Martin Jung

TL;DR: Estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.

TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.

TL;DR: An estimate of global land evapotranspiration from 1982 to 2008 is provided using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm, which suggests that increasing soil-moisture limitations on evapOTranspiration largely explain the recent decline of the global land-evapotranpiration trend.

TL;DR: Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, it is shown that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions.

TL;DR: In this article, the authors present a comprehensive global database for forest ecosystems, which includes carbon budget variables (fluxes and stocks), ecosystem traits (e.g., leaf area index, age), as well as ancillary site information such as management regime, climate, and soil characteristics.