Fabian Gans

TL;DR: The study indicates that spatial climate covariation drives the global carbon cycle response and helps to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance.

TL;DR: In this article, the authors use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties.

TL;DR: In this paper, the authors provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods and sets of predictor variables were employed.

TL;DR: A systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing datasets, and sets of predictor variables were employed as mentioned in this paper.

TL;DR: In this paper, a simple power transfer hierarchy based on the assumption that available wind power will not geographically vary with increased extraction for an estimate of 68 TW was proposed. But this was based on a simple momentum balance model to estimate maximum extractability which was then applied to reanalysis climate data.