Measurements and modelling of CO2 in Switzerland: The CarboCount-CH projectDominik Brunner1, Stephan Henne1, Tesfaye Berhanu2, Nina Buchmann3, Edouard Davin4, Werner Eugster3, Nicolas Gruber5, Markus Leuenberger2, Yu Liu5, Brian Oney1 , Ece Satar2, Sonia Seneviratne4 and Lukas Emmenegger1

1 Empa, Air Pollution and Environmental Technology, Switzerland, 2 University of Bern, Climate and Environ. Physics Div., Physics Institute & Oeschger Centre for Climate Change Research, Switzerland, 3 ETH Zurich, Institute of Agricultural Sciences, Switzerland, 4 ETH Zurich, Atmospheric and Climate Science, Switzerland, 5 ETH Zurich, Environmental Physics, Switzerland

The project CarboCount-CH

Contact:dominik.brunner@empa.ch / www.empa.ch/abt503

Acknowledgements:Funded by the Swiss National Science Foundation as part of the the Sinergia project CarboCount CH

CarboCount-CH was a collaborative Swiss SNSF Sinergia project 2012-2015. It developed a prototype greenhouse gas (GHG) observing and modelling system with the aim to quantify and better understand anthropogenic emissions and natural fluxes of CO2 and CH4 in Switzerland.

It successully developed the following components:

Observation network with four regionally representative sites for high-precision observations of atmospheric concentrations of CO2, CH4 and CO.

Two complementary mesoscale atmospheric model systems for CO2 and CH4 using a Eulerian and Lagrangian approach, respectively

Two independent inverse modelling systems

Simulations of the European carbon cycle with COSMO-CLM2

Characterization of CO2 observations

CO2 and CH4 measurement network

4 sites equipped with Picarro G2401 or G2301 plus meteorology

212 m tall tower Beromünster with 5 inlet heights (Berhanu et al., 2016)

Central calibration at Empa, traceable to WMO standards

For overview and characterization of sites see Oney et al. (2015)

Measurements at Beromünster and other sites were characterized for seasonal and diurnal cycles (Satar et al., 2016) mean footprints and local and regional influences (Oney et al., 2015). equivalence (or not) of measurements at a mountain site and at tall tower

when measuring at same elevation (Bamberger et al., 2017).

Fig. 2: Monthly mean diurnal cycles of CO2 at Beromünster (Satar et al., 2016)

Fig. 3: Monthly mean CO2 diurnal cycle at highest level at Beromünster(blue) and difference to mountain top site (red) (Bamberger et al., 2017).

Fig. 1: Overview of measurement network and instrumentation.

Transport and inverse modelling system

Two transport models built around weather prediction model COSMO:

Lagrangian model FLEXPART-COSMO

Eulerian model COSMO-tracer

Two inverse modeling systems: FLEXPART-COSMO + Bayesian; COSMO-tracer + CarbonTracker-EnKF

High resolution anthropogenic emission and biosphere fluxes

Components of the atmospheric CO2 signalMeasured CO2

FLEXPART-COSMO simulated CO2 (sum ofall components)

MACC background CO2

Jungfraujoch background

Simulated anthropogenic CO2

based on inventories

CO-based anthropogenic CO2

Simulated biospheric CO2

based on VPRM

Residual biospheric CO2

Biospheric CO2 component (CO2,B) of measured signal can be derived asresidual by subtracting simulated background and anthropogenic CO2

(blue). Oney et al. (2017) obtained more plausible CO2,B when subtractingJungfraujoch background (CO2,BG) and anthropogenic CO2 deduced fromobserved CO (red):

Fig. 4: Model- vs observation-based CO2 components (Oney et al., 2017)

Fig. 5: Annual mean surface CO2 from (a) anthropogenic, (c) biospheric,and (d) background. (b) Sum of all components. (Yu et al., 2017)

Conclusions High-quality observation and modelling system for CO2 established Network suitable to estimate Swiss CH4 emissions (Henne et al., 2016) Anthropogenic CO2 dominates in winter, biospheric CO2 in summer. Fossil fuel CO2 accounts for >50% of CO2 variance in most of Europe CO holds promise to estimate anthropogenic CO2, but important

questions remain (Oney et al. 2017), e.g. why is ratio CO/CO2ff from14C so much higher than wintertime ratio CO/CO2?

Measurements and modelling continue in several new projects.

References:- Oney et al., The CarboCount CH sites: characterization of a dense greenhouse gas observation network, Atmos. Chem. Phys., 15, doi:10.5194/acp-15-11147-2015, 2015.- Berhanu et al., Measurements of greenhouse gases at Beromünster tall tower station in Switzerland, Atmos. Meas. Tech., 9, doi.org/10.5194/amt-9-2603-2016, 2016.- Satar et al., Continuous CO2/CH4/CO measurements (2012–2014) at Beromünster tall tower station in Switzerland, Biogeosc., 13, doi.org/10.5194/bg-13-2623-2016, 2016.- Bamberger et al., Observations of Atmospheric Methane and Carbon Dioxide Mixing Ratios: Tall-Tower or Mountain-Top Stations? Bound. Lay. Met., 164, 2017.- Oney et al., A CO based method to determine the regional biospheric signal in atmospheric CO2, Tellus B, 69, doi: 10.1080/16000889.2017.1353388, 2017.- Liu et al., Spatiotemporal patterns of the fossil-fuel CO2 signal in central Europe: Results from a high-resolution atmospheric transport model, ACP, under review, 2017.- Henne et al., Validation of the Swiss CH4 emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, do:10.5194/acp-16-3683-2016, 2016- Berhanu et al., Estimation of the fossil-fuel component in atmospheric CO2 based on radiocarbon measurements at the Beromünster tall tower, Switzerland, ACPD, in review.