Sea ice protists in arctic marine sedaDNA records: origins, diversity, and vertical export of DNA from the sea surface to the seafloor 

2021 ◽  
Author(s):  
Sara Harðardóttir ◽  
Connie Lovejoy ◽  
Marit-Solveig Seidenkrantz ◽  
Sofia Ribeiro
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Ancient Dna ◽  
Genetic Material ◽  
Amplicon Sequencing ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Gene Copy ◽  
Marine Biodiversity ◽  
Vertical Export

<p>Arctic sea ice is declining at an unprecedented pace as the Arctic Ocean heads towards ice-free summers within the next few decades. Because of the role of sea ice in the Earth System such as ocean circulation and ecosystem functioning, reconstructing its past variability is of great importance providing insight into past climate patterns and future climate scenarios. Today, much of our knowledge of past sea-ice variability derives from a relatively few microfossil and biogeochemical tracers, which have limitations, such as preservation biases and low taxonomic resolution. Marine sedimentary ancient DNA (marine <em>seda</em>DNA) has the potential to capture more of the arctic marine biodiversity compared to other approaches. However, little is known about how well past communities are represented in marine <em>seda</em>DNA. The transport and fate of DNA derived from sea-ice associated organisms, from surface waters to the seafloor and its eventual incorporation into marine sediment records is poorly understood.  Here, we present results from a study applying a combination of methods to examine modern and ancient DNA to material collected along the Northeast Greenland Shelf. We characterized the vertical export of genetic material by amplicon sequencing the hyper-variable V4 region of the 18S rDNA at three water depths, in surface sediments, and in a dated sediment core.  The amplicon sequencing approach, as currently applied, includes some limitations for quantitative reconstructions of past changes such as primer competition, PCR errors, and variation of gene copy numbers across different taxa. For these reasons we quantified amplicons from a single species, the circum-polar sea ice dinoflagellate <em>Polarella glacialis</em> in the marine <em>seda</em>DNA, using digital droplet PCR. The results will increase our understanding on the taphonomy of DNA in sea ice environments, how sedimentation differs among taxonomic groups, and provide indications to potentially useful marine <em>seda</em>DNA-based proxies for climate and environmental reconstructions.</p>

The dawn of Molecular Genetics in Paleoceanography: tracing Arctic change during the Holocene with marine sedaDNA records from Greenland

2020 ◽  
Author(s):  
Sofia Ribeiro ◽  
Sara Hardardottir ◽  
Jessica Louise Ray ◽  
Stijn De Schepper ◽  
Audrey Limoges ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Full Range ◽  
Sediment Cores ◽  
Critical Role ◽  
Genetic Material ◽  
Digital Pcr ◽  
The Arctic ◽  
Taxonomic Resolution ◽  
Gene Copy

<p>As we move towards a “blue” Arctic Ocean in the summer within the next decades, predicting the full range of effects of climate change on the marine arctic environment remains a challenge. This is partly due to the paucity of long-term data on ocean-biosphere-cryosphere interactions over time and partly because, today, much of our knowledge on past ocean variability derives from microfossil and biogeochemical tracers that all have considerable limitations such as preservation biases and low taxonomic resolution or coverage.</p><p>Recent studies have revealed sedaDNA as a potential “game-changer” in our ability to reconstruct past ocean conditions, due to the preservation of DNA at low temperatures, and the possibility to capture a much larger fraction of the Arctic marine biome diversity than with classical approaches. However, while sedaDNA has been used in terrestrial, archeological, and lake studies for some years, its application to marine sediment records is still in its infancy.</p><p>Here, we will present new results from material recently collected along the two Arctic Ocean outflow shelves off Greenland (Greenland Sea/Fram Strait and Northern Baffin Bay/Nares Strait). We have used a combination of modern and ancient DNA methods applied to seawater, surface sediments, and sediment cores covering the past ca. 12 000 years with the objectives of: 1) characterizing the vertical export of sea ice-associated genetic material through the water column and into the sediments following sea ice melt and 2) exploring the potential of sedaDNA from the circum-polar sea ice dinoflagellate Polarella glacialis as a new sea ice proxy. For the first objective, we followed a comparative metabarcoding approach while the second objective included designing species-specific primers followed by gene copy number quantification by a droplet digital PCR assay. </p><p>We argue that sedaDNA will have a critical role in expanding the Paleoceanography “toolbox” and lead to the establishment of a new cross-disciplinary field.</p><p> </p>

scholarly journals Present-Day Arctic Sea Ice Variability in the Coupled ECHAM5/MPI-OM Model

2010 ◽  
Vol 23 (10) ◽  
pp. 2520-2543 ◽  
Author(s):  
Nikolay V. Koldunov ◽  
Detlef Stammer ◽  
Jochem Marotzke
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Heat Content ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Max Planck ◽  
Arctic Sea

Abstract As a contribution to a detailed evaluation of Intergovernmental Panel on Climate Change (IPCC)-type coupled climate models against observations, this study analyzes Arctic sea ice parameters simulated by the Max-Planck-Institute for Meteorology (MPI-M) fully coupled climate model ECHAM5/Max-Planck-Institute for Meteorology Hamburg Primitive Equation Ocean Model (MPI-OM) for the period from 1980 to 1999 and compares them with observations collected during field programs and by satellites. Results of the coupled run forced by twentieth-century CO2 concentrations show significant discrepancies during summer months with respect to observations of the spatial distribution of the ice concentration and ice thickness. Equally important, the coupled run lacks interannual variability in all ice and Arctic Ocean parameters. Causes for such big discrepancies arise from errors in the ECHAM5/MPI-OM atmosphere and associated errors in surface forcing fields (especially wind stress). This includes mean bias pattern caused by an artificial circulation around the geometric North Pole in its atmosphere, as well as insufficient atmospheric variability in the ECHAM5/MPI-OM model, for example, associated with Arctic Oscillation/North Atlantic Oscillation (AO/NAO). In contrast, the identical coupled ocean–ice model, when driven by NCEP–NCAR reanalysis fields, shows much increased skill in its ice and ocean circulation parameters. However, common to both model runs is too strong an ice export through the Fram Strait and a substantially biased heat content in the interior of the Arctic Ocean, both of which may affect sea ice budgets in centennial projections of the Arctic climate system.

scholarly journals Halogen-based reconstruction of Russian Arctic sea ice area from the Akademii Nauk ice core (Severnaya Zemlya)

2015 ◽  
Vol 9 (4) ◽  
pp. 4407-4436 ◽  
Author(s):  
A. Spolaor ◽  
T. Opel ◽  
J. R. McConnell ◽  
O. J. Maselli ◽  
G. Spreen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Ice Core ◽  
Iodine Concentration ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Russian Arctic ◽  
Ice Surface ◽  
Spring Sea Ice ◽  
Severnaya Zemlya

Abstract. The role of sea ice in the Earth climate system is still under debate, although it is known to influence albedo, ocean circulation, and atmosphere-ocean heat and gas exchange. Here we present a reconstruction of AD 1950 to 1998 sea ice in the Laptev Sea based on the Akademii Nauk ice core (Severnaya Zemlya, Russian Arctic). The halogens bromine (Br) and iodine (I) are strongly influenced by sea ice processes. Bromine reacts with the sea ice surface in auto-catalyzing "Bromine explosion" events causing an enrichment of the Br / Na ratio and the bromine excess (Brexc) in snow compared to that in seawater. Iodine is emitted from algal communities growing under sea ice. The results suggest a connection between Brexc and spring sea ice area, as well as a connection between iodine concentration and summer sea ice area. These two halogens are therefore good candidates for extended reconstructions of past sea ice changes in the Arctic.

scholarly journals Sea ice biota: Excerpts from the State of the Arctic Marine Biodiversity Report by the Sea Ice Biota Expert Network/CBMP

2018 ◽  
Author(s):  
Haakon Hop ◽  
Bodil A. Bluhm ◽  
Igor A. Melnikov ◽  
Michel Poulin ◽  
Mikko Vihtakari ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Algal Community ◽  
The Arctic ◽  
Marine Biodiversity ◽  
Cascading Effects ◽  
Marine Areas ◽  
Monitoring Protocols ◽  
Expert Network

Sea ice is an important Arctic habitat that supports a high diversity of species—with over 1276 protist taxa alone. Multi-year sea ice is being replaced by first-year ice and open water, which will cause shifts in ice algal communities with cascading effects on the ice-associated ecosystem. Documentation of ice biota composition, abundance and natural variability is critical for evaluating responses to the decline in Arctic sea ice. The Sea-ice Biota Expert Network, therefore, aggregated and reviewed data on status and trends of ice-associated Bacteria, Archaea, microalgae, meiofauna, and under-ice macrofauna Focal Ecosystem Components (FECs) across eight Arctic Marine Areas as well as current monitoring. Sea ice biota monitoring has occurred most frequently in the central Arctic, Svalbard area, Barrow (Alaska) and the Canadian Arctic, with recent sites in northern Greenland. Sea ice algal community structure has possibly changed in the central Arctic between the 1980s and 2010s, and ice-amphipod abundance and biomass have declined in the Svalbard area since the 1980s. Consistent monitoring protocols, equipment and methodology should be implemented. The presentation also evaluates dominant drivers of observed trends, and knowledge and monitoring gaps.

Evaluation of a multilayer snow scheme in NEMO-LIM3

2020 ◽  
Author(s):  
Gaëlle Gilson ◽  
Thierry Fichefet ◽  
Olivier Lecomte ◽  
Pierre-Yves Barriat ◽  
Jean Sterlin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Climate Models ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimum Number ◽  
Ice Growth ◽  
Arctic Sea ◽  
The Impact

<p>Arctic sea ice is a major component of the Earth’s climate system and has been experiencing a drastic decline over the past decades, with important consequences regionally and globally. With the sustained warming of the Arctic, sea ice loss is expected to continue in the future. However, the estimation of its magnitude is model-dependent. As a result, the representation of sea ice in climate models requires further consideration. A major issue relates to the long-standing misrepresentation of snow properties on sea ice. However, the presence of snow strongly impacts sea ice growth and surface energy balance. Through its high albedo, snow reflects more solar radiation than bare sea ice does. When a snow cover is present, sea ice growth is reduced because snow is an effective insulator, with a thermal conductivity an order of magnitude lower than that of sea ice. Ocean circulation models usually use multiple layers to resolve sea ice thermodynamics but only one single layer for snow. Lecomte et al. (2013) developed a multilayer snow scheme for ocean circulation models and improved the snow depth distribution by considering the macroscopic effects of wind packing and redeposition. Since then, this snow scheme has been revisited and implemented in a more recent and much more robust NEMO-LIM version, using a simpler technical approach. In addition, new instrumental observations of snow thickness, distribution and density are available since these exploratory works. They are used in the current study to: 1) evaluate the performance of the multilayer snow scheme for sea ice in the NEMO-LIM3 model, and 2) investigate the climatic importance of this snow scheme. Here, we present results of simulations with a varying number of snow layers. By comparing these to the latest observational datasets, we recommend an optimum number of snow  layers to be used in ocean circulation models in both hemispheres. Finally, we explore the impact of a few specific parameterizations of snow thermophysical properties on the representation of sea ice in climate models.</p>

scholarly journals Sea ice biota: Excerpts from the State of the Arctic Marine Biodiversity Report by the Sea Ice Biota Expert Network/CBMP

2018 ◽  
Author(s):  
Haakon Hop ◽  
Bodil A. Bluhm ◽  
Igor A. Melnikov ◽  
Michel Poulin ◽  
Mikko Vihtakari ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Algal Community ◽  
The Arctic ◽  
Marine Biodiversity ◽  
Cascading Effects ◽  
Marine Areas ◽  
Monitoring Protocols ◽  
Expert Network

Sea ice is an important Arctic habitat that supports a high diversity of species—with over 1276 protist taxa alone. Multi-year sea ice is being replaced by first-year ice and open water, which will cause shifts in ice algal communities with cascading effects on the ice-associated ecosystem. Documentation of ice biota composition, abundance and natural variability is critical for evaluating responses to the decline in Arctic sea ice. The Sea-ice Biota Expert Network, therefore, aggregated and reviewed data on status and trends of ice-associated Bacteria, Archaea, microalgae, meiofauna, and under-ice macrofauna Focal Ecosystem Components (FECs) across eight Arctic Marine Areas as well as current monitoring. Sea ice biota monitoring has occurred most frequently in the central Arctic, Svalbard area, Barrow (Alaska) and the Canadian Arctic, with recent sites in northern Greenland. Sea ice algal community structure has possibly changed in the central Arctic between the 1980s and 2010s, and ice-amphipod abundance and biomass have declined in the Svalbard area since the 1980s. Consistent monitoring protocols, equipment and methodology should be implemented. The presentation also evaluates dominant drivers of observed trends, and knowledge and monitoring gaps.

scholarly journals Equilibrium simulations of Marine Isotope Stage 3 climate

2018 ◽  
Author(s):  
Chuncheng Guo ◽  
Kerim H. Nisancioglu ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Zhongshi Zhang
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Southern Oscillation ◽  
Arctic Sea Ice ◽  
Meridional Overturning Circulation ◽  
Marine Isotope Stage ◽  
The Arctic ◽  
Quasi Equilibrium ◽  
Near Surface ◽  
Marine Isotope Stage 3

Abstract. An equilibrium simulation of Marine Isotope Stage 3 (MIS3) climate with boundary conditions characteristic of Greenland Interstadial 8 (GI-8; 38 ka BP) is carried out with the Norwegian Earth System Model (NorESM). A computationally efficient configuration of the model enables long integrations at relatively high resolution, with the simulations reaching a quasi-equilibrium state after 2500 years. We assess the characteristics of the simulated large-scale atmosphere and ocean circulation, precipitation, ocean hydrography, sea ice distribution, and internal variability. The simulated MIS3 interstadial near surface air temperature is 2.9 °C cooler than the pre-industrial (PI). The Atlantic Meridional Overturning Circulation (AMOC) is deeper and intensified (by ~ 13 %). There is a decrease in the volume of Antarctic Bottom Water (AABW) reaching the Atlantic. However, there is an increase in ventilation of the Southern Ocean, associated with a significant expansion of Antarctic sea ice and intensified brine rejection, invigorating ocean convection. In the central Arctic, sea ice is ~ 2 m thicker, with an expansion of sea ice in the Nordic Seas during winter. Simulated MIS3 inter-annual variability of the El Niño-Southern Oscillation (ENSO) and the Arctic Oscillation are weaker compared to the pre-industrial. Attempts at triggering a non-linear transition to a cold stadial climate state by varying atmospheric CO2 concentrations and Laurentide Ice Sheet height, suggest that the simulated MIS3 interstadial state in the NorESM is relatively stable, thus questioning the potential for unforced abrupt transitions in Greenland climate during the last glacial.

Prediction skill of Arctic sea ice in decadal climate simulations of the EC-Earth3 model

2020 ◽  
Author(s):  
Pasha Karami ◽  
Tim Kruschke ◽  
Tian Tian ◽  
Torben Koenigk ◽  
Shuting Yang
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Lower Troposphere ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
The Mean ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>Arctic sea ice variability and long-term trend play a major role in affecting the climate of polar and lower latitudes via complex coupling with the polar atmospheric circulation and the North Atlantic Ocean circulation. Moreover, sea ice conditions in the Arctic have direct impacts on socio-economy (e.g. the key shipping regions) and on the ecosystem. Understanding and improving predictions of Arctic sea ice on seasonal to decadal time scales is therefore crucial. <span>We investigate the skill of decadal climate prediction simulations of the EC-Earth3 model (T255L91, ORCA1L75) with a focus on Arctic sea ice. In line with the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP), we launched 59 hindcasts/forecasts from 1960 to 2018. Each hindcast/forecast has 15 ensemble members which were initialized on 1 November and integrated for 10 years (+ 2 months). Anomaly initialization approach for the ocean and sea-ice (based on data from the ORA-S5-reanalysis) and full-field initialization for the atmosphere/land surface (based on ERA-Interim/ERA-Land) were applied. We first present a comparison of our hindcasts to observations for global key parameters and provide quantitative estimates of hindcast skill by using </span><span>common deterministic metrics such as</span><span> correlation and the Mean Squared Error Skill Score. We focus particularly on the skill regarding sea ice concentration and area in the Arctic’s sub-basins and its relation to the temperature and circulation of lower troposphere as well as the mean state of the ocean </span><span>outside the Arctic</span><span>. We </span><span>also </span><span>explore relevant processes and how the ocean state and natural climate variability can </span><span>a</span><span>ffect our prediction skills to improve the prediction system.</span></p>

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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