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In his comment (Kowalski 2023) on our recent publication (Heiskanen et al. 2022) where we present the Integrated Carbon Observation System (ICOS) research infrastructure, Andrew Kowalski introduces three important and, [...]

Emissions of biogenic volatile organic compounds (BVOCs) are a crucial component of biosphere-atmosphere interactions. In northern latitudes, climate change is amplified by feedback processes in which BVOCs have a recognized, yet poorly quantified role, mainly due to a lack of measurements and concomitant modeling gaps. Hence, current Earth system models mostly rely on temperature responses measured on vegetation from lower latitudes, rendering their predictions highly uncertain. Here, we show how tundra isoprene emissions respond vigorously to temperature increases, compared to model results. Our unique dataset of direct eddy covariance ecosystem-level isoprene measurements in two contrasting ecosystems exhibited Q10 (the factor by which the emission rate increases with a 10 °C rise in temperature) temperature coefficients of up to 20.8, that is, 3.5 times the Q10 of 5.9 derived from the equivalent model calculations. Crude estimates using the observed temperature responses indicate that tundra vegetation could enhance their isoprene emissions by up to 41% (87%)-that is, 46% (55%) more than estimated by models-with a 2 °C (4 °C) warming. Our results demonstrate that tundra vegetation possesses the potential to substantially boost its isoprene emissions in response to future rising temperatures, at rates that exceed the current Earth system model predictions.

The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis–Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the root-mean-square error (RMSE), from 0.044 to 0.023 gC m -2 d -1 , and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH4 data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed. [ABSTRACT FROM AUTHOR]

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 using CH4 eddy covariance flux observations from 14 different natural boreal and temperate wetlands. The objective is to derive a single set of calibrated parameter values. These parameters are then used in the model to validate its CH4 flux output against 5 different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from different boreal and temperate 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 distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE were observed. The validation experiment results indicate that four out of 5 sites successfully reduced RMSE, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the study. [ABSTRACT FROM AUTHOR]

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO 2), methane (CH 4), and nitrous oxide (N 2 O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km 2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO 2 uptake, it also revealed small but widespread CH 4 uptake in upland vegetation types, almost surpassing the high wetland CH 4 emissions at the landscape scale. Average N 2 O fluxes were negligible. CO 2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH 4 fluxes by soil moisture (increases net emissions) and vegetation type, and N 2 O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO 2 fluxes across the tundra landscape but suggest that CH 4 uptake in dry upland soils might play a significant role in the regional GHG budget. [ABSTRACT FROM AUTHOR]

The processes responsible for methane (CH4) emissions from boreal wetlands are complex, and hence their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions are highlight the need for a calibrated and optimised version of LPJGUESS. In this study we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis Hastings (MH) based Markov Chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with reduced risk of sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period 2005 to 2014 from the Siikaneva wetlands in southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. As a part of this work, knowledge about how the CH4 data can constrain the parameters and processes is derived. Though the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed. [ABSTRACT FROM AUTHOR]

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO2 uptake, it also revealed small but widespread CH4 uptake in upland vegetation types, shifting this region to an average net CH4 sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N2O fluxes were negligible. CO2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH4 fluxes by soil moisture (increases net emissions) and vegetation type, and N2O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO2 fluxes across the tundra landscape, but suggest that CH4 uptake might play a significant role in the regional GHG budget. [ABSTRACT FROM AUTHOR]

Termites play an essential role in decomposing dead plant material in tropical ecosystems and are thus major sources of gaseous C emissions in many environments. In African savannas, fungus-growing termites are among the ecologically most influential termite species. We studied the gas exchange from mounds of two closely related fungus-growing species (Macrotermes subhyalinus and M. michaelseni, respectively) in shaded and open habitats together with soil fluxes around the mounds. The fluxes from active termite mounds varied from 116 to 2105 mg CO2-C m-2 h-1 for CO2 and from 0.06 to 3.66 mg CH4-C m-2 h-1 for CH4 fluxes. Mound CO2 fluxes varied seasonally with a 64 % decrease and 35 % increase in the fluxes from dry to wet season at the open grassland and shaded bushland sites, respectively. During the wet season, the CO2 fluxes were significantly correlated with termite mound volume. The diurnal measurements from two M. michaelseni mounds suggest that the gas fluxes peak during daytime and midnight supporting the previously presented model of thermally driven air circulation inside the mound and possibly enhanced ventilation during the night by the opening of foraging routes. The stability of the mound gas fluxes over diurnal and annual scales coincides with the constant nature of the nest internal gas and thermal environment that guarantees continuously favorable conditions for the fungal symbiont. Soil fluxes of both CO2 and CH4 were enhanced at up to 2 m distance from the mounds compared to the local soil respiration, indicating that, in addition to mound ventilation structures, a small proportion of the produced metabolic gases leave the nest also via surrounding soils. [ABSTRACT FROM AUTHOR]

The ecosystem-atmosphere flux of biogenic volatile organic compounds (BVOCs) has important impacts on tropospheric oxidative capacity and the formation of secondary organic aerosols, influencing air quality and climate. Here we present within-canopy measurements of a set of dominant BVOCs in a managed spruce- and pine-dominated boreal forest located at the ICOS station Norunda in Sweden, collected using proton transfer reaction mass spectrometry (PTR-MS) during 2014–2016, and vertical emission profiles derived from these data. Ozone concentrations were simultaneously measured in conjunction with these PTR-MS measurements. The main BVOCs investigated with the PTR-MS were isoprene, monoterpenes, methanol, acetaldehyde, and acetone. The distribution of BVOC sources and sinks in the forest canopy was explored using Lagrangian dispersion matrix methods, in particular continuous near-field theory. The forest canopy was found to contribute ca. 86 % to the total monoterpene emission in summertime, whereas the below-canopy and canopy emission was comparable (ca. 42 % and 58 % respectively) during the autumn period. This result indicates that boreal forest litter and other below-canopy emitters are a principle source for total forest monoterpene emissions during autumn months. During night, our results for methanol, acetone, and acetaldehyde seasonally present strong sinks in the forest canopy, especially in the autumn, likely due to the nighttime formation of dew on vegetation surfaces. [ABSTRACT FROM AUTHOR]

The reasons for spatial and temporal variation of methane emission from mire ecosystems are not fully understood. Stable isotope signatures of the emitted methane can offer cues to the causes of these variations. We measured the methane emission and 13C-signature of emitted methane by automated chambers at a temperate mire for two growing seasons. In addition, we used ambient methane mixing ratios and δ13C-CH4 to calculate a mire-scale 13C signature using a nocturnal boundary-layer accumulation approach. Microbial methanogenic and methanotrophic communities were determined by a captured metagenomics analysis. The chamber measurements showed large and systematic spatial variations in δ13C-CH4 of up to 15 ‰ but smaller and less systematic temporal variation. The trophic status of methanogenesis was the dominant factor explaining the spatial variation. Genetic analysis indicated that methanogenic communities at all sample locations were able to utilize both hydrogenotrophic and acetoclastic pathways and could thus adapt to trophic status. The temporal variation of methane emission and δ13C-CH4 over the growing seasons showed hysteresis-like behavior, indicative of time-lagged responses to temperature and trophic status. The up-scaled chamber measurements and nocturnal boundary-layer accumulation measurements showed similar average δ13C-CH4 values of -81.3 ‰ and -79.3 ‰, respectively, lending confidence to the use of mire scale isotopic signatures to be used in e.g. atmospheric inversion modelling of methane sources. The results obtained can constrain our theories on the variability of methane emission from mire ecosystems and be useful in development of numerical models of mire biogeochemistry. [ABSTRACT FROM AUTHOR]

Recent climate change mitigation strategies rely on the reduction of methane (CH4) emissions. δ13CCH4 and δ²HCH4 measurements can be used to distinguish sources and thus to understand the CH4 budget better. The CH4 emission estimates by models are sensitive to the isotopic signatures assigned to each source category, so it is important to provide representative estimates of the different CH4 source isotopic signatures worldwide. We present new measurements of isotope signatures of various, mainly anthropogenic, CH4 sources in Europe, which represent a substantial contribution to the global dataset of source isotopic measurements from the literature, especially for δ²HCH4. They improve the definition of δ13CCH4 from waste sources, and demonstrate the use of δ²HCH4 for fossil fuel source attribution. Results from previous studies were combined in a common database that we updated with our new measurements, as well as with additional literature. We found that microbial sources are generally well characterised. The large variability in fossil fuels isotopic compositions requires particular care in the choice of weighting criteria for the calculation of a representative global value. The global dataset could be further improved by measurements from African, South American and Asian countries, as well as more measurements from pyrogenic sources. The final version of the European methane isotope database coupled with a global inventory of fossil and non-fossil δ13CCH4 and δ²HCH4 source signature measurements, is available at: https://doi.org/10.24416/UU01-4PO56T (Menoud et al., 2021a). [ABSTRACT FROM AUTHOR]

Microbial communities of methane (CH4) producing methanogens and consuming methanotrophs play an important role for Earth's atmospheric CH4 budget. Despite their global significance, knowledge on how much they control the spatial variation in CH4 fluxes from peatlands is poorly understood. We studied variation in CH4 producing and consuming communities in a natural peatland dominated by Eriophorum vaginatum, via a metagenomics approach using custom designed hybridization-based oligonucleotide probes to focus on taxa and functions associated with methane cycling. We hypothesized that sites with different magnitudes of methane flux are occupied by structurally and functionally different microbial communities, despite the dominance of a single vascular plant species. To investigate this, nine plant-peat mesocosms dominated by the sedge Eriophorum vaginatum, with varying vegetation coverage, were collected from a temperate natural wetland and subjected to a simulated growing season. During the simulated growing season, measurements of CH4 emission, carbon dioxide (CO2) exchange and δ13C signature of emitted CH4 were made. Mesocosms 1 through 9 were classified into three categories according to the magnitude of CH4 flux. Gross primary production and ecosystem respiration followed the same pattern as CH4 fluxes, but this trend was not observed in net ecosystem exchange. We observed that genetic functional potential was of minor importance in explaining spatial variability of CH4 fluxes with only small shifts in taxonomic community and functional genes. In addition, a higher β-diversity was observed in samples with high CH4 emission. Among methanogens, Methanoregula, made up over 50% of the community composition. This, in combination with the remaining hydrogenotrophic methanogens matched the δ13C isotopic signature of emitted CH4. However, the presence of acetoclastic and methylotrophic taxa and type I, II and Verrucomicrobia methanotrophs indicates that the microbial community holds the ability to produce and consume CH4 in multiple ways. This is important in terms of future climate scenarios, where peatlands are expected to alter in nutrient status, hydrology, and peat biochemistry. Due to the high functional potential, we expect the community to be highly adaptive to future climate scenarios. [ABSTRACT FROM AUTHOR]

The role of precipitation (P) variability on evapotranspiration (ET) and its two components, transpiration (T) and evaporation (E) from savannas, continues to draw significant research interest given its relevance to a number of eco-hydrological applications. Our study reports on six years of measured ET and estimated T and E from a grazed savanna grassland in Welgegund, South Africa. Annual P varied significantly in amount (508 to 672 mm yr-1), with dry years characterized by infrequent early-season rainfall. T was determined using annual water-use efficiency and gross primary production estimates derived from eddy covariance measurements of latent heat flux and net ecosystem CO2 exchange rates. The computed annual T was nearly constant, 331 ± 11 mm yr-1 (T/ET=0.52), for the four wet years with frequent early wet-season rainfall, whereas annual T was 268 and 175 mm yr-1 during the dry years. Annual T/ET was linearly related to the early wet-season storm frequency. The constancy of annual T during wet years is explained by the moderate water stress of C4 grass and constant annual tree transpiration covering 15% of the landscape. However, grass transpiration declines during dry spells. Moreover, grasses respond to water availability with a dieback-regrowth pattern, reducing leaf area and transpiration during drought. These changes lead to an anomalous monthly T/ET relation to leaf-area index (LAI). The results highlight the role of the C4 grass layer in the hydrological balance and suggest that the grass response to dry spells and drought is reasonably described by precipitation timing. [ABSTRACT FROM AUTHOR]