Anne Morgenstern’s research while affiliated with Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and other places

The Siberian Arctic is warming rapidly, causing permafrost to thaw and altering the biogeochemistry of aquatic environments, with cascading effects on the coastal and shelf ecosystems of the Arctic Ocean. The Lena River, one of the largest Arctic rivers, drains a catchment dominated by permafrost. Baseline discharge biogeochemistry data are necessary to understand present and future changes in land-to-ocean fluxes. Here, we present a high-frequency 4.5-year-long dataset from a sampling program of the Lena River's biogeochemistry, spanning April 2018 to August 2022. The dataset comprises 587 sampling events and measurements of various parameters, including water temperature, electrical conductivity, stable oxygen and hydrogen isotopes, dissolved organic carbon concentration and ¹⁴C, colored and fluorescent dissolved organic matter, dissolved inorganic and total nutrients, and dissolved elemental and ion concentrations. Sampling consistency and continuity and data quality were ensured through simple sampling protocols, real-time communication, and collaboration with local and international partners. The data are available as a collection of datasets separated by parameter groups and periods at https://doi.org/10.1594/PANGAEA.913197 (Juhls et al., 2020b). To our knowledge, this dataset provides an unprecedented temporal resolution of an Arctic river's biogeochemistry. This makes it a unique baseline on which future environmental changes, including changes in river hydrology, at temporal scales from precipitation event to seasonal to interannual can be detected.

The ice-covered period of large Arctic rivers is shortening. To what extent will this affect biogeochemical processing of nutrients? Here we reveal, with silicon isotopes (δ³⁰Si), a key winter pathway for nutrients under river ice. During colder winter phases in the Lena River catchment, conditions are met for frazil ice accumulation, which creates microzones. These are conducive to a lengthened reaction time for biogeochemical processes under ice. The heavier δ³⁰Si values (3.5 ± 0.5 ‰) in river water reflect that 39 ± 11% of the Lena River discharge went through these microzones. Freezing-driven amorphous silica precipitation concomitant to increased ammonium concentration and changes in dissolved organic carbon aromaticity in Lena River water support microbially mediated processing of nutrients in the microzones. Upon warming, suppressing loci for winter intra-river nitrogen processing is likely to modify the balance between N2O production and consumption, a greenhouse gas with a large global warming potential.

The Siberian Arctic is warming rapidly, causing permafrost to thaw and altering the biogeochemistry of aquatic environments, with cascading effects on the coastal and shelf ecosystems of the Arctic Ocean. The Lena River, one of the largest Arctic rivers, drains a catchment dominated by permafrost. Baseline discharge biogeochemistry data is necessary to understand present and future changes in land-to-ocean fluxes. Here, we present a high-frequency, 4.5-year-long dataset from a sampling program of the Lena River’s biogeochemistry, spanning April 2018 to August 2022. The dataset comprises 587 sampling events and measurements of various parameters, including water temperature, electrical conductivity, stable oxygen and hydrogen isotopes, dissolved organic carbon concentration and 14C, coloured and fluorescent dissolved organic matter, dissolved inorganic and total nutrients, and dissolved elemental and ion concentrations. Sampling consistency and continuity and data quality were ensured through simple sampling protocols, real-time communication, and collaboration with local and international partners. The data is available as a collection of datasets separated by parameter groups and periods at https://doi.org/10.1594/PANGAEA.913197 (Juhls et al., 2020b). To our knowledge, this dataset provides an unprecedented temporal resolution of an Arctic river’s biogeochemistry. This makes it a unique baseline on which future environmental changes, including changes in river hydrology, at temporal scales from precipitation event to seasonal to interannual, can be detected.

Arctic landscapes are characterized by diverse water bodies, which are covered with ice for most of the year. Ice controls surface albedo, hydrological properties, gas exchange, and ecosystem services, but freezing processes differ between water bodies. We studied the influence of geomor- phology and meteorology on winter ice of water bodies in the Lena Delta, Siberia. Electrical conductivity (EC) and stable water isotopes of ice cores from four winters and six water bodies were measured at unprecedented resolution down to 2-cm increments, revealing differences in freezing systems. Open-system freezing shows near-constant isotopic and EC gradients in ice, whereas closed-system freezing shows decreasing isotopic composition with depth. Lena River ice displays three zones of isotopic composition within the ice, reflecting open-system freezing that records changing water sources over the winter. The isotope composition of ice covers in landscape units of different ages also reflects the individual water reservoir settings (i.e., Pleistocene vs. Holocene ground ice thaw). Ice growth models indicate that snow properties are a dominant determinant of ice growth over winter. Our findings provide novel insights into the winter hydrochemistry of Arctic ice covers, including the influences of meteorology and water body geomorphology on freezing rates and processes.

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

In the past decades the Arctic has experienced stronger temperature increases than any other region globally. Shifts in hydrological regimes and accelerated permafrost thawing have been observed and are likely to increase mobilization of organic carbon and its transport through rivers into the Arctic Ocean. In order to better quantify changes to the carbon cycle, Arctic rivers such as the Lena River in Siberia need to be monitored closely. Since 2018, a sampling program provides frequent in situ observations of dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) of the Lena River. Here, we utilize this ground truth dataset and aim to test the potential of frequent satellite observations to spatially and temporally complement and expand these observations. We explored all available overpasses (~3250) of the Ocean and Land Colour Instrument (OLCI) on Sentinel-3 within the ice-free periods (May – October) for four years (2018 to 2021) to develop a new retrieval scheme to derive concentrations of DOC. OLCI observations with a spatial resolution of ~300 m were corrected for atmospheric effects using the Polymer algorithm. The results of this study show that using this new retrieval, remotely sensed DOC concentrations agree well with in situ DOC concentrations (MAPD=10.89%, RMSE=1.55 mg L⁻¹, r²=0.92, n=489). The high revisit frequency and wide swath of OLCI allow it to capture the entire range of DOC concentrations and their seasonal variability. Estimated satellite-derived DOC export fluxes integrated over the ice-free periods of 2018 to 2021 show a high interannual variability and agree well with flux estimates from in situ data (RMSD=0.186 Tg C, MAPD=4.05%). In addition, 10-day OLCI composites covering the entire Lena River catchment revealed increasing DOC concentration and local sources of DOC along the Lena from south to north. We conclude that moderate resolution satellite imagers such as OLCI are very capable of observing DOC concentrations in large/wide rivers such as the Lena River despite the relatively coarse spatial resolution. The global coverage of remote sensing offers the expansion to more rivers in order to improve our understanding of the land-ocean carbon fluxes in a changing climate.

The Lena Delta is the largest river delta in the Arctic (about 30 000 km2) and prone to rapid changes due to climate warming, associated cryosphere loss and ecological shifts. The delta is characterized by ice-rich permafrost landscapes and consists of geologically and geomorphologically diverse terraces covered with tundra vegetation and of active floodplains, featuring approximately 6 500 km of channels and over 30 000 lakes. Because of its broad landscape and habitat diversity the delta is a biodiversity hotspot with high numbers of nesting and breeding migratory birds, fish, caribou and other mammals and was designated a State Nature Reserve in 1995. Characterizing plant composition, above ground biomass and application of field spectroscopy was a major focus of a 2018 expedition to the delta. These field data collections were linked to Sentinel-2 satellite data to upscale local patterns in land cover and associated habitats to the entire delta. Here, we describe multiple field datasets collected in the Lena Delta during summer 2018 including foliage projective cover (Shevtsova et al., 2021a), above ground biomass (Shevtsova et al., 2021b), and hyperspectral field measurements (Runge et al., 2022, https://doi.pangaea.de/10.1594/PANGAEA.945982). We further describe a detailed Sentinel-2 satellite image-based classification of habitat types for the central Lena Delta (Landgraf et al., 2022), an upscaled classification for the entire Lena Delta (Lisovski et al., 2022), as well as a synthesis product for disturbance regimes (Heim and Lisovski, 2023, https://doi.org/10.5281/zenodo.7575691) in the delta that is based on the classification, the described datasets, and field expertise. We present context and detailed methods of these openly available datasets and show how they can improve our understanding of the rapidly changing Arctic tundra system. The new Lena Delta habitat distribution dataset represents a first baseline against which future observations can be compared. With the link between such detailed habitat type classifications and disturbance regimes future upscaling efforts may provide a better understanding of how Arctic lowland landscapes will respond to climate change and how this will impact land surface processes.