A one-dimensional (1d) physical-biological-chemical model was developed and tested by Antoine and Morel [1995, AM95 hereafter], with the aim of assessing upper ocean carbon fluxes. This model was specifically designed to be driven by satellite data, and it was used to evaluate the upper ocean CO₂ fluxes at station P in the NE Pacific. Another validation of this model has been carried out at the DYFAMED station (NW Mediterranean), where time series of biological and physical observations are available. This validation is a first step before the basin-scale application to the Mediterranean Sea, as presented here for the period 1998-2000.

Accurate estimates of the spatial distribution of pre-industrial and anthropogenic air-sea carbon fluxes are crucial to understanding the processes driving ocean carbon uptake. We present regional anthropogenic and pre-industrial air-sea fluxes estimated separately from their reconstructed concentrations and Ocean General Circulation Models (OGCM). The ocean interior carbon transports required to explain these fluxes are calculated and their implications for the global carbon cycle are discussed. 

Greenhouse gases Observing SATellite (GOSAT) of Japan is planned to be launched in 2008. GOSAT will be equipped with a FTS to monitor CO₂ column density globally. The FTS has three near infrared bands which cover 0.76 µm, 1.6 µm, and 2.0 µm spectral regions, respectively. Retrieval algorithms to estimate CO₂ and CH₄ column densities from these bands data are now being developed. We have investigated retrieval algorithms under the non-clear sky conditions. As one of these cases, a cirrus cloud parameter estimation was researched. The cirrus vertical profile (i.e., existing height) is estimated from the 0.76 µm band data. Strong water vapor absorption area is included in the 2.0 µm spectral band, so that the reflected radiance from a ground surface is absorbed completely by H₂O in this area. Thus the signal in this area is considered as path radiance caused by the cirrus clouds reflection, because there is little water vapor above the cirrus cloud top. By using this signal, the cirrus optical depth can be estimated, and then column densities of CO₂, CH₄ and H₂O are retrieved precisely.

We consider the ability of an inverse model framework and observations from the Cooperative Air Sampling Network to resolve fluxes at various scales over a 20-year period. During this time the observational network underwent a significant expansion. We calculate the resolution kernel to determine which continental/ocean basin scale fluxes may be resolved, and which spatial aggregations of fluxes are well resolved. In addition, the resolution kernel is used to obtain insights into how source regions are constrained by individual measurement sites.

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 Greenhouse Effect is a natural phenomenon that warms up the earth. It works on the same principles as the ordinary garden glasshouse, which allows the light to get in, but does not allow the heat to get out. The earth is surrounded by a shield of atmospheric gases primarily nitrogen (78 %), and oxygen (21%). The remainder of the air composition is made up of what are called as “trace gases,” which include carbon dioxide (CO₂), methane (CH₄) etc. The earth maintains its temperature through insulation with a 'thermal blanket' of greenhouse gases which allow penetration of the sun's rays but prevent some heat radiating back into space. Light from the sun penetrates the atmosphere and reaches the earth surface, warming it up.

We have developed a carbon flux inversion method for using a mesoscale meteorological model (CSU-RAMS) within a Maximum Likelihood Ensemble Filter (MLEF, Zupanski 2005; Zupanski and Zupanski 2005). The MLEF is a variant of the Ensemble Kalman Filter, and is used to optimize model state variables and parameters based on continuous observations of CO₂ mixing ratio. The method does not require the development of a model adjoint, but rather relies on transformation of variables to efficiently obtain estimates of fluxes with uncertainties and dynamical model error from an ensemble of forward model simulations. We demonstrate this method using a mesoscale simulation of weather, transport, and the surface carbon budget over the continental USA during the summer. The estimation procedure decomposes the total surface flux into photosynthesis and respiration (which are assumed to be modeled correctly to first order), plus an unknown but time-invariant fractional error in each.  These residuals are estimated for each model grid cell over a moving window in time, allowing atmospheric observations to be integrated over sufficient time to obtain constraint. Model error can also be estimated by this procedure, and the method can be extended to larger domains and longer integrations.

We quantify atmosphere-biosphere carbon exchange at the continental scale across North America during the summers of 2003 and 2004. The 2003 campaign features continental transects across the northern portion of North America with significant influence from biomass burning, while the 2004 study focuses on the greater New England and Quebec region. We use a Lagrangian, adjoint atmospheric model [Gerbig et al. 2003a,b; Lin et al. 2003] coupled to a biosphere model derived from the Vegetation Photosynthesis Model [Xiao et al., 2004]. Our analysis of the 2004 airborne data demonstrates the progression of increasing carbon uptake through the boreal zone during the seasonal transition from early spring to late summer. Data from the coast-to-coast transects of the 2003 campaign allow us to quantify large scale carbon exchange across the continent.