CHIOTTO – Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases – is a European Union-funded project which has as objective to build an infrastructure for the continuous monitoring of greenhouse gas concentrations across Europe above the surface layer using tall towers (~300m height). For this purpose a new analysis system for continuous atmospheric measurements was built and tested at Max Planck Institute for Biogeochemistry, Jena, Germany and was recently installed at a 300 m tower close to Bialystok, Poland (Lat 53°14'N, Long 23°01'E, Alt 180m), as part of the “CHIOTTO” tall tower network. Since July 2005 this system is measuring quasi-continuously the atmospheric concentration of CO₂, CH₄, CO, N₂O, SF₆ and the O₂/N₂ ratio as well as meteorological parameters (atmospheric pressure, temperature, humidity; wind speed and direction) from 5 heights on the tower ranging from 5 to 300 m. The measurement devices are: an Oxzilla O₂ fuel cell analyzer, a LiCor 7000 NDIR CO₂ analyzer, an Agilent gas chromatograph (GC) with flame ionization detector (FID) and electronic capture detector (ECD) for CH₄, CO, N₂O, SF₆. The challenge was to build a reliable automatic system which can run continuously with very little maintenance and to fulfill at the same time the high precision requirements for all the measured species prescribed by the CHIOTTO project goals. The high temporal resolution achieved will capture short term events and diurnal variability. In addition, the system is planned to run for at least several years in order to observe long-term trends as well. We describe the technical setup of the measurement system, the region of influence of the station and present the first months of data if available: correlations between species, observed short term variability patterns and their relation to meteorology and air parcel paths.

We carried out airborne campaigns over Europe in order to analyze atmospheric CO₂ variability at the regional scale. Data reveal a higher standard variation in the planetary boundary layer (PBL) against a lower one in the free troposphere (FT), where the air is more well mixed. Ground data generally agree well with airborne measurements when done in the FT, but not in the PBL where they are exposed to local disturbances. Ground stations located in the FT are shown to be representative of a regional scale while PBL observatories provide only locally representative measurements.

Regular vertical profiles over Europe were set up in 2001 as part of the AEROCARB and Carboeurope-IP projects at five locations: Griffin (56°36'N, 3°47'W, Scotland), Orléans (47°50'N, 2°30'E, France), Schauinsland (47°55'N, 7°55'E, Germany), Hegyhatsal (46°57'N, 16°39'E, Hungary), and Bialystok (53.20°N, 22.75°E, Poland). The objective of the program is to measure CO₂, CH₄, N₂O, SF₆, CO, ¹³C and ¹⁸O in CO₂ vertical profiles at a bi-weekly frequency using air samples taken up at several levels from 100m up to 3000 m above the ground surface. One liter flasks are sampled on board small aircraft using a standardised protocol. The samples are analysed at three laboratories (LSCE, MPI-BGC, IUP-UHEI) which are linked through regular intercomparison exercises. We have characterised for each site the CO₂ seasonal cycles within the atmospheric boundary layer (ABL: 14 to 20 ppm) and the free troposphere (FT: 10 to 13 ppm). From these signals we have calculated the difference between ABL and FT, known as the CO₂ 'jump', which will be compared to the simulations from atmospheric transport models. We have also calculated the offset between each airborne sampling site and the time series from Mace Head observatory, used as a maritime reference. For CO₂, the wintertime offsets at the lowest level of the average vertical profiles are ranging from 0 ppm in Scotland up to 10 ppm in all continental sites. Depending of the site the positive offset due to emissions from anthropogenic and biospheric processes may extend up to 300 to 1500 m agl. In summertime we observe a negative gradient in most of the sites with a typical decrease of 5 ppm between 2000m and 100m agl. The average vertical gradients will be compared to the ouput of atmospheric models, and will be analysed with regards to the other trace gas (CO, CH₄, and CO₂ isotopes).

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.

The global biogeochemical cycle of oxygen is closely linked to that of carbon dioxide, because key biological processes, as well as fossil fuel burning, occur with specific stochiometric ratios. In the ocean, however, several processes – carbonate chemistry (buffer effect), physical transport (dilution), and warming/cooling (solubility changes) – decouple O₂ and CO₂ exchanges. Based on a decade of atmospheric O₂/N₂ and CO₂ data, we estimated spatial and temporal patterns of oceanic APO fluxes, using an inversion of atmospheric transport. Seasonal and interannual variations are interpreted in the light of climate variables.

Regular multi-year measurements of atmospheric CO₂ mixing ratios at a network of sites (Fig. 1) give quantitative spatial and temporal information on surface sources and sinks [e.g., Conway et al., 1994]. Using a global atmospheric tracer transport model in a high-resolution (daily, 4x5 degree pixels) inversion setup, we estimate surface-atmosphere CO₂ fluxes that give the best match between modelled and observed CO₂ concentrations. Building on an earlier study [Rödenbeck et al., 2003], this contribution (1) presents new CO₂ flux estimates using methodological developments, and (2) provides an update on interannual fluxes over the most recent anomalous time period 2002-2003.

There is growing evidence that the rate of anthropogenic CO₂ uptake in the ocean is changing over time. Several programs are poised to assess current and future ocean CO₂ uptake rates, but there are issues with how to extrapolate these measurements to decadal-scale changes over entire ocean basins. One possibility is to exploit the growing network of ARGO floats that are collecting profiles throughout the global oceans. We explore the viability of this approach and make recommendations for how the ARGO network might be made more useful for biogeochemical applications.

The Orbiting Carbon Observatory is a NASA ESSP mission that is scheduled for launch in September 2008 [Crisp et al., 2004]. The space-based observatory will sample the dry air, column averaged mole fraction of CO₂ (X_CO2) based on analysis of reflected solar radiation, between ~0.78 and 2.0 microns, acquired by three grating spectrometers. To fulfill the mission’s science objectives, the OCO validation activities are focused on demonstrating that space-based retrievals of X_CO2 have random errors no larger than 0.3% (1 ppm) over a network of ground based validation sites on monthly time scales [Miller et al., 2005]. Furthermore, space-based retrievals of X_CO2 will be compared to measurements from this network of ground-based stations to detect and mitigate geographically coherent biases on regional to continental scales. We describe plans and progress to date of the OCO validation program, which consists primarily of a series of ground-based, Fourier Transform Spectrometers (FTS), that measure X_CO2 in the same spectral regions as the space-based spectrometers.

Mt. Cimone Observatory is a background station for the measurement of greenhouse gases and other atmospheric pollutants located on the top of the highest peak of the Italian Northern Appenines. Continuous Measurements of atmospheric CO₂ were started in March 1979 by the Italian Air Force Meteorological Service using NDIR analysers. A number of case studies are presented in order to show the influence of certain polluted or vegetated areas on the concentration of carbon dioxide. Chemical tracers are used to asses the origin of the air masses together with an analysis of the back trajectories.

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.