Diminishing carryover benefits of earlier spring vegetation growth.

Spring vegetation growth can benefit summer growth by increasing foliage area and carbon sequestration potential, or impair it by consuming additional resources needed for sustaining subsequent growth. However, the prevalent driving mechanism and its temporal changes remain unknown. Using satellite observations and long-term atmospheric CO2 records, here we show a weakening trend of the linkage between spring and summer vegetation growth/productivity in the Northern Hemisphere during 1982-2021. This weakening is driven by warmer and more extreme hot weather that becomes unfavourable for peak-season growth, shifting peak plant functioning away from earlier periods. This is further exacerbated by seasonally growing ecosystem water stress due to reduced water supply and enhanced water demand. Our finding suggests that beneficial carryover effects of spring growth on summer growth are diminishing or even reversing, acting as an early warning sign of the ongoing shift of climatic effects from stimulating to suppressing plant photosynthesis during the early to peak seasons.

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave.

Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.

Dark respiration explains nocturnal stomatal conductance in rice regardless of drought and nutrient stress.

The ecological mechanism underlying nocturnal stomatal conductance (gsn ) in C3 and C4 plants remains elusive. In this study, we proposed a 'coordinated leaf trait' hypothesis to explain gsn in rice plants. We conducted an open-field experiment by applying drought, nutrient stress and the combined drought-nutrient stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, gsn exhibited a strong positive correlation with dark respiration (Rn ). Notably, gsn primed early morning (5:00-7:00) photosynthesis through faster stomatal response time. This photosynthesis priming effect diminished after mid-morning (9:00). Leaves were cooled by gsn -derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of gsn .

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

Does plant ecosystem thermoregulation occur? An extratropical assessment at different spatial and temporal scales.

To what degree plant ecosystems thermoregulate their canopy temperature (Tc ) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability. With global datasets of Tc , air temperature (Ta ), and other environmental and biotic variables from FLUXNET and satellites, we tested the 'limited homeothermy' hypothesis (indicated by Tc & Ta regression slope < 1 or Tc < Ta around midday) across global extratropics, including temporal and spatial dimensions. Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or Tc > Ta around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their Tc -Ta difference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site-mean ΔT exhibits larger variations than the slope indicator, suggesting ΔT is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial-wide ΔT variation (0-6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%). These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the 'limited homeothermy' hypothesis, but their thermoregulation still occurs, implying that slope < 1 or Tc < Ta are not necessary conditions for plant thermoregulation.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions.

Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

Evaluation of effective quantum yields of photosystem II for CO2 leakage monitoring in carbon capture and storage sites.

Vegetation monitoring can be used to detect CO2 leakage in carbon capture and storage (CCS) sites because it can monitor a large area at a relatively low cost. However, a rapidly responsive, sensitive, and cost-effective plant parameters must be suggested for vegetation monitoring to be practically utilized as a CCS management strategy. To screen the proper plant parameters for leakage monitoring, a greenhouse experiment was conducted by exposing kale (Brassica oleracea var. viridis), a sensitive plant, to 10%, 20%, and 40% soil CO2 concentrations. Water and water with CO2 stress treatments were also introduced to examine the parameters differentiating CO2 stress from water stresses. We tested the hypothesis that chlorophyl fluorescence parameters would be early and sensitive indicator to detect CO2 leakage. The results showed that the fluorescence parameters of effective quantum yield of photosystem II (Y(II)), detected the difference between CO2 treatments and control earlier than any other parameters, such as chlorophyl content, hyperspectral vegetation indices, and biomass. For systematic comparison among many parameters, we proposed an indicator evaluation score (IES) method based on four categories: CO2 specificity, early detection, field applicability, and cost. The IES results showed that fluorescence parameters (Y(II)) had the highest IES scores, and the parameters from spectral sensors (380-800 nm wavelength) had the second highest values. We suggest the IES system as a useful tool for evaluating new parameters in vegetation monitoring.

An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity.

The maximum rate of carboxylation (Vcmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating Vcmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant Vcmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in Vcmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily Vcmax,25C (Vcmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal Vcmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant Vcmax,25C across the 10 sites, with a medium root mean square error of 12.3 µmol m-2  s-1 , which suggests that seasonal changes in Vcmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.

Increased atmospheric vapor pressure deficit reduces global vegetation growth.

Atmospheric vapor pressure deficit (VPD) is a critical variable in determining plant photosynthesis. Synthesis of four global climate datasets reveals a sharp increase of VPD after the late 1990s. In response, the vegetation greening trend indicated by a satellite-derived vegetation index (GIMMS3g), which was evident before the late 1990s, was subsequently stalled or reversed. Terrestrial gross primary production derived from two satellite-based models (revised EC-LUE and MODIS) exhibits persistent and widespread decreases after the late 1990s due to increased VPD, which offset the positive CO2 fertilization effect. Six Earth system models have consistently projected continuous increases of VPD throughout the current century. Our results highlight that the impacts of VPD on vegetation growth should be adequately considered to assess ecosystem responses to future climate conditions.

Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem.

Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p < 0.01, r2 = 0.77 and p < 0.01, respectively, compared to top-of-canopy ground-based measurements acquired synchronously with the overflight) over the forested study area. Second, maps of GPP and absorbed photosynthetically active radiation (APAR) were derived using a customised version of the coupled biophysical model Breathing Earth System Simulator (BESS). The model was driven with airborne-derived maps of key forest traits (i.e., leaf chlorophyll content (LCC) and leaf area index (LAI)) and meteorological data providing a high-resolution snapshot of the variables of interest across the study site. The LCC and LAI were accurately estimated (RMSE = 5.66 µg cm-2 and RMSE = 0.51 m2m-2, respectively) through an optimised Look-Up-Table-based inversion of the PROSPECT-4-INFORM radiative transfer model, ensuring the accurate representation of the spatial variation of these determinants of the ecosystem's functionality. The spatial relationships between the measured F and modelled BESS outputs were then analysed to interpret the variability of ecosystem functioning at a regional scale. The results showed that far-red F is significantly correlated with the GPP (r2 = 0.46, p < 0.001) and APAR (r2 = 0.43, p < 0.001) in the spatial domain and that this relationship is nonlinear. Conversely, no statistically significant relationships were found between the red F and the GPP or APAR (p > 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

The impact of the 2015/2016 El Niño on global photosynthesis using satellite remote sensing.

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7 ± 1.2 PgC in 2015, but a slight positive anomaly of 0.05 ± 0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1 ± 0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22 ± 0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33 ± 0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most. We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

FluoSpec 2-An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence.

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products.

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products.

Terrestrial Carbon Cycle Variability.

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO 2 concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget. The CO 2 concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration. At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO 2, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y -1) with respect to a large and uncertain background (123 +/- 4 Pg-C y -1), and detected trends in global primary productivity are even smaller (33 Tg-C y -2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.