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  • Data  (2)
  • 2015-2019  (2)
  • 1985-1989
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  • 1
    Publication Date: 2023-01-13
    Description: Our knowledge of sea ice variability, which contributes to the detection of the Antarctic climate change trends, stems primarily from remotely sensed information. However, sea ice in the Southern Ocean is characterized by large variability that remains unresolved and limits our confidence on the remotely sensed products. Therefore, the in situ sea ice observations presented (according to the ASPeCt protocol) provide a greater understanding of the Antarctic sea ice environment - on a local scale - and allows us to evaluate remotely sensed products.
    Keywords: AGULII201612; AGULII20161207_10; AGULII20161207_11; AGULII20161207_12; AGULII20161207_13; AGULII20161207_14; AGULII20161207_15; AGULII20161207_16; AGULII20161207_17; AGULII20161207_18; AGULII20161207_19; AGULII20161207_20; AGULII20161207_21; AGULII20161207_22; AGULII20161207_23; AGULII20161207_9; AGULII20161208_0; AGULII20161208_1; AGULII20161208_10; AGULII20161208_11; AGULII20161208_12; AGULII20161208_13; AGULII20161208_14; AGULII20161208_15; AGULII20161208_16; AGULII20161208_17; AGULII20161208_18; AGULII20161208_2; AGULII20161208_3; AGULII20161208_4; AGULII20161208_5; AGULII20161208_6; AGULII20161208_7; AGULII20161208_8; AGULII20161208_9; AGULII20161210_0; AGULII20161210_1; AGULII20161210_10; AGULII20161210_11; AGULII20161210_12; AGULII20161210_13; AGULII20161210_14; AGULII20161210_15; AGULII20161210_16; AGULII20161210_17; AGULII20161210_2; AGULII20161210_3; AGULII20161210_4; AGULII20161210_5; AGULII20161210_6; AGULII20161210_7; AGULII20161210_8; AGULII20161210_9; Comment; Comment of event; DATE/TIME; Event label; Floe size; Ice concentration; ICESUR; Ice survey; Ice type; Latitude of event; Lazarev Sea; Longitude of event; S. A. Agulhas II; Sea ice observation according to ASPeCt protocol; Sea ice thickness; Sea ice topography; Snow thickness; Snow type; South Atlantic Ocean; Station label; Temperature, air; Temperature, water; Weddell Sea; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 603 data points
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2024-04-20
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Acer; ACER; Agrostemma githago; Allium-type; Alnus; Anagallis arvensis; Anagallis tenella; Anemone nemorosa-type; Anthemis-type; Apiaceae; Armeria; Artemisia; Asphodelus-type; Asteraceae; Aster-type; Basilicata, southern Italy; Betula; Boraginaceae; Brassicaceae; Bryophyta; Bupleurum-type; Buxus; Calligonum-type; Campanulaceae; Cannabaceae; Carpinus betulus; Caryophyllaceae; Castanea; CDRILL; Cedrus; Celtis; Centaurea cyanus-type; Centaurea nigra-type; Ceratophyllum; cf. Alchemilla; cf. Androsace; cf. Callitriche; cf. Centaurium; cf. Chrysosplenium; cf. Circaea; cf. Daphne; cf. Elatine; cf. Euphorbia; cf. Frangula/Bruckenthalia; cf. Hypericum; cf. Mercurialis; cf. Onobrychis-type; cf. Platanus; cf. Samolus valerandi; cf. Trichomanes; Chenopodiaceae; Cirsium; Cladium; Consolida ajecis-type; Core drilling; Cornaceae; Corylus; Counting, palynology; Cupressaceae-type; Cyperaceae; DEPTH, sediment/rock; Digitalis-type; Dipsacaceae; Dryopteris; Echium-type; Ephedra distachya; Ephedra fragilis-type; Epilobium; Equisetum; Ericaceae; Euonymus europaeus; Fagus; Filipendula; Fraxinus; Fraxinus excelsior-type; Fraxinus ornus; Fritillaria-type; Fumana; Galium-type; Geranium; Hedera; Helianthemum; Helleborus foetidus; Helleborus viridis; Herbs; Hippophae; Ilex; Juglans; Lago_Grande_di_Monticchio; Larix; Leguminosae; Lemnaceae; Liliaceae; Linum; Lithospermum; Lonicera; Lotus; Malvaceae; Marrubium vulgare; Mentha-type; Menyanthes; Myriophyllum; Myriophyllum alterniflorum; Myriophyllum spicatum-type; Myriophyllum verticillatum; Nuphar; Nymphaea; Olea; Ononis; Ostrya; Oxyria-type; Papaveraceae; Pedicularis; Phillyrea; Picea; Pinus; Pistacia; Plantago; Poaceae; Pollen indeterminata; Polygonum amphibium; Polygonum bistorta-type; Polygonum persicaria-type; Polypodiales; Polypodium; Populus; Potamogeton; Potentilla-type; Primulaceae; Prunus spinosa; Pteridium; Pterocarya; Quercus; Quercus ilex; Ranunculus; Resedaceae; Rhamnaceae; Rosaceae; Rumex; Salix; Sambucus; Sample ID; Sanguisorba minor; Sanguisorba officinalis; Saxifragaceae; Scilla-type; Scrophulariaceae; Scutellaria-type; Serratula-type; Sorbus; Sphagnum; Tamarix; Taxus; Thalictrum; Tilia; Trifolium-type; Typha angustifolia; Typha latifolia; Ulmus; Unknown; Urtica-type; Valeriana; Veronica-type; Viburnum; Vicia; Viola palustris-type; Viscum; Vitis; Woody taxa
    Type: Dataset
    Format: text/tab-separated-values, 89430 data points
    Location Call Number Expected Availability
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