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Figure 7.5 Step-by-step process of adaptation planning (Steps A through E, as in Table 7.4), overlain with the more general adaptation steps shown in Figure 7.2 (based on Eyzaguirre and Warren, 2014).
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Figure 7.4 Links between increasing unpredictability of weather and human impacts and adaptation among Inuit Nunangat communities in Canada (Nickels et al., 2005).
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Figure 7.1 Feedbacks and interactions between adaptation and changing environmental and socio-economic conditions.
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Figure 7.2 Stages and steps in the adaptation process (Eyzaguirre and Warren, 2014). The adaptation process involves deepening levels of engagement (phases), as well as actions that can be taken in support of decision-making (the numbered steps). While illustrated here in a sequential manner, organizations may take different pathways as they move through these phases and steps. This graphic was compiled by integrating common elements of different adaptation planning frameworks.
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Figure 7.3 The Building Resilience Against Climate Effects (BRACE) framework (Marinucci et al., 2014).
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Figure 6.6 Four scenarios for the future of the Arctic, based on future demand for Arctic resources and the stability of Arctic governance (based on work by Brigham and colleagues: Brigham, 2008, and PAME, 2009). These scenarios are ‘Arctic Race’, in which ad hoc and poorly developed governance does not keep up with the high demand for Arctic resources, resulting in a gold-rush approach; ‘Arctic Saga’, in which high demand is tempered by stable governance structures, leading to development balanced with the needs of local peoples and ecosystems; ‘Polar Lows’, in which demand does not materialize and limited governance leads to little change or development for the region; and ‘Polar Preserve’, in which stable governance pursues extensive protection of Arctic ecosystems and peoples without external geopolitical pressure.
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Figure 6.5 Population dynamics of 11 communities in Alaska’s Northwest Arctic Borough, where food insecurity and the impacts of climate change are being actively experienced (population and birth/death numbers are graphed from different baselines, but with comparable y-axis scales) (Hamilton et al., 2014).
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Figure 4.21 Change in population over recent decades in Chukotka, plus projections to 2050 (Russian Federation Federal Statistical Service. Official population forecast, www.fedstat.ru/indicator/36727).
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Figure 4.22 Forecast of demographic indices in medium variant scenario for Chukotka (C) and the Russian Federation (RF) (Russian Federation Federal Statistical Service. Official demographic forecast, www.fedstat.ru/indicator/36727).
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Figure 4.16 Shifts in Arctic phytoplankton phenology within the Arctic Circle (>66.58°N). The histogram shows three types of annual phytoplankton bloom cycle (flat, single bloom, double bloom) for three consecutive periods since the late 1990s. The circumpolar map shows percentage change in autumn/secondary bloom occurrence between the earlier and later periods for 19 Arctic areas. The minimum September sea-ice extent in 2012 is shown in dark blue (Ardyna et al., 2014).
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Figure 1.1 The three regions for the project Adaptation Actions for a Changing Arctic. Blue lines delimit Large Marine Ecosystems (LMEs).
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Figure 1.2 The Bering-Chukchi-Beaufort region. The southern terrestrial boundary of the BCB region generally follows the treeline–tundra boundary, while the marine boundaries conform to the boundaries of the large marine ecosystem (LME) areas defined by PAME (Skjoldal and Mundy, 2013).
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Figure 1.3 Bridging the chasm between science and decision-making: The collaborative science-based decision-making scheme (Madison River Group, LLC).
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Figure 2.1 Areas of study. The letters that accompany the pinned locations refer to the different stakeholder perspectives in Section 2.3.
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Figure 2.2 Wainwright and Alaska’s North Slope, showing the dominant offshore currents (Matthew L. Druckenmiller, NSIDC). Piruġaġnaq, the dominant current off Utqiagvik and Wainwright, flows from the northeast and is more frequent during winter. This current typically creates conditions that make it safe to be out on the ice. Qaisaġniq, the more seaward current flowing from the southwest, is more common after late May and is known to bring warm water that can accelerate the melt and break-up of shorefast ice. Qaisaġniq is also known as the current that brings the animals.
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Figure. 3.1 Geographic place names in the BCB region.
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Figure 3.2 Ocean circulation in the BCB region. The major currents and water masses of the region are depicted, as are the approximate southern ice-edge locations of March and September (orange lines) (adapted from Wiese et al., 2013; Moore and Stabeno, 2015).
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Figure 3.3 Mackenzie River discharge plume, 15 June 1998. Image provided by the SeaWiFS Project, NASA Goddard Space Flight Center, and ORBIMAGE.
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Figure 3.4 Seasonal evolution of stratification and dynamics of the mixed surface layer in the BCB region (Peralta-Ferriz and Woodgate, 2015).
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Figure 3.5 Distribution of sea ice concentration, sea surface temperature, and snow depth from September 2012 to June 2013 (images provided by Arctic Data Archive System, https://ads.nipr.ac.jp/vishop/#/monitor).
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Figure 3.6 Spatial distribution of permafrost in the BCB region, adjacent lands, and subsea areas (Brown et al., 2001). Permafrost is defined as the ground that remains at or below 1°C for more than two years. It is differentiated by its spatial extent into continuous (90–100% areal coverage), discontinuous (50–90%), sporadic (10–50%), and isolated (0–10%) permafrost.
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Figure 3.7 Hydrograph for the Mackenzie River at Arctic Red River (Yang et al., 2015).
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Figure 3.8 Distribution of urban centers and areas of economic development in Chukotka (deserted settlements: Litvinenko, 2013; actual population 2014 and employment 2015: Russian Federation Federal State Statistics Service; populations of shift settlements: official CAO website and websites of mining companies).
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Figure 3.9 Minerals of the Chukotka district (Federal Agency of Geodesy and Cartography, 2005, 2008; Mineral Information and Analysis Center, 2015).
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Figure 3.10 Arctic shipping routes. Graphic shows the convergence of Arctic shipping (Bering, Chukchi, Beaufort seas) through the Bering Strait, the Northern Sea Route (along the coast of the Russian Federation), the Northwest Passage (along the coasts of the United States and Canada), and potential transpolar routes across the North Pole. (Base map courtesy of the US National Aeronautics and Space Administration, showing the sea ice minimum from 10 September 2016.) 

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Table 3.8 Location of benthic hotspots in the BCB region (Dunton et al., 2005, 2006, 2012; Darnis et al., 2012; Grebmeier and Maslowki, 2014; Grebmeier et al., 2015; Moore and Stabeno, 2015).
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Figure 3.11 Locations of Important Bird Areas (IBAs) in the BCB region (Audubon Alaska, 2014; Bird Studies Canada, 2015; BirdLife International, 2015).
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Figure 3.12 Generalized range and seasonal migration of bowhead whales (Quakenbush, L., Alaska Department of Fish and Game).
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Figure 3.13 Physiography of the BCB region (CAVM Team, 2003).
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Figure 3.14 Distribution of caribou herds in the BCB region (CAFF, 2015b).
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Figure 3.15 Global flyways for migratory waterbirds (Ganter and Gaston, 2013).
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Figure 4.1 Historical trends in surface air temperature. Graphic shows the spatial pattern of Arctic warming for the period 1961–2014 in the cold season and warm season. Warm colors indicate warming trends and cool colors indicate cooling trends (NASA GISTEMP http://data.giss.nasa.gov/gistemp/maps/).
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Figure 4.2 Historical trends in Arctic sea ice extent. Graphic shows anomalies in sea ice extent for March (month of maximum extent) and September (month of minimum extent). The anomaly value for each year is the difference in ice extent relative to the mean values for 1981–2010. The straight lines show least squares linear regressions and indicate ice losses of 2.6% (March) and 13.3% (September) per decade (Perovich et al., 2015).
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Figure 4.3 Historical trends in sea ice cover. Graphic shows spatial patterns in the number of ice-covered days per sea ice season over the period 1979-2013. Warm colors indicate shorter ice cover duration and cool colors indicate longer ice cover duration (Parkinson, 2014).
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Figure 4.4 Historical trends in snow cover on land. Graphic shows spatial patterns in annual snow cover duration (the number of days with snow on the ground) from the NOAA Climate Data Record data set (Robinson et al., 2012) for the period 1972–2014. Warm colors indicate shorter snow seasons and cool colors indicate longer snow seasons.
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Figure 4.5 Historical trends in air temperature in the BCB region. Plots show spatially-averaged annual mean surface air temperature anomalies for the Chukotka region (based on RosHydromet, 2014); annual mean air temperature at Inuvik (from Environment Canada’s Adjusted and Homogenized Climate Data and based on Vincent et al., 2012); annual mean air temperature averaged across northern Canada (based on Wang et al., 2013); and change in regional average (relative to 1951-1960) of the 20-year return values of extreme high daily maximum temperature and extreme low daily minimum temperature and the annual mean temperature (Wang et al., 2013). Return values represent the level that is attained, on average only once in 20 years.
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Figure 4.6 Historical trends in snow amount and duration in the BCB region. Plots show trends in annual maximum snow water equivalent (SWE) for the period 1979–2009, as estimated from the snow cover reconstruction of Liston and Hiemstra (2011), and trends in annual snow cover duration, as estimated from the NOAA CDR satellite record (Robinson et al., 2012) and the Liston and Hiemstra (2011) reconstruction.
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Figure 4.7 Historical trends in ground temperature in Alaska. Graphic shows time series of annual mean ground temperature at depths of 9 to 26 m below the surface at selected measurements sites in Alaska in both the zone of cold continuous permafrost and in the more southerly zone of discontinuous permafrost. Temperatures are measured at or near zero annual amplitude. Based on Romanovsky et al. (2012, 2014, 2015).
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Figure 4.8 Time series of daily sea-ice extent in the Bering Sea for 1979-2017, highlighting winter (Feb-Mar) extremes. There are no summer extremes because the sea ice completely melts in summer in this area. Based on data from the Arctic and Antarctic Research Institute, St. Petersburg, Russia (wdc.aari.ru/datasets/d0042/).
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Figure 4.9 Historical trends in ocean surface waves in the Beaufort-Chukchi seas. The plots show the regional mean time series of significant wave height and mean wave period (Wang et al., 2015). The trend estimates are expressed as a percentage of the 1970–1999 climatological mean (the numbers in parentheses). Trends shown are significant at the 5% level or above.
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Figure 4.10 CMIP5 multi-model ensemble projections of future change in surface air temperature (relative to the 1986–2005 average) for summer and winter, based on the RCP4.5 scenario. Results are shown for three future periods: 2016–2035 (‘2020s’), 2046–2065 (‘2050s’) and 2081–2100 (‘2080s’). Panels illustrate the 25th, 50th, and 75th percentiles of change as projected by the 29 CMIP5 models listed in Table 4.1. Graphic based on re-gridded CMIP5 data available at ccds-dscc.ec.gc.ca
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Figure 4.11 CMIP5 multi-model ensemble projections of future change in snow depth (relative to the 1986–2005 average) for summer and winter, based on the RCP4.5 scenario. Results are shown for three future periods: 2016–2035 (‘2020s’), 2046–2065 (‘2050s’) and 2081–2100 (‘2080s’). Panels illustrate the 25th, 50th, and 75th percentiles of change as projected by the 29 CMIP5 models listed in Table 4.1. Graphic based on re-gridded CMIP5 data available at ccds-dscc.ec.gc.ca
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Figure 4.12 CMIP5 multi-model ensemble projections of change in sea ice thickness (relative to the 1986–2005 average) for summer and winter, based on the RCP4.5 scenario. Results are shown for three future periods: 2016–2035 (‘2020s’), 2046–2065 (‘2050s’) and 2081–2100 (‘2080s’). Panels illustrate the 25th, 50th, and 75th percentiles of change as projected by the 29 CMIP5 models listed in Table 4.1. Graphic based on re-gridded CMIP5 data available at ccds-dscc.ec.gc.ca
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Figure 4.13 CMIP5 multi-model ensemble projections of future change in sea ice concentration (relative to the 1986–2005 average) for summer and winter, based on the RCP4.5 scenario. Results are shown for three future periods: 2016–2035 (‘2020s’), 2046–2065 (‘2050s’) and 2081–2100 (‘2080s’). Panels illustrate the 25th, 50th, and 75th percentiles of change as projected by the 29 CMIP5 models listed in Table 4.1. Graphic based on re-gridded CMIP5 data available at ccds-dscc.ec.gc.ca
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Figure 4.14 CMIP5 multi-model ensemble projections of future changes in near-surface wind speed (relative to the 1986-2005 average) for summer and winter, based on the RCP4.5 scenario. Results are shown for three future periods: 2016-2035 (‘2020s’), 2046-2065 (‘2050s’) and 2081-2100 (‘2080s’). Panels illustrate the 25th, 50th, and 75th percentiles of change as projected by the 29 CMIP5 models listed in Table 4.1. Graphic based on re-gridded CMIP5 data available at ccds-dscc.ec.gc.ca
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Figure 4.15 Multi-model comparison based on output from six Earth System models under the RCP8.5 scenario for the Beaufort Sea and Bering Sea. Plots show annual mean sea surface temperature, sea surface salinity, sea ice concentration (March and September), surface nitrate (summer JJA and winter DJF), annual mean primary production, and pH. Error bars indicate multi-model standard deviation. Data points are 20-year averages and are centered in the midpoint of the 20-year range. Modified from Steiner et al. (2013, 2014).
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Figure 4.17 Biodiversity-related changes projected to result from projected high-range warming as of 2050 (relative to 2001–2005 means). Plots show species invasion intensity, local extinction intensity, and species turnover. Species redistribution models using ESM output and current species distributions based on selected environmental variables (temperature, salinity, oxygen) to project future change indicate the Arctic could become a hotspot for species invasions and species turnover (i.e., extinction of local species and replacement by invading species) (Cheung et al., 2009).
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Figure 4.18 Mean seasonal changes in vegetation phenology as indicated by NDVI-derived indices, 1982–2010, for May through September (David Douglas, US Geological Survey, Alaska Science Center).
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Figure 4.19 Change in population over recent decades in northern Alaska, plus projections to 2042 (based on ADL 2014 and Hamilton et al., 2016).
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Figure 4.20 Population dynamics of Kotzebue, a community in Alaska’s Northwest Arctic Borough, where food insecurity and the impacts of climate change are being actively experienced (population and birth/death numbers are graphed from different baselines, but with comparable y-axis scales) (Hamilton et al., 2014).
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Figure 4.23 Exploration licenses and significant discovery areas in the Canadian Beaufort Sea as of November 2012 (based on BREA, 2013).
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Figure 4.24 Catches and landed value of marine fisheries from the Canadian Arctic region (data from Sea Around Us, www.seaaroundus.org), and the relative contribution of individual species (Cheung et al., 2016).
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Figure 4.25 Types of freight cargo in the main ports of the Chukotka district in 2014 (All Freight of Russia, 2015).
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Figure 4.26 Ice road and tundra travel season lengths on the North Slope of Alaska, 1970–2016 (updated from Alaska Department of Environmental Conservation, 2014).
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Figure 6.1 Comparison of household composition for Alaska and Chukotka (based on SLiCA data, as reported by West, 2011).
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Figure 6.2 Self-reported ability to make ends meet, Alaska and Chukotka (based on SLiCA data, as reported by West, 2011).
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Figure 6.3 Heuristic presentation of thresholds and regime change in resilience. Resilience is represented by the height and slope of the ‘hill’ between the system’s current state and an alternative stable state (Loring, 2017).
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Figure 6.4 Population dynamics of nine communities along Alaska’s Norton Sound, where king salmon fisheries collapsed in 2000 (population and birth/death numbers are graphed from different baselines but with comparable y-axis scales) (Hamilton et al., 2014).
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Figure 7.6. Knowledge co-production (adapted from Knapp and Trainor, 2015). Two-way, iterative communication is an important feature of knowledge co-production. In some cases, scientists and decision-makers communicate directly. Boundary organizations can facilitate this communication as well as the transfer of knowledge from scientists to decision-makers.
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Figure 5.1 Mean serum concentrations of total PCBs measured in Indigenous populations of the Russian Arctic, as sampled from pregnant women, fetal umbilical cord tissue, and newborn male and female babies. (Source: AMAP, 2000.)
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Figure 5.2 Incidence of diseases potentially associated with human exposure to PCBs in Indigenous populations of Chukotka (Russian Ministry of Public Health, 2011).
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Figure 5.3 Effects of thermal abrasion on a coastline of unconsolidated, ice-rich sediments, example from Lorino, Chukotka Autonomous Okrug.
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Figure 5.4 Projected 21st century runoff from the West River at Cape Bounty, Melville Island, in the High Arctic, based on the IPCC A2 scenario. Event timings are given in terms of Julian days. The box plots on the left indicate the ranges of modeled hydrological response based on measured meteorological data (red; 1949–1997, Mould Bay) and historical model data (green; 1961–1996, Canadian Global Climate Model v. 3) (Lamoureux et al., 2015, as adapted from Lewis and Lamoureux, 2010).
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Figure 5.5 Proportion of energy derived from traditional foods among Inuit by age and sex. Data collected from the Inuit Health Survey, based on respondents’ 24-hour recall of food intake. Redrawn from Young et al. (2015).
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Figure 5.6 Zoning of the Russian territory according to specific power of the wind flow at a height of 100 m above the ground (Roshydromet, 2014).
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Figure 5.7 Aerial imagery of tundra and ocean, showing the impacts of coastal erosion near Drew Point, Alaska. Between 1955 and 2009, approximately 2800 ha of land were washed into the sea along a 64-km stretch of Beaufort Sea coastline that included the parts shown here. For the periods 1955–1979, 1979–2002, and 2002–2009, the annual rates of coastline erosion were 6, 8, and 17 m/y, respectively. For year-to-year reference, note the large lake near the center of the photo. The colored lines in the 2009 image outline the locations of the shoreline in the years indicated (Benjamin M. Jones, US Geological Survey).
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Figure 5.8 Topography and bathymetry of the Canadian Beaufort Sea region with physiographic subdivisions (gray bars), after Forbes et al. (2014). The green squares show the locations of Geological Survey of Canada coastal erosion monitoring sites. The color-coding along the coastline indicates erosion hazard index, after Solomon and Gareau (2004). (Modified from Lamoureux et al., 2015.)
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Figure 8.1 Summary of socio-economic scenarios for the BCB region, based on a synthesis of prior scenario activities from the region.
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Figure 8.2 Socio-economic scenarios for the BCB subregion: Chukotka.
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Figure 8.3 Socio-economic scenarios for the BCB subregion: northern Alaska.
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Figure 8.4 Socio-economic scenarios for the BCB subregion: Beaufort in the western Canadian Arctic.
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Figure 8.5 Effective institutions must be resilient but also nimble. Adaptive institutions should therefore occupy the space within the circle. Ideal institutions (orange star) perform optimally in robustness and responsiveness (adapted from Lindgren and Bandhold, 2009).
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Figure 8.6 Implications of agreements or disagreements among stakeholders and the scientific community regarding the value of different kinds of data. The data needs of stakeholders for decision-making are compared against data availability, which is a function of research priorities and investments defined by science policy.
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Figure 8.1 Summary of socio-economic scenarios for the BCB region, based on a synthesis of prior scenario activities from the region.
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Figure 8.2 Socio-economic scenarios for the BCB subregion: Chukotka.
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Figure 8.3 Socio-economic scenarios for the BCB subregion: northern Alaska.
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Figure 8.4 Socio-economic scenarios for the BCB subregion: Beaufort in the western Canadian Arctic.
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Figure 8.5 Effective institutions must be resilient but also nimble. Adaptive institutions should therefore occupy the space within the circle. Ideal institutions (orange star) perform optimally in robustness and responsiveness (adapted from Lindgren and Bandhold, 2009).
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Figure 8.6 Implications of agreements or disagreements among stakeholders and the scientific community regarding the value of different kinds of data. The data needs of stakeholders for decision-making are compared against data availability, which is a function of research priorities and investments defined by science policy.
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