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.
Highlights
Engineers could make use of probabilistic data to assess the potential duration of sea ice presence for an infrastructure they are planning to build; mariners could use the data to estimate the best departure date from their home port to reach their final destination according to a certain sea ice concentration probability threshold; fauna specialists could use the data to estimate the risk encountered by species dependent on sea ice cover for their fitness, such as polar bears and seals; and Inuit communities could use the tool to evaluate if their planned travel routes are risky for a given period of the year given the known history of the sea ice spatio-temporal behaviour
Last but not least, the effect of ponds appearing during the melt is that the resulting maps tend to underestimate sea ice concentration (SIC) % during summer since there is confusion between open water areas and melt ponds on top of sea ice. Another underestimation of the SIC % results from thinner ice types which do not act as a radiometric insulator for the passive microwave frequencies around 19 and 37 GHz that are the base of the OSI-409 and OSI-430 datasets (Eastwood et al, 2015). These different error sources in the process of estimating SIC % using passive microwave imagery are known to have an impact on the reliability of the data, especially during the freeze-up and the melt periods, as brought forward by Agnew and Howell (2003), who noticed that the underestimation of ice extent in the Hudson Bay during summer, when compared to Canadian Ice Service (CIS) maps, could go up to 43.5 ± 27.9 %
IcePAC generates spatialized sea ice probabilistic information that can be used in any geographic informa
Summary
Numerous scientific projects have recognized the link between climate change and changes in the spatio-temporal sea ice distribution (Andrews et al, 2017; Cavalieri and Parkinson, 2012; Comiso, 2011, 2002; Comiso et al, 2008; Gloersen et al, 1999; Johannessen et al, 2004; Rothrock et al, 1999; Stocker, 2014; Stroeve et al, 2007; Stroeve et al, 2014; Stroeve et al, 2012; Wang and Overland, 2009). Sea ice extent has displayed an important decline in the last decades (Cavalieri and Parkinson, 2012), as it can be observed with remote sensing data, such as passive microwave data, which have been acquired since 1978 Another important source of information on sea ice cover is model predictions which come from deterministic models (Hunke et al, 2017; Rousset et al, 2015; Weaver et al, 2001), based on dynamical and thermodynamic equations evolving in synergy inside a modelling framework, or from statistical models, based on statistical tools such as simple and multiple regression analysis (Ahn et al, 2014; Drobot, 2007; Pavlova et al, 2014) to explain an expected sea ice parameter value (e.g. sea ice extent, sea ice area, sea ice concentration, sea ice thickness). The datasets used and protocols followed in the IcePAC tool to model SIC % distributions at every grid cell in the HBS are described
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