Explore the vast range of titles available.

Significance Feedbacks from terrestrial ecosystems to atmospheric CO ₂ concentrations contribute the second-largest uncertainty to projections of future climate. These feedbacks, acting over huge regions and long periods of time, are extraordinarily difficult to observe and quantify directly. We evaluated in situ, atmospheric, and simulation estimates of the effect of CO ₂ on carbon storage, subject to mass balance constraints. Multiple lines of evidence suggest significant tropical uptake for CO ₂, approximately balancing net deforestation and confirming a substantial negative global feedback to atmospheric CO ₂ and climate. This reconciles two approaches that have previously produced contradictory results. We provide a consistent explanation of the impacts of CO ₂ on terrestrial carbon across the 12 orders of magnitude between plant stomata and the global carbon cycle. Feedbacks from the terrestrial carbon cycle significantly affect future climate change. The CO ₂ concentration dependence of global terrestrial carbon storage is one of the largest and most uncertain feedbacks. Theory predicts the CO ₂ effect should have a tropical maximum, but a large terrestrial sink has been contradicted by analyses of atmospheric CO ₂ that do not show large tropical uptake. Our results, however, show significant tropical uptake and, combining tropical and extratropical fluxes, suggest that up to 60% of the present-day terrestrial sink is caused by increasing atmospheric CO ₂. This conclusion is consistent with a validated subset of atmospheric analyses, but uncertainty remains. Improved model diagnostics and new space-based observations can reduce the uncertainty of tropical and temperate zone carbon flux estimates. This analysis supports a significant feedback to future atmospheric CO ₂ concentrations from carbon uptake in terrestrial ecosystems caused by rising atmospheric CO ₂ concentrations. This feedback will have substantial tropical contributions, but the magnitude of future carbon uptake by tropical forests also depends on how they respond to climate change and requires their protection from deforestation.

Water availability plays a critical role in shaping terrestrial ecosystems, particularly in low- and mid-latitude regions. The sensitivity of vegetation growth to precipitation strongly regulates global vegetation dynamics and their responses to drought, yet sensitivity changes in response to climate change remain poorly understood. Here we use long-term satellite observations combined with a dynamic statistical learning approach to examine changes in the sensitivity of vegetation greenness to precipitation over the past four decades. We observe a robust increase in precipitation sensitivity (0.624% yr −1 ) for drylands, and a decrease (−0.618% yr −1 ) for wet regions. Using model simulations, we show that the contrasting trends between dry and wet regions are caused by elevated atmospheric CO 2 (eCO 2 ). eCO 2 universally decreases the precipitation sensitivity by reducing leaf-level transpiration, particularly in wet regions. However, in drylands, this leaf-level transpiration reduction is overridden at the canopy scale by a large proportional increase in leaf area. The increased sensitivity for global drylands implies a potential decrease in ecosystem stability and greater impacts of droughts in these vulnerable ecosystems under continued global change. Changes in vegetation responses to precipitation may be hydroclimate dependent. Here the authors reveal contrasting trends of vegetation sensitivity to precipitation in drylands vs. wetter ecosystems over the last 4 decades and identify increased CO2 as a major contributing factor.

Vegetation impacts on ecosystem functioning are mediated by mycorrhizas, plant–fungal associations formed by most plant species. Ecosystems dominated by distinct mycorrhizal types differ strongly in their biogeochemistry. Quantitative analyses of mycorrhizal impacts on ecosystem functioning are hindered by the scarcity of information on mycorrhizal distributions. Here we present global, high-resolution maps of vegetation biomass distribution by dominant mycorrhizal associations. Arbuscular, ectomycorrhizal, and ericoid mycorrhizal vegetation store, respectively, 241 ± 15, 100 ± 17, and 7 ± 1.8 GT carbon in aboveground biomass, whereas non-mycorrhizal vegetation stores 29 ± 5.5 GT carbon. Soil carbon stocks in both topsoil and subsoil are positively related to the community-level biomass fraction of ectomycorrhizal plants, though the strength of this relationship varies across biomes. We show that human-induced transformations of Earth’s ecosystems have reduced ectomycorrhizal vegetation, with potential ramifications to terrestrial carbon stocks. Our work provides a benchmark for spatially explicit and globally quantitative assessments of mycorrhizal impacts on ecosystem functioning and biogeochemical cycling. Mycorrhizas—mutualistic relationships formed between fungi and most plant species—are functionally linked to soil carbon stocks. Here the authors map the global distribution of mycorrhizal plants and quantify links between mycorrhizal vegetation patterns and terrestrial carbon stocks.

Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R 2  = 0.47, p  < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits. Ecosystem productivity generally declines under drought. Here, the authors show that spring droughts are linked to increases in gross primary productivity in energy-limited ecosystems in the Northern Hemisphere, and that terrestrial biosphere models tend not to capture this.

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions.

It is unclear to what extent seasonal water stress impacts on plant productivity over Amazonia. Using new Greenhouse gases Observing SATellite (GOSAT) satellite measurements of sun-induced chlorophyll fluorescence, we show that midday fluorescence varies with water availability, both of which decrease in the dry season over Amazonian regions with substantial dry season length, suggesting a parallel decrease in gross primary production (GPP). Using additional SeaWinds Scatterometer onboard QuikSCAT satellite measurements of canopy water content, we found a concomitant decrease in daily storage of canopy water content within branches and leaves during the dry season, supporting our conclusion. A large part (r2 = 0.75) of the variance in observed monthly midday fluorescence from GOSAT is explained by water stress over moderately stressed evergreen forests over Amazonia, which is reproduced by model simulations that include a full physiological representation of photosynthesis and fluorescence. The strong relationship between GOSAT and model fluorescence (r2 = 0.79) was obtained using a fixed leaf area index, indicating that GPP changes are more related to environmental conditions than chlorophyll contents. When the dry season extended to drought in 2010 over Amazonia, midday basin-wide GPP was reduced by 15 per cent compared with 2009.

Aim: Many macroecological analyses are based on analyses of climatological data, within which evapotranspiration estimates are of central importance. In this paper we evaluate and review the use of evapotranspiration models and data in studies of geographical ecology to test the likely sensitivity of the analyses to variation in the performance of different metrics of potential evapotranspiration. Location: Analyses are based on: (1) a latitudinal transect of sites (FLUXNET) for 11 different land-cover types; and (2) globally gridded data. Methods: First, we review the fundamental concepts of evapotranspiration, outline basic evapotranspiration models and describe methods with which to measure evapotranspiration. Next, we compare three different types of potential evapotranspiration models - a temperature-based (Thornthwaite type), a radiation-based (Priestley-Taylor) and a combination (Penman-Monteith) model - for 11 different land-cover types. Finally, we compare these models at continental and global scales. Results: At some sites the models differ by less than 7%, but generally the difference was greater than 25% across most sites. The temperature-based model estimated 20-30% less than the radiation-based and combination models averaged across all sites. The combination model often gave the highest estimates (22% higher than the radiation-based model averaged across all sites). For continental and global averages, the potential evapotranspiration was very similar across all models. However, the difference in individual pixels was often larger than 150 mm year⁻¹ between models. Main conclusions: The choice of evapotranspiration model and input data is likely to have a bearing on model fits and predictions when used in analyses of species richness and related phenomena at geographical scales of analysis. To assist those undertaking such analyses, we provide a guide to selecting an appropriate evapotranspiration model.

Recent advances in the retrieval of Chl fluorescence from space using passive methods (solar-induced Chl fluorescence, SIF) promise improved mapping of plant photosynthesis globally. However, unresolved issues related to the spatial, spectral, and temporal dynamics of vegetation fluorescence complicate our ability to interpret SIF measurements. We developed an instrument to measure leaf-level gas exchange simultaneously with pulse-amplitude modulation (PAM) and spectrally resolved fluorescence over the same field of view – allowing us to investigate the relationships between active and passive fluorescence with photosynthesis. Strongly correlated, slope-dependent relationships were observed between measured spectra across all wavelengths (Fλ , 670–850 nm) and PAM fluorescence parameters under a range of actinic light intensities (steady-state fluorescence yields, F t) and saturation pulses (maximal fluorescence yields, F m). Our results suggest that this method can accurately reproduce the full Chl emission spectra – capturing the spectral dynamics associated with changes in the yields of fluorescence, photochemical (ΦPSII), and nonphotochemical quenching (NPQ). We discuss how this method may establish a link between photosynthetic capacity and the mechanistic drivers of wavelength-specific fluorescence emission during changes in environmental conditions (light, temperature, humidity). Our emphasis is on future research directions linking spectral fluorescence to photosynthesis, ΦPSII, and NPQ.

Wildfire prediction models that can be applied across diverse regions at fine scales (< 100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km 2 from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R 2  = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) introducing new predictors that represent spatial structure in the data, and (3) training our model on half the fires and predicting the other half of the fires. We found that after increasing sample spacing, model accuracy declined. However, we found declines in model accuracy were more impacted by decreased training set size compared to the distance spacing-indicating that the models are likely accurately capturing fine-scale processes. Scalability of random forest models was also found to be more challenging for regression problems but was accurate for classification of burned pixel occurrence (total pixel accuracy of 67%). These results provide promising results for application of random forest models to predict fine-scale fire severity and occurrence with applications for fire management.