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Research infrastructures play a key role in launching a new generation of integrated long-Term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-Access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT). We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a) reduces the estimated global land sink of CO2, and (b) shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

We report on 10 yr of airborne measurements of atmospheric CO2 mole fraction from continuous and flask systems, collected between 2002 and 2012 over the Atmospheric Radiation Measurement Program Climate Research Facility in the US Southern Great Plains (SGP). These observations were designed to quantify trends and variability in atmospheric mole fraction of CO2 and other greenhouse gases with the precision and accuracy needed to evaluate ground-based and satellite-based column CO2 estimates, test forward and inverse models, and help with the interpretation of ground-based CO2 mole-fraction measurements. During flights, we measured CO2 and meteorological data continuously and collected flasks for a rich suite of additional gases: CO2, CO, CH4, N2O, 13CO2, carbonyl sulfide (COS), and trace hydrocarbon species. These measurements were collected approximately twice per week by small aircraft (Cessna 172 initially, then Cessna 206) on a series of horizontal legs ranging in altitude from 460 m to 5500 m a.m.s.l. Since the beginning of the program, more than 400 continuous CO2 vertical profiles have been collected (2007–2012), along with about 330 profiles from NOAA/ESRL 12-flask (2006–2012) and 284 from NOAA/ESRL 2-flask (2002–2006) packages for carbon cycle gases and isotopes. Averaged over the entire record, there were no systematic differences between the continuous and flask CO2 observations when they were sampling the same air, i.e., over the one-minute flask-sampling time. Using multiple technologies (a flask sampler and two continuous analyzers), we documented a mean difference of < 0.2 ppm between instruments. However, flask data were not equivalent in all regards; horizontal variability in CO2 mole fraction within the 5–10 min legs sometimes resulted in significant differences between flask and continuous measurement values for those legs, and the information contained in fine-scale variability about atmospheric transport was not captured by flask-based observations. The CO2 mole fraction trend at 3000 m a.m.s.l. was 1.91 ppm yr−1 between 2008 and 2010, very close to the concurrent trend at Mauna Loa of 1.95 ppm yr−1. The seasonal amplitude of CO2 mole fraction in the free troposphere (FT) was half that in the planetary boundary layer (PBL) (~ 15 ppm vs. ~ 30 ppm) and twice that at Mauna Loa (approximately 8 ppm). The CO2 horizontal variability was up to 10 ppm in the PBL and less than 1 ppm at the top of the vertical profiles in the FT.

資料番号: PA1110076000

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability.

This study evaluates the potential impact of clouds on ecosystem CO2 and CO2 isotope fluxes (“isofluxes”) in two contrasting ecosystems (a broadleaf deciduous forest and a C4 grassland) in a region for which cloud cover, meteorological, and isotope data are available for driving the isotope-enabled land surface model (ISOLSM). Our model results indicate a large impact of clouds on ecosystem CO2 fluxes and isofluxes. Despite lower irradiance on partly cloudy and cloudy days, predicted forest canopy photosynthesis was substantially higher than on clear, sunny days, and the highest carbon uptake was achieved on the cloudiest day. This effect was driven by a large increase in light-limited shade leaf photosynthesis following an increase in the diffuse fraction of irradiance. Photosynthetic isofluxes, by contrast, were largest on partly cloudy days, as leaf water isotopic composition was only slightly depleted and photosynthesis was enhanced, as compared to adjacent clear-sky days. On the cloudiest day, the forest exhibited intermediate isofluxes: although photosynthesis was highest on this day, leaf-to-atmosphere isofluxes were reduced from a feedback of transpiration on canopy relative humidity and leaf water. Photosynthesis and isofluxes were both reduced in the C4 grass canopy with increasing cloud cover and diffuse fraction as a result of near-constant light limitation of photosynthesis. These results suggest that some of the unexplained variation in global mean δ 18O of CO2 may be driven by large-scale changes in clouds and aerosols and their impacts on diffuse radiation, photosynthesis, and relative humidity.

We use the radon tracer method to estimate monthly average net ecosystem exchange (NEE) of carbon dioxide in the Southern Great Plains of the USA for the year 2007. These estimates are compared with optimized flux estimates of NEE from NOAA CarbonTracker, sampled with a Lagrangian particle dispersion model to identify the upwind area influencing each measurement. The radon-tracer equation is very simple: F(CO2) = F(Rn) x delta(CO2)/delta(Rn), where F(CO2) is NEE, F(Rn) is the flux of radon out of the soil, and delta(X) is the discrepancy between a measured concentration and "background" levels, caused by near-field terrestrial fluxes. The F(Rn) term presents a challenge in applying the radon-tracer method, since neither its mean value nor seasonality are well known. We present two lines of evidence to help constrain the seasonal cycle of radon flux in the Southern Great Plains during 2007 (a year with record high early-summer soil moisture), increasing our confidence in applying the radon-tracer method. The first line of evidence comes from a comparison between the observed seasonal cycle of radon at ARM-CART SGP and simulations made by 14 global transport models as part of the "Transcom4" experiment which assumed constant radon emissions. The second line of evidence uses the radon-tracer approach in reverse, using measured eddy covariance CO2 fluxes and nighttime accumulation of CO2 and radon to calculate radon fluxes.