In order to facilitate future decision-making regarding regional carbon fluxes, it is essential to better quantify uncertainty in inverse carbon flux models. At Colorado State University, research is being performed in order to better quantify sources and sinks and associated uncertainties on a mesoscale level, through a coupled atmospheric (RAMS and PCTM) and terrestrial carbon flux (SiB3) model (Denning, 2003).  Carbon-dioxide flux and mixing ratio data were collected from a ring of towers (WLEF tall tower and nearby smaller towers) in northern Wisconsin over the summer of 2004.  The fully coupled terrestrial-atmospheric model, SiB/RAMS, will be forced with 2004 reanalysis data to predict fine scale weather in the vicinity of these towers for the summer of 2004. Relevant portions of this simulated weather, including wind fields and pertinent turbulence components, are extracted and used to create backward-in-time Lagrangian Particle Dispersion Modeled (LPDM) influence functions.  Pseudo spatial carbon-dioxide mixing ratio and flux data created by SiB/Rams is then used as input to several different estimation routines in order to try and predict pseudo tower data at different heights.  Different temporal and spatial aggregation lengths are considered as means of data reduction. Particular attention will be paid to Ensemble Kalman Filter (EnKF) techniques as well as geo-statistical methods as a means of estimation.

Current predictions of future CO₂ sink strength vary widely as a result of different model representations of the carbon cycle. A sound characterization of these prediction uncertainties is crucial for the design of economically efficient carbon management strategies. We use a mechanistically sound and statistically tractable model of the global carbon cycle to (1) assimilate historical observations of atmospheric CO₂ concentrations and oceanic CO₂ fluxes, (ii) derive probabilistic predictions of future CO₂ concentrations and fluxes, and (iii) compare the utility of terrestrial and oceanic observations to constrain predictive uncertainties. We found that terrestrial and oceanic flux observations have nearly equal ability to constrain these uncertainties, if terrestrial observations include both net primary productivity (NPP) and respiration. Model predictions are dependent on the choice of historical land use emissions dataset. The probability density function (PDFs) of model parameter estimates are not normally distributed, and neglecting autocorrelation in the CO₂ concentration signal during model calibration causes overconfident results.

Numerical models of the carbon cycle are becoming increasingly sophisticated. One result of this is that these models now require fossil-fuel carbon-dioxide emissions data with sub-annual (e.g., seasonal) time resolution. They also require finer spatial resolution than national averages (i.e., than one point per nation). Finer spatial resolution is especially needed for countries as large in area as the United States of America (U.S.A.). Here we present a summary of monthly data for the entire nation, and annual data for each state in the U.S.A.

We address an ongoing debate regarding the geographic distribution of interannual variability in ocean - atmosphere carbon exchange. We find that, for 1983-1998, both novel high-resolution atmospheric inversion calculations and global ocean biogeochemical models place the primary source of global CO₂ air-sea flux variability in the Pacific Ocean. In ocean biogeochemical models, this variability is clearly associated with the El Niño / Southern Oscillation cycle. Both inversion and models indicate that the Southern Ocean is the second-largest source of air-sea CO₂ flux variability, and that variability is small throughout the Atlantic, including the North Atlantic, in contrast to previous studies.

Measurements of atmospheric O₂/N₂ ratio and CO₂ concentration are presented over the period 1989 to present from the Scripps Institution of Oceanography global flask sampling network. The data are used to make estimates of land and ocean sinks over various time scales. The oceanic and land biotic sinks are estimated to be 1.9±0.6 (ocean) and 1.2±0.8 Pg C/yr (land) over the period Jan. 1990-Jan. 2000 and 2.2±0.5 (ocean) and 0.5±0.7 Pg C/yr (land) over the period Jan. 1993-Jan. 2003. These estimates make allowance for oceanic O₂ and N₂ outgassing based on observed changes in ocean heat content and estimates of the relative outgassing per unit warming. The recent ocean sink is consistent, to within the uncertainties, with estimates of the accumulation of anthropogenic CO₂ in the ocean since 1800, assuming the oceanic sink varied over time as predicted by a box-diffusion model. The possibility that the ocean sink is being reduced slightly by climate feedbacks, as predicted by some models, is not ruled out, however.

The Total Carbon Column Observing Network is a new network of ground-based solar observatories, dedicated to column measurements of greenhouse gases.  We present CO₂ column abundances observed in Park Falls, Wisconsin and Lauder, New Zealand during May 2004 – June 2005.  In Park Falls, Wisconsin, the peak-to-peak variation of column-average CO₂ is approximately 13 ppmv.  In Lauder, New Zealand, the peak-to-peak variation of column-average CO₂ is approximately 4 ppmv.  Assuming a secular trend of 2 ppmv yr^-1, we infer a peak-to-peak seasonal amplitude of 11 ppmv and 2 ppmv for Park Falls and Lauder respectively.  These values are higher than model predictions by Olsen and Randerson [2003].

We have constructed an estimate of annual-mean surface fluxes of carbon dioxide for the period 1992-6 using observational constraints from the atmosphere and from the ocean interior. The method interprets in situ observations of carbon dioxide concentration in the ocean and atmosphere using transport estimates from global circulation models.