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Aim: To investigate the importance of autumn phenology in controlling interannual variability of forest net ecosystem productivity (NEP) and to derive new phenological metrics to explain the interannual variability of NEP. Location: North America and Europe. Method: Flux data from nine deciduous broadleaf forests (DBF) and 13 evergreen needleleaf forests (ENF) across North America and Europe (212 site-years) were used to explore the relationships between the yearly anomalies of annual NEP and several carbon flux based phenological indicators, including the onset/end of the growing season, onset/end of the carbon uptake period, the spring lag (time interval between the onset of growing season and carbon uptake period) and the autumn lag (time interval between the end of the carbon uptake period and the growing season). Meteorological variables, including global shortwave radiation, air temperature, soil temperature, soil water content and precipitation, were also used to explain the phenological variations. Results: We found that interannual variability of NEP can be largely explained by autumn phenology, i.e. the autumn lag. While variation in neither annual gross primary productivity (GPP) nor in annual ecosystem respiration (R e ) alone could explain this variability, the negative relationship between annual NEP and autumn lag was due to a larger R e /GPP ratio in years with a prolonged autumn lag. For DBF sites, a longer autumn lag coincided with a significant decrease in annual GPP but showed no correlation with annual R e . However, annual GPP was insensitive to a longer autumn lag in ENF sites but annual R e increased significantly. Main conclusions: These results demonstrate that autumn phenology plays a more direct role than spring phenology in regulating interannual variability of annual NEP. In particular, the importance of respiration may be potentially underestimated in deriving phenological indicators.

Plant biomass is largely dictated by the total amount of light intercepted by the plant [daily light integral (DLI) — intensity × photoperiod]. Continuous light (CL, 24 h lighting) has been hypothesized to increase plant biomass and yield if CL does not cause any injury. However, lighting longer than 18 h causes leaf injury in tomato characterized by interveinal chlorosis and yield is no longer increased with further photoperiod extension in tomatoes. Our previous research indicated the response of cucumbers to long photoperiod of lighting varies with light spectrum. Therefore, we set out to examine greenhouse tomato production under supplemental CL using an alternating red (200 µmol m−2 s−1, 06:00–18:00) and blue (50 µmol m−2 s−1, 18:00–06:00) spectrum in comparison to a 12 h supplemental lighting treatment with a red/blue mixture (200 µmol m−2 s−1 red + 50 µmol m−2 s−1 blue, 06:00–18:00) at the same DLI. Our results indicate that tomato plants grown under supplemental CL using the red and blue alternating spectrum were injury-free. Furthermore, parameters related to photosynthetic performance (i.e., Pnmax, quantum yield, and Fv/Fm) were similar between CL and 12 h lighting treatments indicating no detrimental effect of growth under CL. Leaves under CL produced higher net carbon exchange rates (NCER) during the subjective night period (18:00–06:00) compared to plants grown under 12 h lighting. Notably, 53 days into the treatment, leaves grown under CL produced positive NCER values (photosynthesis) during the subjective night period, a period typically associated with respiration. At 53 days into the growth cycle, it is estimated that leaves under CL will accumulate approximately 800 mg C m−2 more than leaves under 12 h lighting over a 24 h period. Leaves grown under CL also displayed similar diurnal patterns in carbohydrates (glucose, fructose, sucrose, and starch) as leaves under 12 h lighting indicating no adverse effects on carbohydrate metabolism under CL. Taken together, this study provides evidence that red and blue spectral alternations during CL allow for injury-free tomato production. We suggest that an alternating spectrum during CL may alleviate the injury typically associated with CL production in tomato.

Elevated temperatures during berry ripening have been shown to affect grape quality. The crop forcing technique (summer pruning that ‘force’ the vine to start a new cycle) has been shown to improve berry quality by delaying the harvest date. However, yield is typically reduced on forced vines, which is attributed to vine low carbon availability soon after forcing and likely incomplete inflorescence formation. The present study aims to estimate the carbon balance of forced vines and evaluate vine responses to changes in carbon patterns due to forcing. Three treatments were studied on Tempranillo cultivar: non-forced vines (Control), vines forced shortly after fruit set (CF early ) and vines forced one month later at the beginning of bunch closure (CF late ). Whole canopy net carbon exchange was modelled and validated using two whole canopy gas exchange chambers. In addition, non-structural carbohydrate reserves at budburst, forcing date and harvest, were analysed. Yield, yield components and vegetative growth were also evaluated. Harvest date was delayed by one and two months in the CF early and CF late , respectively, which increased must acidity. However, yield was lower in the forced treatments compared to the Control (49% lower for CF early and 82% for CF late ). In the second year, at the time when CF early and CF late dormant buds were unlocked (forced budburst), forced vines had significantly lower non-structural carbohydrates than Control vines at budburst. Although the time elapsed from budburst to reach maximum net carbon exchange was longer for the Control treatment (80 days) than for the forced treatments (about 40 days), average daily net carbon exchange until harvest was comparable between Control (60.9 g CO 2 /vine/day) and CF early (55.9 g CO 2 /vine/day), but not for CF late (38.7 g CO 2 /vine/day). In addition, the time elapsed from budburst to harvest was shorter in forced treatments (about 124 days) than for the Control (172 days). As a result, the cumulative net carbon exchange until harvest was reduced by 35% (CF early ) and 55% (CF late ) in the forced treatments. However, no differences in carbon reserves at harvest were observed between treatments partly helped by the higher source:sink ratio observed in forced than Control vines.

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH4 fluxes. The model underestimated the EC‐measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape. Key Points The CLM‐microbe model is able to simulate microtopographical impacts on CO2 and CH4 flux in the Arctic Substrates availability for methanogenesis is the most important factor determining CH4 emission The strong microtopographic impacts call for a model‐data integration framework to better understand and predict C flux in the Arctic

With global warming, snowmelt is occurring earlier and growing seasons are becoming longer around the Arctic. It has been suggested that this would lead to more uptake of carbon due to a lengthening of the period in which plants photosynthesize. To investigate this suggestion, 8 consecutive years of eddy covariance measurements at a northeastern Siberian graminoid tundra site were investigated for patterns in net ecosystem exchange, gross primary production (GPP) and ecosystem respiration (Reco). While GPP showed no clear increase with longer growing seasons, it was significantly increased in warmer summers. Due to these warmer temperatures however, the increase in uptake was mostly offset by an increase in Reco. Therefore, overall variability in net carbon uptake was low, and no relationship with growing season length was found. Furthermore, the highest net uptake of carbon occurred with the shortest and the coldest growing season. Low uptake of carbon mostly occurred with longer or warmer growing seasons. We thus conclude that the net carbon uptake of this ecosystem is more likely to decrease rather than to increase under a warmer climate. These results contradict previous research that has showed more net carbon uptake with longer growing seasons. We hypothesize that this difference is due to site‐specific differences, such as climate type and soil, and that changes in the carbon cycle with longer growing seasons will not be uniform around the Arctic. Key Points Longer growing seasons do not increase net carbon uptake Highest uptake occurs in cold or short growing seasons Greenhouse gas function of this tundra ecosystem is likely to diminish

Forests are critical in regulation of carbon and water cycles and mitigation of climate change. Forest water-use efficiency (WUE) refers to the ratio of biomass produced (or assimilated carbon) to the amount of water used by forests, which indicates how effectively a forest utilizes water to achieve productivity. Climate change and its impact on forest WUE are important research directions that explore the complex relationship between global environmental change and the forest ecosystem dynamics. The global intensification of climate change underscores the need for an inclusive understanding of forest water use and makes it crucial to know how forests balance carbon and water resources, which is essential for effective forest management and predicting ecosystem responses to climate change. This study aims to comprehensively and objectively analyze current research trends and future directions related to the response of forest WUE to climate change. Our database included 1755 research papers from the Web of Science Core Collection, spanning from 2000 to 2023. Our analysis included cooperative networks of countries, authors, and institutions, as well as the most frequently cited journals and articles, keyword co-occurrence analysis, and a keyword burst analysis. The results showed that the top cooperative country, author, and institution is PR China, Prof. Dr. Jesús Julio Camarero from the Consejo Superior de Investigaciones Científicas (CSIC), and the Chinese Academy of Sciences, respectively. The leading journal in this field is “Global Change Biology”. Critical research hot topics include gas exchange, modeling, altitudinal gradients, tree growth dynamics, net carbon exchange, global change drivers, tropical forests, nitrogen stoichiometry, Northern China plains, and extreme drought conditions. Frontier topics that have emerged in recent years include studies on China’s Loess Plateau, stable isotopes, radial growth, gross primary productivity, and Scots pine. The insights from this analysis are vital for researchers, decision-makers, and forestry professionals aiming to mitigate the impacts of climate change on forest WUE and overall ecosystem health and resilience. This study emphasizes the importance of sustained research efforts and global research collaboration in addressing the intricate challenges posed by climate change to forest ecosystems.

Aim: We investigated how ozone pollution and climate change/variability have interactively affected net primary productivity (NPP) and net carbon exchange (NCE) across China's forest ecosystem in the past half century. Location: Continental China. Methods: Using the dynamic land ecosystem model (DLEM) in conjunction with 10-km-resolution gridded historical data sets (tropospheric O₃ concentrations, climate variability/change, and other environmental factors such as land-cover/land-use change (LCLUC), increasing CO₂ and nitrogen deposition), we conducted nine simulation experiments to: (1) investigate the temporo-spatial patterns of NPP and NCE in China's forest ecosystems from 1961-2005; and (2) quantify the effects of tropospheric O₃ pollution alone or in combination with climate variability and other environmental stresses on forests' NPP and NCE. Results: China's forests acted as a carbon sink during 1961-2005 as a result of the combined effects of O₃, climate, CO₂, nitrogen deposition and LCLUC. However, simulated results indicated that elevated O₃ caused a 7.7% decrease in national carbon storage, with O₃-induced reductions in NCE (Pg C year⁻¹ ) ranging from 0.4-43.1% among different forest types. Sensitivity experiments showed that climate change was the dominant factor in controlling changes in temporo-spatial patterns of annual NPP. The combined negative effects of O₃ pollution and climate change on NPP and NCE could be largely offset by the positive fertilization effects of nitrogen deposition and CO₂. Main conclusions: In the future, tropospheric O₃ should be taken into account in order to fully understand the variations of carbon sequestration capacity of forests and assess the vulnerability of forest ecosystems to climate change and air pollution. Reducing air pollution in China is likely to increase the resilience of forests to climate change. This paper offers the first estimate of how prevention of air pollution can help to increase forest productivity and carbon sequestration in China's forested ecosystems.

Understanding the response of grassland production and carbon exchange to intra-annual variation in precipitation and nitrogen addition is critical for sustainable grassland management and ecosystem restoration. We introduced growing-season drought treatments of different lengths (15, 30, 45 and 60 d drought) by delaying growing-season precipitation in a long-term nitrogen addition experiment in a low diversity meadow steppe in northeast China. Response variables included aboveground biomass (AGB), ecosystem net carbon exchange (NEE), and leaf net carbon assimilation rate (A). In unfertilized plots drought decreased AGB by 13.7% after a 45-d drought and 31.7% after a 60-d drought (47.6% in fertilized plots). Progressive increases in the drought response of NEE were also observed. The effects of N addition on the drought response of productivity increased as drought duration increased, and these responses were a function of changes in AGB and biomass allocation, particularly root to shoot ratio. However, no significant effects of drought occurred in fertilized or unfertilized plots in the growing season a year after the experiment, N addition did limit the recovery of AGB from severe drought during the remainder of the current growing season. Our results imply that chronic N enrichment could exacerbate the effects of growing-season drought on grassland productivity caused by altered precipitation seasonality under climate change, but that these effects do not carry over to the next growing season.

To determine factors controlling the carbon dynamics of an intensively managed landscape, we measured net CO2exchange with the atmosphere using eddy covariance and soil CO2fluxes using static chambers along a chronosequence of slash pine (Pinus elliottii var. elliottii) plantations consisting of a recent clearcut, a mid-rotation (10-yr-old) stand, and a rotation-aged (24-yr-old) stand. Daytime net ecosystem exchange of CO2($NEE_{day}$) at the clearcut was not significantly different than zero during the growing season of the first year following harvest and reached levels that were ~40% of those at the older stands during the second growing season.$NEE_{day}$was similar at the mid-rotation and rotation-aged sites, reflecting the similar leaf areas of these stands. Nighttime net ecosystem exchange of CO2($NEE_{night}$) was an exponential function of air or soil temperature at all sites. However, low decomposition rates of litter and flooding of the site following harvest likely constrained$NEE_{night}$at the clearcut, and drought affected rates at the mid-rotation site. Annual net ecosystem exchange of CO2($NEE_{yr}$) was estimated at$-1269 and -882 g C\\cdot m^{-2}\\cdot yr^{-1}$at the clearcut, and$576 and 603 g C\\cdot m^{-2}\\cdot yr^{-1}$at the mid-rotation stand in 1998 and 1999, respectively. For comparison,$NEE_{yr}$was 741 and$610 g C\\cdot m^{-2}\\cdot yr^{-1}$at the rotation-aged stand in 1996 and 1997, respectively. In contrast, annual ecosystem respiration ($R_{eco}$) was similar in magnitude at all sites during all years. Although$R_{eco}$is similar in magnitude,$NEE_{yr}$is highly dynamic across this intensively managed landscape, with a maximum range of$\\sim 2000 g C\\cdot m^{-2}\\cdot yr^{-1}$. This range exceeds that across all the sites in both the Ameriflux and Euroflux networks and illustrates the need to include the range of stand ages and disturbance histories in landscape- to regional-scale flux estimates.

The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square (R2), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R2 was selected from the models with a prediction accuracy of R2 > 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R2 > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1.