Preprints
https://doi.org/10.5194/essd-2022-175
https://doi.org/10.5194/essd-2022-175
 
21 Jun 2022
21 Jun 2022
Status: this preprint is currently under review for the journal ESSD.

Near real-time CO2 fluxes from CarbonTracker Europe for high resolution atmospheric modeling

Auke Marijn van der Woude1,2, Remco de Kok2,3, Naomi Smith2, Ingrid T. Luijkx2, Santiago Botia4, Ute Karstens5, Linda Maria Johanna Kooijmans2, Gerbrand Koren6,2, Harro Meijer1, Gert-Jan Steeneveld2, Ida Storm3,5, Ingrid Super7, Bert Augustinus Scheeren1, Alex Vermeulen3,4, and Wouter Peters2,1 Auke Marijn van der Woude et al.
  • 1Centre for Isotope Research, Energy and Sustainability Research Institute Groningen, University of Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands
  • 2Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
  • 3ICOS ERIC, Carbon Portal, Geocentrum II, Sölvegatan 1222362 Lund, Sweden
  • 4Max Planck Institute for Biogeochemistry, Hans-Knoell-Straße 10, 07745 Jena, Germany
  • 5Department of Physical geography and Ecosystem Sciences, Lund University, Box 117, SE-221 00, Lund, Sweden
  • 6Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
  • 7Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands

Abstract. We present the CarbonTracker Europe High-Resolution system that estimates carbon dioxide (CO2) exchange over Europe at high-resolution (0.1 x 0.2°) and in near real-time (about 2 months latency). It includes a dynamic fossil fuel emission model, which uses easily available statistics on economic activity, energy-use, and weather to generate fossil fuel emissions with dynamic time profiles at high spatial and temporal resolution (0.1 x 0.2°, hourly). Hourly net biosphere exchange (NEE) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEE is downscaled to 0.1 x 0.2° using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map, and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. An ocean flux extrapolation and downscaling based on wind speed and temperature for Jena CarboScope ocean CO2 fluxes is included in our product. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.

We assess the ability of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 drought, (b) to capture the reduction of fossil fuel emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ERA5, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city centre of Amsterdam (The Netherlands).

We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and fossil fuel emissions (COVID-19 pandemic in 2020). After transport with TM5, the CTE-HR fluxes have lower root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor-person inversion). RSMEs are close to those of the reanalysis with the data assimilation system CarbonTracker Europe (CTE). This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.

We furthermore compare CO2 observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different sectors in summer and winter. Interestingly, in periods where synoptic scale transport variability dominates CO2 variations, the CTE-HR fluxes perform similar to low-resolution fluxes (5–10x coarsened). The remaining 10 % of simulated CO2 mole fraction differ by > 2ppm between the low-resolution and high-resolution flux representation, and are clearly associated with coherent structures ("plumes") originating from emission hotspots, such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely-populated region like the Amsterdam city centre, our fluxes underestimate the magnitude of measured eddy-covariance fluxes, but capture their substantial diurnal variations in summer- and wintertime well.

We conclude that our product is a promising tool to model the European carbon budget at a high-resolution in near real-time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near real-time monitoring and modeling, for example as a-priori flux product in a CO2 data-assimilation system. The data is available at https://doi.org/10.18160/20Z1-AYJ2.

Auke Marijn van der Woude et al.

Status: open (until 16 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Auke Marijn van der Woude et al.

Data sets

Near real-time, high-resolution CO2 fluxes over Europe Auke van der Woude, Remco de Kok, Ute Karstens, Wouter Peters https://doi.org/10.18160/20Z1-AYJ2

Auke Marijn van der Woude et al.

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Short summary
To monitor the progress towards the CO2 emission goals set out in the Paris Agreement, the European Union requires an independent validation of emitted CO2. For this validation, atmospheric measurements of CO2 can be used, together with first-guess estimates of CO2 emissions and uptake. To quickly inform end-users, it is imperative that this happens in near real-time. To aid these efforts, we create estimates of European CO2 exchange in high-resolution in the near real-time.