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Abstract Realistic 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 (V_cmax,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 observedR₂₅andV_cmax,25vary 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 inV_cmax,25(73% and 54% of the variations inR₂₅). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread inr(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 CO₂uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters. 

The efflux of carbon dioxide (CO₂) from woody stems, a proxy for stem respiration, is a critical carbon flux from ecosystems to the atmosphere, which increases with temperature on short timescales. However, plants acclimate their respiratory response to temperature on longer timescales, potentially weakening the carbon-climate feedback. The magnitude of this acclimation is uncertain despite its importance for predicting future climate change. We develop an optimality-based theory dynamically linking stem respiration with leaf water supply to predict its thermal acclimation. We show that the theory accurately reproduces observations of spatial and seasonal change. We estimate the global value for current annual stem CO₂efflux as 27.4 ± 5.9 PgC. By 2100, incorporating thermal acclimation reduces projected stem respiration without considering acclimation by 24 to 46%, thus reducing land ecosystem carbon emissions. 

Abstract. Global fire-vegetation models are widely used to assessimpacts of environmental change on fire regimes and the carbon cycle and toinfer relationships between climate, land use and fire. However,differences in model structure and parameterizations, in both the vegetationand fire components of these models, could influence overall modelperformance, and to date there has been limited evaluation of how welldifferent models represent various aspects of fire regimes. The Fire ModelIntercomparison Project (FireMIP) is coordinating the evaluation ofstate-of-the-art global fire models, in order to improve projections of firecharacteristics and fire impacts on ecosystems and human societies in thecontext of global environmental change. Here we perform a systematicevaluation of historical simulations made by nine FireMIP models to quantifytheir ability to reproduce a range of fire and vegetation benchmarks. TheFireMIP models simulate a wide range in global annual total burnt area(39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burntarea is within observational uncertainty (345–468 Mha). Benchmarking scoresindicate that seven out of nine FireMIP models are able to represent thespatial pattern in burnt area. The models also reproduce the seasonality inburnt area reasonably well but struggle to simulate fire season length andare largely unable to represent interannual variations in burnt area.However, models that represent cropland fires see improved simulation offire seasonality in the Northern Hemisphere. The three FireMIP models whichexplicitly simulate individual fires are able to reproduce the spatialpattern in number of fires, but fire sizes are too small in key regions, andthis results in an underestimation of burnt area. The correct representationof spatial and seasonal patterns in vegetation appears to correlate with abetter representation of burnt area. The two older fire models included inthe FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less wellglobally than other models, but it is difficult to distinguish between theremaining ensemble members; some of these models are better at representingcertain aspects of the fire regime; none clearly outperforms all othermodels across the full range of variables assessed. 

Abstract Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.