Name
ID
Status
Kind
Modified
Options

AMAP Assessment 2006: Acidifying Pollutants, Arctic Haze and Acidification in the Arctic
75
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2009: Arctic Pollution Status
76
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2009: Human Health in the Arctic
77
Active
Folder
25.09.26 | 06:33

AMAP Assessment Report: Arctic Pollution Issues
78
Active
Folder
25.09.26 | 06:33

Arctic Climate Impact Assessment (ACIA)
79
Active
Folder
25.09.26 | 06:33

Arctic Oil and Gas 2007
80
Active
Folder
25.09.26 | 06:33

Mercury Emissions
85
Active
Folder
25.09.26 | 06:33

Arctic Pollution Issues 2011 (Mercury)
310
Active
Folder
25.09.26 | 06:33

AMAP graphics
350
Active
Folder
25.09.26 | 06:33

Arctic Climate Issues 2011: Changes in Arctic Snow, Water, Ice and Permafrost
382
Active
Folder
25.09.26 | 06:33

AACA Perspectives from the Bering-Chukchi-Beaufort Regional Report 2017
559
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2015: Temporal Trends in Persistent Organic Pollutants in the Arctic
560
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2018: Biological Effects of Contaminants on Arctic Wildlife and Fish
562
Active
Folder
25.09.26 | 06:33

Adaptation Actions for a Changing Arctic: Perspectives from the Barents Area
569
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2016: Chemicals of Emerging Arctic Concern
576
Active
Folder
25.09.26 | 06:33

Global Mercury Assessment (GMA) Scientific Report 2017
578
Active
Folder
25.09.26 | 06:33

AMAP Assessment 2018: Arctic Ocean Acidification
584
Active
Folder
25.09.26 | 06:33

Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017
586
Active
Folder
25.09.26 | 06:33

Impacts of Short-Lived Climate Forcers on Arctic Climate, Air Quality, and Human Health Summary For Policy-Makers
646
Active
Folder
25.09.26 | 06:33

POPs and Chemicals of Emerging Arctic Concern: Influence of Climate Change
647
Active
Folder
25.09.26 | 06:33

POPs and Chemicals of Emerging Arctic Concern: Influence of Climate Change
648
Active
Folder
25.09.26 | 06:33

2021 AMAP Mercury Assessment Summary for Policy-makers
650
Active
Folder
25.09.26 | 06:33

AACA Perspectives from the Baffin Bay/Davis Strait Regional Report 2017
651
Active
Folder
25.09.26 | 06:33

Under future climate change conditions the Arctic will be subject to numerous changes including loss of ice, snow, and permafrost, increasing human activity and development, and changes in wind and ocean circulation patterns – all of which could alter the sources, pathways, distribution and fate of contaminants.
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Key climate-related changes influencing contaminant levels and fate in Arctic ecosystems
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Examples of studies where climate change-related effects on Arctic contaminants have been observed.
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Climate-related changes in contaminant sources, pathways, and environmental fate are further acted upon by effects of abundance, habitat range, seasonality, prey accessibility, and physiology of individual wildlife species in Arctic ecological communities. The highly connected nature of Arctic food webs and various potential influences of climate change makes it difficult to estimate the overall impacts on contaminant exposure on Arctic species.
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In some locations, the decline of certain POPs is slowing, and even reversing direction, potentially in association with climate-related changes. Levels of PCB-52 and PCB-101 in air are now increasing at the coastal site of Stórhöfði, Iceland in close proximity to retreating ice caps, after earlier decreases.
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Updated global mercury budget and related fluxes for the Arctic Ocean (area indicated on map) and land north of 60°N.
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Conceptual diagram highlighting key physical and ecological changes occurring in the Arctic and links to the environmental and biological fate of mercury
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Figure 1.1 The three AACA pilot regions. The Bering-Chukchi-Beaufort region. The Baffin Bay/Davis Strait region. The Barents study area.
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Figure 10.1 Typical colorful, one-family gable-roofed housing in Sarfannguaq, Greenland (photo by Tove Lading).
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Figure 10.2 Example of Euro-Canadian house layouts introduced to the North in the 1950s. This example is from the community of Pond Inlet, Baffin Island (photo by M. Fortier).
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Figure 10.3 Thermographic image of a typical Greenland-style single-family house in Sisimiut, showing poor insulation, especially around windows and structural elements (from Vladyková, 2007). The colors on the image represent surface temperature in °C.
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Figure 10.4 Blok P in Nuuk (left, photo by Vincent van Zeijst, CC BY-SA 3.0), once the largest building in Greenland, and Blokland in Albertslund, Denmark (right, photo by Thomas Helsted), built at approximately the same time. Blok P was demolished in 2012, whereas the Blokland is now undergoing a major refurbishment.
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Figure 10.5 Permafrost temperature regime. Blue curve: Minimum ground temperature. Red curve: Maximum ground temperature. Black curve: Average ground temperature. Gray curves: Temperature profiles at specific times of the year. The “zaa” label shows the depth of zero annual amplitude.
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Figure 10.6 Examples of types of massive ground ice (panels A–C) and ground cryostructures (panels D–H) encountered in permafrost over the BBDS region: (A) Massive ground ice mound – a frost blister, (B) Buried massive glacier ice, (C) An ice wedge, (D) Structureless – porous non-visible ice, (E) Lenticular – microlenticular segregated ice lenses, (F) Layered – wavy, segregated ice lenses, (G) Irregular reticulate – segregated ice lenses, and (H) Suspended/reticulate – segregated ice lenses (photos by E. L’Hérault).
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Figure 10.7 Schematic representation of variation in soil strength as a function of temperature (modified from Andersland and Ladanyi, 2004).
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Figure 10.8 The three main risk factors in permafrost engineering are (1) the geological setting, (2) water/ice availability, and (3) changes to the thermal regime. Certain combinations result in a high risk of thaw settlement, frost heave, slope instability, or other problems. If a risk factor is removed or limited (e.g., if water availability is reduced by proper drainage), then the risk of failure is reduced.
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Figure 10.9 Deglacial upper marine limit (maximum relative sea level in meters above present sea level, m.a.s.l.) in the BBDS region (modified from Dyke et al., 2005). In the absence of detailed local observations, the upper marine limit can be used as a first indicator of risk in permafrost regions. At elevations below the upper marine limit, fine-grained ice-rich deposits may be encountered, depending on local depositional history and ground temperature regime. Contour interval is 25 m (the 0 m contour is dashed).
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Figure 10.10 An active-layer detachment slide in water-saturated soil at Cape Bounty, Nunavut (photo by Scott Lamoureux). Even though this terrain disturbance is localized, it can have significant downstream impacts – for example, by increasing sediment loads into freshwater ecosystems.
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Figure 10.11 Water ponding in a depression created by thaw subsidence at the bottom of an embankment. This subsidence was facilitated by the thermal impact of a snow bank previously at the road’s edge (photo by E. L’Hérault).
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Figure 10.12 The Kangerlussuaq airport and its supporting settlement are located on the river terrace of a glaciomarine delta, in close proximity to the Greenland Ice Sheet (visible in the background) (photo by Thomas Ingeman-Nielsen).
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Figure 10.13 New repairs of pavement cracking and settlement at the west end of the Kangerlussuaq runway, November 2015 (photo by Thomas Ingeman-Nielsen).
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Figure 10.14 Building S385 in Kangerlussuaq suffered differential settlement of approximately 35 cm in its southeast corner after power was cut to an artificial cooling system installed below the building (photo by Thomas Ingeman-Nielsen).
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Figure 10.15 Runway, Thule Air Base: 3000 m of flexible asphalt pavement (photo by Kevin Bjella).
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Figure 10.16 Runway condition (top) and two alternatives for addressing thaw-settlement problems (lower) (modified from Bjella, 2013).
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Figure 10.17 Excavation and placement of pier-and-footer style foundation for new dormitory, Thule Air Base (photo by Kevin Bjella).
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Figure 10.18 Aircraft hangar under-floor cooling plenums, Thule Air Base (photo by Kevin Bjella).
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Figure 10.19 Over-excavation for construction of slab-on-grade structure, Thule Air Base (photo by Kevin Bjella).
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Figure 10.20 Map of surficial deposits in the vicinity of the Iqaluit airport (from Allard et al., 2012a). The red lines show the locations of the most evident frost cracks and ice wedge furrows. Full legend available at http://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=289503. (This map is a copy of an official work published by the Government of Canada. This reproduction has not been produced in affiliation with or with the endorsement of the Government of Canada)
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Figure 10.21 Linear depression caused by permafrost degradation at the Iqaluit airport (photo by E. L’Hérault).
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Figure 10.22 Hydrological changes associated with construction and expansion of the Iqaluit airport. The left panels show the natural hydrological settings of two areas (1948). The right panels show post-construction and post-expansion conditions (2006) (reproduced from Mathon-Dufour, 2011).
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Figure 10.23 Top: Pangnirtung, a community on Baffin Island, located in the continuous permafrost zone (photo by A.-S. Carbonneau). Lower: Sisimiut, second largest town in West Greenland, located in the discontinuous permafrost zone (photo by T. Ingeman-Nielsen).
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Figure 10.24 Municipal garage showing visible deformation (central depression) due to permafrost thaw settlement, Pangnirtung, Nunavut. Drillings next to this building revealed a very ice-rich surficial deposit (photo by E. L’Hérault).
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Figure 10.25 Community of Pangnirtung, established on a slightly inclined sedimentary terrace of Pangnirtung Fjord (from Carbonneau, 2014). The red rectangle on the top photo indicates the community location. Original base images are from Landsat 7 (top), all rights reserved, and Worldview-2 (lower), all rights reserved.
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Figure 10.26 Extreme peak discharge of the Duval River in June 2008 resulted in downcutting of the riverbed, generating thermo-erosion and causing terrain collapse and the destruction of the Pangnirtung bridges (from Gosselin, 2013, and Carbonneau, 2014).
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Figure 10.27 Excavation and in situ casting of piles for the new district court house in Ilulissat, August 2015. The photo illustrates the current practice of excavating to bedrock for in situ casting of piles anchored in the bedrock, followed by backfilling (photo by Thomas Ingeman-Nielsen).
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Figure 10.28 Cover page of the homeowner’s guide to permafrost produced by the Government of Nunavut in collaboration with permafrost specialists (reproduced from Government of Nunavut, 2013, with permission; Government of Nunavut, Department of Environment, © Government of Nunavut).
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Figure 10.29 Instrumented culvert at the Beaver Creek experimental site, Alaska Highway, Yukon, Canada (sources: Périer et al., 2014, and Périer, 2015).
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Figure 11.1 Environmental variability and coping ranges (modified from Smit and Wandel, 2006). The coping range of the past (light blue) handles historical variability in the external environment, with rare extremes overwhelming this capacity. Going forward, increased adaptive capacity (darker blue) will be needed to handle increasing environmental variability and new extreme events. Among the different types of variability, climate variability is the one for which science can best provide long-term projections, with estimates of uncertainty. Socio-economic and political variability and extremes are less predictable.
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Figure 11.2 An adaptation continuum: climate-centered to vulnerability-centered. The categories of climate-centered adaptations (CCAs) and vulnerability-centered adaptations (VCAs) are not distinct and mutually exclusive. Adaptation options can fall anywhere along the continuum, as illustrated by these examples from the key options reviewed in Chapter 12. “SAR” = search and rescue.
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Figure 11.3 Primary pathways of the impacts of sea ice change on Arctic humans (redrawn from Eamer et al., 2013). These pathways can be direct or indirect; some are associated with changes in Arctic biodiversity. Changing sea ice is one of the most well-known and well-characterized effects of Arctic climate change, and this change poses challenges as well as benefits – both of which call for adaptation.
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Figure 12.1 Steps in a prioritized climate change adaptation plan for Arctic communities (based on Champalle et al., 2015).
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Figure 2.1 The BBDS marine and terrestrial regions.
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Figure 2.2 The BBDS region lies mostly within the Low Arctic and High Arctic climate zones; only a small area in South Greenland is Subarctic. Adapted from CAVM Team (2003) data set.
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Figure 2.3 Bioclimate subzones and protected areas in the vicinity of the BBDS region. The protected areas include ecologically or biologically significant marine areas (EBSAs), bird protection areas, wildlife protection areas, national parks, and marine protected areas. Adapted from CAVM Team (2003) data set, DFO (2011), Parks Canada (2013), and Environment Canada (2016).
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Figure 2.4 Major surface ocean currents (black lines) and bathymetric features in the BBDS region. Derived from Curry et al. (2014).
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Figure 2.5 Known polynyas in the BBDS region. “NOW” indicates the North Water Polynya. Adapted from Stirling (1980), Barber and Massom (2007), and Hannah et al. (2009).
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Figure 2.6 Annual cycle in sea ice cover in Baffin Bay and Davis Strait in 2010. Images based on multichannel microwave radiometer data (Advanced Scanning Microwave Radiometer, ASMR, and Scanning Multichannel Microwave Radiometer, SMMR). Red and magenta indicate very dense ice (8/10 to 10/10), while yellow indicates somewhat looser ice. The loosest ice (1/10 to 3/10) is not recorded. (Data source: Danish Meteorological Institute.)
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Figure 2.7 Distribution of breeding colonies of selected seabird species in the BBDS region. Note the high density of little auk (dovekie) colonies along the northwest Greenland coast adjacent to the area of the North Water Polynya (see Figure 2.5). (Data from the Greenland Seabird Colony Register and Seabirds.net: Circumpolar Seabird Data Portal.)
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Figure 2.8 Towns and major villages, administrative units (municipalities in Greenland), NAFO fishing areas, major fish plants, and transportation facilities in the BBDS region. (NAFO data were extracted from www.nafo.int/Data. Transportation facilities data were obtained from Tables 10.2 and 10.3.)
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Figure 2.9 Number of individuals in the Qikiqtaaluk region in 2011, classified by sex and age groups. (Data from Statistics Canada.)
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Figure 2.10 Number of individuals on the Greenland side of the BBDS region in 2015, classified by sex and age groups. (Data from Statistics Greenland.)
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Figure 2.11 Mining operations and hydrocarbon exploration and utilization activities in the BBDS region, as of 2014. The Mary River mine became operational in 2015. (Data for Canada were obtained from www.aadnc-aandc.gc.ca/eng/1100100036298/1100100036301, cngo.ca/app/uploads/Exploration_Overview-2014-Map-English.pdf, and Baffinland Iron Mines Corporation, 2017; data for Greenland were obtained from www.govmin.gl/petroleum/current.)
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Figure 2.12 Concept plan for the Plateau Subdivision in Iqaluit, Nunavut. The plan incorporates development practices to build a more sustainable neighbourhood while also considering community needs and values, available resources, and climate. For example, the subdivision includes a variety of lot sizes, including smaller, more affordable lots; road and lot orientations that minimize snow drift and maximize solar exposure, as well as views of Frobisher Bay; and a mixture of land uses that support the community’s cultural and socio-economic diversity. (Source: City of Iqaluit and FoTenn Consultants, 2012).
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Figure 3.1 Observed and projected anomalies in 2-meter air temperature averaged over the land portion of the BBDS region, relative to the 1986–2005 average. The black lines represent observations (specifically, the CRU TS 3.23 tmp observational product) (Harris et al., 2014). The colored envelopes represent the likely evolution of the 20-year averages up to 2090 under the RCP4.5 (green) and RCP8.5 (red) emissions scenarios, based on CMIP5 simulations for (a) winter (December-January-February, DJF), (b) spring (March-April-May, MAM), (c) summer (June-July-August, JJA), and (d) autumn (September-October-November, SON). The CRU data are presented up to 2014. Average anomalies for each simulation are first calculated for each year and then averaged over 20-year blocks from 1986–2005 (attributed here to the year 1996) through 2080–2099 (attributed to 2090). For each attribution year, the 10th and 90th percentiles among the simulations are next calculated; fourth-order fits on these two percentile times series define the envelope boundaries.
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Figure 3.2 Average 2-meter air temperature anomalies (ΔT) relative to 1986–2005 for the BBDS region: (a) land areas for 2025 (2016–2035), (b) sea areas for 2025, (c) land areas for 2090 (2080–2099), and (d) sea areas for 2090. Each circle represents one simulation. A filled circle indicates that the anomaly is larger than the 1986–2005 standard deviation in that same simulation. A total of 95 CMIP5 simulations are used: 56 for RCP4.5 (green) and 39 for RCP8.5 (red).
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Figure 3.3 Changes in annual mean near-surface air temperature (°C) for RCP4.5 and RCP8.5 for the time periods 2016–2035 and 2081–2100 (relative to 1986–2005): 25th, 50th, and 75th percentiles. The 50th percentile corresponds to the median value, and the 25th and 75th percentiles correspond to the values dividing the distribution of projected changes into the coldest 25% and warmest 25% of models, respectively. (Data source: IPCC, 2013b.)
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Figure 3.4 Selected winter climate scenarios for Clyde River in Nunavut (70°28'26" N, 68°35'10" W), based on the r1i1p1 member of the RCP8.5 experiment performed with the following global climate models: (a) FIO-ESM (green), (b) MIROC-ESM (orange), and (c) GFDL-CM3 (cyan). Gridded (10×10 km) data from Natural Resources Canada (Hopkinson et al., 2011) are used as observations over 1962–2010 (black line). Climate scenarios over 2011–2035 are obtained by statistically adjusting the simulations with a procedure termed quantile mapping (Grenier et al., 2015). Linear trends over 15-year segments are represented in red when positive and blue when negative. The gray envelope represents the time-smoothed 10th and 90th percentiles for yearly values in an ensemble of 15 RCP8.5-based climate scenarios (comprising the three presented here).
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Figure 3.5 Same as Figure 3.1, but for anomalies in total (liquid and solid) precipitation. Changes are expressed as percentage differences with respect to the 1986–2005 average. The observational product is CRU TS 3.23 (pre), and the simulations are the same as those used for Figure 3.1.
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Figure 3.6 Same as Figure 3.2, but for anomalies in mean wind speed (ΔV10m), expressed as percentage differences with respect to the 1986–2005 average.
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Figure 3.7 Left: Mean annual number of days with snow on the ground (snow cover duration, SCD) from the NOAA IMS 24 km daily snow cover analysis (Helfrich et al., 2007) over snow seasons 1998/99 to 2013/14. Right: Mean annual maximum snow water equivalent (SWE) (mm) over the 1979/80 to 2008/09 snow seasons, from the snow cover reconstruction of Liston and Hiemstra (2011).
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Figure 3.8 Left panels: Projected change (%) in BBDS-averaged maximum snow water equivalent (SWEmax) relative to 1986–2005, from 16 CMIP5 models: 25th, 50th and 75th percentiles (fourth-order polynomial smoothing). Right panels: Projected change (%) in monthly snow water equivalent (SWE) over BBDS non-glacier land areas for the year 2055 under the RCP4.5 and RCP8.5 scenarios: 25th, 50th, and 75th percentiles of 16 CMIP5 models. Results for the 2025 and 2090 periods are provided in the supplementary material for this subchapter (Langen et al., 2016).
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Figure 3.9 Top panels: Projected change (%) in annual snow cover duration relative to 1986–2005, averaged over non-glacier land points in the BBDS region, from 16 CMIP5 models: 25th, 50th, and 75th percentiles. Bottom panels: Projected change (%) in snow cover duration over the first half (SCD Autumn) and second half (SCD Spring) of the snow season, relative to 1986–2005, from 16 models: 50th percentiles.
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Figure 3.10 Permafrost temperatures (derived from Christiansen et al., 2010; Smith et al., 2013) and permafrost zones (from the map of Brown et al., 2014). The temperatures represent mean annual ground temperature at the depth of zero annual amplitude (the depth below which there is no significant seasonal variation in ground temperature) or at the depth of the closest measurement. The data were generally collected since 2008. The permafrost zone categories, indicated by the dark-to-light gray shading, are continuous (90–100% cover), discontinous (50–90% cover), and sporadic (10–50% cover).
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Figure 3.11 Permafrost temperature (annual mean) time-series records (reproduced from Smith et al., 2015) for Canadian Forces Station (CFS) Alert, at depths of 15 and 24 m (updated from Smith et al., 2012, and Romanovsky et al., 2015) and three communities on Baffin Island (data from Ednie and Smith, 2015, and Romanovsky et al., 2015). Alert BH1 is located near the coast and has greater snow cover than the other two Alert sites. (This figure is a copy of an official work published by the Government of Canada. This reproduction has not been produced in affiliation with or with the endorsement of the Government of Canada.)
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Figure 3.12 Projected change for BBDS permafrost, expressed as the change in average temperature (°C) at 5 m depth for the periods 2016–2035 (left panels) and 2081–2100 (right panels). The results are based on the GIPL2 transient permafrost model forced with the CCSM4 GCM and emission scenarios RCP4.5 (upper panels) and RCP8.5 (lower panels). Changes are computed relative to the reference period 1986–2005.
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Figure 3.13 Summer (August to October) temperature (left panel) and salinity (right panel) in the upper 100 m of the Baffin Bay/Davis Strait water column. Derived from available archived data between 1910 and 2009. The transect line indicates the location of the cross-sections shown in Figure 3.14. (Modified from Hamilton and Wu, 2013; see their report for data-set details. © Her Majesty in Right of Canada, as presented by the Minister of Fisheries and Oceans.)
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Figure 3.14 Davis Strait cross-sections of mean summer temperature (T, top) and salinity (S, bottom). Derived from available archived data between 1910 and 2009. The three-layer structure of Baffin Bay is also reflected in these Davis Strait data. The location of the cross-sections is indicated by the black transect line in Figure 3.13. (Reproduced from Hamilton and Wu, 2013; see their report for data-set details. © Her Majesty in Right of Canada, as presented by the Minister of Fisheries and Oceans.)
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Figure 3.15 Mean freshwater fluxes (mSv) into Baffin Bay, based on observations reported by the referenced investigators. The coloring of the block arrows denotes the portion of the flux that is liquid (blue) versus ice (white).
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Figure 3.16 Labrador Sea/Baffin Bay mean near-surface current velocities (0–50 m) over the reference period (1986–2005), plus projected changes for the periods 2016–2035 and 2081–2100 under the RCP8.5 scenario. The projected changes are expressed as differences between the future period and the historical period. Each row shows the output of a different model.
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Figure 3.17 Mean dates (1981–2010) of sea ice freeze-up (upper panel) and break-up (lower panel) (Canadian Ice Service, 2011b.) (These maps are copies of official works published by the Government of Canada. These reproductions have not been produced in affiliation with or with the endorsement of the Government of Canada.)
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Figure 3.18 Trends in monthly average ice concentration (%) over the Canadian Arctic and adjacent waters, 1979–2012, expressed as percent change per decade (based on the passive microwave satellite data set of Cavalieri et al., 1996, updated to 2012).
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Figure 3.19 Regionally averaged annual anomalies in maximum ice thickness, 1958–2013 (with respect to a 1959–1987 average) from six Canadian BBDS stations with long-term weekly ice thickness measurements. (Ice thickness data were obtained from the Canadian Ice Service, www.ec.gc.ca/glaces-ice/.)
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Figure 3.20 Projected change in autumn (SON) and winter (DJF) sea ice concentration (change in percent concentration relative to the 1986–2005 average) for the RCP4.5 scenario, according to a 29-member CMIP5 multi-model simulation. Results are shown for three periods in the future: 2016–2035 (labeled 2020s), 2046–2065 (labeled 2050s), and 2081–2100 (labeled 2080s). The figures illustrate the 25th, 50th, and 75th percentile changes projected by the CMIP5 models. For a list of the 29 models, see Table 4.1 of AMAP (2017a).
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Figure 3.21 Projected change in autumn (SON) and winter (DJF) sea ice thickness (change in meters relative to the 1986–2005 average) for the RCP4.5 scenario, according to a 29-member CMIP5 multi-model simulation. See Figure 3.20 for an explanation of the panels.
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Figure 3.22 Present-day vertical crustal motion estimated by the ICE-6G glacial isostatic adjustment (GIA) model (described in Peltier et al., 2015). Regions of large uplift (warm colors) indicate former centers of the Laurentide Ice Sheet; subsiding regions (cool colors) were close to the ice-sheet margin or were peripheral to the ice sheets. In Greenland, the modest amounts of crustal uplift and subsidence are largely due to modeled changes in the Greenland Ice Sheet.
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Figure 3.23 Projected relative sea-level change at the year 2100 (in cm) for the median RCP8.5 value at coastal locations in the Canadian sector of the Baffin Bay/Davis Strait region (after James et al., 2014).Values range from −84 cm to −1 cm and are relative to 1986–2005. Locations mentioned in the text are labeled.
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Figure 3.24 Projected relative sea-level change for Iqaluit and Alert (after James et al., 2014), based on the IPCC AR5 (Church et al., 2013a, 2013b) and also vertical crustal motion (uplift rate, given to nearest 0.5 mm/yr) derived from GPS observations. Projections are given through the century for the RCP2.6, RCP4.5, and RCP8.5 scenarios. The rectangles show the 90% confidence interval (5% to 95%) of the average projection for the period 2081–2100 for each of those three scenarios and also RCP6.0. The dashed red line gives the 95th percentile value for RCP8.5. The projected value at 2100 is also given for a scenario in which West Antarctica contributes an additional 65 cm of global sea level rise, added to the median projection of RCP8.5 (RCP8.5+W.Ant; green triangle).
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Figure 3.25 Proportion of Inuktitut (Inuit language) speakers in different parts of Nunavut (data compiled from Statistics Canada, 2011b).
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Figure 3.26 The BBDS scenario framework formulated by the AACA Baffin Bay/Davis Strait regional integration team. The two macro-drivers are climate change and the development of extractive industries (oil/gas and hard minerals) in the BBDS region. Each scenario is to be considered in terms of two timeframes: through the year 2030 and also the year 2080.
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Figure 3.27 Global mean temperature (relative to 1986–2005) for the four RCP scenarios considered in the Fifth Assessment Report of the IPCC (modified from Collins et al., 2013). The vertical bars indicate the corresponding likely ranges for global temperature change by the end of the 21st century. The BBDS framework scenarios consider the cases of high emissions (RCP8.5; red) and medium-low emissions (RCP4.5; blue).
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Figure 3.28 Projected annual mean near-surface air temperature changes (°C, relative to 1986–2005) in the (a, c) near-term future, 2016–2035, and (b, d) long-term future, 2081–2100 (based on data from IPCC, 2013). a, b: Upper range (RCP8.5, 75th percentile). c, d: Lower range (RCP4.5, 50th percentile).
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Figure 4.1 An illustration of the challenges of managing water supply in a reservoir in Nunavut. The upper panel indicates total water storage in a reservoir throughout the year. The lower panel shows a typical river runoff hydrograph. Most of the water is available during the spring snowmelt period, but consumption in a community is relatively constant throughout the year. To maintain adequate water supply for the entire winter period, then, water managers need to consider augmenting water supply during the spring snowmelt period (to yield the greater reservoir quantities indicated by the dashed line). Summer rainfall alone is rarely adequate to accomplish a year-round supply and is, moreover, unpredictable. (Source: S. Lamoureux)
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Figure 4.2 Health service network and the typical referral pathways that serve Nunavut communities. (Source: Government of Nunavut, Department of Health.)
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Figure 5.1 Students involved per year in various types and levels of educational activities in Greenland, 2003–2012 (Designed by the editor on the basis of data from Statbank Greenland, bank.stat.gl, 2014). Percentages are expressed relative to number of active students.
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Figure 5.2 Student demographics in Greenland, 2005–2010 (Designed by the editor on the basis of data from Statbank Greenland, bank.stat.gl, 2014). (Note that the “no answer” fraction of the responses is large enough that it may affect the validity of the other figures.)
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Figure 5.3 High school graduation rates in Nunavut and in the Qikiqtaaluk region, 1999–2014 (data source: Nunavut Bureau of Statistics, 2016).
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Figure 6.1 Map of the two major ocean current systems in the BBDS region (Pacific-derived = blue arrows and blue shading; Atlantic water = orange arrows and orange shading). Marine biological hotspots and EBSAs discussed in the text are shown by the numbered circles: (1) Hudson Strait, (2) Hatton Basin, (3) Lancaster Sound, (4) Smith Sound, (5) the Greenland shelf from Uummannaq to Melville, (6) Disko Bay, (7) Store Hellefiskebanke, and (8) the Maniitsoq area and Fyllas Bank. The white block arrows denote inputs of fresh water and icebergs from the Greenland Ice Sheet. The solid lines near the coasts mark the 250-meter isobath that delineates shelf areas. (Base map generated using Ocean Data View; Schlitzer, 2016.)
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Figure 6.2 East–west cross-sections of selected ocean properties in Smith Sound (North Water) between Ellesmere Island and Greenland, 0–150 m: temperature, salinity, nitrate concentration, and relative chlorophyll fluorescence (an index of phytoplankton biomass) (data from the 2005 ArcticNet expedition).
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Figure 6.3 Satellite-based estimates of annual primary production (PP) in open (ice-free) waters of the BBDS, averaged over the period 1998–2014. The black boxes indicate the four areas considered for the analysis of temporal trends presented in Figure 6.4.
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Figure 6.4 Temporal trends in satellite-based primary production, 1998–2014, in open waters of (a) Smith Sound (North Water), (b) central Baffin Bay, (c) the central Labrador Sea, and (d) the southwest Greenland shelf. The locations of these four areas are shown in Figure 6.3. The dark blue lines are moving averages adjusted to the data.
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Figure 6.5 Generalized terrestrial food web. The lines represent major trophic relationships. The species shown in red is mainly boreal, those shown in pale blue are typically Arctic, and those in brown are widespread boreal and Arctic. “Subzone C” is one of five Arctic bioclimate subzones. (Figure from Ims and Ehrich, 2013.)
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Figure 6.6 Conceptual model for science-based management of natural resources that face climate and development/land-use impacts. The process begins with a conceptual model of the key drivers affecting the resource or ecosystem of interest, such as breeding habitat. Projected changes and impacts from climate change and development are incorporated based on local observations/expert knowledge, modeled projections, or examples from similar ecosystems elsewhere to develop a hypothesis of how the resource might change. A research, inventory, and/or monitoring plan designed to understand the most important drivers of future changes is designed and implemented to track the actual changes that occur over time. As sufficient data are collected, scientists, managers, and communities adapt their management choices to better mitigate the impacts as they are understood. Ongoing long-term monitoring is needed to continue to examine the resiliency of the resource or ecosystem in response to ongoing climate and land-use changes. (Figure by W.M. Loya.)
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Figure 6.7 Distribution and intensity of the trawl fishery in West Greenland waters, 1999–2013, based on logbook information. Boxes 1 and 2 indicate the traditional Greenland halibut fishing grounds in West Greenland, while box 3 shows the area of inshore Greenland halibut fishery. The remaining fishery in West Greenland is aimed at shrimp. The trawl intensity is graduated from low (yellow) to high (red), with the sailed distance (km/km2) being summed over all years. The thin lines show the 3–, 12–, and 200–nautical mile zones. (GIS analysis by Karl Brix Zinglersen, GINR. Analogous data were not available for the Nunavut side).
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Figure 6.8 Numbers of hunters reporting harvest of at least one species within a species group in West Greenland (GL) and the Canadian (CA) Baffin area (Arctic Bay, Clyde River, Grise Fiord, Iqaluit, Kimmirut, Pangnirtung, Pond Inlet, Qikiqtarjuaq, Resolute Bay, and Cape Dorset). The distinct peak in the harvest of terrestrial mammals in West Greenland in the early 2000s reflects a spike in caribou hunting. (Canada data from Priest and Usher, 2004; Greenland data from Piniarneq, 2014.)
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Figure 6.9 Geographic delineation of polar bear populations in Canada and Greenland. Within the BBDS region: FB = Foxe Basin, GB = Gulf of Boothia, LS = Lancaster Sound, KB = Kane Basin, BB = Baffin Bay & DS = Davis Strait. (Figure reproduced from PBSG, 2017a.)
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Figure 7.1 Overview map of mineral licenses and major mining projects in Greenland, as of 2014 (from Government of Greenland, 2016a). Up-to-date information about current licenses can be found on the Minerals and Petroleum Licence Map of the Government of Greenland: licence-map.bmp.gl/
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Figure 7.2 Mineral exploration expenditures in Greenland, 2005–2014, in DKK millions (from Government of Greenland, 2014). (As of 5 June 2017, 1 Danish krone = 0.20 Canadian dollar.)
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Figure 7.3 Mineral exploration expenditures in Nunavut, 1999–2014, in CAD millions (data for 1999–2008 are from Natural Resources Canada, 2009; data for 2009–2012 are from AANDC, 2015a). (As of 5 June 2017, 1 Canadian dollar = 4.90 Danish kroner.)
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Figure 8.1 Map of tourist activities in the BBDS region (information compiled by J. Dawson).
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Figure 8.3 Map of the total volume of passenger ships in the BBDS region, 1990–2013 (data from NORDREG and Greenpost).
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Figure 8.4 Map of the total volume of pleasure craft vessels in the BBDS region, 1990–2013 (data from NORDREG and Greenpost).
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Figure 8.5 Travel motivations to Canada and Greenland.
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Figure 8.2 Vessel trends in the BBDS region: passenger ships and pleasure crafts, 2004–2014 (from database discussed in Pizzolato et al., 2016, and Dawson et al., 2017a).
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Figure 9.1 Map of traffic patterns (total density of ships) for the BBDS region, 1990–2013 (based on data from the vessel traffic reporting systems NORDREG and GREENPOST).
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Figure 9.2 Diagram of marine jurisdictional zones (from Symonds et al., 2009; Geoscience Australia). For Canada, the territorial sea limit is 12 nautical miles, as shown on the figure; for Greenland, the limit is 3 nautical miles. M = nautical mile; m = meter; TSB = territorial sea baseline. (Figure from Geoscience Australia, www.ga.gov.au/ausgeonews/ausgeonews200903/limits.jsp; licensed under the Creative Commons Attribution 3.0 Australia License: creativecommons.org/licenses/by/3.0/au/.)
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Figure 9.3 Identification of ecologically valuable and sensitive sea areas in the Greenland part of the BBDS region (from Christensen et al., 2012). The color of the area labels indicates priority: Priority 1 in red, Priority 2 in orange, and Priority 3 in green. Especially important “core areas” are marked by red shading. In area V7, the critical resources (whelping seals and foraging seabirds and whales) are associated with the marginal ice zone, which is highly dynamic within a single year and also between years – increasingly so due to climate change impacts. Accordingly, the associated core areas are not mapped here, as they must be equally dynamic.
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Figure 9.4 Relative environmental sensitivity within the Disko Bay and Store Hellefiskebanke (V5) area, including five subareas (1–5) where heightened awareness in relation to the impacts of shipping may be needed (from Christensen et al., 2015). The colors indicate sensitivity within each grid cell (2.5 × 2.5 km), based on an assessment of the sensitivity of existing species and ecosystem components to environmental impacts from shipping (oil, noise/disturbance, and organic garbage). Relative sensitivity is expressed in 5% bins, with red being the most sensitive and blue being the least sensitive.
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Figure 9.5 Map of optimal Arctic September shipping routes from the Pacific to the Atlantic: (A) 2006–2015, modeled and (B) 2040–2059, projected (reproduced from Smith and Stephenson, 2013, with permission). The red lines indicate the fastest available trans-Arctic routes for Polar Class 6 (PC6) ships (i.e., with medium ice strengthening of the hull), and the blue lines indicate the fastest transits for common open water (OW) ships (no ice strengthening of the hull). Where navigation routes overlap or coincide, the line widths indicate the number of successful transits along that route. The dashed lines represent the limits of the exclusive economic zones (EEZs).
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Figure A. Some socio-economic drivers (blue boxes) and climatic drivers (red) of change in the Arctic, and the temporal and spatial scales over which they act (modified from Arctic Council, 2013). Although changes in the social drivers are more difficult to project with scientific rigor than are changes in the climatic drivers, they are usually more important than environmental/climatic drivers in shaping policies and personal decisions (see Andrew, 2014; IPCC, 2014). Climate change effects include changing seasonality and an increase in the incidence of very localized extreme weather events, which can last from hours (e.g., heavy rains or snowfall, flash floods, storms) to days, weeks, or months (e.g., heat waves or cold waves, droughts). These types of changes are already being felt much more than the changes in long-term average conditions (for example, ice conditions and mean temperature) that are affecting most of the Arctic and the globe.
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Figure B. Adaptation options can be related to the changing patterns of climate to varying extents. Some options are clearly “climate centered” in that they address specific challenges posed by a changing climate – for example, preparing infrastructure for conditions of thawing permafrost. Other long-term options are just “good development.” These “vulnerability-centered” options address existing vulnerabilities that could be exacerbated by changes in a variety of external factors, climatic as well as socio-economic (see further explanations in Chapter 11, Figure 11.2, and the summary table in Chapter 12).
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