Carbon flux estimation for Siberia by inverse modeling constrained by aircraft and tower CO₂ measurements

Abstract

[1] Being one of the largest carbon reservoirs in the world, the Siberian carbon sink however remains poorly understood due to the limited numbers of observation. We present the first results of atmospheric CO 2 inversions utilizing measurements from a Siberian tower network (Japan-Russia Siberian Tall Tower Inland Observation Network; JR-STATION) and four aircraft sites, in addition to surface background flask measurements by the National Oceanic and Atmospheric Administration (NOAA). Our inversion with only the NOAA data yielded a boreal Eurasian CO 2 flux of À0.56 AE 0.79 GtC yr À1 , whereas we obtained a weaker uptake of À0.35 AE 0.61 GtC yr À1when the Siberian data were also included. This difference is mainly explained by a weakened summer uptake, especially in East Siberia. We also found the inclusion of the Siberian data had significant impacts on inversion results over northeastern Europe as well as boreal Eurasia. The inversion with the Siberian data reduced the regional uncertainty by 22% on average in boreal Eurasia, and further uncertainty reductions up to 80% were found in eastern and western Siberia. Larger interannual variability was clearly seen in the inversion which includes the Siberia data than the inversion without the Siberia data.In the inversion with NOAA plus Siberia data, east Siberia showed a larger interannual variability than that in west and central Siberia. Finally, we conducted forward simulations using estimated fluxes and confirmed that the fit to independent measurements over central Siberia, which were not included in inversions, was greatly improved.Citation: Saeki, T., et al. (2013), Carbon flux estimation for Siberia by inverse modeling constrained by aircraft and tower CO 2 measurements,

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

“…Performing this type of assessment for multiple inversions constrained by different types of measurements but using the same transport model can provide insight into whether seasonal biases in the inversion are caused by seasonal biases in an observing system or by seasonal biases in the transport model (e.g., Houweling et al, 2014). More generally, vertical transport bias can be assessed by comparing the vertical gradients of posterior vertical profiles to those of observed profiles (e.g., Pickett-Heaps et al, 2011;Saeki et al, 2013b;Liu and Bowman, 2016), because vertical gradients provide information about vertical mixing and convection.…”

“…However, the informational value and robustness of the information provided by satellite observations is still the subject of ongoing research, and thus their use as constraints in inversions requires special consideration of the impacts of any potential biases. Several studies have included satellite total column or mixing ratio data as an additional constraint on a model otherwise constrained only by in situ concentration measurements to determine whether remotely sensed total column concentrations provide a significant amount of additional information (e.g., Alexe et al, 2015;Houweling et al, 2014;Nassar et al, 2011;Pandey et al, 2016;Saeki et al, 2013a). An inversion constrained only by in situ measurements may also be compared to an inversion constrained only by satellite measurements (e.g., Cressot et al, 2014).…”

“…Such experiments can be used to estimate the uncertainty reduction (see Sect. 3.2) that could potentially be achieved by assimilating more observations over or downwind from poorly constrained regions as well as the effects of a more extensive observational network on the estimated spatial and temporal variability of fluxes (e.g., Butler et al, 2010;Saeki et al, 2013b;Kadygrov et al, 2015;Jiang et al, 2014;Peters et al, 2010). They can also be used to determine the value of episodic versus continuous observations (e.g., Peters et al, 2010).…”

Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation

“…The resolution of the input fluxes was matched to that of FLEX-PART. Model results were compared with observations from the World Data Centre for Greenhouse Gases (WDCGG, 2015) and the Siberian observations obtained by the Center for Global Environmental Research (CGER) of the National Institute for Environmental Studies (NIES) and the Russian Academy of Science (RAS), from six tower sites (JR-STATION) as described by Sasakawa et al (2010). The selected site locations are shown in Fig.…”

“…Most of them are concentrated in the temperate latitudes of the Northern Hemisphere, where the variations in CO 2 concentration are most noticeable. Siberia is assumed to be a substantial source and sink of CO 2 , with high uncertainties in the fluxes describing them (McGuire et al, 2009;Hayes et al, 2011;Saeki et al, 2013). As a result, CTMs tend to reproduce the interannual variability of CO 2 quite poorly.…”