Antarktis-bibliografi er en database over den norske Antarktis-litteraturen.
Hensikten med bibliografien er å synliggjøre norsk antarktisforskning og annen virksomhet/historie i det ekstreme sør. Bibliografien er ikke komplett, spesielt ikke for nyere forskning, men den blir oppdatert.
Norsk er her definert som minst én norsk forfatter, publikasjonssted Norge eller publikasjon som har utspring i norsk forskningsprosjekt.
Antarktis er her definert som alt sør for 60 grader. I tillegg har vi tatt med Bouvetøya.
Det er ingen avgrensing på språk (men det meste av innholdet er på norsk eller engelsk). Eldre norske antarktispublikasjoner (den eldste er fra 1894) er dominert av kvalfangst og ekspedisjoner. I nyere tid er det den internasjonale polarforskninga som dominerer. Bibliografien er tverrfaglig; den dekker både naturvitenskapene, politikk, historie osv. Skjønnlitteratur er også inkludert, men ikke avisartikler eller upublisert materiale.
Til høyre finner du en «HELP-knapp» for informasjon om søkemulighetene i databasen. Mange referanser har lett synlige lenker til fulltekstversjon av det aktuelle dokumentet. For de fleste tidsskriftartiklene er det også lagt inn sammendrag.
Bibliografien er produsert ved Norsk Polarinstitutts bibliotek.
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Results 2 resources
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Dynamical modeling is widely utilized for Antarctic sea ice prediction. However, the relative impact of initializing different model components remains unclear. We compare three sets of hindcasts of the Norwegian Climate Prediction Model (NorCPM), which are initialized by ocean, ocean/sea-ice, or atmosphere data and referred to as the OCN, OCNICE, and ATM hindcasts hereafter. The seasonal cycle of sea ice extent (SIE) in the ATM reanalysis shows a slightly better agreement with observations than the OCN and OCNICE reanalyzes. The trends of sea ice concentration (SIC) in the OCN and OCNICE reanalyzes compare well to observations, but the ATM reanalysis is poor over the western Antarctic. The OCNICE reanalysis yields the most accurate estimation of sea ice variability, while the OCN and ATM reanalyzes are comparable. Evaluation of the hindcasts reveals the predictive skill varies with region and season. Austral winter SIE of the western Antarctic can be skillfully predicted 12 months ahead, while the predictive skill in the eastern Antarctic is low. Austral winter SIE predictability can be largely attributed to high sea surface temperature predictability, thanks to skillful initialization of ocean heat content. The ATM hindcast from July or October performs best due to the effective initialization of sea-ice thickness, which enhances prediction skills until early austral summer via its long memory. Meanwhile, the stratosphere-troposphere coupling contributes to the prediction of springtime. The comparable skill between the OCN and OCNICE hindcasts implies limited benefits from SIC data on prediction when using ocean data.
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Abstract In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S) projects was evaluated by a probabilistic metric, the spatial probability score (SPS). Both projects provide subseasonal to seasonal scale forecasts of multiple coupled dynamical systems. We found that predictions by individual dynamical systems remain skillful for up to 38 days (i.e., the ECMWF system). Regionally, dynamical systems are better at predicting the sea ice edge in the West Antarctic than in the East Antarctic. However, the seasonal variations of the prediction skill are partly system-dependent as some systems have a freezing-season bias, some had a melting-season bias, and some had a season-independent bias. Further analysis reveals that the model initialization is the crucial prerequisite for skillful subseasonal sea ice prediction. For those systems with the most realistic initialization, the model physics dictates the propagation of initialization errors and, consequently, the temporal length of predictive skill. Additionally, we found that the SPS-characterized prediction skill could be improved by increasing the ensemble size to gain a more realistic ensemble spread. Based on the C3S systems, we constructed a multi-model forecast from the above principles. This forecast consistently demonstrated a superior prediction skill compared to individual dynamical systems or statistical observation-based benchmarks. In summary, our results elucidate the most important factors (i.e., the model initialization and the model physics) affecting the currently available subseasonal Antarctic sea ice prediction systems and highlighting the opportunities to improve them significantly.
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Topic
- klimatologi
- atmosfæren (1)
- havis (2)
- klimaendringer (1)
- klimamodeller (1)
- meteorologi (1)
- oseanografi (2)
- sjøis (2)
- Sørishavet (2)
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- Journal Article (2)
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Online resource
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