View : 73 Download: 0

Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Title
Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation
Authors
Kim Y.Kim H.-R.Choi Y.-S.Kim W.M.Kim H.-S.
Ewha Authors
최용상김원무
SCOPUS Author ID
최용상scopus; 김원무scopus
Issue Date
2016
Journal Title
Asia-Pacific Journal of Atmospheric Sciences
ISSN
1976-7633JCR Link
Citation
vol. 52, no. 5, pp. 467 - 477
Keywords
arcticMarkovian modelSea icesolar radiationstatistical prediction
Publisher
Korean Meteorological Society
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model. © 2016, Korean Meteorological Society and Springer Science+Business Media Dordrecht.
DOI
10.1007/s13143-016-0031-y
Appears in Collections:
엘텍공과대학 > 환경공학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE