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Field studies reveal that the woody surfaces of upland trees are a substantial global sink for methane, a potent greenhouse gas. The findings help to fill a hole in the global methane budget and should improve the accuracy of climate models. Methane flux quantified at the woody surfaces of upland trees.

At each location, the authors observed the same vertical pattern of methane exchange: net emissions were produced at the tree base (below about 1.3 metres), whereas net uptake occurred higher up the stems (up to 2 m above ground). Gauci et al. also observed that floodplain trees, which usually act as net methane sources, could exhibit net uptake during dry periods. [...]the authors report a clear positive correlation between the mean methane uptake of trees at a stem height of 2 m and the mean annual temperature at each location. The findings have important implications for estimates of the potential of forests to mitigate climate change, and for our understanding of the global methane budget.

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

Several studies have proposed aerobic methane (CH4) emissions by plants. If confirmed, these findings would further increase the imbalance in the global CH4 budget which today underestimates CH4sinks. Oxidation by OH‐radicals in the troposphere is the major identified sink followed by smaller contribution from stratospheric loss and oxidation by methano‐ and methylotrophic bacteria in soils. This study directly investigated CH4 exchange by plants in their natural environment. At a forest site in central Sweden, in situ branch chamber measurements were used to study plant ambient CH4 exchange by spruce (Picea abies), birch (Betula pubescens), rowan (Sorbus aucuparia) and pine (Pinus sylvestris). The results show a net uptake of CH4 by all the studied plants, which might be of importance for the methane budget. Key Points In situ measurements show that boreal plants are a significant sink of methane

Non-technical summaryAgriculture has been dominated by annual plants, such as all cereals and oilseeds, since the very beginning of civilization over 10,000 years ago. Annual plants are planted and uprooted every year which results in severe disturbance of the soil and disrupts ecosystem services. Science has shown that it is possible to domesticate completely new perennial grain crops, i.e. planted once and harvested year after year. Such crops would solve many of the problems of agriculture, but their development and uptake would be at odds with the current agricultural technology industry.Technical summaryAgriculture is arguably the most environmentally destructive innovation in human history. A root cause is the reliance on annual crops requiring uprooting and restarting every season. Most environmental predicaments of agriculture can be attributed to the use of annuals, as well as many social, political, and economic ones. Advances in domestication and breeding of novel perennial grain crops have demonstrated the possibility of a future agricultural shift from annual to perennial crops. Such a change could have many advantages over the current agricultural systems which are to over 80% based on annual crops mainly grown in monocultures. We analyze and review the prospects for such scientific advances to be adopted and scaled to a level where it is pertinent to talk about a perennial revolution. We follow the logic of E.O. Wright's approach of Envisioning Real Utopias by discussing the desirability, viability, and achievability of such a transition. Proceeding from Lakatos' theory of science and Lukes' three dimensions of power, we discuss the obstacles to such a transition. We apply a transition theory lens to formulate four reasons of optimism that a perennial revolution could be imminent within 3–5 decades and conclude with an invitation for research.

Management of drained forested peatlands has important implications for carbon budgets, but contrasting views exist on its effects on climate. This study utilised the dynamic ecosystem model ForSAFE-Peat to simulate biogeochemical dynamics over two complete forest rotations (1951–2088) in a nutrient-rich drained peatland afforested with Norway spruce (Picea abies) in southwestern Sweden. Model simulations aligned well with observed groundwater levels (R2=0.78) and soil temperatures (R2≥0.76) and captured seasonal and annual net ecosystem production patterns, although daily variability was not always well represented. Simulated carbon exchanges (a positive sign indicates gains, and a negative sign indicates losses) were analysed considering different system boundaries (the soil; the ecosystem; and the ecosystem and the fate of harvested wood products, named ecosystem–HWP) using the net carbon balance (NCB) and the integrated carbon storage (ICS) metrics. Model results indicated negative NCB and ICS across all system boundaries, except for a positive NCB calculated by the end of the simulation at the ecosystem–HWP level. The soil exhibited persistent carbon losses primarily driven by peat decomposition. At the ecosystem level, net carbon losses were reduced as forest growth partially offset soil losses until harvesting. NCB was positive (2307 gCmsoil-2) at the ecosystem–HWP level due to the slow decay of harvested wood products, but ICS was negative (-0.59×106 gCyrmsoil-2) due to the large initial carbon losses. This study highlights the importance of system boundary selection and temporal dynamics in assessing the carbon balance of forested drained peatlands.

Given the severe land-use and land-cover change pressure on tropical forests and the high demand for field observations of ecosystem characteristics, it is crucial to collect such data both in pristine tropical forests and in the converted deforested land-cover classes. To gain insight into the ecosystem characteristics of pristine tropical forests (EFs), regrowth forests (RFs), and cashew plantations (CPs), we established an ecosystem monitoring site in Phnom Kulen National Park, Cambodia. Here, we present the first observed datasets at this site of forest inventories, leaf area index (LAI), leaf traits of woody species, a fraction of intercepted photosynthetically active radiation (fPAR), and soil and meteorological conditions. Using these data, we aimed to assess how land-cover change affects stand structure, species diversity, leaf functional traits, and soil conditions among the three land-cover classes and to evaluate the feasibility of locally calibrated diameters at breast height (DBHs) and tree height (H) allometries for improving aboveground biomass (AGB) estimation. We found significant differences in these ecosystem characteristics, caused by the anthropogenic land-cover conversion, which underlines land-cover change's profound impact on stand structure, species diversity, leaf functional traits, and soil conditions in these tropical forest regions. Our results further demonstrated the feasibility of locally updating aboveground biomass estimates using power-law functions based on relationships between DBH and H. These datasets and findings can contribute to enriching tropical forest research databanks and supporting sustainable forest management.

In this study, we use a data assimilation framework based on the adaptive Markov chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH4 eddy-covariance flux observations from 14 different natural boreal, temperate, and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH4 flux output against independent CH4 flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from boreal, temperate, and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH4 emission estimation using the optimised model resulted in 28.16 Tg C yr−1 and for regions above 60 ° N it resulted in 7.46 Tg C yr−1.

In this study, we use a data assimilation framework based on the adaptive Markov chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH.sub.4 eddy-covariance flux observations from 14 different natural boreal, temperate, and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH.sub.4 flux output against independent CH.sub.4 flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH.sub.4 fluxes from boreal, temperate, and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH.sub.4 fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH.sub.4 emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH.sub.4 emission estimation using the optimised model resulted in 28.16 Tg C yr.sup.-1 and for regions above 60 ° N it resulted in 7.46 Tg C yr.sup.-1.