Abstract
Abstract. Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ 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 study 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 timescale 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 d 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, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.
Highlights
Human activities have significantly influenced the carbon cycle of the earth system since industrialization, with the accumulation of greenhouse gases (GHG) in the atmosphere leading to radiative forcing and climate change (IPCC, 2014)
Based on the Vegetation Photosynthesis Respiration Model (VPRM) model, we developed a forecasting model that can predict biospheric net ecosystem exchange (NEE) for the 5 d and assess the error contribution from each aspect of forecasting
This CO2 flux forecast model is a crucial component in an atmospheric CO2 forecasting system, in which hourly to dayto-day CO2 flux variability plays an important role
Summary
Human activities have significantly influenced the carbon cycle of the earth system since industrialization, with the accumulation of greenhouse gases (GHG) in the atmosphere leading to radiative forcing and climate change (IPCC, 2014). The carbon exchange between the surface and the atmosphere still remains largely uncertain due to the complexity of processes and a lack of observations (Le Quéré et al, 2009). More measurements are needed, especially over emission hotspots and regions lacking observations. Field campaigns to measure greenhouse gases, such as research flights and measurements in remote areas, can fill the observation gap in the troposphere and over regions not covered by existing networks, but they are often time limited. To make the best use of these limited measurements, field campaigns require careful planning. An atmospheric CO2 forecast on synoptic timescales (∼ days) can be helpful in such cases, for it provides an estimate of what signals are expected during the experiment and a physical explanation of the observations
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