Nicolas Viovy

TL;DR: An increase in future drought events could turn temperate ecosystems into carbon sources, contributing to positive carbon-climate feedbacks already anticipated in the tropics and at high latitudes.

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

TL;DR: In the past 50 years, the fraction of CO2 emissions that remains in the atmosphere each year has likely increased, from about 40% to 45%, and models suggest that this trend was caused by a decrease in the uptake of CO 2 by the carbon sinks in response to climate change and variability as mentioned in this paper.

Abstract: This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphere-ocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrology-vegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

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.