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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2019-173
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-173
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Jul 2019

23 Jul 2019

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model

Jinxuan Chen1, Christoph Gerbig1, Julia Marshall1, and Kai Uwe Totsche2 Jinxuan Chen et al.
  • 1Department Biogeochemical Systems, Max Plank Institute for Biogeochemistry, Jena, 07745, Germany
  • 2Friedrich Schiller University, Jena, 07743, Germany

Abstract. Forecasting atmospheric CO2 concentrations on synoptic time scales (~ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes poses a major challenge. This research aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the time scale of a few days. As input the VPRM model requires downward shortwave radiation, 2 m temperature, and Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e. using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 day flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, which fail to accurately predict cloud cover, leading to overestimated shortwave radiation in the model. The error contribution from all error sources is similar at each flux observation site, and is not significantly dependent on vegetation type.

Jinxuan Chen et al.

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Jinxuan Chen et al.

Model code and software

Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model - Model code and output J. Chen and C. Gerbig https://doi.org/10.17617/3.2d

Jinxuan Chen et al.

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Latest update: 04 Aug 2020
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Short summary
One of the essential challenge for atmospheric CO2 forecasting is predicting CO2 flux variation on sub-daily time scale. For CAMS CO2 forecast, a process-based vegetation model is used. In this research we evaluate another type of model (i.e. the light-use-efficiency model VPRM), which is a data-driven approach and thus ideal for realistic estimation, on its flux prediction ability. Errors from different sources are assessed, and overall the model is capable of CO2 flux prediction.
One of the essential challenge for atmospheric CO2 forecasting is predicting CO2 flux variation...
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