Sea ice in the Canadian Arctic: inter-annual variability and predictability
Bibliography: p. 189-202 Some pages are in colour. This dissertation investigates the utility of statistical methods towards the development of predictive sea ice models in support of seasonal forecasting efforts at the North American Ice Service (NAIS), the government agency responsible for relayin...
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ftunivcalgary:oai:prism.ucalgary.ca:1880/104031 2023-08-27T04:06:33+02:00 Sea ice in the Canadian Arctic: inter-annual variability and predictability Tivy, Adrienne Yackel, John 2009 xix, 202 leaves : ill. 30 cm. application/pdf http://hdl.handle.net/1880/104031 https://doi.org/10.11575/PRISM/3030 eng eng University of Calgary Calgary Tivy, A. (2009). Sea ice in the Canadian Arctic: inter-annual variability and predictability (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/3030 http://dx.doi.org/10.11575/PRISM/3030 http://hdl.handle.net/1880/104031 University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. doctoral thesis 2009 ftunivcalgary https://doi.org/10.11575/PRISM/3030 2023-08-06T06:25:53Z Bibliography: p. 189-202 Some pages are in colour. This dissertation investigates the utility of statistical methods towards the development of predictive sea ice models in support of seasonal forecasting efforts at the North American Ice Service (NAIS), the government agency responsible for relaying ice information to the public. Seasonal sea ice forecasting models are required to a) predict the dates of actual sea ice events and b) predict the general pattern of break-up. Statistical methods are employed because deterministic models do not yet adequately represent seasonal or inter-annual variability in sea ice to the level of accuracy required for forecasting. The Canadian Ice Service Digital Archive (CISDA) dataset of weekly ice charts is used as the foundation dataset. The issue of changing sensors over time in CISDA is addressed allowing the full dataset, which begins almost 20 years before the satellite era, to be used in model development. Model development is focused on the Hudson Bay region of Canada where the greatest reduction in summer sea ice cover is observed. Two forecasting models are developed. The first is an exploratory method based on multiple linear regression. Tested on forecasting the opening date of the shipping route to Churchill (OWRC), the model explains 76% of the variability in the OWRC time-series with a forecast success rate of 77%. The methodology has been automated and the regression based seasonal forecasting model (CIS-RSF) will be used operationally at NAIS for the first time in 2009. An initial evaluation of CIS-RSF on 21 time-series of sea ice events in the Arctic produced 7 forecast models; the skill of each model is greater than persistence and the current NAIS forecast technique. The second model uses Canonical Correlation Analysis (CCA). To date, it is the only sea ice forecasting model capable of predicting the spatial distribution of sea ice. The model is used to predict the spatial pattern of July ice using North Atlantic SST anomalies in the preceding fall (6 to ... Doctoral or Postdoctoral Thesis Arctic Arctic Hudson Bay North Atlantic Sea ice PRISM - University of Calgary Digital Repository Arctic Canada Hudson Hudson Bay |
institution |
Open Polar |
collection |
PRISM - University of Calgary Digital Repository |
op_collection_id |
ftunivcalgary |
language |
English |
description |
Bibliography: p. 189-202 Some pages are in colour. This dissertation investigates the utility of statistical methods towards the development of predictive sea ice models in support of seasonal forecasting efforts at the North American Ice Service (NAIS), the government agency responsible for relaying ice information to the public. Seasonal sea ice forecasting models are required to a) predict the dates of actual sea ice events and b) predict the general pattern of break-up. Statistical methods are employed because deterministic models do not yet adequately represent seasonal or inter-annual variability in sea ice to the level of accuracy required for forecasting. The Canadian Ice Service Digital Archive (CISDA) dataset of weekly ice charts is used as the foundation dataset. The issue of changing sensors over time in CISDA is addressed allowing the full dataset, which begins almost 20 years before the satellite era, to be used in model development. Model development is focused on the Hudson Bay region of Canada where the greatest reduction in summer sea ice cover is observed. Two forecasting models are developed. The first is an exploratory method based on multiple linear regression. Tested on forecasting the opening date of the shipping route to Churchill (OWRC), the model explains 76% of the variability in the OWRC time-series with a forecast success rate of 77%. The methodology has been automated and the regression based seasonal forecasting model (CIS-RSF) will be used operationally at NAIS for the first time in 2009. An initial evaluation of CIS-RSF on 21 time-series of sea ice events in the Arctic produced 7 forecast models; the skill of each model is greater than persistence and the current NAIS forecast technique. The second model uses Canonical Correlation Analysis (CCA). To date, it is the only sea ice forecasting model capable of predicting the spatial distribution of sea ice. The model is used to predict the spatial pattern of July ice using North Atlantic SST anomalies in the preceding fall (6 to ... |
author2 |
Yackel, John |
format |
Doctoral or Postdoctoral Thesis |
author |
Tivy, Adrienne |
spellingShingle |
Tivy, Adrienne Sea ice in the Canadian Arctic: inter-annual variability and predictability |
author_facet |
Tivy, Adrienne |
author_sort |
Tivy, Adrienne |
title |
Sea ice in the Canadian Arctic: inter-annual variability and predictability |
title_short |
Sea ice in the Canadian Arctic: inter-annual variability and predictability |
title_full |
Sea ice in the Canadian Arctic: inter-annual variability and predictability |
title_fullStr |
Sea ice in the Canadian Arctic: inter-annual variability and predictability |
title_full_unstemmed |
Sea ice in the Canadian Arctic: inter-annual variability and predictability |
title_sort |
sea ice in the canadian arctic: inter-annual variability and predictability |
publisher |
University of Calgary |
publishDate |
2009 |
url |
http://hdl.handle.net/1880/104031 https://doi.org/10.11575/PRISM/3030 |
geographic |
Arctic Canada Hudson Hudson Bay |
geographic_facet |
Arctic Canada Hudson Hudson Bay |
genre |
Arctic Arctic Hudson Bay North Atlantic Sea ice |
genre_facet |
Arctic Arctic Hudson Bay North Atlantic Sea ice |
op_relation |
Tivy, A. (2009). Sea ice in the Canadian Arctic: inter-annual variability and predictability (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/3030 http://dx.doi.org/10.11575/PRISM/3030 http://hdl.handle.net/1880/104031 |
op_rights |
University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. |
op_doi |
https://doi.org/10.11575/PRISM/3030 |
_version_ |
1775347438970732544 |