Measurements of the partial pressure of CO₂ in surface seawater (pCO₂^w) have been made frequently and extensively in the western North Pacific (3-35°N, 132-142°E) since 1990. Based on the time series analysis of pCO₂^w data, we obtained a “climatological view” of seasonal variation in pCO₂^w in the western North Pacific. We have examined the relationship between pCO₂^w and sea surface temperature (SST). The pCO₂^w–SST relationship varies spatially and temporally. The pCO₂^w showed an average growth rate of 1.6 µatm yr^-1 (nearly equal to that of the air, pCO₂^a) with large variability (±8.9µatm yr^-1). In 1998, larger growth rates of pCO₂^w occurred in the subtropical gyre and the western equatorial Pacific, which was probably associated with the 1997/98 El Niño phenomena. To know processes affecting long-term variations in pCO₂^w, we have examined seasonal variation in growth rate of pCO₂^w. The linear growth rate of pCO₂^w during the winter season ranged from 1.3±0.2 to 2.1±0.2µatm yr^-1 with an average of 1.7±0.2µatm yr^-1. During spring/summer seasons, the average growth rate of pCO₂^w was larger than 2µatm yr^-1 north of 27°N, and within the range from 0 to 1µatm yr^-1 in the North Equatorial Current. These increases were mostly caused by the oceanic uptake of anthropogenic CO₂, and to some extent, other processes controlling the pCO₂^w change: thermodynamic effect, lateral transport and vertical mixing, and biological activity.

GOSAT is a satellite to measure the column densities of CO₂ and CH₄ from space globally, and it is scheduled to be launched in 2008. It has a short wavelength infrared (SWIR) Fourier transform spectrometer (FTS) which measures both the ground surface scattered solar light over land and the right reflected light (sun-glint) over ocean. Column densities of CO₂ and CH₄ will be retrieved from the SWIR (i.e. 1.6 µm and 2.0 µm bands) data and the optical path length from oxygen A-band (0.76 µm). A cloud and aerosol sensor composed of three spectral image sensors (0.380, 0.678 and 1.62 µm) is equipped, viewing the wider area than FTS. This is a joint project among Ministry of Environment of Japan (MOE), National Insitutite for Environmental Studies (NIES) and Japan Aerospace Exploration Agency (JAXA).

The remote sensing of CO₂ from satellites is an exciting new and rapidly developing field in carbon cycle research. Satellite sensors have the potential to provide a wealth of information on atmospheric CO₂, covering many regions that are scarsely monitored the ground based observational networks. Satellite measurements could significantly strengthen the power of inverse modelling computations in tracing sources and sinks of CO₂. The main challenge, however, is to reach the measurement accuracy needed to resolve the important CO₂ concentration gradients. The current generation of satellite instruments from which CO₂ can be retrieved is expected to meet the requirements only partly, as the instruments were not originally designed to measure CO₂. Nevertheless interesting results come out as we will show for the Sciamachy instrument. A particularly difficult aspect is the determination of the airmass factor, which is needed to translate the observed optical thickness into a column averaged dry air mixing ratio. The airmass factor is influenced by e.g. clouds, aerosols, air pressure, and orography. So far the uncertainty assessments have mainly relied on theoretical investigations and ground-based measurements. The measurements from Sciamachy allow us to verify these studies, and some of the methods that have been proposed to reduce or eliminate the errors. We will demonstrate this with the main focus on aerosols. Error assessments using in-flight data will be indispensable for improving future instruments.

The influence of air-sea fluxes on atmospheric oxygen can be separated from terrestrial influences using the tracer Atmospheric Potential Oxygen (APO).  Data collected by the Scripps atmospheric oxygen flask sampling network exhibits interannual variability in APO coherent over the northern hemisphere.  The timing of these changes correlates with climatic changes in the North Pacific.

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.

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.

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.

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.

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.