The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015

Abstract

Abstract. Data assimilation systems are used increasingly to constrain the budgets of reactive and long-lived gases measured in the atmosphere. Each trace gas has its own lifetime, dominant sources and sinks, and observational network (from flask sampling and in situ measurements to spacebased remote sensing) and therefore comes with its own optimal configuration of the data assimilation. The CarbonTracker Europe data assimilation system for CO 2 estimates global carbon sources and sinks, and updates are released annually and used in carbon cycle studies. CarbonTracker Europe simulations are performed using the new modular implementation of the data assimilation system: the CarbonTracker Data Assimilation Shell (CTDAS). Here, we present and document this redesign of the data assimilation code that forms the heart of CarbonTracker, specifically meant to enable easy extension and modification of the data assimilation system. This paper also presents the setup of the latest version of CarbonTracker Europe (CTE2016), including the use of the gridded state vector, and shows the resulting carbon flux estimates. We present the distribution of the carbon sinks over the hemispheres and between the land biosphere and the oceans. We show that with equal fossil fuel emissions, 2015 has a higher atmospheric CO 2 growth rate compared to 2014, due to reduced net land carbon uptake in later year. The European carbon sink is especially present in the forests, and the average net uptake over 2001-2015 was 0.17 ± 0.11 PgC yr −1 with reductions to zero during drought years. Finally, we also demonstrate the versatility of CTDAS by presenting an overview of the wide range of applications for which it has been used so far.

Sources of Uncertainty in Regional and Global Terrestrial CO2 Exchange Estimates

“…The combination of these two results hints at differences in the sensitivity of NBP from inversions and DGVMs to changes in water availability or temperature related with ENSO (section 3.5.1), or possibly also to changes in atmospheric circulation/mixing patterns linked to ENSO. The opposing sign in the sensitivity to temperature between inversions and DGVMs ( Figure 5) may be due to an overestimate of the sensitivity of respiration to temperature (Bastos et al, 2018;van Schaik et al, 2018), or indirectly linked to the higher sensitivity of NBP to water availability in DGVMs compared to inversions. Models differ in prescribed soil depth and root water access for transpiration, but in general do not have deep rooting and ground water access of plants (Fan et al, 2017), which may explain why the sensitivity of regional CO 2 fluxes to water availability in DGVMs is higher than that of inversions with TWS.…”

“…Le Quéré, Andrew, Friedlingstein, Sitch, Hauck, et al (2018b) proposed that regional-level analyses may uncover sources of errors in the inferred fluxes. For example, comparisons of net CO 2 surface flux estimates from atmospheric inversions and dynamic global vegetation models (DGVMs) allowed assessing in detail the response of the tropical net CO 2 exchange to the 2015-2016 El Niño event, and evaluating the DGVMs' ability to simulate the anomalies in carbon fluxes in 2015/16 (Bastos et al, 2018;Gloor et al, 2018;van Schaik et al, 2018). Likewise, the studies produced in the framework of the REgional Carbon Cycle Assessment and Processes (RECCAP; Canadell et al, 2012) provided a first evaluation of global and regional budgets, budget component fluxes and uncertainties.…”

Quantification and attribution of urban fossil fuel emissions through atmospheric measurements

“…At this rural site, CO and CO 2 mixing ratios are observed at 60 m a.g.l. (Van der Laan et al, 2009b;Van der Laan et al, 2016). These four stations together describe a transect from the city towards rural areas.…”

“…At the rural site (Lutjewad), the urban plume has become mixed with other signals and the urban plume is difficult to distinguish. This site (at ~200 km from the Rijnmond area) is therefore too far removed to specifically constrain the Rijnmond emissions, although it was shown to constrain emissions from the larger urban conglomerate of the Randstad quite well (Van der Laan et al, 2009a;Van der Laan et al, 2010). The semi-urban site (Cabauw) detects urban plumes from Rijnmond which have already become well-mixed during transport.…”

“…It has been applied successfully to constrain biogenic fluxes of CO 2 by combining continental monitoring networks (like the Integrated Carbon Observation System) with regional to global transport models, leading to flux estimates at large spatiotemporal scales (0.5 to 10° and weeks to seasons) (e.g. (Basu et al, 2016;Broquet et al, 2013;Liu and Bowman, 2016;Meesters et al, 2012;Peters et al, 2007;Peters et al, 2010;Ray et al, 2014;Rödenbeck et al, 2009;Tolk et al, 2011;Van der Laan-Luijkx et al, 2015;Van der Laan-Luijkx et al, 2017)). However, most of the anthropogenic CO 2 emissions come from cities and therefore monitoring should be done at much smaller scales.…”