Regional variability of a projected sea ice‐free Arctic during the summer months

Laliberté, Frédéric; Howell, S.; Kushner, Paul J.

doi:10.1002/2015gl066855

Cited by 79 publications

(72 citation statements)

References 28 publications

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“…We obtain the multi-model mean of trends and their statistical significance at each grid point by creating the distribution of trends through a Monte Carlo simulation. We use a t distribution for the interannual variability and build a noise model to account for internal variability as in Swart et al (2015) and Laliberté et al (2016). We obtain the multi-model mean of Pearson correlations and their statistical significance by first performing a Fisher transform and then applying the same method as for the trends.…”

Section: Modelsmentioning

confidence: 99%

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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: Modelsmentioning

confidence: 99%

“…A noise model is created to ensure that internal variability is comparable for models with different ensemble sizes, following Swart et al (2015) and Laliberté et al (2016). To generate the noise model, we discard models that have fewer than two realizations.…”

Section: Appendix Amentioning

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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“…CC BY 4.0 License. The probability of sea ice free conditions by 2050 for regions of the Canadian Arctic calculated from the CMIP-5 multimodel ensemble are shown in Figure 13 (see Laliberté et al, 2016, for a full description of the sea ice free probability methodology). Use of two ice area thresholds, 5% and 30%, applied to each grid cell, indicates the sensitivity in timing to the definition of minimum ice area.…”

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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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“…Turning to sea ice, we recall that summertime sea-ice area or extent is biased low in CanESM2 (Stroeve et al, 2012;Merryfield et al 2013a;Laliberté et al 2016), which is borne out in the Canadian Arctic sector (top two panels of Fig. 8) , 15 where CanESM2 has less than half of the observed sea-ice coverage in the Beaufort Sea-Arctic Ocean sector.…”

Section: Canesm2 Climatology and Trendsmentioning

confidence: 99%

Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System

Kushner

Mudryk

Merryfield

et al. 2017

Preprint

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Abstract. This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate 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) archive, and initial condition ensembles of CanESM2 and other 25 models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as coldregion 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 cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational 30 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 modeling 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 35The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-157 Manuscript under review for journal The Cryosphere Discussion started: 5 September 2017 c Author(s) 2017. CC BY 4.0 License. 2 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.

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Regional variability of a projected sea ice‐free Arctic during the summer months

Cited by 79 publications

References 28 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)

Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System

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