Lateral thermokarst patterns in permafrost peat plateaus in northern Norway

Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon. Our ability to quantify the evolution of such permafrost landscapes in numerical models is critical for providing robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions has so far been hampered by small-scale physical processes that create a high spatial variability of thermal surface conditions, affecting the ground thermal regime and thus permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (northern Norway) over a three-year period using drone-based repeat high-resolution photogrammetry. Our results show thermokarst degradation is concentrated on the edges of the plateau, representing 77 % of observed subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to an annual volume change of 0.13±0.07 m3 yr−1 per meter of retreating edge (orthogonal to the retreat direction). Using the CryoGrid3 land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. By performing a sensitivity test for snow depths on the plateau under steady-state climate forcing, we obtain a threshold behavior for the start of edge degradation. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. For plateau snow depths in the range of field measurements, the simulated annual volume changes are broadly in agreement with the results of the drone survey. As snow depths are clearly correlated with ground surface temperatures, our results indicate that the approach can potentially be used to simulate climate-driven dynamics of edge degradation observed at our study site and other peat plateaus worldwide. Thus, the model approach represents a first step towards simulating climate-driven landscape development through thermokarst in permafrost peatlands

The Bayelva high Arctic permafrost long-term observation site: an opportunity for joint international research on permafrost, atmosphere, ecology and snow

At present, the Arctic climate is changing much more rapidly than the rest of the globe, and yet observational data available in the region is poor. The positive feedback between climate warming a nd permafrost carbon emissions depends on changing land - atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual time scales, and permafrost, which changes over longer time periods. This understanding mandates long - term observational data sets. There is also a need to realistically incorporate permafrost into global modelling frameworks such as Earth System Models. Evaluating and parameterising of process - based models require simultaneous measurements of interacting variables. Here we present an example of such a long -term data set, from the Bayelva site at Ny- Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, and testing permafrost as a component in Earth System Models. There is a great need for continuous monitoring at more sites, to span the full range of permafrost conditions. The data show that mean annual, summer and winter soil temperature data from shallow to deeper depths have been warming over the period of record, indicating the degradation of permafrost at this site

The Endoplasmic Reticulum Stress Response in Neuroprogressive Diseases: Emerging Pathophysiological Role and Translational Implications

The endoplasmic reticulum (ER) is the main cellular organelle involved in protein synthesis, assembly and secretion. Accumulating evidence shows that across several neurodegenerative and neuroprogressive diseases, ER stress ensues, which is accompanied by over-activation of the unfolded protein response (UPR). Although the UPR could initially serve adaptive purposes in conditions associated with higher cellular demands and after exposure to a range of pathophysiological insults, over time the UPR may become detrimental, thus contributing to neuroprogression. Herein, we propose that immune-inflammatory, neuro-oxidative, neuro-nitrosative, as well as mitochondrial pathways may reciprocally interact with aberrations in UPR pathways. Furthermore, ER stress may contribute to a deregulation in calcium homoeostasis. The common denominator of these pathways is a decrease in neuronal resilience, synaptic dysfunction and even cell death. This review also discusses how mechanisms related to ER stress could be explored as a source for novel therapeutic targets for neurodegenerative and neuroprogressive diseases. The design of randomised controlled trials testing compounds that target aberrant UPR-related pathways within the emerging framework of precision psychiatry is warranted

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Time-resolved dual transcriptomics reveal early induced Nicotiana benthamiana root genes and conserved infection-promoting Phytophthora palmivora effectors

BACKGROUND: Plant-pathogenic oomycetes are responsible for economically important losses in crops worldwide. Phytophthora palmivora, a tropical relative of the potato late blight pathogen, causes rotting diseases in many tropical crops including papaya, cocoa, oil palm, black pepper, rubber, coconut, durian, mango, cassava and citrus. Transcriptomics have helped to identify repertoires of host-translocated microbial effector proteins which counteract defenses and reprogram the host in support of infection. As such, these studies have helped in understanding how pathogens cause diseases. Despite the importance of P. palmivora diseases, genetic resources to allow for disease resistance breeding and identification of microbial effectors are scarce. RESULTS: We employed the model plant Nicotiana benthamiana to study the P. palmivora root infections at the cellular and molecular levels. Time-resolved dual transcriptomics revealed different pathogen and host transcriptome dynamics. De novo assembly of P. palmivora transcriptome and semi-automated prediction and annotation of the secretome enabled robust identification of conserved infection-promoting effectors. We show that one of them, REX3, suppresses plant secretion processes. In a survey for early transcriptionally activated plant genes we identified a N. benthamiana gene specifically induced at infected root tips that encodes a peptide with danger-associated molecular features. CONCLUSIONS: These results constitute a major advance in our understanding of P. palmivora diseases and establish extensive resources for P. palmivora pathogenomics, effector-aided resistance breeding and the generation of induced resistance to Phytophthora root infections. Furthermore, our approach to find infection-relevant secreted genes is transferable to other pathogen-host interactions and not restricted to plants.This work was supported by the Gatsby Charitable Foundation (RG62472), by the Royal Society (RG69135) and by the European Research Council (ERC-2014-STG, H2020, 637537)

A Comparison between Simulated and Observed Surface Energy Balance at the Svalbard Archipelago

The surface energy balance at the Svalbard Archipelago has been simulated at high resolution with the Weather Research and Forecasting Model and compared with measurements of the individual energy fluxes from a tundra site near Ny-Ålesund (located north of Norway), as well as other near-surface measurements across the region. For surface air temperature, a good agreement between model and observations was found at all locations. High correlations were also found for daily averaged surface energy fluxes within the different seasons at the main site. The four radiation components showed correlations above 0.5 in all seasons (mostly above 0.9), whereas correlations between 0.3 and 0.8 were found for the sensible and latent heat fluxes. Underestimation of cloud cover and cloud optical thickness led to seasonal biases in incoming shortwave and longwave radiation of up to 30%. During summer, this was mainly a result of distinct days on which the model erroneously simulated cloud-free conditions, whereas the incoming radiation biases appeared to be more related to underestimation of cloud optical thickness during winter. The model overestimated both sensible and latent heat fluxes in most seasons. The model also initially overestimated the average Bowen ratio during summer by a factor of 6, but this bias was greatly reduced with two physically based model modifications that are related to frozen-ground hydrology. The seasonally averaged ground/snow heat flux was mostly in agreement with observations but showed too little short-time variability in the presence of thick snow. Overall, the model reproduced average temperatures well but overestimated diurnal cycles and showed considerable biases in the individual energy fluxes on seasonal and shorter time scales

Modeling the degradation of ice-rich permafrost landscapes

Thawing of permafrost potentially affects the global climate system through the mobilization of greenhouse gases, and poses a risk to human infrastructure in the Arctic. The response of ice-rich permafrost landscapes to a changing climate is particularly uncertain, and challenging to be addressed with numerical models. A main reason for this is the rapidly changing surface topography resulting from melting of ground ice, which is referred to as thermokarst. It is expressed in characteristic landforms which alter the hydrology, the surface energy balance, and the redistribution of snow of the entire landscapes. Polygonal patterned tundra which is underlain by massive ice-wedges, is a prototype of a sensitive permafrost system which is increasingly subjected to thermokarst activity throughout the Arctic. In this talk I will present a scalable modeling approach, based on the CryoGrid land surface model, to investigate the degradation of ice-wedges. The numerical model takes into account lateral fluxes of heat, water, and snow between different topographic units of polygonal tundra and simulates topographic changes resulting from melting of excess ground ice (i.e., thermokarst), and from lateral erosion of sediment. We applied the model to investigate the influence of hydrological conditions on the development of different types of ice-wedge polygons in a study area in northern Siberia. We further used projections of future climatic conditions to confine the evolution of ice-wedge polygons in a changing climate, and assessed the amount of organic matter which could thaw under different scenarios. In a related study for a study site in northern Alaska, we demonstrated that the model setup can be used to study the effect of infrastructure on the degradation of ice-wedges. Altogether, our modeling approach can be seen as a blueprint to investigate complexly inter-related processes in ice-rich permafrost landscapes, and marks a step forward towards an improved representation of these landscapes in large-scale land surface models