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The Canadian Arctic Archipelago (CAA) serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. We use observations from Sentinel-1, RADARSAT-2, the RADARSAT Constellation Mission (RCM), and CryoSat-2, together with the Canadian Ice Service ice charts, to quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage shipping route. Results indicate that the CAA primarily exports ice to the Arctic Ocean and Baffin Bay, with an average annual (October to September) ice area flux of 137 ± 72 × 10 3 km 2 and a volume flux of 58 ± 68 km 3. The CAA contributes a larger area but smaller volume of ice downstream to the North Atlantic than what is delivered via Nares Strait. The average annual ice area flux from the Queen Elizabeth Islands to Parry Channel was 27 ± 10 × 10 3 km 2 and the volume flux was 34 ± 12 km 3 , with a majority occurring through Byam Martin Channel, which is directly above the central region of Northwest Passage. Over our study period, annual multi-year ice (MYI) replenishment within the CAA was resilient, with an average of 14 ± 38 × 10 3 km 2 imported from the Arctic Ocean and an average of 56 ± 36 × 10 3 km 2 of first-year ice (FYI) retained following the melt season. The considerable ice flux to Parry Channel, together with sustained MYI replenishment, emphasizes the continued risk that sea ice poses to practical utilization of key shipping routes in the CAA, including the Northwest Passage. [ABSTRACT FROM AUTHOR]

This study presents a long-term winter sea ice thickness proxy product for the Canadian Arctic based on a random forest regression model – applied to ice charts and scatterometer data, trained on CryoSat-2 observations, and applying an ice type–sea ice thickness correction using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) – that provides 25 years of sea ice thickness in the Beaufort Sea, Baffin Bay, and, for the first time, the Canadian Arctic Archipelago. An evaluation of the product with in situ sea ice thickness measurements shows that the presented sea ice thickness proxy product correctly estimates the magnitudes of the ice thickness and accurately captures spatial and temporal variability. The product estimates sea ice thickness within 30 to 50 cm uncertainty from the model. The sea ice thickness proxy product shows that sea ice is thinning over most of the Canadian Arctic, with a mean trend of - 0.82 cm yr -1 in April over the whole study area (corresponding to 21 cm thinning over the 25-year record), but that trends vary locally. The Beaufort Sea and Baffin Bay show significant negative trends during all months, though with peaks in November (- 2.8 cm yr -1) and April (- 1.5 cm yr -1), respectively. The Parry Channel, which is part of the Northwest Passage and relevant for shipping, shows significant thinning in autumn. The sea ice thickness proxy product provides, for the first time, the opportunity to study long-term trends and variability in sea ice thickness in the Canadian Arctic, including the narrow channels in the Canadian Arctic Archipelago. [ABSTRACT FROM AUTHOR]

As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014 and Sentinel-1B (S1) in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent five spaceborne SAR sensors that together routinely cover the pan-Arctic sea ice domain. Here, we describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using SAR images from S1 and RCM. We applied the ECCC-ASITS to the incoming image streams of S1 and RCM from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (7 d 25 km and 3 d 6.25 km) by combining the image stream outputs of S1 and RCM (S1 + RCM). Results indicate that S1 + RCM SIM provides more coverage in the Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and directly over the North Pole compared to SIM from S1 alone. Based on the resolvable S1 + RCM SIM grid cells, the 7 d 25 km spatiotemporal scale is able to provide the most complete picture of SIM across the pan-Arctic from SAR imagery alone, but considerable spatiotemporal coverage is also available from 3 d 6.25 SIM products. S1 + RCM SIM is resolved within the narrow channels and inlets of the Canadian Arctic Archipelago, filling a major gap from coarser-resolution sensors. Validating the ECCC-ASITS using S1 and RCM imagery against buoys indicates a root-mean-square error (RMSE) of 2.78 km for dry ice conditions and 3.43 km for melt season conditions. Higher speeds are more apparent with S1 + RCM SIM as comparison with the National Snow and Ice Data Center (NSIDC) SIM product and the Ocean and Sea Ice Satellite Application Facility (OSI SAF) SIM product indicated an RMSE of u=4.6 km d -1 and v=4.7 km d -1 for the NSIDC and u=3.9 km d -1 and v=3.9 km d -1 for OSI SAF. Overall, our results demonstrate the robustness of the ECCC-ASITS for routinely generating large-scale SIM entirely from SAR imagery across the pan-Arctic domain. [ABSTRACT FROM AUTHOR]

Melt ponds form on the surface of Arctic sea ice during spring, influencing how much solar radiation is absorbed into the sea ice–ocean system, which in turn impacts the ablation of sea ice during the melt season. Accordingly, melt pond fraction (fp) has been shown to be a useful predictor of sea ice area during the summer months. Sea ice dynamic and thermodynamic processes operating within the narrow channels and inlets of the Canadian Arctic Archipelago (CAA) during the summer months are difficult for model simulations to accurately resolve. Additional information on fp variability in advance of the melt season within the CAA could help constrain model simulations and/or provide useful information in advance of the shipping season. Here, we use RADARSAT-2 imagery to predict and analyze peak melt pond fraction (fpk) and evaluate its utility to provide predictive information with respect to sea ice area during the melt season within the CAA from 2009–2018. The temporal variability of RADARSAT-2 fpk over the 10-year record was found to be strongly linked to the variability of mean April multi-year ice area with a statistically significant detrended correlation (R) of R=- 0.89. The spatial distribution of RADARSAT-2 fpk was found to be in excellent agreement with the sea ice stage of development prior to the melt season. RADARSAT-2 fpk values were in good agreement with fpk observed from in situ observations but were found to be ∼ 0.05 larger compared to MODIS fpk observations. Dynamically stable sea ice regions within the CAA exhibited higher detrended correlations between RADARSAT-2 fpk and summer sea ice area. Our results show that RADARSAT-2 fpk can be used to provide predictive information about summer sea ice area for a key shipping region of the Northwest Passage. [ABSTRACT FROM AUTHOR]

Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first-year (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n=186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m -3 or 10.9 %) and strong retrieval skill (R2= 0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer type. The resulting dataset contains 58 882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m -3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m -3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density. [ABSTRACT FROM AUTHOR]

Arctic landfast ice extent and duration are examined from observations, ice assimilations, ocean reanalyses and coupled models. From observations and assimilations, it is shown that in areas where landfast ice conditions last more than 5 months the first-year ice typically grows to more than 2 m and is rarely less than 1 m. The observed spatial distribution of landfast ice closely matches assimilation products but less so for ocean reanalyses and coupled models. Although models generally struggle to represent the landfast ice necessary to emulate the observed import/export of sea ice in regions favourable to landfast ice conditions, some do exhibit both a realistic climatology and a realistic decline of landfast ice extent under an anthropogenic forcing scenario. In these more realistic simulations, projections show that an extensive landfast ice cover should remain for at least 5 months of the year, well into the end of the 21st century. This is in stark contrast with the simulations that have an unrealistic emulation of landfast ice conditions. In these simulations, slow and packed ice conditions shrink markedly over the same period. In all simulations and in areas with landfast ice that lasts more than 5 months, the end-of-winter sea ice thickness remains between 1 and 2 m, well beyond the second half of the century. It is concluded that in the current generation of climate models, projections of winter sea ice conditions in the Canadian Arctic Archipelago and the Laptev Sea are overly sensitive to the representation of landfast ice conditions and that ongoing development in landfast ice parameterization will likely better constrain these projections. [ABSTRACT FROM AUTHOR]

The Canadian Sea Ice and Snow Evolution (Can- SISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020-2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5-10% per decade (or 15-30% in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10% per decade (30% in total) are projected across southern Canada. [ABSTRACT FROM AUTHOR]

The Canadian Sea Ice and Snow Evolution (Can- SISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time. [ABSTRACT FROM AUTHOR]

Remarkably low mean September sea ice area in the Canadian Arctic Archipelago (CAA) was observed in 2011 (146×103 km2), a record-breaking level that was nearly exceeded in 2012 (150×103 km2). These values were lower than previous September records set in 1998 (200×103 km2) and 2007 (220×103km2), and are ∼ 60% lower than the 1981-2010 mean September climatology. In this study, the processes contributing to the extreme light years of 2011 and 2012 were investigated, compared to previous extreme minima of 1998 and 2007, and contrasted against historic summer seasons with above average September ice area. The 2011 minimum was associated with positive June through September (JJAS) surface air temperature (SAT) and net solar radiation(K" ) anomalies that facilitated rapid melt, coupled with atmospheric circulation that restricted multi-year ice (MYI) inflow from the Arctic Ocean into the CAA. The 2012 minimum was also associated with positive JJAS SAT and K" anomalies with coincident rapid melt, but further ice decline was temporarily mitigated by atmospheric circulation which drove Arctic Ocean MYI inflow into the CAA. Atmospheric circulation was comparable between 2011 and 1998 (impeding Arctic Ocean MYI inflow) and 2012 and 2007 (inducing Arctic Ocean MYI inflow). However, preconditioning was more apparent leading up to 2011 and 2012 than 1998 and 2007. The rapid melt process in 2011 and 2012 was more intense than observed in 1998 and 2007 because of the thinner ice cover being more susceptible to anomalous thermodynamic forcing. The thinner sea ice cover within the CAA in recent years has also helped counteract the processes that facilitate extreme heavy ice years. The recent extreme light years within the CAA are associated with a longer navigation season within the Northwest Passage. [ABSTRACT FROM AUTHOR]