• Energy Research

AbstractTibetan Plateau (TP) has experienced a slowdown of the vegetation greening since the late 1990s. This structural change (i.e., greening) along with canopy physiology (i.e., potential photosynthetic productivity) regulates vegetation gross primary productivity (GPP). However, it remains unclear how the joint regulation influences the trend of alpine GPP under climate change. Here, we validate a universal productivity model against flux‐based and satellite‐derived observations at TP and diagnose the long‐term climatic impacts on GPP via canopy physiology and structure. We found an increasing but weakening trend of GPP after 1998. About 3/4 of this slowdown was attributed to the slowing greening after 1998, which was caused by the fact that the stress of atmospheric aridity and reduced benefits of warming overwhelmed the positive effects of CO2 fertilization and radiation enhancement. This study highlights the coupling between canopy structure and productivity for the long‐term period.

AbstractRealistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C (Vcmax,25) and the leaf respiration rate at 25°C (R25)) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R25 and Vcmax,25 vary substantially both temporally and spatially within the same PFT (C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in Vcmax,25 (73% and 54% of the variations in R25). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Abstract Recent increases in vegetation cover, observed over much of the world, reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming appears to be declining. Here we examine changes in vegetation cover on the Tibetan Plateau over the past 35 years. Although the climate trends are similar across the Plateau, drier regions have become greener by 0.31±0.14% yr−1 while wetter regions have become browner by 0.12±0.08% yr–1. This divergent response is predicted by a universal model of primary production accounting for optimal carbon allocation to leaves, subject to constraint by water availability. Rising CO2 stimulates production in both greening and browning areas; increased precipitation enhances growth in dry regions, but growth is reduced in wetter regions because warming increases below-ground allocation costs. The declining sensitivity of vegetation to climate change reflects a shift from water to energy limitation.

AbstractBoth anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation.

Stomatal conductance (gs) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of gs that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model1 and the leaf and wood economics spectrum2, 3. We also demonstrate a global relationship with climate. These findings provide a robust theoretical framework for understanding and predicting the behaviour of gs across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate. This research was supported by the Australian Research Council (ARC MIA Discovery Project 1433500-2012-14). A.R. was financially supported in part by The Next-Generation Ecosystem Experiments (NGEE-Arctic) project, which is supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the United States Department of Energy contract No. DE-AC02-98CH10886 to Brookhaven National Laboratory. M.O.d.B. acknowledges that the Brassica data were obtained within a research project financed by the Belgian Science Policy (OFFQ, contract number SD/AF/02) and coordinated by K. Vandermeiren at the Open-Top Chamber research facilities of CODA-CERVA (Tervuren, Belgium).

SummaryInteractions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO2 and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf‐level photosynthetic capacity. Whole‐plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO2 also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO2 fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections.