IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
-
Published:2019-02-06
Issue:2
Volume:13
Page:451-468
-
ISSN:1994-0424
-
Container-title:The Cryosphere
-
language:en
-
Short-container-title:The Cryosphere
Author:
Gignac CharlesORCID, Bernier MoniqueORCID, Chokmani KaremORCID
Abstract
Abstract. A reliable knowledge and assessment of the sea ice conditions and their
evolution in time is a priority for numerous decision makers in the domains
of coastal and offshore management and engineering as well as in commercial
navigation. As of today, countless research projects aimed at both modelling
and mapping past, actual and future sea ice conditions were completed using
sea ice numerical models, statistical models, educated guesses or remote
sensing imagery. From this research, reliable information helping to
understand sea ice evolution in space and in time is available to
stakeholders. However, no research has, until present, assessed the evolution
of sea ice cover with a frequency modelling approach, by identifying the
underlying theoretical distribution describing the sea ice behaviour at a
given point in space and time. This project suggests the development of a
probabilistic tool, named IcePAC, based on frequency modelling of historical
1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT
OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters
of the Hudson Bay system in northeast Canada. Grid-cell-scale models are
based on the generalized beta distribution and generated at a weekly temporal
resolution. Results showed coherence with the Canadian Ice Service 1981–2010
Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous
coastal communities in the study area and showed that it is possible to
evaluate a range of plausible events, such as the shortest and longest
probable ice-free season duration, for any given location in the simulation
domain. Results obtained in this project pave the way towards various
analyses on sea ice concentration spatio-temporal distribution patterns that
would gain in terms of information content and value by relying on the kind
of probabilistic information and simulation data available from the IcePAC
tool.
Funder
Natural Resources Canada
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
Reference112 articles.
1. Agnew, T. and Howell, S.: The use of operational ice charts for evaluating
passive microwave ice concentration data, Atmos. Ocean, 41, 317–331, 2003. 2. Ahn, J., Hong, S., Cho, J., Lee, Y.-W., and Lee, H.: Statistical Modeling of
Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in
the Barents and Kara Seas, 1979–2012, Remote Sens., 6, 5520–5540, 2014. 3. Akaike, H.: Information theory and an extension of the maximum likelihood
principle, in: Selected Papers of Hirotugu Akaike, Springer, 1998. 4. Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. J. G., Williams, T. D.,
Bertino, L., and Bergh, J.: On the future navigability of Arctic sea routes:
High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy, 75,
300–317, 2017. 5. Andersen, S., Tonboe, R., Kern, S., and Schyberg, H.: Improved retrieval of
sea ice total concentration from spaceborne passive microwave observations
using numerical weather prediction model fields: An intercomparison of nine
algorithms, Remote Sens. Environ., 104, 374–392, 2006.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|