The Long and Short of It: Semi-arid forests cover close to 18% of Earth's land surface. If climate change were to stimulate carbon accumulation in these areas, resulting changes in the forests could both promote climate cooling and warming: On one hand, forest growth would draw CO ₂ from the atmosphere, providing a cooling effect on climate; on the other, as forests grew and became more dense, their albedo would decrease, which would warm climate. Rotenberg and Yakir (p. 451 ; see the Perspective by Schimel ) now report that a shift in peak photosynthetic activities from summer to early spring would, indeed, cause carbon accumulation by the forests, but that a suppression of reflected longwave radiation effect would complement the better-known (shortwave) albedo effect, doubling the amount of potential warming. Several decades of carbon accumulation would thus be necessary to counteract these radiative changes.

The leaf economics spectrum^1,2 and the global spectrum of plant forms and functions³ revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species². Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities⁴. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability^4,5. Here we derive a set of ecosystem functions⁶ from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems^7,8.

For the past decade, observations of carbonyl sulfide (OCS or COS) have been investigated as a proxy for carbon uptake by plants. OCS is destroyed by enzymes that interact with CO₂ during photosynthesis, namely carbonic anhydrase (CA) and RuBisCO, where CA is the more important one. The majority of sources of OCS to the atmosphere are geographically separated from this large plant sink, whereas the sources and sinks of CO₂ are co-located in ecosystems. The drawdown of OCS can therefore be related to the uptake of CO₂ without the added complication of co-located emissions comparable in magnitude. Here we review the state of our understanding of the global OCS cycle and its applications to ecosystem carbon cycle science. OCS uptake is correlated well to plant carbon uptake, especially at the regional scale. OCS can be used in conjunction with other independent measures of ecosystem function, like solar-induced fluorescence and carbon and water isotope studies. More work needs to be done to generate global coverage for OCS observations and to link this powerful atmospheric tracer to systems where fundamental questions concerning the carbon and water cycle remain.

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θ_crit). Better quantification of θ_crit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θ_crit. We find an average global θ_crit of 0.19 m³/m³, varying from 0.12 m³/m³ in arid ecosystems to 0.26 m³/m³ in humid ecosystems. θ_crit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θ_crit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θ_crit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.