The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat to biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved sea ice extent indicator covering the European regional seas, the new IceMap500 algorithm has been developed to classify sea ice and water at a resolution of 500 m at nadir. IceMap500 features a classification strategy built upon previous MODIS sea ice extent algorithms and a new method to reclassify areas affected by resolution-breaking features inherited from the MODIS cloud mask. This approach results in an enlargement of mapped area, a reduction of potential error sources and a better delineation of the sea ice edge, while still systematically achieving accuracies above 90 %, as obtained by manual validation. Swath maps have been aggregated at a monthly scale to obtain sea ice extent with a method that is sensitive to spatio-temporal variations in the sea ice cover and that can be used as an additional error filter. The resulting dataset, covering the months of maximum and minimum sea ice extent (i.e. March and September) over 2 decades (from 2000 to 2019), demonstrates the algorithm's applicability as a monitoring tool and as an indicator, illustrating the sea ice decline at a regional scale. The European sea regions located in the Arctic, NE Atlantic and Barents seas display clear negative trends in both March (−27.98 ± 6.01 × 10³ km²yr⁻¹) and September (−16.47 ± 5.66 × 10³ km²yr⁻¹). Such trends indicate that the sea ice cover is shrinking at a rate of ∼ 9 % and ∼ 13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼ 70 % greater in March.

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.

The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat on biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved European sea ice extent indicator, the IceMap250 algorithm has been reworked to generate improved sea ice extent maps at 500 m resolution at nadir. Changes in the classification approach and a new method to correct artefacts arising from the MODIS cloud mask allow the enlargement of the mapped area, the reduction of potential error sources and a qualitative improvement of the resulting maps, while systematically achieving accuracies above 90 %. Monthly sea ice extent maps have been derived using a new synthesis method which acts as an additional error filter. Our results, covering the months of maximum (March) and minimum (September) sea ice extent during two decades (from 2000 to 2019), are a proof of the algorithm's applicability as an indicator, illustrating the sea ice decline in the European regional seas. We observed no significant trends in the Baltic (−2.75 ± 2.05 × 10³ km² yr⁻¹) although, on the contrary, the European Arctic seas display clear negative trends both in March (−27.98 ± 6.01 × 10³ km² yr⁻¹) and September (−16.47 ± 5.66 × 10³ km² yr⁻¹). Such trends indicate that the sea ice cover in March and September is shrinking at a rate of ∼9 % and ∼13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼70 % greater in March. Therefore, according to the trends and without taking into account the variability of the sea ice cover, the loss of sea ice extent over two decades in the study area would be comparable to the area of continental France in the case of the March maximum, and to that of Finland in the case of the September minimum.

The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat on biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved European sea ice extent indicator, the IceMap250 algorithm has been reworked to generate improved sea ice extent maps at 500 m resolution at nadir. Changes in the classification approach and a new method to correct artefacts arising from the MODIS cloud mask allow the enlargement of the mapped area, the reduction of potential error sources and a qualitative improvement of the resulting maps, while systematically achieving accuracies above 90 %. Monthly sea ice extent maps have been derived using a new synthesis method which acts as an additional error filter. Our results, covering the months of maximum (March) and minimum (September) sea ice extent during two decades (from 2000 to 2019), are a proof of the algorithm's applicability as an indicator, illustrating the sea ice decline in the European regional seas. We observed no significant trends in the Baltic (−2.75 ± 2.05 × 10³ km² yr⁻¹) although, on the contrary, the European Arctic seas display clear negative trends both in March (−27.98 ± 6.01 × 10³ km² yr⁻¹) and September (−16.47 ± 5.66 × 10³ km² yr⁻¹). Such trends indicate that the sea ice cover in March and September is shrinking at a rate of ∼9 % and ∼13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼70 % greater in March. Therefore, according to the trends and without taking into account the variability of the sea ice cover, the loss of sea ice extent over two decades in the study area would be comparable to the area of continental France in the case of the March maximum, and to that of Finland in the case of the September minimum.

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 realized using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From these research, reliable information helping to understand sea ice evolution in space and in time are available to stakeholders. However, no research has, as of today, assessed the evolution of the 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 EUMETSAT OSI-409 product, to study the sea ice spatiotemporal behaviour in the waters of the Hudson Bay System in Northeastern Canada. Pixel scale models are based on the generalized Beta distribution and generated at a weekly temporal resolution. Results showed coherent with the Canadian Ice Service 1980–2010 Sea Ice Climatic Atlas average freeze-up and meltdown 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 on the simulation domain. This innovative research opens a path towards various analyses on sea ice dynamics that would gain in informative content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.