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Figure 2.9. Distribution of Atlantic cod in 2004 and 2013 recorded in autumn during the joint IMR-PINRO ecosystem survey (Prozorkevich and Gjøsæter, 2013).
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Figure 2.10 Distribution of red king crab in the Barents Sea (Norwegian Institute of Marine Research).
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Figure 2.7 Mean biomass of krill recorded during joint Norwegian-Russian autumn surveys by trawl sampling in the upper 60 m of the Barents Sea. Based on Eriksen and Dalpadado (2011) with updates for 2010–2015 (Institute of Marine Research, Norway, unpubl. data).
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Figure 1.2 The Barents area, as defined in this pilot study. The terrestrial areas follow relevant administrative boundaries within the four countries.
The marine area comprises the Barents Sea Large Marine Ecosystem (LME).
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Figure 1.1 The three pilot regions for the project Adaptation Actions for a Changing Arctic. Blue lines delimit Large Marine Ecosystems (LMEs).
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Figure 2.1 AACA Barents area.
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Figure 2.2 Present-day mean annual temperature (left) and mean annual total precipitation (right) estimated for the period 1960–2015. The data are based on station records from the European Climate Assessment & Dataset (ECA&D) project with methodology as per Benestad et al. (2016).
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Figure 2.3 Distribution of forest types and ecozones in the Barents area.
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Figure 2.4 Distribution of permafrost in the circumpolar north (http://maps.grida.no/arctic/).
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Figure 2.5 Mosaic of a Palsa mire (left), demonstrating a collapsing pals (right).
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Figure 2.6 Temporal patterns in ocean temperature, sea-ice extent, zooplankton biomass and fish biomass in the Barents Sea. Ocean temperature (50–200 m in the Atlantic Water in the Vardø-North section in August–September), September sea-ice extent, August–September zooplankton biomass (wet weight), and August–September pelagic fish biomass (capelin, polar cod, herring) and demersal fish biomass (cod, haddock). 

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Figure 2.8 Total catches of the most important fish stocks in the Barents Sea since the mid-1960s. The data include catches in all ICES areas: I, IIb and IIa (i.e. along the Norwegian seas and the Norwegian coast south to 62°N). Redfish refers to Sebastes mentella (ICES, 2014a). 

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Figure 2.11 Overview of industries in the Barents area (based on ÅF-Infraplan, 2005).
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Figure 2.12 Main indices of economic and social condition in Nordic (upper) and Russian (lower) regions of the Barents area in 2012.
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Figure 2.13 At-sea transport routes, major ports and land infrastructure (Nordic Council of Ministers, 2011) (map layout: José Sterling and Julien Grunfelder, Nordregio).
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Figure 2.14 Mineral mines and deposits in northern Fennoscandia (Nordic Council of Ministers, 2011; map layout: Johanna Roto, Nordregio).
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Figure 2.15 Industrial ports and shipped goods in the Barents area (ÅF-Infraplan, 2005).
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Figure 2.16 National jurisdictions in the Barents Sea, and the corresponding ICES fishery management areas (Norwegian Mapping Authority).
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Figure 4.1 Comparison of observed temperature and computed winter-mean temperature for the period 1900–2100 at Svalbard airport for the RCP2.6, RCP4.5, and RCP8.5 emission scenarios (Benestad et al., 2016).
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Figure 4.2 Projected change in a typical warm winter mean temperature (DJF; left) and annual total precipitation (right) for a typical wet year between 2015 and 2080 based on empirical-statistical downscaling of the 95-percentile of CMIP5 ensemble following the RCP4.5 emissions scenario. The results are based on Benestad et al. (2016).
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Figure 4.3 Projected change in seasonal temperature across the Barents and Scandinavian region using the regional climate model COSMO-CLM driven by the global MPI-ESM-LR Earth System Model under three emission scenarios. RCP2.6 (left), RCP4.5 (middle) and RCP8.5 (right) for the period 2071–2100 relative to 1971–2000. The dark and light blue lines indicate the northern extent of an ice-free sea for at least 20% of the time in the future and historical period, respectively. The biggest temperature changes occur between the two lines, i.e. where the sea ice is retreating regularly. Numbers at the lower right of each plot give the mean climate change signal over the domain shown (Dobler et al., 2016).

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Figure 4.4 Calculated seasonal precipitation sensitivity over the Barents and Scandinavian region using the regional climate model COSMO-CLM driven by the global MPI-ESM-LR Earth System Model. Absolute (left) and relative (right) sensitivities following the RCP8.5 scenario for the period 2071–2100 relative to 1971–2000. Numbers at the lower right of each plot give the mean precipitation sensitivity over the domain shown (Dobler et al., 2016).

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Figure 4.5 Avalanche in Longyearbyen, Svalbard on 19 December 2015.
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Figure 4.6 Development of the synoptic storm on 18 and 19 December 2015 responsible for two fatalities and considerable damage to buildings near Longyearbyen, Svalbard. The plots show archived forecasts from HIRLAM8 (Met Norway). Contours show mean sea-level pressure, green shading indicates wind gusts, and blue areas show the regions in which precipitation fell for a period of 24 hours.

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Figure 4.7 Projected seasonal change in daily maximum 10-m wind speed over the Barents and Scandinavian region using the regional climate model COSMO-CLM driven by the global MPI-ESM-LR earth system model under the RCP8.5 scenario for the period 2071–2100 against a 1971–2000 baseline. The green lines indicate the northern extent of an area that is ice-free sea for at least 20% of the time in the future (dark green) and historical period (light green) (Dobler et al., 2016).

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Figure 4.8 Polar low off the Finnmark coast, 23 March 2011 (MET Norway / NOAA).
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Figure 4.9 Seasonal distribution of polar lows recorded at MET Norway for the first two decades of the 21st century (note different scales on axes) (MET Norway).

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Figure 4.10 Kernel density estimates for two equally sized intervals of the data set. (Kvammen, 2014; Rojo et al., 2015).

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Figure 4.11 Black carbon emissions in the Barents area (excluding wildfires). Graphic based on the global emission dataset (IIASA-GAINS, ECLIPSE v5) used in the most recent AMAP assessment on black carbon and tropospheric ozone (AMAP 2015a,b) and the ECLIPSE project (Stohl et al., 2015; Klimont et al., 2017).
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Figure 4.12 Change in sea surface temperature in March for downscaled GISS (left), NCAR (middle) and NorESM (right). The left and middle plots are from Sandø et al. (2014a) and show change between present (1981–2000, data from the 20C3M control run) and future (2046–2065, A1B scenario). The right plots shows change between 2010–2019 and 2060–2069 using the RCP4.5 scenario (Bentsen et al., 2013).

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Figure 4.13 Change in sea ice concentration and thickness in March. Downscaled GISS sea ice concentration (upper left), NCAR sea ice concentration (upper right), NorESM sea ice concentration (lower left), and NorESM sea ice thickness (lower right). The upper plots are from Sandø et al. (2014a) and show change between present (1981–2000, data from the 20C3M control run) and future (2046–2065, A1B scenario). In the lower plots, change is between 2010–2019 and 2060–2069 using the RCP4.5 scenario (Bentsen et al., 2013).

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Figure 4.14 Change in salinity (left) and stratification (right) in the upper 50 m in March based on downscaled NorESM data. Present (2010-2019) and future (2060-2069) using the RCP4.5 scenario.
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Figure 4.15 Time series of ground temperature at depths of 10 to 20 m below the surface at selected measurement sites that fall roughly within the continuous to discontinuous and warm permafrost zones in the Barents area, including Scandinavia, Svalbard, and Russia. Data updated from Christiansen et al. (2010) and Romanovsky et al. (2014, 2015).

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Figure 4.16 Distribution and recent change in thickness for glaciers in the Barents-Kara Sea region. Colors indicate rate of change in thickness based on ICESat laser altimetry for the period 2003–2009. Glaciers on the Eurasian continent are shown in black, with exaggerated extent for better visibility.

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Figure 4.17 Key global drivers in the ~1200 scenarios assessed during the latest IPCC assessment. To enhance clarity, only the median is shown within each scenario category. The dotted lines at 2030 and 2080 are to help highlight the increase in uncertainty with time horizon. The number of scenarios used to generate each plot is shown at the bottom right. Based on the IPCC scenario database (Krey et al., 2014).

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Figure 4.18 Example of positive feedbacks in the ‘environment-socio-economic’ system with possible intervention via adaptation actions. Human actions (green and yellow boxes), environmental responses (blue boxes).
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Figure 5.1 The SSPs were developed to cover different challenges in relation to adaptation and mitigation (based on O’Neill et al., 2017).
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Figure 5.2 Methodology for producing an extended Shared Socioeconomic Pathway (SSP).
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Figure 5.3 Locations of scenario workshops conducted for the Barents area pilot studies.
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Figure 5.4 Pajala workshop: results of brainstorming and ‘voting’ in response to the question: What future changes may influence this region economically, environmentally and socially within one to two generations? (Nilsson et al., 2015).
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Figure 5.5 Kirovsk workshop: results of brainstorming and ‘voting’ in response to the question: What future changes may influence this region economically, environmentally and socially within one to two generations? (van Oort et al., 2015).
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Figure 5.6 Bodø workshop: results of brainstorming and ‘voting’ in response to the question: What future changes may influence this region economically, environmentally and socially within one to two generations? (van Oort et al., 2015).
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Figure 5.7 Gávnnadeapmi workshop: results of brainstorming and ‘voting’ in response to the question: What future changes may influence this region economically, environmentally and socially within one to two generations?
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Figure 6.1 Important elements in the Arctic terrestrial and freshwater ecosystems (Bob van Oort, Cicero).
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Figure 6.2 Comparison of the abundance of fish communities in the Barents Sea between 2004 and 2012 (Fossheim et al., 2015).
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Figure 6.3 Distribution of benthic biomass in the Barents Sea for three survey periods (after Brotskaya and Zenkevich, 1939; Antipova, 1975; Kiyko and Pogrebov, 1997) (Institute of Marine Research).
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Figure 6.4. Nests occupied by glaucous gull at Bjørnøya between 1987 and 2010 at a study plot. No monitoring was undertaken in 1989, 1990, 1994, 1996 and 1997. Open symbols indicate explorations based on the PROC EXPAND procedure which is a tool to work with time series. Erikstad et al. (2013).
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Figure 6.5 Overview of the major regional pressures, human activities, and state of the ecosystem within the Barents Sea. Line width indicates the relative importance of individual links (ICES, 2015b).
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Figure 6.6 Impact of bottom trawling on the benthic community in the Barents Sea: before trawling (upper) and after trawling (lower) (photos Mareano/Institute of Marine Research, Norway).
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Figure 6.7 Sources of impact and biological responses in marine ecosystems associated with the offshore oil and gas sector.
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Figure 6.8 Ten areas of heightened ecological significance in the Barents Sea LME (AMAP/CAFF/SDWG, 2013).
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Figure 6.9 The ballast water cycle (International Maritime Organization).
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Figure 6.10 Integrated growth of Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula spp.) under the current climate and under projected future climates in Finland. From left to right: total current growth and percentage change in total forest growth for 1991–2020, 2021–2050 and 2070–2099. The numbers on the maps refer to the Finnish Forest Centres. Kellomäki et al. (2005).
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Figure 6.11 Production of farmed salmonids in Norway (Statistics Norway).
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Figure 6.12 Numbers of cruise trips and tourists visiting the national park ‘Russian Arctic’ (Russkaya Arktika) between 2012 and 2016 (Russkaya Arktika).
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Figure 6.13 Oil supply in western Arctic Russia and Arctic Norway for a high and low oil price scenario. The high (low) oil price scenario assumes a cost per barrel of oil equivalent of USD 70 (58) by 2020 rising to USD 126 (80) by 2030 and then remaining unchanged (Lindholt and Glomsrød, 2013).
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Figure 6.14 Gas supply in western Arctic Russia and Arctic Norway (Lindholt and Glomsrød, 2013).
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Figure 6.15 Observed daily discharge in River Seurujoki in Finnish Lapland for the period 1971–2000 and as projected over the short term (2010–2039) and long term (2040–2069). The data show maximum, average and minimum discharge (based on unpublished SYKE data). The inset shows the position of the Kittilä mine – the treated waste water from the Kittilä gold mine is discharged into the River Seurujoki.
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Figure 6.16 Fastest available September trans-Arctic routes from future climate projections. Routes for low emissions (equivalent to achieving the Paris Agreement; RCP2.6, upper row) and high emissions (business-as-usual, RCP8.5; lower row) split into three periods (early-, mid- and late-century), each containing 15 consecutive Septembers. Line weights indicate the relative number of transits using the same route. Faint dashed grey lines indicate the Exclusive Economic Zones (Melia et al., 2016).
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Figure 7.1 Infrastructure in Finnmark, Norway: present (left) and planned (right) (Wilbert van Rooij / Plansup).
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Figure 7.2 Projected infrastructure development in the Nenets Autonomous Okrug, Russia (Wilbert van Rooij / Plansup).
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Figure 7.3 Annual and seasonal mean air temperature in coastal Finnmark, Norway (Nordreisa), inland Finnmark, Norway (Karasjok) and the Yamal Nenets AO, Russia (Salekhard). Solid lines show the observed 1961–1990 mean. Dotted lines show the mean for 50 downscaled climate models for 2085 (based on Magga et al., 2011).

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Figure 7.4 Trend in mean species abundance (MSA) in the Arctic for the baseline scenario of the Rethinking Study (based on data from the Netherlands Environmental Assessment Agency, 2010).

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Figure 7.5 Mean species abundance (MSA) in Finnmark from GLOBIO3 showing present-day (2011; left) and future (2030; right) impacts on biodiversity of land use, infrastructure development, land fragmentation and climate change (Wilbert van Rooij / Plansup).
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Figure 7.6 Mean species abundance (MSA) in Finnmark from GLOBIO3 showing the combined impacts of land use, infrastructure development, land fragmentation and climate change on current (2011) and future (2030) biodiversity (Wilbert van Rooij / Plansup).
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Figure 7.7 Total mean species abundance (MSA) in Finnmark from GLOBIO3 under current (2011) and future (2030) conditions, overlain by seasonal reindeer migration routes, with insets showing the reindeer herding districts Fálá,Fiettar and Gearretnjárga and their calving grounds (Levi Westerveld, GRID-Arendal).
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Figure 7.8 Mean species abundance (MSA) across the Nenets AO in 2009 (upper). Local disturbance is centered around the city of Naryan Mar, and the surrounding extensive oil fields to the north and south. The lower plot shows MSA across the Nenets AO for the future infrastructure scenario. (Wilbert van Rooij / Plansup).
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Figure 8.1 Conceptual model of a social-ecological system (Carson and Sommerkorn et al., 2016).
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Figure 8.2 Co-evolution of social and ecological systems (Carson and Sommerkorn et al., 2016).
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Figure 8.3 Five key ‘ingredients’ of social-ecological resilience. The two in the upper tier are cross-cutting and contribute resilience to each of those in the lower tier.
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Figure 9.1 Interacting factors and cumulative effects on society from biophysical and socio-economic impacts. Interacting socio-economic drivers of change (orange boxes), effects of climate change impacts on natural systems (white boxes) and the cumulative impacts on a selection of key industries/sectors (blue boxes). This graphic shows examples of the different impacts and drivers of change and is by no means exhaustive. The purpose of the graphic is to illustrate the complexities, and interactions of cumulative effects (Halvor Dannevig).
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