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Canadian Snow and Sea Ice: Historical Trends and Projections

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Canadian Snow and Sea Ice: Historical Trends and Projections The Cryosphere, 12, 1157–1176, 2018 https://doi.org/10.5194/tc-12-1157-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Canadian snow and sea ice: historical trends and projections Lawrence R. Mudryk1, Chris Derksen1, Stephen Howell1, Fred Laliberté1, Chad Thackeray2, Reinel Sospedra-Alfonso3, Vincent Vionnet4, Paul J. Kushner5, and Ross Brown6 1Climate Research Division, Environment and Climate Change Canada, Toronto, Canada 2Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada 3Climate Research Division, Environment and Climate Change Canada, Victoria, Canada 4Centre National de Recherches Météorologiques, Centre d’Etudes de la Neige, Grenoble, France 5Department of Physics, University of Toronto, Toronto, Canada 6Climate Research Division, Environment and Climate Change Canada, Montréal, Canada Correspondence: Lawrence R. Mudryk ([email protected]) Received: 6 September 2017 – Discussion started: 9 October 2017 Revised: 9 February 2018 – Accepted: 20 February 2018 – Published: 4 April 2018 Abstract. The Canadian Sea Ice and Snow Evolution (Can- 1 Introduction SISE) Network is a climate research network focused on de- veloping and applying state of the art observational data to advance dynamical prediction, projections, and understand- Seasonal terrestrial snow and sea ice influence short-term ing of seasonal snow cover and sea ice in Canada and the weather and longer-term climate by altering the surface en- circumpolar Arctic. Here, we present an assessment from the ergy budget, modifying both the surface reflectivity and ther- CanSISE Network on trends in the historical record of snow mal conductivity (Serreze et al., 2007; Flanner et al., 2011; cover (fraction, water equivalent) and sea ice (area, concen- Gouttevin et al., 2012). Snow also influences freshwater stor- tration, type, and thickness) across Canada. We also assess age through soil moisture recharge and surface runoff (Bar- projected changes in snow cover and sea ice likely to oc- nett et al., 2005). Understanding historical and projected cur by mid-century, as simulated by the Coupled Model In- changes to snow and ice is essential to assess both the im- tercomparison Project Phase 5 (CMIP5) suite of Earth sys- portance of physical changes to the climate system and their tem models. The historical datasets show that the fraction of consequent impacts and risks. A previous assessment of the Canadian land and marine areas covered by snow and ice is Canadian cryosphere (snow, sea ice, freshwater ice, land ice, decreasing over time, with seasonal and regional variability frozen ground) was compiled as part of the International Po- in the trends consistent with regional differences in surface lar Year (IPY) in 2007–2008 and is described in Derksen temperature trends. In particular, summer sea ice cover has et al. (2012). The current study updates the IPY analysis decreased significantly across nearly all Canadian marine re- pertaining to terrestrial snow and sea ice by adding nearly gions, and the rate of multi-year ice loss in the Beaufort Sea a decade of data and including climate model projections and Canadian Arctic Archipelago has nearly doubled over of changes over the next 30 to 40 years. This study is fo- the last 8 years. The multi-model consensus over the 2020– cused on Canadian territory, which is nearly completely cov- 2050 period shows reductions in fall and spring snow cover ered by snow and sea ice for parts of each year with near- fraction and sea ice concentration of 5–10 % per decade (or continuous coverage over high-latitude and high-elevation 15–30 % in total), with similar reductions in winter sea ice regions. Snow and sea ice are recognized as critical com- concentration in both Hudson Bay and eastern Canadian wa- ponents of Canada’s natural environment, ecosystems, and ters. Peak pre-melt terrestrial snow water equivalent reduc- economy. With respect to snow cover, real-time information tions of up to 10 % per decade (30 % in total) are projected on the amount of snow on the ground (i.e. depth and water across southern Canada. equivalent) is used in operational decision making for wa- ter resource planning (Turcotte et al., 2007), snow clearing, evaluation of avalanche risk (Conlan and Jamieson, 2017), Published by Copernicus Publications on behalf of the European Geosciences Union. 1158 L. R. Mudryk et al.: Canadian snow and sea ice: historical trends and projections and initialization of Canada’s global weather forecast system ice cover important but the type of sea ice present is (Brasnett, 1999). Historical snow cover data are used in a as well, specifically whether it is thin first-year ice or wide range of applications including ecological studies (Luus thicker multi-year ice (Maslanik et al., 2011). et al., 2013), water resources (Kang et al., 2014), forest man- Other process drivers. agement (Hanewinkel et al., 2008), estimation of snow loads – While surface temperature plays for infrastructure design (Hong and Ye, 2014), impacts on a major role in influencing snow and ice, there are other ground frost penetration (Zhang et al., 2008), and evaluation important drivers of change. For instance, increased pre- of climate and hydrological models (Verseghy et al., 2017; cipitation in sufficiently cold regions may offset shorter Ganji et al., 2017). Snow also makes a significant direct con- snow seasons (Brown and Mote, 2009). Sea ice dynam- tribution to the Canadian economy through winter recreation ics (driven by wind and ocean currents) can play a ma- (Archambault et al., 2003 as cited in Scott et al., 2007). With jor role in regional sea ice conditions independent of respect to sea ice, knowledge of both historical and future surface temperature (Howell et al., 2013a). sea ice conditions in Canadian waters is important for opera- – Snow–ice forcing of climate anomalies. Variations in tional ship navigation to ensure economic and safe shipping, snow and ice cover may also generate feedbacks to the particularly in the Northwest Passage. In addition, informa- atmosphere–ocean circulation that influence climate on tion on sea ice (e.g. coverage, type, and thickness) is required seasonal to decadal scales (Cohen et al., 2007; Smith for the initialization and verification of seasonal prediction et al., 2010; Scaife et al., 2014), although there is no models (Lindsay et al., 2012; Sigmond et al., 2013). While clear consensus on how changes in Arctic sea ice and acknowledging the Canadian focus, much of the applied ap- snow cover influence midlatitude climate (Francis and proach will be extended to other regions of interest through Vavrus, 2012, 2015; Francis et al., 2017; Barnes, 2013; upcoming Coupled Model Intercomparison Project Phase 6 Screen et al., 2015). (CMIP6) experiments (Eyring et al., 2016). Previous studies have shown that warming temperatures, The first objective of this paper is to provide an overview which are amplified at higher latitudes as a natural response of observed changes to seasonal terrestrial snow and sea ice to increasing greenhouse gases (Serreze et al., 2009; Pithan across Canadian territory using the longest available time se- and Mauritsen, 2014), reduce the spatial extent and mass of ries of validated gridded datasets. We use a multi-dataset ap- snow and ice (for example, see Derksen et al., 2012). In real- proach, averaging multiple estimates of terrestrial snow vari- ity, the linkage between warming temperatures and snow–sea ables together for more robust trends and using an integrated, ice reduction is more nuanced due to the following: multi-source dataset for analysis of sea ice change. The sec- ond objective is to compare these recent historical changes to – Regional and seasonal climate variability. For example, projected changes of snow and sea ice over a similar length surface temperature warming across Canadian land and of time from the near future to the middle of the 21st cen- ocean areas is not uniform in space and time, but con- tury (2020–2050). We use simulations from state of the art tains regional and seasonal variability driven by natural climate models with confidence levels that account for un- climatic processes, such as the El Niño–Southern Os- certainty in the regional temperature response (note that the cillation (ENSO) and other oceanic teleconnections as focus of this study is not model evaluation – that analysis is well as inter-seasonal and inter-annual changes in the described in Kushner et al., 2018, which is a companion pa- preferred modes of atmospheric circulation (Vincent et per to this study). Results are presented in two sub-sections, al., 2015). The impact of warm temperature departures separated by the observational analysis period (Sect. 3.1) and on the cryosphere varies with season: during spring they the climate projections (Sect. 3.2). Details on the datasets and are directly linked to the timing and magnitude of melt methodology are provided in Sect. 2. We summarize our key onset, while during fall warm temperatures can be as- findings in Section 4 and present remaining points of discus- sociated with delayed snow cover onset and ice forma- sion in Sect. 5. tion, the impacts of which may not become apparent for many months. 2 Data – The selection of cryospheric variables. The onset, accu- mulation/growth and melt of snow and sea ice are in- 2.1 Historical datasets fluenced by many factors. Different metrics are relevant for assessing different impacts on the environment and 2.1.1 Terrestrial snow data ecosystems, and these metrics do not always vary coher- ently with each other (Bokhorst et al., 2016). Changes Following Mudryk et al. (2015), we took a multi-dataset ap- in snow can be reflected in the timing of snow onset in proach to analyze observed snow cover change in order to ac- the fall and melt in the spring, the annual maximum ac- count for observational uncertainty.
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