Plant phenology and species-specific traits control plant CH4 emissions in a northern boreal fen.

Aerenchymatic transport is an important mechanism through which plants affect methane (CH4 ) emissions from peatlands. Controlling environmental factors and the effects of plant phenology remain, however, uncertain. We identified factors controlling seasonal CH4 flux rate and investigated transport efficiency (flux rate per unit of rhizospheric porewater CH4 concentration). We measured CH4 fluxes through individual shoots of Carex rostrata, Menyanthes trifoliata, Betula nana and Salix lapponum throughout growing seasons in 2020 and 2021 and Equisetum fluviatile and Comarum palustre in high summer 2021 along with water-table level, peat temperature and porewater CH4 concentration. CH4 flux rate of C. rostrata was related to plant phenology and peat temperature. Flux rates of M. trifoliata and shrubs B. nana and S. lapponum were insensitive to the investigated environmental variables. In high summer, flux rate and efficiency were highest for C. rostrata (6.86 mg m-2  h-1 and 0.36 mg m-2  h-1 (µmol l-1 )-1 , respectively). Menyanthes trifoliata showed a high flux rate, but limited efficiency. Low flux rates and efficiency were detected for the remaining species. Knowledge of the species-specific CH4 flux rate and their different responses to plant phenology and environmental factors can significantly improve the estimation of ecosystem-scale CH4 dynamics in boreal peatlands.

Stronger negative priming effect and lower basal respiration rates in nutrient-poor as compared to nutrient-rich forestry-drained peatland.

RATIONALE: Land-use changes, e.g., forestry drainage, modify the characteristics of peatland soil and affect the peatland carbon (C) balance. Peat soil nutrient status, related mainly to the original peatland type, also has an impact on the C balance after drainage, as observed earlier at the ecosystem scale for two forestry-drained sites in Southern Finland. Here the aim was to compare the soil CO2 fluxes from the two sites, nutrient-poor and nutirent-rich forestry-drained peatlands, and study the effect of plant photosynthates on the decomposition of peat C. Therefore, the respiration rates and priming effect (PE) of peat soils with variable nutrient status were examined in the laboratory. METHODS: Half of the samples were labelled with 13 C-glucose to study the effect of fresh C addition on the soil decomposition. The 13 CO2 -samples were analysed with isotope ratio mass spectrometry. A two-pool mixing model was applied to separate the soil- and sugar-derived respirations and to determine the PE. RESULTS: The nutrient-rich peat soil respired generally more than the nutrient-poor peat. A negative PE was observed in both peat soils, suggesting that the addition of fresh C did not increase the soil decomposition, but on the contrary decreased it. The negative PE was significantly more pronounced in nutrient-poor peat soil than in the nutrient-rich peat treatments, suggesting that the higher nutrient availability suppresses the negative PE. CONCLUSIONS: These results imply that microbes prefer utilizing fresh C instead of old C in the short term and that the peat decomposition is suppressed in the presence of fresh C inputs from vegetation at forestry-drained peatlands. These effects are even stronger in peat soils with less nutrients available. Ecosystem scale and soil process models could be improved with the help of these results.

Identifying main uncertainties in estimating past and present radiative forcing of peatlands.

Reconstructions of past climate impact, that is, radiative forcing (RF), of peatland carbon (C) dynamics show that immediately after peatland initiation the climate warming effect of CH4 emissions exceeds the cooling effect of CO2 uptake, but thereafter the net effect of most peatlands will move toward cooling, when RF switches from positive to negative. Reconstructing peatland C dynamics necessarily involves uncertainties related to basic assumptions on past CO2  flux, CH4 emission and peatland expansion. We investigated the effect of these uncertainties on the RF of three peatlands, using either apparent C accumulation rates, net C balance (NCB) or NCB plus C loss during fires as basis for CO2 uptake estimate; applying a plausible range for CH4 emission; and assuming linearly interpolated expansion between basal dates or comparatively early or late expansion. When we factored that some C would only be stored temporarily (NCB and NCB+fire), the estimated past cooling effect of CO2 uptake increased, but the present-day RF was affected little. Altering the assumptions behind the reconstructed CO2  flux or expansion patterns caused the RF to peak earlier and advanced the switch from positive to negative RF by several thousand years. Compared with NCB, including fires had only small additional effect on RF lasting less than 1000 year. The largest uncertainty in reconstructing peatland RF was associated with CH4 emissions. As shown by the consistently positive RF modelled for one site, and in some cases for the other two, peatlands with high CH4 emissions and low C accumulation rates may have remained climate warming agents since their initiation. Although uncertainties in present-day RF were mainly due to the assumed CH4 emission rates, the uncertainty in lateral expansion still had a significant effect on the present-day RF, highlighting the importance to consider uncertainties in the past peatland C balance in RF reconstructions.

Methane production and oxidation potentials along a fen-bog gradient from southern boreal to subarctic peatlands in Finland.

Methane (CH4 ) emissions from northern peatlands are projected to increase due to climate change, primarily because of projected increases in soil temperature. Yet, the rates and temperature responses of the two CH4 emission-related microbial processes (CH4 production by methanogens and oxidation by methanotrophs) are poorly known. Further, peatland sites within a fen-bog gradient are known to differ in the variables that regulate these two mechanisms, yet the interaction between peatland type and temperature lacks quantitative understanding. Here, we investigated potential CH4 production and oxidation rates for 14 peatlands in Finland located between c. 60 and 70°N latitude, representing bogs, poor fens, and rich fens. Potentials were measured at three different temperatures (5, 17.5, and 30℃) using the laboratory incubation method. We linked CH4 production and oxidation patterns to their methanogen and methanotroph abundance, peat properties, and plant functional types. We found that the rich fen-bog gradient-related nutrient availability and methanogen abundance increased the temperature response of CH4 production, with rich fens exhibiting the greatest production potentials. Oxidation potential showed a steeper temperature response than production, which was explained by aerenchymous plant cover, peat water holding capacity, peat nitrogen, and sulfate content. The steeper temperature response of oxidation suggests that, at higher temperatures, CH4 oxidation might balance increased CH4 production. Predicting net CH4  fluxes as an outcome of the two mechanisms is complicated due to their different controls and temperature responses. The lack of correlation between field CH4  fluxes and production/oxidation potentials, and the positive correlation with aerenchymous plants points toward the essential role of CH4 transport for emissions. The scenario of drying peatlands under climate change, which is likely to promote Sphagnum establishment over brown mosses in many places, will potentially reduce the predicted warming-related increase in CH4 emissions by shifting rich fens to Sphagnum-dominated systems.

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.

Decreased carbon accumulation feedback driven by climate-induced drying of two southern boreal bogs over recent centuries.

Northern boreal peatlands are important ecosystems in modulating global biogeochemical cycles, yet their biological communities and related carbon dynamics are highly sensitive to changes in climate. Despite this, the strength and recent direction of these feedbacks are still unclear. The response of boreal peatlands to climate warming has received relatively little attention compared with other northern peatland types, despite forming a large northern hemisphere-wide ecosystem. Here, we studied the response of two ombrotrophic boreal peatlands to climate variability over the last c. 200 years for which local meteorological data are available. We used remains from plants and testate amoebae to study historical changes in peatland biological communities. These data were supplemented by peat property (bulk density, carbon and nitrogen content), 14 C, 210 Pb and 137 Cs analyses and were used to infer changes in peatland hydrology and carbon dynamics. In total, six peat cores, three per study site, were studied that represent different microhabitats: low hummock (LH), high lawn and low lawn. The data show a consistent drying trend over recent centuries, represented mainly as a change from wet habitat Sphagnum spp. to dry habitat S. fuscum. Summer temperature and precipitation appeared to be important drivers shaping peatland community and surface moisture conditions. Data from the driest microhabitat studied, LH, revealed a clear and strong negative linear correlation (R2  = .5031; p < .001) between carbon accumulation rate and peat surface moisture conditions: under dry conditions, less carbon was accumulated. This suggests that at the dry end of the moisture gradient, availability of water regulates carbon accumulation. It can be further linked to the decreased abundance of mixotrophic testate amoebae under drier conditions (R2  = .4207; p < .001). Our study implies that if effective precipitation decreases in the future, the carbon uptake capacity of boreal bogs may be threatened.

Refining the role of phenology in regulating gross ecosystem productivity across European peatlands.

The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.

Ecosystem carbon response of an Arctic peatland to simulated permafrost thaw.

Permafrost peatlands are biogeochemical hot spots in the Arctic as they store vast amounts of carbon. Permafrost thaw could release part of these long-term immobile carbon stocks as the greenhouse gases (GHGs) carbon dioxide (CO2 ) and methane (CH4 ) to the atmosphere, but how much, at which time-span and as which gaseous carbon species is still highly uncertain. Here we assess the effect of permafrost thaw on GHG dynamics under different moisture and vegetation scenarios in a permafrost peatland. A novel experimental approach using intact plant-soil systems (mesocosms) allowed us to simulate permafrost thaw under near-natural conditions. We monitored GHG flux dynamics via high-resolution flow-through gas measurements, combined with detailed monitoring of soil GHG concentration dynamics, yielding insights into GHG production and consumption potential of individual soil layers. Thawing the upper 10-15 cm of permafrost under dry conditions increased CO2 emissions to the atmosphere (without vegetation: 0.74 ± 0.49 vs. 0.84 ± 0.60 g CO2 -C m-2  day-1 ; with vegetation: 1.20 ± 0.50 vs. 1.32 ± 0.60 g CO2 -C m-2  day-1 , mean ± SD, pre- and post-thaw, respectively). Radiocarbon dating (14 C) of respired CO2 , supported by an independent curve-fitting approach, showed a clear contribution (9%-27%) of old carbon to this enhanced post-thaw CO2 flux. Elevated concentrations of CO2 , CH4 , and dissolved organic carbon at depth indicated not just pulse emissions during the thawing process, but sustained decomposition and GHG production from thawed permafrost. Oxidation of CH4 in the peat column, however, prevented CH4 release to the atmosphere. Importantly, we show here that, under dry conditions, peatlands strengthen the permafrost-carbon feedback by adding to the atmospheric CO2 burden post-thaw. However, as long as the water table remains low, our results reveal a strong CH4 sink capacity in these types of Arctic ecosystems pre- and post-thaw, with the potential to compensate part of the permafrost CO2 losses over longer timescales.

The uncertain climate footprint of wetlands under human pressure.

Significant climate risks are associated with a positive carbon-temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the "cost" of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse-response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange.

Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area?

Earth observing systems are now routinely used to infer leaf area index (LAI) given its significance in spatial aggregation of land surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing season energy budget, surface conductance (Gs ), water- and light-use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance (EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multilayer soil-vegetation-atmosphere transfer model as well as by a bulk Gs representation. The LAI variations significantly alter radiation regime, within-canopy microclimate, sink/source distributions of CO2 , H2 O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem-scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared with other energy budget components, dry-canopy evapotranspiration (ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0 m2 m-2 . Both ET and Gs experienced a minimum in the LAI range 1-2 m2 m-2 caused by opposing nonproportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse (LAI ~2 m2 m-2 ) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands (LAI <3 m2 m-2 ). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI dependency varies with physiological traits such as light-saturated water-use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests.

Latent heat exchange in the boreal and arctic biomes.

In this study latent heat flux (λE) measurements made at 65 boreal and arctic eddy-covariance (EC) sites were analyses by using the Penman-Monteith equation. Sites were stratified into nine different ecosystem types: harvested and burnt forest areas, pine forests, spruce or fir forests, Douglas-fir forests, broadleaf deciduous forests, larch forests, wetlands, tundra and natural grasslands. The Penman-Monteith equation was calibrated with variable surface resistances against half-hourly eddy-covariance data and clear differences between ecosystem types were observed. Based on the modeled behavior of surface and aerodynamic resistances, surface resistance tightly control λE in most mature forests, while it had less importance in ecosystems having shorter vegetation like young or recently harvested forests, grasslands, wetlands and tundra. The parameters of the Penman-Monteith equation were clearly different for winter and summer conditions, indicating that phenological effects on surface resistance are important. We also compared the simulated λE of different ecosystem types under meteorological conditions at one site. Values of λE varied between 15% and 38% of the net radiation in the simulations with mean ecosystem parameters. In general, the simulations suggest that λE is higher from forested ecosystems than from grasslands, wetlands or tundra-type ecosystems. Forests showed usually a tighter stomatal control of λE as indicated by a pronounced sensitivity of surface resistance to atmospheric vapor pressure deficit. Nevertheless, the surface resistance of forests was lower than for open vegetation types including wetlands. Tundra and wetlands had higher surface resistances, which were less sensitive to vapor pressure deficits. The results indicate that the variation in surface resistance within and between different vegetation types might play a significant role in energy exchange between terrestrial ecosystems and atmosphere. These results suggest the need to take into account vegetation type and phenology in energy exchange modeling.

Plant-mediated CH4 exchange in wetlands: A review of mechanisms and measurement methods with implications for modelling.

Plant-mediated CH4 transport (PMT) is the dominant pathway through which soil-produced CH4 can escape into the atmosphere and thus plays an important role in controlling ecosystem CH4 emission. PMT is affected by abiotic and biotic factors simultaneously, and the effects of biotic factors, such as the dominant plant species and their traits, can override the effects of abiotic factors. Increasing evidence shows that plant-mediated CH4 fluxes include not only PMT, but also within-plant CH4 production and oxidation due to the detection of methanogens and methanotrophs attached to the shoots. Despite the inter-species and seasonal differences, and the probable contribution of within-plant microbes to total plant-mediated CH4 exchange (PME), current process-based ecosystem models only estimate PMT based on the bulk biomass or leaf area index of aerenchymatous plants. We highlight five knowledge gaps to which more research efforts should be devoted. First, large between-species variation, even within the same family, complicates general estimation of PMT, and calls for further work on the key dominant species in different types of wetlands. Second, the interface (rhizosphere-root, root-shoot, or leaf-atmosphere) and plant traits controlling PMT remain poorly documented, but would be required for generalizations from species to relevant functional groups. Third, the main environmental controls of PMT across species remain uncertain. Fourth, the role of within-plant CH4 production and oxidation is poorly quantified. Fifth, the simplistic description of PMT in current process models results in uncertainty and potentially high errors in predictions of the ecosystem CH4 flux. Our review suggest that flux measurements should be conducted over multiple growing seasons and be paired with trait assessment and microbial analysis, and that trait-based models should be developed. Only then we are capable to accurately estimate plant-mediated CH4 emissions, and eventually ecosystem total CH4 emissions at both regional and global scales.

Modeling the response of Norway spruce tree-ring carbon and oxygen isotopes to selection harvest on a drained peatland forest.

Continuous cover forestry (CCF) has gained interest as an alternative to even-aged management particularly on drained peatland forests. However, relatively little is known about the physiological response of suppressed trees when larger trees are removed as a part of CCF practices. Consequently, studies concentrating on process-level modeling of the response of trees to selection harvesting are also rare. Here, we compared, modeled and measured harvest response of previously suppressed Norway spruce (Picea abies) trees to a selection harvest. We quantified the harvest response by collecting Norway spruce tree-ring samples in a drained peatland forest site and measuring the change in stable carbon and oxygen isotopic ratios of wood formed during 2010-20, including five post-harvest years. The measured isotopic ratios were compared with ecosystem-level process model predictions for ${\kern0em }^{13}$C discrimination and ${\kern0em }^{18}$O leaf water enrichment. We found that the model predicted similar but lower harvest response than the measurements. Furthermore, accounting for mesophyll conductance was important for capturing the variation in ${\kern0em }^{13}$C discrimination. In addition, we performed sensitivity analysis on the model, which suggests that the modeled ${\kern0em }^{13}$C discrimination is sensitive to parameters related to CO2 transport through stomata to the mesophyll.

Active afforestation of drained peatlands is not a viable option under the EU Nature Restoration Law.

The EU Nature Restoration Law (NRL) is critical for the restoration of degraded ecosystems and active afforestation of degraded peatlands has been suggested as a restoration measure under the NRL. Here, we discuss the current state of scientific evidence on the climate mitigation effects of peatlands under forestry. Afforestation of drained peatlands without restoring their hydrology does not fully restore ecosystem functions. Evidence on long-term climate benefits is lacking and it is unclear whether CO2 sequestration of forest on drained peatland can offset the carbon loss from the peat over the long-term. While afforestation may offer short-term gains in certain cases, it compromises the sustainability of peatland carbon storage. Thus, active afforestation of drained peatlands is not a viable option for climate mitigation under the EU Nature Restoration Law and might even impede future rewetting/restoration efforts. Instead, restoring hydrological conditions through rewetting is crucial for effective peatland restoration.

A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates.

Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude [Formula: see text]) sites. MDS systematically overestimates the carbon dioxide (CO[Formula: see text]) emissions of carbon sources and underestimates the CO[Formula: see text] sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.

The synergistic role of sulfuric acid, ammonia and organics in particle formation over an agricultural land.

Agriculture provides people with food, but poses environmental challenges. Via comprehensive observations on an agricultural land at Qvidja in Southern Finland, we were able to show that soil-emitted compounds (mainly ammonia and amines), together with available sulfuric acid, form new aerosol particles which then grow to climate-relevant sizes by the condensation of extremely low volatile organic compounds originating from a side production of photosynthesis (compounds emitted by ground and surrounding vegetation). We found that intensive local clustering events, with particle formation rates at 3 nm about 5-10 times higher than typical rates in boreal forest environments, occur on around 30% of all days. The requirements for these clustering events to occur were found to be clear sky, a low wind speed to accumulate the emissions from local agricultural land, particularly ammonia, the presence of low volatile organic compounds, and sufficient gaseous sulfuric acid. The local clustering will then contribute to regional new particle formation. Since the agricultural land is much more effective per surface area than the boreal forest in producing aerosol particles, these findings provide insight into the participation of agricultural lands in climatic cooling, counteracting the climatic warming effects of farming.

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

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.

Effects of drought and meteorological forcing on carbon and water fluxes in Nordic forests during the dry summer of 2018.

The Nordic region was subjected to severe drought in 2018 with a particularly long-lasting and large soil water deficit in Denmark, Southern Sweden and Estonia. Here, we analyse the impact of the drought on carbon and water fluxes in 11 forest ecosystems of different composition: spruce, pine, mixed and deciduous. We assess the impact of drought on fluxes by estimating the difference (anomaly) between year 2018 and a reference year without drought. Unexpectedly, the evaporation was only slightly reduced during 2018 compared to the reference year at two sites while it increased or was nearly unchanged at all other sites. This occurred under a 40 to 60% reduction in mean surface conductance and the concurrent increase in evaporative demand due to the warm and dry weather. The anomaly in the net ecosystem productivity (NEP) was 93% explained by a multilinear regression with the anomaly in heterotrophic respiration and the relative precipitation deficit as independent variables. Most of the variation (77%) was explained by the heterotrophic component. Six out of 11 forests reduced their annual NEP with more than 50 g C m-2 yr-1 during 2018 as compared to the reference year. The NEP anomaly ranged between -389 and +74 g C m-2 yr-1 with a median value of -59 g C m-2 yr-1. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.