Alfred Wegener Institute for Polar and Marine Research
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Sea-ice melt determines seasonal phytoplankton dynamics and delimits the habitat of temperate Atlantic taxa as the Arctic Ocean atlantifies
Get PDFThe Arctic Ocean is one of the regions where anthropogenic environmental change is progressing most rapidly and drastically. The impact of rising temperatures and decreasing sea ice on Arctic marine microbial communities is yet not well understood. Microbes form the basis of food webs in the Arctic Ocean, providing energy for larger organisms. Previous studies have shown that Atlantic taxa associated with low light are robust to more polar conditions. We compared to which extent sea ice melt influences light-associated phytoplankton dynamics and biodiversity over two years at two mooring locations in the Fram Strait. One mooring is deployed in pure Atlantic water, and the second in the intermittently ice-covered Marginal Ice Zone. Time-series analysis of amplicon sequence variants abundance over a 2-year period, allowed us to identify communities of co-occurring taxa that exhibit similar patterns throughout the annual cycle. We then examined how alterations in environmental conditions affect the prevalence of species. During high abundance periods of diatoms, polar phytoplankton populations dominated, while temperate taxa were weakly represented. Furthermore, we found that polar pelagic and ice-associated taxa, such as Fragilariopsis cylindrus and Melosira arctica, were more common in Atlantic conditions, while temperate taxa, such as Odontella aurita and Proboscia alata, were less abundant under polar conditions. This suggests that sea ice melt may act as a barrier to the northward expansion of temperate phytoplankton, preventing their dominance in regions still strongly influenced by polar conditions. Our findings highlight the complex interactions between sea ice melt, phytoplankton dynamics, and biodiversity in the Arctic
Hydroclimatic anomalies detected by a sub-decadal diatom oxygen isotope record of the last 220 years from Lake Khamra, Siberia
Get PDFNorthern latitudes have been significantly impacted by recent climate warming, which has increased the probability of experiencing extreme weather events. To comprehensively understand hydroclimate change and reconstruct hydroclimatic anomalies such as drought periods, appropriate proxy records reaching further back in time beyond meteorological measurements are needed. Here we present a 220-year (2015–1790 CE), continuous, stable oxygen isotope record of diatoms (δ18Odiatom) from Lake Khamra (59.99° N, 112.98° E) in eastern Siberia, an area highly sensitive to climate change and for which there is a demand for palaeohydrological data. This high-resolution proxy record was obtained from a 210Pb–137Cs-dated sediment short core and analysed to reconstruct hydroclimate variability at a sub-decadal scale. The interpretation of the δ18Odiatom record is supported by meteorological data, modern isotope hydrology and geochemical analyses of the same sediment, which is indicative of the conditions in the lake and catchment. A comparison with meteorological data going back to 1930 CE revealed that the δ18Odiatom record of Lake Khamra is primarily influenced by regional precipitation changes rather than the air temperature. We identified winter precipitation, which enters the lake as isotopically depleted snowmelt water, as the key process impacting the diatom isotope variability. We related the overall depletion of δ18Odiatom in recent decades to an observed increase in winter precipitation in the area, likely associated with the global air temperature rise, Arctic sea ice retreat and increased moisture transport inland. Available palaeoclimate proxy records, including a fire reconstruction for the same lake, support the idea that the new record is a valuable hydroclimate proxy that is indicative of precipitation deficits and excludes solar insolation and air temperature as primary driving forces, even before the first meteorological recordings. We propose two possible hydroclimatic anomalies that were detected in the Lake Khamra δ18Odiatom record: one at the beginning of the 19th century and a second prominent event in the 1950s. Both are interpreted as prolonged dry periods associated with enriched δ18Odiatom values likely caused by reduced winter precipitation, which coincide with phases of reconstructed severe wildfires in the region. Despite the apparent pristine lake area, we observed a three- to fourfold increase in mercury concentrations and accumulation rates within the sediment record since the early 20th century, which is partly attributed to human air pollution
How ice anisotropy contributes to fold and ice stream in large-scale ice-sheet models
Get PDFSatellite and airborne sensors have provided detailed data on ice surface flow velocities, englacial structures of ice sheets and bedrock elevations. These data give insight into the flow behaviour of ice sheets and glaciers. One significant phenomenon observed is large-scale folds (over 100 m in amplitude) in the englacial stratigraphy in the Greenland ice sheet. A large population of folds is located at ice streams, where the flow is distinctly faster than in the surroundings, such as the North-East Greenland Ice Stream (NEGIS). While there is no consensus regarding the formation of large-scale folds, unraveling the underlying mechanisms presents significant potential for enhancing our understanding of the formation and dynamics of ice streams.
Ice in ice sheets is a ductile material, i.e., it can flow as a thick viscous fluid with a power-law rheology. Furthermore, ice is significantly anisotropic in its flow properties due to its crystallographic preferred orientation (CPO). Here, we use the Full-Stokes code Underworld2 (Mansour et al.,2022) for 3D modelling of the power-law and transversely isotropic ice flow, also in comparison with the isotropic ice models.
Our simulated folds with anisotropic ice show complex patterns on a bumpy bedrock, and are classified into three types: large-scale folds (fold amplitudes >100 m), small-scale folds (fold amplitudes <<100 m, wavelength <<km) and recumbent basal-shear folds. Our results indicate that bedrock topography contributes to perturbations in ice layers, and that ice anisotropy due to the CPO amplifies these into large-scale folds in convergent flow by horizontal shortening. As for our ice stream model, we simulate convergent flow as initial condition, which subsequently initiates the development of shear margins due to the rotation of the ice crystal basal planes. As soon as the shear margins develop, the ice stream starts to propagate upstream in a short time and narrows in the upstream part. Our modeling shows that the anisotropic rheology of ice and CPO change play a significant role for large-scale folding and for the initiation of ice streams with distinct shear margins. Hence, we promote the implementation of ice anisotropy in large-scale ice-sheet evolution models as it holds the potential to introduce novel perspectives to the glaciological community on the dynamics of ice flow
Effective Spatial Degrees of Freedom of Natural Temperature Variability as a Function of Frequency
Get PDFA fundamental statistic of climate variability is its spatiotemporal correlation function. Its complex structure can be concisely summarized by a frequency-dependent measure of the effective spatial degrees of freedom (ESDOF). Here we present, for the first time, frequency-dependent ESDOF estimates of global natural surface temperature variability from purely instrumental measurements, using the HadCRUT4 dataset (1850-2014). The approach is based on a newly developed method for estimating the frequency-dependent spatial correlation function from gappy data fields. Results reveal a multicomponent structure of the spatial correlation function, including a large-amplitude short-distance component (with weak time scale dependence) and a small-amplitude long-distance component (with increasing relative amplitude toward the longer time scales). Two frequency-dependent ESDOF measures are applied, each responding mainly to either of the two components. Both measures exhibit a significant ESDOF reduction from monthly to multidecadal time scales, implying an increase of the effective spatial scale of natural surface temperature fluctuations. Moreover, it is found that a good approximation to the global number of equally spaced samples needed to estimate the variance of global mean temperature is given, at any frequency, by the greater one of the two ESDOF measures, decreasing from ;130 at monthly to ;30 at multidecadal time scales. Finally, the multicomponent structure of the correlation function together with the detected ESDOF scaling properties indicate that the ESDOF reduction toward the longer time scales cannot be explained simply by diffusion acting on stochastically driven anomalies, as it might be suggested f rom simple stochastic-diffusive energy balance models
Spatial and temporal stable water isotope data from the upper snowpack at the EastGRIP camp site, NE Greenland, sampled in summer 2018
Get PDFAbstract. Stable water isotopes stored in snow, firn and ice are used to reconstruct climatic parameters. The imprint of these parameters at the snow surface and their preservation in the upper snowpack are determined by a number of processes influencing the recording of the environmental signal. Here, we present a dataset of approximately 3800 snow samples analysed for their stable water isotope composition, which were obtained during the summer season next to the deep drilling site of the East Greenland Ice Core Project in northeast Greenland (75.635411° N, 36.000250° W). Sampling was carried out every third day between 14 May and 3 August 2018 along a 39 m long transect. Three depth intervals in the top 10 cm were sampled at 30 positions with a higher resolution closer to the surface (0–1 and 1–4 cm depth vs. 4–10 cm). The sample analysis was carried out at two renowned stable water isotope laboratories that produced isotope data with the overall highest uncertainty of 0.09 ‰ for δ18O and 0.8 ‰ for δD. This unique dataset shows the strongest δ18O variability closest to the surface, damped and delayed variations in the lowest layer, and a trend towards increasing homogeneity towards the end of the season, especially in the deepest layer. Additional information on the snow height and its temporal changes suggests a non-uniform spatial imprint of the seasonal climatic information in this area, potentially following the stratigraphic noise of the surface. The data can be used to study the relation between snow height (changes) and the imprint and preservation of the isotopic composition at a site with 10–14 cm w.e. yr−1 accumulation. The high-temporal-resolution sampling allows additional analyses on (post-)depositional processes, such as vapour–snow exchange. The data can be accessed at https://doi.org/10.1594/PANGAEA.956626 (Zuhr et al., 2023a).
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The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms
Get PDFCentral 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.</jats:p
