The decline of Svalbard land-fast sea ice extent as a result of climate change

• Autorzy: Urbański Jacek A. , Litwicka Dagmara

Identyfikatory

DOI

10.1016/j.oceano.2022.03.008

• Języki publikacji: EN

Abstrakty

EN

The Svalbard Archipelago has experienced some of the most severe temperature increases in the Arctic in the last three decades. This temperature rise has accelerated sea-ice melting along the coast of the archipelago, thus bringing changes to the local environment. In view of the importance of the near-future distribution of land-fast sea ice along the Svalbard coast, the available observation data on the ice extent between 1973 and 2018 are used herein to create a random forest (RF) model for predicting the daily ice extent and its spatial distribution according to the cumulative number of freezing and thawing degree days and the duration of the ice season. Two RF models are constructed by using either regression or classification algorithms. The regression model makes it possible to estimate the extent of land-fast ice with a root mean square error (RMSE) of 800 km2, while the classification model creates a cluster of submodels in order to forecast the spatial distribution of land-fast ice with less than 10% error. The models also enable the reconstruction of the past ice extent, and the prediction of the near-future extent, from standard meteorological data, and can even analyze the real-time spatial variability of land-fast ice. On average, the minimum two-monthly extent of land-fast sea ice along the Svalbard coast was about 12,000 km2 between 1973 and 2000. In 2005–2019, however, the ice extent declined to about 6,000 km2. A further increase in mean winter air temperatures by two degrees, which is forecast in 10 to 20 years, will result in a minimum two-monthly land-fast ice extent of about 1,500 km2, thus indicating a trend of declining land-fast ice extent in this area.

Słowa kluczowe

EN

Svalbard; fast ice; warming; machine learning; Random Forest

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2022
• Tom: No. 64 (3)
• Strony: 535--547
• Opis fizyczny: Bibliogr, 35 poz., map., rys., tab., wykr.

Twórcy

autor

Urbański Jacek A.

• GIS Laboratory, Institute of Oceanography, University of Gdańsk, Gdynia, Poland

• https://orcid.org/0000-0003-3842-4143

autor

Litwicka Dagmara

• Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).