Variability in climate exerts a strong influence on vegetation productivity (gross primary
productivity; GPP), and therefore has a large impact on the land carbon sink. However, no
direct observations of global GPP exist, and estimates rely on models that are constrained by
observations at various spatial and temporal scales. Here, ...
Variability in climate exerts a strong influence on vegetation productivity (gross primary
productivity; GPP), and therefore has a large impact on the land carbon sink. However, no
direct observations of global GPP exist, and estimates rely on models that are constrained by
observations at various spatial and temporal scales. Here, we assess the consistency in GPP
from global products which extend for more than three decades; two observation-based
approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing
derived light-use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models
(TRENDYv6). At local scales, we find high correlations in annual GPP amongst the
products, with exceptions in tropical and high northern latitudes. On longer timescales, the
products agree on the direction of trends over 58% of the land, with large increases across
northern latitudes driven by warming trends. Further, tropical regions exhibit the largest
interannual variability in GPP, with both rainforests and savannas contributing substantially.
Variability in savanna GPP is likely predominantly driven by water availability, although
temperature could play a role via soil moisture – atmosphere feedbacks. There is, however,
no consensus on the magnitude and driver of variability of tropical forests, which suggest
uncertainties in process representations and underlying observations remain. These results
emphasise the need for more direct long-term observations of GPP along with an extension of
in-situ networks in underrepresented regions (e.g. tropical forests). Such capabilities would
support efforts to better validate relevant processes in models, to more accurately estimate
GPP.