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. Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns bybetter predicting the location of important gradients. One aspect of this,accurately predicting the day-to-day variation in biospheric fluxes, poses amajor challenge. This study aims to investigate the feasibility of using adiagnostic light-use-efficiency model, the Vegetation PhotosynthesisRespiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwaveradiation, 2 m temperature, and enhanced vegetation index (EVI) and landsurface water index (LSWI), both of which are calculated from MODISreflectance measurements. Flux forecasts were performed by extrapolating themodel input into the future, i.e., using downward shortwave radiation andtemperature from a numerical weather prediction (NWP) model, as well asextrapolating the MODIS indices to calculate future biospheric CO2fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in2014 has been done and compared to eddy covariance flux measurements toassess the uncertainty from different aspects of the forecasting system. Intotal the range-normalized mean absolute error (normalized) of the 5 dflux forecast at daily timescales is 7.1 %, while the error for the modelitself is 15.9 %. The largest forecast error source comes from themeteorological data, in which error from shortwave radiation contributesslightly more than the error from air temperature. The error contributionfrom all error sources is similar at each flux observation site and is notsignificantly dependent on vegetation type.

geoscientific model development

Short-term forecasting of regional biospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2)