Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Isolating significant signals from temporal variations in autotrophic and heterotrophic components of ecosystem respiration (Reco) is required to better quantify the temperature sensitivity of the land carbon cycle processes. Here we present diurnal and seasonal patterns in field and model-based components of respiration and investigate their responses to environmental conditions at a dry eucalypt forest, the Cumberland Plain SuperSite of the Australian Terrestrial Ecosystem Research Network. We conducted measurement campaigns of total CO2 flux from the soil surface (Rsoil), soil microbial respiration (Rmicrobe), root respiration (Rroot), litter respiration (Rlitter), and stem respiration (Rstem) in 2018. In total, six infrared gas analyzers with closed, dynamic auto-chambers and six forced diffusion auto-chambers were used for periodic campaigns. Further, Reco and its components were simulated using the Community Atmosphere-Biosphere Land Exchange model (CABLE), constrained by eddy covariance measurements and chamber measurements of Rsoil. A new version of CABLE was implemented with the Dual Arrhenius Michaelis Menten (DAMM) formulation to assess the importance of substrate availability for simulating Rmicrobe. We found that respiration rates showed similar diurnal patterns among the components, showing diurnal hysteresis between respiration components and temperature. In this dry ecosystem, the respiratory components were more responsive to seasonally increasing temperature in wet than in dry periods, and the responses were dependent on atmospheric relative humidity affecting the litter layer moisture content. The temperature sensitivity was significantly higher in Rstem than in other components. Based on observed fluxes of Rmicrobe in trenched plots and Rsoil in intact soil plots, the mean contribution of Rroot to Rsoil was less than 20 % for the dry seasons, while mean Rstem was two times greater than mean Rsoil suggesting that Rstem should be not overlooked in ecosystem flux estimations. This study highlights that partitioning the respiratory components and accounting for their different temperature-responses will be necessary to reduce uncertainty in modelling carbon-climate feedbacks.