Microbial methane cycling in sediments of Arctic thermokarst lagoons

Thermokarst lagoons represent the transition state from a freshwater lacustrine to a marine environment, and receive little attention regarding their role for greenhouse gas production and release in Arctic permafrost landscapes. We studied the fate of methane (CH4) in sediments of a thermokarst lagoon in comparison to two thermokarst lakes on the Bykovsky Peninsula in northeastern Siberia through the analysis of sediment CH4 concentrations and isotopic signature, methane-cycling microbial taxa, sediment geochemistry, lipid biomarkers, and network analysis. We assessed how differences in geochemistry between thermokarst lakes and thermokarst lagoons, caused by the infiltration of sulfate-rich marine water, altered the microbial methane-cycling community. Anaerobic sulfate-reducing ANME-2a/2b methanotrophs dominated the sulfate-rich sediments of the lagoon despite its known seasonal alternation between brackish and freshwater inflow and low sulfate concentrations compared to the usual marine ANME habitat. Non-competitive methylotrophic methanogens dominated the methanogenic community of the lakes and the lagoon, independent of differences in porewater chemistry and depth. This potentially contributed to the high CH4 concentrations observed in all sulfate-poor sediments. CH4 concentrations in the freshwater-influenced sediments averaged 1.34 ± 0.98 μmol g−1, with highly depleted δ13C-CH4 values ranging from −89‰ to −70‰. In contrast, the sulfate-affected upper 300 cm of the lagoon exhibited low average CH4 concentrations of 0.011 ± 0.005 μmol g−1 with comparatively enriched δ13C-CH4 values of −54‰ to −37‰ pointing to substantial methane oxidation. Our study shows that lagoon formation specifically supports methane oxidizers and methane oxidation through changes in pore water chemistry, especially sulfate, while methanogens are similar to lake conditions

Direct nitrous oxide emissions from oilseed rape cropping - a meta-analysis

Oilseed rape is one of the leading feedstocks for biofuel production in Europe. The climate change mitigation effect of rape methyl ester (RME) is particularly challenged by the greenhouse gas (GHG) emissions during crop production, mainly as nitrous oxide (N2O) from soils. Oilseed rape requires high nitrogen fertilization and crop residues are rich in nitrogen, both potentially causing enhanced N2O emissions. However, GHG emissions of oilseed rape production are often estimated using emission factors that account for crop-type specifics only with respect to crop residues. This meta-analysis therefore aimed to assess annual N2O emissions from winter oilseed rape, to compare them to those of cereals and to explore the underlying reasons for differences. For the identification of the most important factors, linear mixed effects models were fitted with 43 N2O emission data points deriving from 12 different field sites. N2O emissions increased exponentially with N-fertilization rates, but interyear and site-specific variability were high and climate variables or soil parameters did not improve the prediction model. Annual N2O emissions from winter oilseed rape were 22% higher than those from winter cereals fertilized at the same rate. At a common fertilization rate of 200 kg N ha−1 yr−1, the mean fraction of fertilizer N that was lost as N2O-N was 1.27% for oilseed rape compared to 1.04% for cereals. The risk of high yield-scaled N2O emissions increased after a critical N surplus of about 80 kg N ha−1 yr−1. The difference in N2O emissions between oilseed rape and cereal cultivation was especially high after harvest due to the high N contents in oilseed rape's crop residues. However, annual N2O emissions of winter oilseed rape were still lower than predicted by the Stehfest and Bouwman model. Hence, the assignment of oilseed rape to the crop-type classes of cereals or other crops should be reconsidered

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Patterns in the multiannual course of growing season in Central Europe since the end of the 19th century

The research identified patterns in the multiannual course of start and end dates, and length of growing sea son (GS) in Central Europe since the end of the 19th century in selected cities of Central Europe in the period 1893-2020. GS start in the analysed stations was characterised by high year-to-year variability, particularly in those located more southwards, i.e. in Prague and Vienna. A smaller variability occurred in GS end dates. The GS was subject to prolon gation, although these changes in particular cities were uneven and had different causes. In Toruń and Potsdam, its increase was caused by a greater shift of the end date, and in the remaining stations, it was determined by its earlier start date. Two subperiods were distinguished that differ in terms of intensity of changes of the start and end dates, as well as the length of the GS. The intensification was observed recently

Deep Yedoma permafrost: A synthesis of depositional characteristics and carbon vulnerability

Permafrost is a distinct feature of the terrestrial Arctic and is vulnerable to climate warming. Permafrost degrades in different ways, including deepening of a seasonally unfrozen surface and localized but rapid development of deep thaw features. Pleistocene ice-rich permafrost with syngenetic ice-wedges, termed Yedoma deposits, are widespread in Siberia, Alaska, and Yukon, Canada and may be especially prone to rapid-thaw processes. Freeze-locked organic matter in such deposits can be re-mobilized on short time-scales and contribute to a carbon cycle climate feedback. Here we synthesize the characteristics and vulnerability of Yedoma deposits by synthesizing studies on the Yedoma origin and the associated organic carbon pool. We suggest that Yedoma deposits accumulated under periglacial weathering, transport, and deposition dynamics in non-glaciated regions during the late Pleistocene until the beginning of late glacial warming. The deposits formed due to a combination of aeolian, colluvial, nival, and alluvial deposition and simultaneous ground ice accumulation. We found up to 130 gigatons organic carbon in Yedoma, parts of which are well-preserved and available for fast decomposition after thaw. Based on incubation experiments, up to 10% of the Yedoma carbon is considered especially decomposable and may be released upon thaw. The substantial amount of ground ice in Yedoma makes it highly vulnerable to disturbances such as thermokarst and thermo-erosion processes. Mobilization of permafrost carbon is expected to increase under future climate warming. Our synthesis results underline the need of accounting for Yedoma carbon stocks in next generation Earth-System-Models for a more complete representation of the permafrost-carbon feedback

Relationships between greenhouse gas production and landscape position during short-term permafrost thaw under anaerobic conditions in the Lena Delta

Soils in the permafrost region have acted as car- bon sinks for thousands of years. As a result of global warming, permafrost soils are thawing and will potentially release greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2). However, small-scale spatial heterogeneities of GHG production have been neglected in previous incubation studies. Here, we used an anaerobic incubation experiment to simulate permafrost thaw along a transect from upland Yedoma to the floodplain on Kurungnakh Island. Potential CO2 and CH4 production was measured during incubation of the active layer and permafrost soils at 4 and 20 ◦C, first for 60 d (approximate length of the growing season) and then continuing for 1 year. An assessment of methanogen abundance was performed in parallel for the first 60 d. Yedoma samples from upland and slope cores remained in a lag phase during the growing season simulation, while those located in the floodplain showed high production of CH4 (6.5 × 103 μg CH4-C g−1 C) and CO2 (6.9 × 103 μg CO2-C g−1 C) at 20 ◦C. The Yedoma samples from the permafrost layer started producing CH4 after 6 months of incubation. We conclude that landscape position is a key factor triggering CH4 production during the growing season time on Kurungnakh Island

Lake browning counteracts cyanobacteria responses to nutrients: Evidence from phytoplankton dynamics in large enclosure experiments and comprehensive observational data

Lakes worldwide are affected by multiple stressors, including climate change. This includes massive loading of both nutrients and humic substances to lakes during extreme weather events, which also may disrupt thermal stratification. Since multi-stressor effects vary widely in space and time, their combined ecological impacts remain difficult to predict. Therefore, we combined two consecutive large enclosure experiments with a comprehensive time-series and a broad-scale field survey to unravel the combined effects of storm-induced lake browning, nutrient enrichment and deep mixing on phytoplankton communities, focusing particularly on potentially toxic cyanobacterial blooms. The experimental results revealed that browning counteracted the stimulating effect of nutrients on phytoplankton and caused a shift from phototrophic cyanobacteria and chlorophytes to mixotrophic cryptophytes. Light limitation by browning was identified as the likely mechanism underlying this response. Deep-mixing increased microcystin concentrations in clear nutrient-enriched enclosures, caused by upwelling of a metalimnetic Planktothrix rubescens population. Monitoring data from a 25-year time-series of a eutrophic lake and from 588 northern European lakes corroborate the experimental results: Browning suppresses cyanobacteria in terms of both biovolume and proportion of the total phytoplankton biovolume. Both the experimental and observational results indicated a lower total phosphorus threshold for cyanobacterial bloom development in clearwater lakes (10-20 mu g P L-1) than in humic lakes (20-30 mu g P L-1). This finding provides management guidance for lakes receiving more nutrients and humic substances due to more frequent extreme weather events

Uncertainty of biomass contributions from agriculture and forestry to renewable energy resources under climate change

In the future, Germany's land-use policies and the impacts of climate change on yields will affect the amount of biomass available for energy production. We used recent published data on biomass potentials in the federal states of Germany to assess the uncertainty caused by climate change effects in the potential supply of biomass available for energy production. In this study we selected three climate scenarios representing the maximum, mean and minimum temperature increase for Germany out of 21 CMIP5-projections driven by the Representative Concentration Pathways (RCP) 8.5 scenario. Each of the three selected projections was downscaled using the regional statistical climate model STARS. We analysed the yield changes of four biomass feedstock crops (forest, short-rotation coppices (SRC), cereal straw (winter wheat) and energy maize) for the period 2031–2060 in comparison to 1981–2010. The mean annual yield changes of energy wood from forest and short-rotation coppices were modelled using the process-based forest growth model 4C. The yield changes of winter wheat and energy maize from agricultural production were simulated with the statistical yield model IRMA. Germany's annual biomass potential of 1500 PJ varies between minus 5 % and plus 8 % depending on the climate scenario realisation. Assuming that 1500 PJ of biomass utilisation can be achieved, climate change effects of minus 75 (5 %) PJ or plus 120 (8 %) PJ do not impede overall bioenergy targets of 1287 PJ in 2020 and 1534 PJ in 2050. In five federal states the climate scenarios lead to decreasing yields of energy maize and winter wheat. Impacts of climate scenarios on forest yields are mainly positive and show both positive and negative effects on yields of SRC