IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

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.

Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region

“…For this range of dates, 30 d forecasts would have been launched between 1 September and 31 December, trained on data from 1 August to 31 October (for the September model It is worthwhile to consider how our results compare with those of similar studies in the region. Gignac et al (2019) and Dirkson et al (2019) developed methods for probabilistic forecasting based on fitting probability distribution functions (PDFs) to historical passive-microwave sea ice concentration data. Gignac et al (2019) in particular focused on the same geographic region as the present study, choosing a beta PDF to fit the data and define a model from which they could query the probability of ice, given a date.…”

“…Gignac et al (2019) and Dirkson et al (2019) developed methods for probabilistic forecasting based on fitting probability distribution functions (PDFs) to historical passive-microwave sea ice concentration data. Gignac et al (2019) in particular focused on the same geographic region as the present study, choosing a beta PDF to fit the data and define a model from which they could query the probability of ice, given a date. Following the same definition of breakup and freeze-up as used here, they found their approach was able to capture freezeup and breakup within 1 or 2 weeks of dates provided by the Canadian Ice Service (CIS) ice atlas, with the exception of Sanirajak (formerly known as Hall Beach), similar to the results reported here.…”

“…We note this resolution is similar to that used in other studies on seasonal forecast-ing that have been developed with mariners in mind. For example, passive-microwave data were used for development of the probabilistic approach of Gignac et al (2019) and for validation of subseasonal-to-seasonal sea ice predictions (Zampieri et al, 2018). While passive-microwave sea ice concentration data are often gridded to 25 km, the spatial resolution of the brightness temperature data used to generate the sea ice concentration is typically coarser.…”