It's not only the carbon in the trees: Forest loss affects climate not just because of the impacts it has on the carbon cycle, but also because of how it affects the fluxes of energy and water between the land and the atmosphere. Evaluating global impact is complicated because deforestation can produce different results in different climate zones, making it hard to determine large-scale trends rather than more local ones. Alkama and Cescatti conducted a global assessment of the biophysical effects of forest cover change. Forest loss amplifies diurnal temperature variations, increases mean and maximum air temperatures, and causes a significant amount of warming when compared to CO ₂ emission from land-use change. Science , this issue p. 600

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R² < 0.5), ecosystem respiration (R² > 0.6), gross primary production (R²> 0.7), latent heat (R² > 0.7), sensible heat (R² > 0.7), and net radiation (R² > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R² > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R² < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO₂ and mitigating global climate change.

A decline in the carbon fertilization effect: One source of uncertainty in climate science is how the carbon fertilization effect (CFE) will contribute to mitigation of anthropogenic climate change. Wang et al. explored the temporal dynamics of CFE on vegetation photosynthesis at the global scale. There has been a decline over recent decades in the contribution of CFE to vegetation photosynthesis, perhaps owing to the limiting effects of plant nutrients such as nitrogen and phosphorus. This declining trend has not been adequately accounted for in carbon cycle models. CFE thus has limitations for long-term mitigation of climate change, and future warming might currently be underestimated. Science , this issue p. 1295

Extra water loss from earlier spring greening dries soils locally in summer, but can moisten soils in areas downwind.

Increasing terrestrial biomass has important impacts on the climate that affects it.