The lack of a standardized database of eddy covariance observations has been an obstacle for data‐driven estimation of terrestrial CO₂ fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data‐driven estimation of gross primary productivity (GPP) and net ecosystem CO₂ exchange (NEE). Data‐driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site‐level evaluation of the estimated CO₂ fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r² = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor‐based Sun‐induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r² = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere‐land CO₂ fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO₂ fluxes from SVR‐NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO₂ fluxes. These data‐driven estimates can provide a new opportunity to assess CO₂ fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Fire causes dramatic short-term changes in vegetation and ecosystem function, and may promote rapid vegetation change by creating recruitment opportunities. Climate warming likely will increase the frequency of wildfire in the Arctic, where it is not common now. In 2007, the unusually severe Anaktuvuk River fire burned 1039 km ² of tundra on Alaska's North Slope. Four years later, we harvested plant biomass and soils across a gradient of burn severity, to assess recovery. In burned areas, above-ground net primary productivity of vascular plants equalled that in unburned areas, though total live biomass was less. Graminoid biomass had recovered to unburned levels, but shrubs had not. Virtually all vascular plant biomass had resprouted from surviving underground parts; no non-native species were seen. However, bryophytes were mostly disturbance-adapted species, and non-vascular biomass had recovered less than vascular plant biomass. Soil nitrogen availability did not differ between burned and unburned sites. Graminoids showed allocation changes consistent with nitrogen stress. These patterns are similar to those seen following other, smaller tundra fires. Soil nitrogen limitation and the persistence of resprouters will likely lead to recovery of mixed shrub–sedge tussock tundra, unless permafrost thaws, as climate warms, more extensively than has yet occurred.

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO₂ fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V_cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r² = 0.76; Nash–Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r² = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r² = 0.42, MEF = 0.14) and and net ecosystem CO₂ exchange (NEE, r² = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r² values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r² values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r² < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V_cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V_cmax value.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions^1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores³. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms^4,5.

Rising atmospheric CO₂ concentration ([CO₂]) enhances photosynthesis and reduces transpiration at the leaf, ecosystem, and global scale via the CO₂ fertilization effect. The CO₂ fertilization effect is among the most important processes for predicting the terrestrial carbon budget and future climate, yet it has been elusive to quantify. For evaluating the CO₂ fertilization effect on land photosynthesis and transpiration, we developed a technique that isolated this effect from other confounding effects, such as changes in climate, using a noisy time series of observed land-atmosphere CO₂ and water vapor exchange. Here, we evaluate the magnitude of this effect from 2000 to 2014 globally based on constraint optimization of gross primary productivity (GPP) and evapotranspiration in a canopy photosynthesis model over 104 global eddy-covariance stations. We found a consistent increase of GPP (0.138 ± 0.007% ppm⁻¹; percentile per rising ppm of [CO₂]) and a concomitant decrease in transpiration (−0.073% ± 0.006% ppm⁻¹) due to rising [CO₂]. Enhanced GPP from CO₂ fertilization after the baseline year 2000 is, on average, 1.2% of global GPP, 12.4 g C m⁻² yr⁻¹ or 1.8 Pg C yr⁻¹ at the years from 2001 to 2014. Our result demonstrates that the current increase in [CO₂] could potentially explain the recent land CO₂ sink at the global scale.