Abstract
Abstract. We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.
Highlights
Atmospheric measurements of CO2 show that on average half of the anthropogenic emissions of CO2 are taken up each year by the land and oceans (Ballantyne et al, 2012)
The range of flux estimates from different data sets is purely determined by the coverage difference between different sampling modes and the type of measurement, while the differences between the flux estimates from pseudo-obs generated by different models is a measure of the intermodel transport difference as sampled by a particular observing mode/network
We have used five different transport models in an observing system simulation experiments (OSSEs) to estimate the uncertainty in inversion-derived flux estimates due to the uncertainty of the modeled transport in flux inversions
Summary
Atmospheric measurements of CO2 show that on average half of the anthropogenic emissions of CO2 are taken up each year by the land and oceans (Ballantyne et al, 2012). Allocating this global sink to specific regions, or even partitioning it between land and oceans, has proved challenging (Schimel et al, 2014). Bottom-up methods, such as biosphere models and ocean biogeochemistry models, calculate the surface exchange of CO2 between two reservoirs by modeling the physical processes in the reservoirs that lead to such exchanges. Generally speaking, infer surface fluxes of CO2 from measured spatiotemporal gradients in tracer concentrations in either reservoir
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