Nitrous oxide (N2O) is a potent greenhouse gas and major driver of stratospheric ozone depletion. Isotopic data constrain bottom-up models, yet global natural-abundance patterns remain poorly resolved. We compiled a global database of in situ soil-emitted N2O isotopes from chamber- or probe-based studies to examine the spatiotemporal variability and environmental controls. Croplands showed the greatest variability, with mean delta 15Nbulk, delta 18O, and delta 15NSP values of -15.0 parts per thousand, 35.3 parts per thousand, and 13.7 parts per thousand; natural soils were slightly 15N-enriched, consistent with less anthropogenic influence. Using the Time-resolved Fractionation and Mixing Evaluation (TimeFRAME) model, we estimated bacterial denitrification as the major N2O source process (45%-63%) and found a mean N2O reduction potential of 43%. Global distribution of isotope signatures reflected the edaphic drivers including positive delta 15NSP-pH and negative delta 15Nbulk-moisture relationship. Overall, this global inventory provides empirically constrained isotopic end-members for improved source-sink attribution, new insights into terrestrial N2O cycling, and a benchmark for model evaluation.

Nitrous oxide (N 2 O) is a potent greenhouse gas and major driver of stratospheric ozone depletion. Isotopic data constrain bottom‐up models, yet global natural‐abundance patterns remain poorly resolved. We compiled a global database of in situ soil‐emitted N 2 O isotopes from chamber‐ or probe‐based studies to examine the spatiotemporal variability and environmental controls. Croplands showed the greatest variability, with mean δ 15 N bulk , δ 18 O, and δ 15 N SP values of −15.0‰, 35.3‰, and 13.7‰; natural soils were slightly 15 N‐enriched, consistent with less anthropogenic influence. Using the Time‐resolved Fractionation and Mixing Evaluation (TimeFRAME) model, we estimated bacterial denitrification as the major N 2 O source process (45%–63%) and found a mean N 2 O reduction potential of 43%. Global distribution of isotope signatures reflected the edaphic drivers including positive δ 15 N SP –pH and negative δ 15 N bulk –moisture relationship. Overall, this global inventory provides empirically constrained isotopic end‐members for improved source–sink attribution, new insights into terrestrial N 2 O cycling, and a benchmark for model evaluation.

Nitrous oxide (N₂O), a potent greenhouse gas, contributes significantly to climate change, with agricultural soils being a major source. In sub-Saharan Africa (SSA), increasing fertilization to boost productivity is expected to elevate N₂O emissions, however data scarcity and regional variability challenge accurate predictions. Thus, quantifying these fluxes remains a major challenge for both science and policy. Here, we present a process-based modelling study of N2O emissions using the CN-model, recently introduced as a mechanistic tool for simulating carbon-nitrogen coupling in terrestrial ecosystems [1], extended here for soil nitrogen transformations and N2O emissions. We apply the CN-model to an experimental maize cropping site in Eldoret, Kenya, as part of the N₂O-SSA project, which investigates greenhouse gas emissions in sub-Saharan African agroecosystems. The site in Eldoret (Kenya), features two annual rainfed maize and potato cropping seasons, with varied nitrogen fertilization regimes (0, 50, 100, and 125 kg N ha-¹ yr-¹). Our analysis covers the 2024 growing period (April 2024-January 2025), during which high-frequency flux measurements of N₂O, CH₄, and CO₂ were collected. The CN-model simulates microbial nitrification and denitrification pathways, soil moisture interactions, and fertilization impacts, providing process-level insights into observed N₂O flux dynamics. Model outputs are evaluated against measured greenhouse gas fluxes to assess predictive performance and to explore the effects of nitrogen input levels, precipitation patterns, and cropping cycles. Simulations under both current and future climate scenarios are used to assess potential trajectories under alternative management practices. This modeling framework is critical for improving nitrogen budgeting by enabling more precise and efficient fertilizer use, reducing unnecessary nitrogen losses, and supporting climate-smart agricultural practices. Preliminary results show that the CN-model captures both background and event-driven emissions effectively, highlighting the sensitivity of N₂O emissions to rainfall timing and nitrogen inputs. This work illustrates the value of combining mechanistic modelling with targeted field observations in sub-Saharan African smallholder systems to better constrain N₂O budgets and inform mitigation strategies under a changing climate.ACKNOWLEDGEMENTThis research was generously supported by the Swiss National Science Foundation (SNSF) under grant number 200021_207348.REFERENCE1. Stocker, B. D. & Prentice, I. C. CN-model: A dynamic model for the coupled carbon and nitrogen cycles in terrestrial ecosystems. bioRxiv, 2024.2004.2025.591063 (2024). https://doi.org/10.1101/2024.04.25.591063

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.Scientific Contribution:In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Natural aerosol components such as particulate methanesulfonic acid (MSAp) play an important role in the Arctic climate. However, numerical models struggle to reproduce MSAp concentrations and seasonality. Here we present an alternative data-driven methodology for modeling MSAp at four High Arctic stations (Alert, Gruvebadet, Pituffik (formerly Thule), and Utqia & gdot;vik (formerly Barrow)). In our approach, we create input features that consider the ambient conditions experienced during atmospheric transport (e.g., dimethyl sulfide (DMS) emission, temperature, radiation, cloud cover, precipitation) for use in two data-driven models: a random forest (RF) regressor and an additive model (AM). The most important features were selected through automatic selection procedures, and their relationships with MSAp model output was investigated. Although the overall performance of our data-driven models on test data is modest (max. R2=0.29), the models can capture variability in the data well (max. Pearson correlation coefficient = 0.77), outperform the current numerical models and reanalysis products, and produce physically interpretable results.The data-driven models selected features which can be grouped into three categories, the sources, chemical processing, and removal of MSAp, with specific differences between stations. The seasonal cycles and selected features suggest gas-phase oxidation is relatively more important during peak concentration months at Alert, Gruvebadet, and Pituffik (Thule), while aqueous-phase oxidation is relatively more important at Utqia & gdot;vik (Barrow). Alert and Pituffik (Thule) appear to be more influenced by processes aloft than in the boundary layer. Our models usually selected chemical-processing-related features as the main factors influencing MSAp predictions, highlighting the importance of properly simulating oxidation-related processes in numerical models.

Isotopic measurements of trace gases such as N2O, CO2, and CH4 contain valuable information about production and consumption pathways. Quantification of the underlying pathways contributing to variability in isotopic time series can provide answers to key scientific questions, such as the contribution of nitrification and denitrification to N2O emissions under different environmental conditions or the drivers of multiyear variability in atmospheric CH4 growth rate. However, there is currently no data analysis package available to solve isotopic production, mixing, and consumption problems for time series data in a unified manner while accounting for uncertainty in measurements and model parameters as well as temporal autocorrelation between data points and underlying mechanisms. Bayesian hierarchical models combine the use of expert information with measured data and a mathematical mixing model while considering and updating the uncertainties involved, and they are an ideal basis to approach this problem. Here we present the Time-resolved FRactionation And Mixing Evaluation (TimeFRAME) data analysis package. We use four different classes of Bayesian hierarchical models to solve production, mixing, and consumption contributions using multi-isotope time series measurements: (i) independent time step models, (ii) Gaussian process priors on measurements, (iii) Dirichlet–Gaussian process priors, and (iv) generalized linear models with spline bases. We show extensive testing of the four models for the case of N2O production and consumption in different variations. Incorporation of temporal information in approaches (i)–(iv) reduced uncertainty and noise compared to the independent model (i). Dirichlet–Gaussian process prior models have been found to be most reliable, allowing for simultaneous estimation of hyperparameters via Bayesian hierarchical modeling. Generalized linear models with spline bases seem promising as well, especially for fractionation estimation, although the robustness to real datasets is difficult to assess given their high flexibility. Experiments with simulated data for δ15Nbulk and δ15NSP of N2O showed that model performance across all classes could be greatly improved by reducing uncertainty in model input data – particularly isotopic end-members and fractionation factors. The addition of the δ18O additional isotopic dimension yielded a comparatively small benefit for N2O production pathways but improved quantification of the fraction of N2O consumed; however, the addition of isotopic dimensions orthogonal to existing information could strongly improve results, for example, clumped isotopes. The TimeFRAME package can be used to evaluate both static and time series datasets, with flexible choice of the number and type of isotopic end-members and the model setup allowing simple implementation for different trace gases. The package is available in R and is implemented using Stan for parameter estimation, in addition to supplementary functions re-implementing some of the surveyed isotope analysis techniques.

Natural aerosols are an important, yet understudied, part of the Arctic climate system. Natural marine biogenic aerosol components (e.g., methanesulfonic acid, MSA) are becoming increasingly important due to changing environmental conditions. In this study, we combine in situ aerosol observations with atmospheric transport modeling and meteorological reanalysis data in a data-driven framework with the aim to (1) identify the seasonal cycles and source regions of MSA, (2) elucidate the relationships between MSA and atmospheric variables, and (3) project the response of MSA based on trends extrapolated from reanalysis variables and determine which variables are contributing to these projected changes. We have identified the main source areas of MSA to be the Atlantic and Pacific sectors of the Arctic. Using gradient-boosted trees, we were able to explain 84 % of the variance and find that the most important variables for MSA are indirectly related to either the gas- or aqueous-phase oxidation of dimethyl sulfide (DMS): shortwave and longwave downwelling radiation, temperature, and low cloud cover. We project MSA to undergo a seasonal shift, with non-monotonic decreases in April/May and increases in June-September, over the next 50 years. Different variables in different months are driving these changes, highlighting the complexity of influences on this natural aerosol component. Although the response of MSA due to changing oceanic variables (sea surface temperature, DMS emissions, and sea ice) and precipitation remains to be seen, here we are able to show that MSA will likely undergo a seasonal shift solely due to changes in atmospheric variables.

The priming effect (PE) is the short-term increase or decrease in the rate of soil organic matter mineralization in response to a stimulus, such as the addition of carbon (C) and/or nitrogen (N) to the soil. Literature has generally framed the PE in terms of CO2 evolved from soil organic carbon mineralization, but fewer publications have focused on how the PE affects the soil N cycle and nitrous oxide (N2O) production from soil organic N mineralization (SOM-N), despite the potency of N2O as a greenhouse gas and ability to destroy stratospheric ozone. This review summarizes our current understanding of how the PE can alter the rates of SOM-N mineralization and subsequently amplify, diminish, or maintain N2O production in and release from soils, henceforth referred to as N2O priming. Additionally, the concept of process priming, the differential augmentation of N2O-producing processes (e.g. priming of nitrification) is introduced. Diverse results across studies suggest that the mechanisms of N2O priming cannot be fully explained by a single hypothesis, and it is currently unclear how significant the contribution of N2O priming to net N2O emissions is, but a preliminary estimate suggests that N2O emissions resulting from priming mechanisms can range from -39 – 76% following C and N amendments compared to a control. To disentangle the complexity of N2O priming, an expansion of current research efforts is required. The promotion of open data sharing and publication of full datasets will facilitate the development and validation of models that can accurately simulate the complexity of soil N dynamics and account for the feedback effects of climate change on N2O priming, which is a key research gap. This is particularly the case in under-studied areas such as permafrost-affected soils of arctic, subarctic, and alpine regions, and vulnerable tropical regions, where climate warming may amplify N2O priming.

Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments in recent years have increased the quality and quantity of data available; however, using current data analysis methods, structures can be assigned to only a small fraction of compounds present in typical mixtures. We present a new data analysis pipeline, “MSEI”, that harnesses data science methodologies to improve structural identification capabilities from tandem mass spectrometry data. In particular, feature vectors for fingerprint calculation are found directly from tandem mass spectra, strongly reducing computational costs, and fingerprint comparison uses an optimised methodology accounting for uncertainty to improve distinction between matching and non-matching compounds. MSEI builds on the identification of a small number of compounds through current state-of-the-art data analysis on UHPLC-HRMS/MS measurements and uses targeted training and tailored molecular fingerprints to focus identification to a particular molecular space of interest. Initial compound identifications are used as training data for a set of random forests which directly predict a custom 75-digit molecular fingerprint from a vectorised MS/MS spectrum. Kendrick mass defects (KMDs) for peaks as well as “lost” fragments removed during fragmentation were found to be useful information for fingerprint prediction. Fingerprints are then compared to potential matches from the PubChem structural database using Euclidean distance, with fingerprint digit weights determined using an SVM to maximise distance between matching and non-matching compounds. Potential matches are additionally filtered for hydrophobicity based on measured retention time, using a newly developed machine learning method for retention time prediction. MSEI was able to correctly assign > 50% of structures in a test dataset and showed > 10% better performance than current state-of-the-art methods, while using an order of magnitude less computational power and a fraction of the training data.

Data and code working together is fundamental to machine learning (ML), but the context around datasets and interactions between datasets and code are in general captured only rudimentarily. Context such as how the dataset was prepared and created, what source data were used, what code was used in processing, how the dataset evolved, and where it has been used and reused can provide much insight, but this information is often poorly documented. That is unfortunate since it makes datasets into black-boxes with potentially hidden characteristics that have downstream consequences. We argue that making dataset preparation more accessible and dataset usage easier to record and document would have significant benefits for the ML community: it would allow for greater diversity in datasets by inviting modification to published sources, simplify use of alternative datasets and, in doing so, make results more transparent and robust, while allowing for all contributions to be adequately credited. We present a platform, Renku, designed to support and encourage such sustainable development and use of data, datasets, and code, and we demonstrate its benefits through a few illustrative projects which span the spectrum from dataset creation to dataset consumption and showcasing.

Soils are the dominant global source of the important greenhouse gas nitrous oxide (N2O). The anthropogenic input of nitrogen (N) into soil ecosystems increases the rate of soil N cycling, and thus enhances soil N2O emissions. N2O is produced during microbial N transformation processes, mainly via oxic nitrification and anoxic denitrification processes. These predominant pathways depend heavily on soil environmental conditions, such as soil moisture, aeration and substrate availability, which are modulated by weather and climate conditions, atmospheric composition and land use. Consequently, N2O emission rates and pathways are likely to be affected by future global changes in climate and atmospheric composition. However, the combined effects of elevated carbon dioxide (eCO2) and elevated air temperature on both N2O emission rates and pathways are unclear, as the effects can be synergistic, antagonistic or additive, and they can be further influenced by additional interacting disturbances (e.g. summer drought). Here we test how soil N2O fluxes and emission pathways respond to environmental changes in a multifactorial climate manipulation experiment, combining warming and eCO2, as well as precipitation manipulation to simulate an extreme drought during the growing season in a managed montane grassland. For the first time, we combine in-situ surface N2O flux measurements with online high-time resolution isotopic measurements, soil N2O isotope depth profiles, molecular microbial ecology, and complementary soil and microclimate measurements. Under future global change conditions, we expect increasing N2O emission rates, as well as an increasing importance of denitrification, due to the effect of large emission pulses following rewetting. In addition, we hypothesize that drought effects overrule other environmental change factors. Our results will provide an unprecedented insight into the effects of global changes on soil N dynamics and soil N2O emissions in managed montane grasslands. Furthermore, these findings will help to improve the modelling of N dynamics at the atmosphere-biosphere interface, which will be used to derive soil N2O production and consumption pathways, based on soil N2O isotope measurements, and to upscale the results to examine their potential global relevance.

Anthropogenic activities, particularly fertilisation, have resulted in significant increases in reactive nitrogen (rN) in soils globally, leading to eutrophication, acidification, poor air quality, and emissions of the important greenhouse gas N2O. Understanding the partitioning of rN losses into different environmental compartments is critical to mitigate negative impacts, however, loss pathways are poorly quantified, and potential changes driven by climate warming and societal shifts are highly uncertain. We present a coupled soil-atmosphere isotope model (IsoTONE; ISOtopic Tracing Of Nitrogen in the Environment) to partition rN losses into leaching, harvest, NH3 volatilization, and production of NO, N2 and N2O based on a global dataset of soil δ15N, as well as numerous other geoclimatic and experimental datasets. The model was optimized in a Bayesian framework using a time series of N2O mixing ratios and isotopic compositions since the preindustrial era, as well as a global dataset of N2O emission factors (EF). The posterior model results showed that the total anthropogenic flux in 2020 (7.8 Tg N2O-N a-1) was dominated by indirect emissions resulting from N deposition, while the growth rate and trend in anthropogenic N2O was driven by both direct N fertilisation and deposition inputs. In contrast, inputs from fixation N drive natural N2O emissions, and were responsible for subdecadal interannual variability in total emissions. Total N gas (N2O + NO + N2) production and N2O losses were strongly dependent on geoclimate and thus spatially variable, therefore the spatial pattern of N inputs strongly impacted resulting EFs and total N2O emissions. The area-weighted global EF for N2O was 1% of anthropogenic N inputs in 2020, similar to the current IPCC default of 1.4%, however the N input-weighted global EF was 4.3%. Shifts in fertilisation inputs from the temperate Northern hemisphere towards warmer regions with higher EFs such as India and China have led to accelerating N2O emissions (1.02±0.7 Tg N2O-N a-1). In addition, N2O emissions have increased over the past decades due to climate warming (0.76±0.4 Tg N2O-N a-1). Predicted increases in fertilisation in India and Africa in the coming decades could further accelerate N2O-driven climate warming, unless mitigation measures are implemented to increase fertiliser N use efficiency and reduce N2O emission factors.

Soils are the dominant global source of the important greenhouse gas nitrous oxide (N2O). The anthropogenic input of nitrogen (N) into soil ecosystems increases the rate of soil N cycling, and thus enhances soil N2O emissions. N2O is produced during microbial N transformation processes, mainly via oxic nitrification and anoxic denitrification processes. These predominant pathways depend heavily on soil environmental conditions, such as soil moisture, aeration and substrate availability, which are modulated by weather and climate conditions, atmospheric composition and land use. Consequently, N2O emission rates and pathways are likely to be affected by future global changes in climate and atmospheric composition. However, the combined effects of elevated carbon dioxide (eCO2) and elevated air temperature on both N2O emission rates and pathways are unclear, as the effects can be synergistic, antagonistic or additive, and they can be further influenced by additional interacting disturbances (e.g. summer drought). Here we test how soil N2O fluxes and emission pathways respond to environmental changes in a multifactorial climate manipulation experiment, combining warming and eCO2, as well as precipitation manipulation to simulate an extreme drought during the growing season in a managed montane grassland. For the first time, we combine in-situ surface N2O flux measurements with online high-time resolution isotopic measurements, soil N2O isotope depth profiles, molecular microbial ecology, and complementary soil and microclimate measurements. Under future global change conditions, we expect increasing N2O emission rates, as well as an increasing importance of denitrification, due to the effect of large emission pulses following rewetting. In addition, we hypothesize that drought effects overrule other environmental change factors. Our results will provide an unprecedented insight into the effects of global changes on soil N dynamics and soil N2O emissions in managed montane grasslands. Furthermore, these findings will help to improve the modelling of N dynamics at the atmosphere-biosphere interface, which will be used to derive soil N2O production and consumption pathways, based on soil N2O isotope measurements, and to upscale the results to examine their potential global relevance.

Anthropogenic nitrogen inputs cause major negative environmental impacts, including emissions of the important greenhouse gas N 2 O. Despite their importance, shifts in terrestrial N loss pathways driven by global change are highly uncertain. Here we present a coupled soil-atmosphere isotope model (IsoTONE) to quantify terrestrial N losses and N 2 O emission factors from 1850-2020. We find that N inputs from atmospheric deposition caused 51% of anthropogenic N 2 O emissions from soils in 2020. The mean effective global emission factor for N 2 O was 4.3 ± 0.3% in 2020 (weighted by N inputs), much higher than the surface area-weighted mean (1.1 ± 0.1%). Climate change and spatial redistribution of fertilisation N inputs have driven an increase in global emission factor over the past century, which accounts for 18% of the anthropogenic soil flux in 2020. Predicted increases in fertilisation in emerging economies will accelerate N 2 O-driven climate warming in coming decades, unless targeted mitigation measures are introduced.

Comparing measured and predicted chromatographic retention time can improve molecular structure assignment in applications such as coupled liquid chromatography-tandem mass spectrometry. We assess a range of different machine learning methods to predict hydrophobicity, a molecular property that can be used as a proxy for retention time. The performance of the models is evaluated on the benchmark Martel and SAMPL7 datasets. We find that more powerful models perform better when predicting in-sample but not necessarily when generalizing to out-of-sample molecular families. We also find that ensemble methods can outperform individual models. Additionally, a multitask learning model shows promise for improving the generalization ability of graph neural networks for hydrophobicity prediction. Finally, we discuss how the ability of graph neural networks to generalize for molecular property prediction could be improved further.

Anthropogenic nitrogen inputs cause major negative environmental impacts, including emissions of the important greenhouse gas N2O. Despite their importance, changes in terrestrial N loss pathways driven by global change and spatial redistribution of N inputs are highly uncertain. We present a novel coupled soil-atmosphere isotope model (IsoTONE) to quantify terrestrial N losses and N2O emission factors from 1850-2020, initialised using a global dataset of natural soil δ15N, and optimized with a tropospheric timeseries of N2O isotopic composition using a Bayesian framework. N inputs from atmospheric deposition caused the majority (51%) of anthropogenic N2O emissions from soils in 2020. Long-term growth in emissions was driven by fertilization and deposition, however biological fixation caused subdecadal variability in emissions. N2O emission factors (EF) show large spatial variability due to climate and soil parameters. The mean effective global EF for N2O (weighted by N inputs) was 4.3±0.3% in 2020, much higher than the land surface area-weighted mean (1.1±0.1%). Climate change and redistribution of fertilisation have driven an increase in global EF over the past century, which accounts for 18% of the anthropogenic soil flux in 2020. Predicted increases in fertilisation in emerging economies will accelerate N2O-driven climate warming in coming decades, unless targeted mitigation measures focussing on fertiliser management and reduced N deposition are introduced.

Nitrous oxide is a powerful greenhouse gas whose atmospheric growth rate has accelerated over the past decade. Most anthropogenic N2O emissions result from soil N fertilization, which is converted to N2O via oxic nitrification and anoxic denitrification pathways. Drought-affected soils are expected to be well oxygenated; however, using high-resolution isotopic measurements, we found that denitrifying pathways dominated N2O emissions during a severe drought applied to managed grassland. This was due to a reversible, drought-induced enrichment in nitrogen-bearing organic matter on soil microaggregates and suggested a strong role for chemo- or codenitrification. Throughout rewetting, denitrification dominated emissions, despite high variability in fluxes. Total N2O flux and denitrification contribution were significantly higher during rewetting than for control plots at the same soil moisture range. The observed feedbacks between precipitation changes induced by climate change and N2O emission pathways are sufficient to account for the accelerating N2O growth rate observed over the past decade.

Climate change is expected to lead to an increase in frequency and severity of extreme climatic events like summer drought. Drought and rewetting have strong impacts on soil respiration, which constitutes the largest flux of CO2 from terrestrial ecosystems to the atmosphere. However, little is known about the role of biotic and abiotic factors in driving CO2 production and transport across the soil profile and how these processes are affected by repeated drought events. Soil CO2 transport can be assessed using the flux-gradient approach, a method which assumes that diffusion is the only transport mechanism for CO2 through soil, with diffusion rates primarily dependent on air-filled pore space. It is therefore generally assumed that the calculated soil CO2 concentration gradient translates directly into soil CO2 efflux, however, a discrepancy between measured soil CO2 efflux and modeled soil CO2 concentration gradients can indicate presence of non-diffusive transport mechanisms.In a multiyear drought and rewetting experiment at a mountain meadow in the Austrian Alps, we compared soil CO2 production, transport and efflux for plots which were exposed to two and twelve subsequent years of experimental summer drought, respectively, versus plots with ambient precipitation and soil moisture. We measured soil respiration using automated chambers and assessed the production and transport of CO2 using the flux-gradient approach on data obtained with solid-state sensors in three soil depths through the soil profile. We tested the hypothesis that drought-driven reduction in soil respiration will be more intense for the 12-year drought treatment, but the CO2 pulse induced by rewetting will be higher. We furthermore expected that non-diffusive transport mechanisms would play a crucial role during drought and would be more pronounced in the 12-year drought treatment compared to the 2-year drought treatment. Data analysis is currently in progress, the findings will be presented at the conference.

As the climate warms, drought events are expected to increase in intensity and frequency, with consequences for the carbon cycle. Soil respiration (R-s) accounts for the largest flux of CO2 from terrestrial ecosystems to the atmosphere. While the drought responses of R-s have been well studied, it is uncertain how they will be modified in a future world, when higher temperatures will occur in combination with elevated atmospheric CO2 concentrations. In a global change experiment in a managed temperate grassland, we studied drought and post-drought responses of R-s dynamics under current versus likely future conditions (+3 degrees, +300 ppm CO2). Furthermore, to understand the soil CO2 production (P-s) and transport dynamics underlying R-s fluxes we continuously monitored in-situ soil CO2 concentrations across the soil profile. Our results show that R-s was higher and that drought-induced reductions in R-s were delayed under future compared to current conditions. Peak drought reductions and post-drought pulses of R-s were more pronounced in the future scenario. Annual R-s was reduced by drought only under current but not under future conditions. An in-depth analysis of soil CO2 gradients and fluxes across the soil profile showed that elevated CO2 stimulated P-s primarily in the main rooting horizon and that warming affected P-s also in deeper soil layers. We found that both in the current and the future scenario drought led to the strongest reductions of P-s in the most productive soil layers, which also exhibited the largest depletion of soil moisture. We conclude that a future warmer climate under elevated CO2 amplifies soil CO2 production and efflux and their peak drought and post-drought responses, but delays the onset of the drought responses and thereby eliminates the overall drought effect on annual soil CO2 emissions.