Recent changes in the exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago

Howell, Stephen E. L.; Wohlleben, Trudy; Dabboor, Mohammed; Derksen, Chris; Komarov, Alexander S.; Pizzolato, Larissa

doi:10.1002/jgrc.20265

Cited by 88 publications

(98 citation statements)

References 33 publications

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“…It is possible that warmer temperatures are associated with an increased flux of thicker multi-year ice into the CAA, which is known to occur (e.g. Howell et al, 2013), but the driving processes responsible for these positive correlations require more investigation. In CMIP5 models, no model exhibits positive corre- lations with temperature that resembles ORA-IP models over the CAA.…”

Section: Ice Thickness Linkages With Snow Depth and Temperaturementioning

confidence: 99%

Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

Howell

Laliberté²,

Kwok

et al. 2016

The Cryosphere

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Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than 5 decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness were statistically significant at Cambridge Bay (−4.31 ±1.4 cm decade −1 ), Eureka (−4.65 ±1.7 cm decade −1 ) and Alert (−4.44 ±1.6 cm decade −1 ) but not at Resolute. Over the 50+-year record, the ice thinned by ∼ 0.24-0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends.Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to 2 orders of magnitude. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

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Section: Ice Thickness Linkages With Snow Depth and Temperaturementioning

confidence: 99%

Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

Howell

Laliberté²,

Kwok

et al. 2016

The Cryosphere

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“…For instance, increased precipitation in sufficiently cold regions may offset shorter snow seasons (Brown and Mote, 2009). Sea ice dynamics (driven by wind and ocean currents) can play a major role in regional sea ice conditions independent of surface temperature (Howell et al, 2013a). …”

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confidence: 99%

Canadian Snow and Sea Ice: Trends (1981–2015) and Projections (2020–2050)

Mudryk

Derksen

Howell

et al. 2017

Preprint

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Abstract. The Canadian Sea Ice and Snow Evolution Network (CanSISE) 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, 15 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-20 year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last eight years. The multimodel 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. 25 IntroductionSeasonal terrestrial snow and sea ice influence short term weather and longer term climate by altering the surface energy budget, modifying both the surface reflectivity and thermal conductivity (Serreze et al., 2007;Flanner et al., 2011;Gouttevin et al., 2012). Snow also influences freshwater storage through soil moisture recharge and surface runoff (Barnett et al., 2005). Understanding historical and projected future changes to snow and ice is essential both to assess the importance of 30 physical changes to the climate system, and to thus assess their consequent impacts and risks. A previous assessment of the The Cryosphere Discuss., https://doi.org/10. 5194/tc-2017-198 Manuscript under review for journal The Cryosphere Previous studies have shown that warming temperatures, which are amplified at higher latitudes as a natural response to increasing greenhouse gases (Serreze et al., 2009;Pithan and Mauritsen, 2014), reduce the spatial extent and mass of snow 45 and ice (for example, see Derksen et al., 2012). In reality, the linkage between warming temperatures and snow/sea ice reduction is more nuanced due to: regional and seasonal climate variability: for example, surface temperature warming across Canadian land and ocean areas is not uniform in space and time, but contains regional and seasonal variability driven by natural climatic processes such as the El Niño Southern Oscillation (ENSO) and other...

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“…Furthermore, wind driven movement of sea ice is restricted by the narrow channels that dominate the CAA [22]. During the melt season, MYI drifts into and subsequently through the CAA from the central Arctic during late summer and early fall and becomes locked in place by FYI that forms in the fall and early winter [23]. This makes for an ideal study area for understanding the evolution of sea ice from winter to summer conditions, without the need for tracking mobile ice.…”

Section: Study Areamentioning

confidence: 99%

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

et al. 2017

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Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

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Recent changes in the exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago

Cited by 88 publications

References 33 publications

Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

Canadian Snow and Sea Ice: Trends (1981–2015) and Projections (2020–2050)

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

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