A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization

Abstract

Recently flux tower data have become available for a variety of ecosystems under different climatic and edaphic conditions. Although Flux tower data represent point measurements with a footprint of typically 1 km × 1 km they can be used to validate models and to spatialize biospheric fluxes at regional and continental scales. In this paper we present a study where biospheric flux data collected in the EUROFLUX project were used to train a neural network simulator to provide spatial (1 km × 1 km) and temporal (weekly) estimates of carbon fluxes of European forests at continental scale. The novelty of the approach is that flux data were used to constrain and parameterize the neural network structure using a limited number of input driving variables. The overall European carbon uptake from this analysis was 0.47 Gt C yr−1 with distinctive differences between boreal and temperate regions. The length of the growing season is longer in the south of Europe (about 32 weeks), compared with north and central Europe, which have a similar length‐growing season (about 27 weeks). A peak in respiration was depicted in spring at continental scale as a coherent signal which parallel the construction respiration increase at the onset of the season as usually shown by leaf level measurements.