laboratory. Here we present preliminary measurements of the natural variability of ¹⁴CO₂ from the SIO network of background air sampling stations.

CO₂ emissions from fossil-fuel combustion can be estimated at the state or monthly level even when full data on fuel combustion are not available. Our hypothesis is that a representative proxy can accurately estimate the pattern of CO₂ emissions if a sufficient fraction of the total can be represented, even if the dataset used does not cover all energy consumption sectors. Our approach employs monthly sales data for each state from the U.S. Department of Energy’s Energy Information Administration (EIA). This is used to estimate the relative proportions of solid, liquid and gaseous fossil fuels for each state for each month.

This presentation will interpret results from the TransCom 3 interannual time dependent inversion. First, the long-term mean carbon exchange will be compared across the three different TransCom 3 inversion levels: the annual mean, seasonal, and interannual control experiments. We will highlight the agreement among these experiments in spite of the differing degrees of freedom, and the differing CO₂ observing networks employed. Comparison will be made to independent decadal estimates of land and ocean carbon uptake and will include the sensitivity to different CO₂ networks. We will also interpret the model mean interannual carbon fluxes as they relate to key indices of climate variability. In particular, correlation to the El Niño/Southern Oscillation index will be made suggesting a propagation carbon flux anomalies from the tropics to the extra tropics following the peak of the ENSO warm phase in the tropical Pacific ocean. These correlations will be explained via anomalies in temperature and precipitation from NCEP reanalysis.

We estimate the natural and anthropogenic components of the air-sea flux of CO₂ in the Indian Ocean. The increase in atmospheric CO₂ driven by human activity has caused the air-sea CO₂ flux, to increase significantly over the industrial era. We estimate the flux in the year 1780 to be approximately 0.2Gt/yr, increasing by 0.26Gt/yr to 0.5Gt/yr in 2000. The estimate of the natural (preindustrial) flux is highly sensitive to uncertainties in modern-day CO₂ disequilibrium measurements. By contrast, the estimate of the anthropogenic flux is only weakly sensitive to these measurements. Our anthropogenic estimate is smaller than other studies due to the removal in our methodology of the widely made weak-mixing and constant-disequilibrium assumptions, both of which cause positive bias.