Global CO 2 fluxes inferred from surface air-sample measurements and from TCCON retrievals of the CO 2 total column (original) (raw)
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Atmospheric Chemistry and Physics, 2004
We infer CO 2 surface fluxes using satellite observations of mid-tropospheric CO 2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO 2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO 2 observations over oceans, spanning 40 • S-40 • N, we find that the hor-5 izontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO 2 distributions are evaluated against independent ship and aircraft CO 2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measure-10 ments are sparse, such as the tropical forests of South America, which the joint inversion suggests was a weak sink of −0.17 ± 0.20 Pg C in 2006. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion yields −1.13 ± 0.21 Pg C for the global ocean, −2.77 ± 0.20 Pg C for the global land biosphere and −3.90 ± 0.29 Pg C for the total global natural flux (defined as the sum of all biospheric, oceanic, and 15 biomass burning contributions but excluding CO 2 emissions from fossil fuel combustion). These global ocean, global land and total global fluxes are shown to be in the range of other inversion results for 2006. To achieve these results, a latitude dependent bias in TES CO 2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations 20 in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO 2 and that the vertical information provided by satellite observations of mid-tropospheric CO 2 combined with measurements of surface CO 2 , provides an important additional constraint for flux inversions. Chevallier et al., 2010a) using in situ observations from instruments at surface stations, towers, ships and aircraft and/or flask samples collected from these platforms, then later analyzed in a laboratory (Conway et al., 1994). Measurement 25 coverage has increased over the years, and forward and inverse modeling techniques have also improved, but a major limitation in achieving further reductions in CO 2 flux uncertainties is the sparse data coverage that remains throughout the tropics, extra-4266
Tellus B, 2003
Spatial and temporal variations of atmospheric CO 2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO 2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed "background" fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air-sea gas exchange. Individual model-estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are
Journal of Geophysical Research, 2010
This paper documents a global Bayesian variational inversion of CO 2 surface fluxes during the period 1988-2008. Weekly fluxes are estimated on a 3.75°× 2.5°(longitudelatitude) grid throughout the 21 years. The assimilated observations include 128 station records from three large data sets of surface CO 2 mixing ratio measurements. A Monte Carlo approach rigorously quantifies the theoretical uncertainty of the inverted fluxes at various space and time scales, which is particularly important for proper interpretation of the inverted fluxes. Fluxes are evaluated indirectly against two independent CO 2 vertical profile data sets constructed from aircraft measurements in the boundary layer and in the free troposphere. The skill of the inversion is evaluated by the improvement brought over a simple benchmark flux estimation based on the observed atmospheric growth rate. Our error analysis indicates that the carbon budget from the inversion should be more accurate than the a priori carbon budget by 20% to 60% for terrestrial fluxes aggregated at the scale of subcontinental regions in the Northern Hemisphere and over a year, but the inversion cannot clearly distinguish between the regional carbon budgets within a continent. On the basis of the independent observations, the inversion is seen to improve the fluxes compared to the benchmark: the atmospheric simulation of CO 2 with the Bayesian inversion method is better by about 1 ppm than the benchmark in the free troposphere, despite possible systematic transport errors. The inversion achieves this improvement by changing the regional fluxes over land at the seasonal and at the interannual time scales.
Journal of Geophysical Research, 1999
The inversion of atmospheric transport of CO 2 may potentially be a means for monitoring compliance with emission treaties in the future. There are two types of errors, though, which may cause errors in inversions: (1) amplification of high-frequency data variability given the information loss in the atmosphere by mixing and (2) systematic errors in the CO 2 flux estimates caused by various approximations used to formulate the inversions. In this study we use simulations with atmospheric transport models and a time independent inverse scheme to estimate these errors as a function of network size and the number of flux regions solved for. Our main results are as follows. When solving for 10 -20 source regions, the average uncertainty of flux estimates caused by amplification of high-frequency data variability alone decreases strongly with increasing number of stations for up to ϳ150 randomly positioned stations and then levels off (for 150 stations of the order of Ϯ0.2 Pg C yr Ϫ1 ). As a rule of thumb, about 10 observing stations are needed per region to be estimated.
Atmospheric Chemistry and Physics, 2010
This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO 2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO 2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO 2 . The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO 2 fluxes and initial concentrations. Forward simulations of column averaged CO 2 (xCO 2 ) mixing ratios vary between the models by σ =0.5 ppm over the continents and σ =0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 10 6 km 2 over land and 0.03 PgC/yr per 10 6 km 2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 10 6 km 2 , and could also limit the overall performance of other CO 2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS Correspondence to: S. Houweling (s.houweling@sron.nl) measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO 2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport.
Possible representation errors in inversions of satellite CO2 retrievals
Journal of Geophysical Research, 2008
1] Owing to global spatial sampling and sheer data volume, satellite CO 2 concentrations can be used in inverse models to enhance our understanding of the carbon cycle. Using column measurements to represent a transport model grid column may introduce spatial, local clear-sky, and temporal sampling errors into inversions: the footprint is smaller than a grid cell, total column concentrations are only retrieved in clear skies, and the mixing ratios are only sampled at one time. To investigate these errors, we used a coupled ecosystem-atmosphere cloud-resolving model to create CO 2 fields over fine ($1°Â 1°) and coarse ($4°Â 4°) grid columns from 1 km 2 and 25 km 2 pixels that utilized explicit microphysics. We performed two simulations in August 2001: one in central North America and one in the Brazilian Amazon. Differences between satellite and grid column concentrations were calculated by subtracting the domain mean column concentration from 10-km-wide simulated satellite measurements. Spatial and local clear-sky errors were less than 0.5 ppm for the fine grid column; however, these errors became large and biased over the coarse grid column in North America. To avoid these errors, transport models should be run at high resolution. Using satellite measurements to represent bimonthly averages created large (>1 ppm) errors for all cases. The errors were negatively biased (approximately À0.4 ppm) in the North American simulation, indicating that inverse models cannot use satellite measurements to represent temporal averages. Simulated representation errors did not arise because of differences in ecosystem metabolism in cloudy versus sunny conditions; rather, they reflected large-scale CO 2 gradients in midlatitudes that were organized along frontal boundaries and masked under regional cloud cover. Such boundaries were not found in the dry-season tropical simulation presented here and may be less prevalent in the tropics in general. To avoid incurring errors, inversions must accurately model synoptic-scale atmospheric transport and CO 2 concentrations must be assimilated at the time and place observed.
Global Biogeochemical Cycles, 2006
1] Monthly CO 2 fluxes are estimated across 1988-2003 for 22 emission regions using data from 78 CO 2 measurement sites. The same inversion (method, priors, data) is performed with 13 different atmospheric transport models, and the spread in the results is taken as a measure of transport model error. Interannual variability (IAV) in the winds is not modeled, so any IAV in the measurements is attributed to IAV in the fluxes. When both this transport error and the random estimation errors are considered, the flux IAV obtained is statistically significant at P 0.05 when the fluxes are grouped into land and ocean components for three broad latitude bands, but is much less so when grouped into continents and basins. The transport errors have the largest impact in the extratropical northern latitudes. A third of the 22 emission regions have significant IAV, including the Tropical East Pacific (with physically plausible uptake/release across the 1997-2000 El Niño/La Niña) and Tropical Asia (with strong release in 1997/1998 coinciding with large-scale fires there). Most of the global IAV is attributed robustly to the tropical/southern land biosphere, including both the large release during the 1997/1998 El Niño and the post-Pinatubo uptake.