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Figure 10.15 Annual precipitation anomalies averaged over land areas for Arctic latitudes from five global precipitation data sets relative to a 1981–2000 climatology (after Hartmann et al., 2013). GPCP (Global Precipitation Climatology Project), GPCC (Global Precipitation Climatology Centre), GHCN (Global Historical Climatology Network) and CRU (Climatic Research Unit).
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Figure 10.14 Detection of permafrost thaw subsidence and thermokarst initiation in burned tundra using multi-temporal Light Detection and Ranging (LiDAR) remote sensing (Jones et al., 2015). (a) Quickbird image from 5 July 2008, the year following fire, showing part of the northern extent of the burn area and the distinction between burned (dark) and unburned (light) tundra. Shaded contour images of the (b) 2009 and (c) 2014 1-m resolution LiDAR digital terrain models (DTMs) showing ice wedge degradation in the burn area. (d) The change in terrain created by subtracting the 2009 DTM from the 2014 DTM.
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Figure 10.13 Thermokarst lake disturbance and recovery cycle in northeastern Siberian yedoma soils. Soil profile from northeastern Siberia, termed yedoma, with massive Pleistocene-aged ice wedges (upper). Thermokarst-lake expansion (thaw bulb shown in yellow; thaw boundary shown as a dotted line) accompanied by Pleistocene-aged methane (CH4) emissions and release of nitrogen and phosphorus (N+P) from yedoma into lakes, stimulating aquatic productivity and carbon dioxide (CO2) uptake, which offset Pleistocene-aged CO2 emissions from yedoma decay (upper middle). Partially drained lake with atmospheric CO2 uptake by plants forming thick Holocene-aged organic carbon deposits (brown) exceeding CH4 emissions from contemporary organic matter decay (lower middle). Refreezing of remaining Pleistocene and Holocene carbon in sediments following complete lake drainage (new ice wedges are shown as triangles), with peatland-type CH4 emissions and CO2 uptake (lower). The thicknesses of CH4 and CO2 arrows are scaled by relative magnitude on a carbon-mass basis. From Walter Anthony et al. (2014).
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Figure 10.12 Proxy records of ecosystem change, highlighting millennial patterns of the past 10,000 years. Plots indicate: millennial composite records of charcoal accumulation rate and fire frequency from sediment charcoal analysis (with 90% confidence intervals); number of records contributing to composite charcoal records; pollen abundance of key conifer species black and white spruce (Picea mariana and P. glauca) and other major taxa at Screaming Lynx Lake, Alaska. Diamond symbols on the P. mariana curve indicate sampling resolution for all pollen data. Figure from Kelly et al. (2013).
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Figure 10.11 Soil organic layer dynamics across fire and successional cycles in taiga forest in interior Alaska. Adapted from Johnstone et al. (2010). NPP: net primary productivity.
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Figure 10.9 False color-infrared satellite image near the Chandler River in Alaska showing tall shrubs (dark red) distributed along streams and sedimentary rock outcrops (left) and a supervised classification of the image, showing expanding alder patches (green) in the centers of the water tracks, stable alder patches on hill slopes (red), and short- or non-shrub tundra elsewhere (white) (right). Each image is 5.5-km wide, with a center point at 68.917°N, 151.765°E; north is up (Tape et al., 2012).
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Figure 10.10 Hypothesized indirect radiative forcing of climate change through the effects of increased fire disturbance on the structure and function of Arctic ecosystems.
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Figure 10.8 Trends in spring sea-ice extent within 100 km of the coast, Summer Warmth Index (SWI), and MaxNDVI for the period 1982–2015, for four areas exhibiting large and contrasting changes in sea-ice extent: East Bering Sea, Beaufort Sea, Baffin Bay and southern Barents Sea (Bhatt et al., 2010 updated to 2015). Sectoral divisions in the Arctic Ocean are according to the Russian Arctic Atlas (Treshnikov, 1985) and the adjacent land areas according the phytogeographic subdivisions (Yurtsev, 1994).
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Figure 10.7 Change over time in annual maximum NDVI (MaxNDVI; left) and time-integrated NDVI (TI-NDVI; right) for the North American Arctic, Eurasian Arctic and Arctic as a whole (1982–2015) (Bhatt et al., 2010, updated to 2015).
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Figure 10.6 Change in annual maximum NDVI (MaxNDVI; left) and time-integrated NDVI (TI-NDVI; right) between 1982 and 2015 (Bhatt et al., 2010, updated to 2015). Both graphics also show change in spring sea-ice concentration. Change in NDVI is determined from the Global Inventory Modeling and Mapping Studies datasets (Pinzon and Tucker, 2014) and change in sea ice from Special Sensor Microwave Imager sea-ice concentrations (Comiso and Nishio, 2008).
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Figure 10.5 NDVI from the Global Inventory Modelling and Mapping Studies (GIMMS3g, Pinzon and Tucker 2014) and related above-ground plant biomass in the Arctic in 2010. The relationship between plant biomass and NDVI, shown in the graph in the upper right, was developed from field sampling of zonal sites in North America and Eurasia (Raynolds et al., 2012).
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Figure 10.4 Typical top-of-atmosphere reflectance of soil, vegetation and water. The spectrum is divided to show the wavelengths used to calculate NDVI. The colored bars show the band-widths measured by the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites.
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Figure 1.1 The Arctic, as defined by AMAP and as used in this assessment.
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Figure 1.2 Schematic representation of major observed changes in the Arctic caused by global warming (graphic adapted from an image created by the U.S. National Center for Atmospheric Research).

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Figure 1.3 Five-tier lexicon describing the likelihood of expected change.
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Figure 1.4 Air temperature anomalies October through December 2016 relative to the 1981–2010 baseline.

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Figure 1.5 Sea-ice extent in November 2016. Red lines show the median climatological ice edge position for the period 1981–2010. Note, in particular the November 2016 sea ice-free regions in the Chukchi Sea to the northwest of Alaska and in the Barents and Kara Seas to the north of western Russia.
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Figure 10.1 Arctic climate system elements involved in abrupt changes and thus representing potential sources of abrupt climate change associated with threshold exceedances (Lenton, 2012). Boreal forest regions have an increased risk of wildfire.
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Figure 10.2 Proximity of different threshold exceedances that may alter the state of the Arctic system (Lenton, 2012). Colors represent the likelihood of a regime shift as a function of the increase in global temperature above the 1980–1999 mean. The transition from white to yellow indicates that the likelihood becomes non-zero, while the transition from yellow to red indicates that a regime shift is as likely as not. Dark red indicates that a regime shift is more likely than not.
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Figure 10.3 A hierarchical framework for examining vegetation greening in relation to snow, water, ice and permafrost. Reading downwards within a hierarchical level gives an impression of the main snow, water, ice, and permafrost factors affecting tundra greening patterns at each scale. The horizontal arrows below the graphic show scales for some of the common monitoring tools used to observe patterns at each scale.
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Figure 10.16 Sea-land moisture exchange within the Arctic and between the Arctic and lower latitudes (Bintanja and Selten, 2014). Blue arrows denote future changes in Arctic moisture exchanges, with arrow size loosely depicting the magnitude of change. The red text and red arrows represent the associated climatic consequences.
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Figure 10.17 Projected changes in precipitation (50th percentile), relative to 1986–2005, for October–March under the RCP4.5 scenario (left panels) and the RCP8.5 scenario (right panels). Upper panels are for the 2050s and lower panels are for the 2080s (G. Flato, Environment Canada).
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Figure 10.18 Projected changes in precipitation (50th percentile), relative to 1986–2005, for April–September under the RCP4.5 scenario (left panels) and the RCP8.5 scenario (right panels). Upper panels are for the 2050s and lower panels are for the 2080s (G. Flato, Environment Canada).
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Figure 11.1 Conceptualized view of the interactions between the three main system elements of the Arctic cryosphere (climate, carbon, freshwater) set within the human dimension, and their emerging links with the mid-latitudes. 

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Figure 11.2 Relative change in multiple Arctic cryospheric indicators since 1970.

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Figure 11.3 Projected change in permafrost area under different greenhouse gas scenarios; the number of models contributing to each RCP estimate is given in brackets, the shaded area is one standard deviation across the models, and the dashed black line represents the present-day total permafrost area (Slater and Lawrence, 2013).
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Figure 11.4 Projected land ice loss (expressed in millimeters sea level equivalence; mmSLE) from different Arctic regions in 2030 and 2080 under the RCP4.5 and RCP8.5 scenarios, based on the 2006–2100 sea level budget described in Section 9.4. The relatively small mainland Eurasian glaciers do not appear here owing to incomplete assessment.
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Figure 2.1 Spatial pattern of Arctic warming for the period 1961–2014 in the cold season (Nov–Apr) and warm season (May–Oct) (NASA GISTEMP http://data.giss.nasa.gov/gistemp/maps/).
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Figure 2.2 Arctic (60-90°N) and global annual surface air temperature anomalies relative to the 1981-2010 mean value (revised from NOAA, 2015).
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Figure 2.3 Temperature anomalies for January–February 2016 relative to the 1981–2010 mean value (left) and meridional wind components (positive northward) for the same months (right) (NOAA/ESRL).
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Figure 2.4 Attribution graphic showing impacts of greenhouse gases (GHG), natural variability (NAT) and other drivers (OTH) on total Arctic temperature change (ALL) during three consecutive periods of the 20th century. NAT is taken in this study to include El Niño/Southern Oscillation effects, volcanic effects and dynamically-induced atmospheric variability such as the cold ocean-warm land. Results in black are from a subset of CMIP5 models found to be the ‘Best 6’; gray bars denote across-model ranges. Red boxes are based on observational data (HadCRUT4 = HadCRUT4 temperatures), with uncertainty ranges indicated by box width (Fyfe et al., 2013).
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Figure 2.5 Decline in multi-year sea ice since the start of the 21st century, images relate to 1 January for each year shown (R. Kwok, NASA/JPL).
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Figure 2.6 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 2.7 Vegetation greenness (NDVI; normalized difference vegetation index) trend derived from satellite data for the period 1982–2011 for Eurasia (upper) and North America (lower) (Xu et al., 2013; NASA Earth Observatory).
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Figure 2.8 Cumulative net area change at the 45 widest and fastest-flowing marine-terminating glaciers of the Greenland Ice Sheet (after Box and Decker, 2011).
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Figure 2.9 Decadal distributions of record-high daily maximum temperatures (left) and record-low daily minimum temperatures (right) for the period 1949–2012 at five first-order weather stations in Alaska. The bars for 2000–2012 have been adjusted to represent the number of events over 10 years (Bieniek and Walsh, 2017).
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Figure 2.10 Contributions of various feedbacks to total Arctic warming in CMIP5 global climate models (Pithan and Mauritsen, 2014).
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Figure 2.11 Schematic representation of the linkage between Arctic warming and extreme weather in mid-latitudes. The dashed link represents the most uncertain part of the chain (Overland et al., 2015).
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Figure 2.12 Total radiative forcing as a function of time under the RCP scenarios through 2300 (negative lines represent anthropogenic aerosol forcing) and the SRES scenarios through 2100 (left), and the contributions of individual forcings to the total forcing at present and under the RCP scenarios (right) (Collins et al., 2013).
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Figure 2.13 Projected changes in near-surface temperature (50th percentile), relative to 1986–2005, for December–February under the RCP4.5 scenario (left panels) and the RCP8.5 scenario (right panels). Upper panels are for the decade of the 2050s, lower panels are for the 2080s (graphic courtesy of G. Flato, Environment and Climate Change Canada).
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Figure 2.14 Projected changes in near-surface temperature (50th percentile), relative to 1986–2005, for June–August under the RCP4.5 scenario (left panels) and the RCP8.5 scenario (right panels). Upper panels are for the decade of the 2050s, lower panels are for the 2080s (graphic courtesy of G. Flato, Environment and Climate Change Canada).
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Figure 2.15 Winter season future temperatures for the northern hemisphere (0–90°N) and Arctic (60–90°N) averaged over 36 CMIP5 global climate models and expressed as departures from the means for the 1981–2005 period. The red line is the ensemble mean for RCP8.5, the blue line is for RCP4.5. Shaded areas denote ± one standard deviation from the ensemble mean (Overland et al., 2014).
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Figure 2.16 Projections of global mean sea level rise relative to the 1986–2005 mean sea level, based on forcing from the CMIP5 models. Medians (solid lines) and ranges (shading) are shown for RCP2.6 and RCP8.5. End-of-century median values and ranges for all RCP scenarios are shown to the right (IPCC, 2013).
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Figure 2.17 Key processes within the Arctic climate system.
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Figure 3.1 Mean annual snow cover duration over Arctic land areas from the NOAA IMS-24 daily snow cover analysis for the snow seasons 1998/99 to 2013/14.
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Figure 3.2 Schematic illustration of some of the important drivers influencing Arctic snow cover.

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Figure 3.3 Seasonal nature of recent warming of Arctic surface air temperatures (difference between 2006–2015 average and 1951–2010 average) from two datasets, CRUTEM4 (Jones et al., 2012) and NOAA (Karl et al., 2015).

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Figure 3.4 Monthly Arctic (land areas >60°N) snow cover extent (SCE) standardized (and thus unitless) anomaly time series for 1967–2016 (with respect to the 1981–2010 mean and standard deviation) from the NOAA CDR product (Estilow et al., 2015) for April, May and June. Solid lines depict 5-year running means.

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Figure 3.5 Trend in snow cover duration (SCD) in the latter half (Feb–Jul) of the snow season from the NOAA CDR dataset (Estilow et al., 2015) over snow seasons 1972/73 to 2015/16.
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Figure 3.6 The upper panels show trends in snow-cover duration (SCD) in the snow-onset (autumn SCD, Aug–Jan) and melt periods (spring SCD, Feb–Jul) and annual SCD over snow seasons 1979/80 to 2009/10 from the Liston and Hiemstra (2011) snow cover reconstruction. Grid points with statistically significant (0.05 level) local trends for decreasing and increasing SCD are shown in the lower panels.

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Figure 3.7 Linear trend estimates in time series of the number of days with snow covering more than 50% of the area around a meteorological station for the period 1966–2014. The inset panel shows climate regions where the spatially-averaged trends (% per decade) were statistically significant at the 0.05 level.

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Figure 3.8 Regionally-averaged variability in annual snow-cover duration (SCD) anomalies (with respect to the 1981–2010 average) over the period 1966–2014 from daily snow depth observations at Russian stations located north of 60°N. Daily snow depths were interpolated to a 200-km grid and the grid values of SCD (days with snow depth >2 cm) used to compute the regional average. The red line is the result of applying a centered 9-term binomial filter. The trend over the period (-3.2 days per decade) is statistically significant at the 0.05 level. 

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Figure 3.9 Regionally-averaged annual snow-cover duration (SCD) series over the North American Arctic north of 66°N (anomalies with respect to a 1971–2000 reference period) from 20 climate stations in the Global Historical Climate Network archive with ≥40 years of daily snow depth data in the 1950/51–2013/14 snow seasons. A 1971–2000 reference period was used to maximize the number of stations in the regional average. The trend over the period (-4.4 days per decade) is statistically significant at the 0.05 level. 

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Figure 3.10 Linear trend estimates (1966–2014) in the time series of maximum winter snow depth (SDmax) at meteorological stations. The inset panel shows climate regions where the spatially-averaged trends (%/decade) were statistically significant at the 0.05 level.
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Figure 3.11 Historical variability in regionally-averaged annual maximum snow depth (SDmax) anomalies (with respect to the 1981–2010 average) for Russian stations in the Arctic following the regional averaging method described in Figure 3.8. The red line is the result of applying a centered 9-term binomial filter. The linear trend over the period is not statistically significant.
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Figure 3.12 Linear trend estimates (1966–2014) in time series of annual maximum snow water equivalent (SWEmax) at meteorological stations in field and forest. The inset panel shows climate regions where the spatially-averaged trends (%/decade) were statistically significant at the 0.05 level. There were no climate regions with statistically significant trends for the forest SWE time series.
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Figure 3.13 Historical variability in regionally-averaged annual maximum snow water equivalent anomalies (SWEmax) (Jan–May period; with respect to the 1981–2010 average) over all snow surveys in the Russian Arctic with at least 40 years of data in the period 1967–2011 following the regional averaging method described in Figure 3.8. The annual SWEmax series is significantly correlated (r=0.80) with the annual maximum snow depth series shown in Figure 3.11. The red line is the result of applying a centered 9-term binomial filter. There is no trend over the period of data shown.
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Figure 3.14 Estimated pan-Arctic trend in snow depth (Jan-Mar) over the period 1948–2006 (Park et al., 2012).
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Figure 3.15 Annual variation and trend in maximum values of Arctic land area snow water storage during March from the GlobSnow V2 dataset. March corresponds approximately to the time of annual maximum snow water equivalent (SWEmax) over non-mountainous Arctic land areas. The trend (-4.2% per decade with respect to the 1981-2010 average) is statistically significant.
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Figure 3.16 Estimated trend in Arctic annual SWEmax over the period 1981–2010 from six datasets (Greenland excluded). The trend in the land area averaged SWEmax series for 60–70°N is shown in each panel. The Liston and Hiemstra trend is computed for the period 1981–2009. Descriptions of the datasets used are provided by Mudryk et al. (2015).
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Figure 3.17 Projected relative change (%) in mean annual maximum monthly snow water equivalent (SWEmax) for RCP4.5 and RCP8.5 from 16 CMIP5 models with respect to 1986–2005. Results are shown for the 25th, 50th and 75th percentile for three 20-year time slices centered on 2025, 2055 and 2090. 

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Figure 3.18 Projected relative percentage change in mean annual snow cover duration (SCD) for RCP4.5 and RCP8.5 from 16 CMIP5 models with respect to 1986–2005. Results are shown for the 25th, 50th and 75th percentile for three 20-year time slices centered on 2025, 2055 and 2090. 

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Figure 3.19 Projected relative change in annual snow cover duration (SCD) over Arctic non-glacier land areas under the RCP4.5 and RCP8.5 emission scenarios with respect to the period 1986–2005. The plots show 25th, 50th and 75th percentiles of 16 CMIP5 model runs. 

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Figure 3.20 Projected relative change in monthly snow water equivalent (SWE) in 2090 over Arctic non-glacier land areas under the RCP4.5 and RCP8.5 emission scenarios with respect to the period 1986–2005. The plots show 25th, 50th and 75th percentiles of 16 CMIP5 model runs. 

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Figure 3.21 Expected year of emergence of climate warming signals in northern hemisphere snow cover in each half of the snow season and for annual maximum snow water equivalent (SWEmax) following de Elía et al. (2013) from an 11-member ensemble of CMIP5 models for emission scenario RCP8.5.
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Figure 3.22 50th percentiles of projected relative change in regionally-averaged snow cover duration (SCD) over non-glacier land areas in the Baffin Bay-Davis Strait region in each half of the snow season under the RCP4.5 and RCP8.5 emission scenarios with respect to the period 1986–2005.
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Figure 4.1 Location of the permafrost monitoring sites shown in Figures 4.2 and 4.3 superimposed on average air temperature anomalies for the period 2000–2014 (relative to the 1971–2000 mean) from the NCEP-reanalysis (Kalnay et al., 1996). Following the subdivisions (regions) presented in Figure 4.2, the sites and their abbreviations are listed from coldest to warmest. Climate data provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado (www.esrl.noaa.gov/psd/).
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Figure 4.2 Time series of annual mean ground temperature at depths of 9 to 26 m below the surface at selected measurement sites (see Figure 4.1 for locations) that fall roughly into the AACA priority regions: (a) cold continuous permafrost of northwestern North America (Beaufort-Chukchi region); (b) discontinuous permafrost in Alaska and northwestern Canada; (c) cold continuous permafrost of eastern and High Arctic Canada (Baffin Davis Strait); and (d) continuous to discontinuous permafrost in Scandinavia, Svalbard, Russia/Siberia (Barents region). Temperatures are measured at or near the level of zero annual amplitude. Data series are updated from Christiansen et al. (2010), Romanovsky et al. (2014, 2015) and Ednie and Smith (2015).
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Figure 4.3 Observed linear trends in ground temperature between 2000 and 2014 as a function of depth for the three Nordic key-monitoring sites at Svalbard (Janssonhaugen) and in Scandinavia (Tarfalaryggen and Juvvasshøe), the Canadian sites Alert BH2 and Norman Wells, and the Alaskan sites Deadhorse, Old Man, and Birch Lake. See Figure 4.1 for average air temperature anomalies at these sites for 2000–2014.
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Figure 4.4. Mean annual temperatures of air, ground surface, permafrost surface, and permafrost at 20 m depth recorded at the Deadhorse permafrost observatory in the period 1987–2014 on the North Slope of Alaska.
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Figure 4.5. Dates of complete freeze-up of the active layer at West Dock, Deadhorse, and Franklin Bluffs on the North Slope of Alaska. See Figure 4.1 for locations.
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Figure 4.6. Long-term change in active-layer thickness in six Arctic regions as observed during the Circumpolar Active Layer Monitoring project. Data are presented as annual percentage deviations from the mean value for the period of observation. Thaw depth observations from the end of the thawing season were used. Only sites with at least 10 years of continuous thaw depth observations are shown (Romanovsky et al., 2015).
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Figure 4.7 Simulated changes in permafrost area (for ALT <3m) between 1960 and 2009 and the sensitivity of simulated changes in permafrost area to changes in atmospheric carbon dioxide, temperature, and precipitation between 1960 and 2009. See Table 4.1 for model details. (McGuire et al., 2016).
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Figure 4.8 Model predictions of initial active-layer thickness (ALT) plotted as functions of mean annual air temperature (MAAT) and the amplitude of the seasonal cycle in surface air temperature, following Koven et al. (2013). Also shown are observations of ALT from the Circumpolar Active Layer Monitoring (CALM) project dataset. See Table 4.1 for model details (McGuire et al., 2016).
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Figure 4.9 Projected northern hemisphere permafrost area from Slater and Lawrence (2013; left) and Arzhanov et al. (2013; right).
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Figure 4.10 Number of climate trajectories with sustainable permafrost in 2099 (Slater and Lawrence, 2013). Numbers in brackets represent the number of different models that were included in the analysis. Present-day continuous and discontinuous permafrost is shown by gray shading.
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Figure 4.11 Mean annual ground temperature at 1-km spatial resolution simulated by the semi-empirical permafrost model CryoGrid 1 driven by remotely sensed and reanalysis data over the period 2003–2012 (Westermann et al., 2015b).
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Figure 4.12 Soil texture categories (upper) and ecotype groups (lower) for northern Alaska. 51 different ecotype-soil texture combinations were used to parameterize ground thermal properties for the 770×770 m computational grid cells for use in model applications. The locations of the temperature monitoring sites by the University of Alaska Geophysical Institute and United States Geological Survey are marked in the upper plot and the locations of the Circumpolar Active Layer Monitoring (CALM) sites are marked in the lower plot (Nicolsky et al., 2017).
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Figure 4.13 A 770×770 m resolution map of the simulated mean active-layer thickness for the Alaska North Slope region for the period 2000–2010 under the RCP4.5 emission scenario. The model was verified using measured ALT at Circumpolar Active Layer Monitoring (CALM) sites (Nicolsky et al., 2017).
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Figure 4.14 Percentage of total ground temperature variability attributed to air temperature (upper) and snow depth (lower) (Anisimov and Sherstukov, 2016).
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Figure 4.15 Departures of the CMIP5 GCM-based estimates from observations averaged over the Eurasian permafrost region (Anisimov and Kokorev, 2013). The upper bars in the left panel extend beyond the x-axis limits, and the numbers show the actual values of departures for these models.
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Figure 4.16 Modeled locations of open taliks in the Laptev Sea Shelf in the case of (left) ‘100-year mean’ and (right) ‘1999–2009 mean’ parameterization of benthic temperature (Nicolsky et al., 2012).
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Figure 4.17 Modeled permafrost dynamics over geological time scales (Anisimov et al., 2012a). Blue curves show modeled changes in sediment temperature (vertical axis on right) over time at near-bottom level (T0), 200 and 1000 m below the sea floor (T200 and T1000, respectively). Red curves show depth (vertical axis on left) to upper (Zpf1) and lower (Zpf2) permafrost boundaries, and the upper (ZH1) and lower (ZH2) boundaries of the methane hydrate stability zone. Time (years) is shown on the horizontal axis in a logarithmic scale with respect to the current period (0).

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Figure 4.18 Thermokarst lakes (a), water-filled troughs above degrading ice wedge (b), and overgrowing of draining lake margins (c), Yamal, Russia (photos M.O.Leibman).
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Figure 4.19 Polygonal network thermal erosion (a), lateral and river thermal erosion (b), bead-shaped stream (c), Yamal, Russia (photos M.O. Leibman).
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Figure 4.20 Thermal abrasion (a), thermal denudation (b) and thermo-erosional gully (c) at the Kara Sea coast, Yugorsky Peninsula, Russia (photos M.O.Leibman).
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Figure 4.21 Solifluction lobes, northern Yamal, Russia (a) (photo from helicopter, M.O. Leibman), active-layer detachments, central Yamal, Russia (b) (photo from helicopter, M.O. Leibman), retrogressive thaw slump, central Yamal, Russia (c) (photo A.A.Gubarkov).
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Figure 4.22 Modern frost-heave mounds in central Yamal, Russia (a) (photo M.O. Leibman), old degrading frost heave mound on the Tazovsky Peninsula, Russia (b) (photo O.E. Ponomareva), and a collapsed pingo on the Tazovsky Peninsula, Russia (c) (photo A.I. Kizyakov).
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Figure 4.23 High-resolution black and white Digital Globe™ satellite imagery (WorldView-1) showing the presence of an 8-m high pingo-like mound on 9 June 2013 (left) and the same location on 15 June 2014 revealing the recently discovered Siberian crater (right). The height of the mound prior to crater formation was estimated using shadow length and sun angle at the time the image was acquired. The red line shows the outline of the surrounding ‘parapet’. The yellow line delimits the area where the chunks of soil ejected from the crater were found (Kizyakov et al., 2015).
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Figure 4.24 Mean changes in bearing capacity of the frozen ground in the Russian permafrost region. Changes are expressed in percentages relative to the 1965-1975 reference period for the periods 1995–2005, 2015–2025 and 2045–2055. Climate forcing is based on an average of results from the six CMIP5 GCM models that most closely reproduce the historical air temperature record for the Russian Arctic (Anisimov and Kokorev, 2013).
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Figure 4.25 Progression of thermokarst from 1949 to 2013 along a section of the Prudhoe Bay Spine Road. The Spine Road was constructed in 1969 so it does not appear on the 1949 image. Thin cloud cover obscures the small lake in the upper left corner of the photo, but most of the thermokarst pits that were present in 1972 are also visible on the 1949 image. In 1972, gravel from road construction occurs on both sides of the road. In 1979, some roadside thermokarst is visible near the road on the north side (above the road in the photo). By 2010, extensive thermokarst is present on both sides of the road, but is most developed south of the road due to periodic flooding from a nearby lake. By 2013, most thermokarst on the south side of the road was connected to Lake Colleen by continuous channels of water in the polygon troughs (Walker et al., 2014).
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Figure 4.26 Landslides of the central Yamal Peninsula. (a and b) Examples of typical cryogenic landslides and thermocirques of the central Yamal Peninsula. (c) Yamal Peninsula and the location of the key site of landslide research at Vaskiny Dachi (Photos: Marina Leibman and Yuri Dvornikov).
(d) Satellite image of highly dissected landscapes caused by cryogenic landslides that are typical of this region. (Images c and d: Google Earth, Digital Globe).
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Figure 4.27 Comparison of overflow waters along the coastal plain portion of the Sagavanirktok River in April of a ‘normal’ year (2014) and during the flood year of 2015. Note the large amount of overflow in the vicinity of Franklin Bluffs in 2015, and in the Sagavanirktok River delta south of Deadhorse adjacent to the Dalton Highway. Images in both years are from Landsat-8. Maps by Lisa Wirth, Geographic Information Network of Alaska, UAF.
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Figure 4.28 Area of major washout during the height of the flood on 19 May 2015 (photo: KTUU News).
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Figure 4.29 Deadhorse vicinity at the northern end of the Dalton Highway during peak flood conditions on 19 May 2015. Looking north with the Deadhorse airstrip on the left (photo: KTUU News).
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Figure 4.30 Area of thermokarst collapse near Deadhorse Airport as the flood water recedes (upper) and after thermokarst collapse and the start of reconstruction on 11 June 2015 (lower). Courtesy of AKDOTandPF.
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Figure 4.31 Large area of massive thermokarst collapse along the Sagavanirktok River, just south of Deadhorse Camp. Looking north (upper). Looking south (lower). Collapse occurred over about 0.5 km along the river and within 20–30 m of the river (photos: D.A. Walker).
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Figure 5.1 March and September Arctic sea-ice extent from the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and linear trend lines (data source: NSIDC Sea Ice Index and OSI-SAF).

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Figure 5.2 Daily Arctic sea-ice extent for 2016–2017 and the record low year of 2012 compared to the 1981–2010 average and two standard deviation range, based on passive microwave data (data source: NSIDC Sea Ice Index).
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Figure 5.3 Linear trends in sea-ice extent (relative to the 1981-2010 average) for September 2016 and March 2017 . Based on the NASA Team algorithm and the NSIDC Sea Ice Index (Fetterer et al., 2016).

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Figure 5.4 ICESat and CryoSat-2 sea-ice volume for late winter (Feb/Mar) and early autumn (Oct/Nov) for two different density values (ρi) (Kwok and Cunningham, 2015).
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Figure 5.5 Uncertainty range for future sea-ice extent projections due to internal variability, model structure, and scenario uncertainty. Estimated from CMIP3 models based on the method of Hawkins and Sutton (2009) (Lique et al., 2016).

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Figure 5.6 Decadal change in snow depth in spring in the Arctic Ocean (Webster et al., 2014). Anomalies were calculated using data from Soviet drifting ice stations (1950–1987), Ice Mass Balance buoys (1993–2013), and the Operation IceBridge snow depth products (2009–2013). The anomaly is the measurement minus the Warren et al. (1999) multi-year average in spring at that location.
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Figure 5.7 Shortwave albedo, transmittance, and absorption of Arctic first-year ice and multi-year ice. The photographs show representative samples of first-year and multi-year ice. The solid line and dashed lines denote the relative freeboard for first-year and multi-year ice. White arrows show results from white ice and blue arrows are for melt ponds. The total arrows show total albedo and transmittance assuming pond fractions of 23% (multi-year ice) and 42% (first-year ice). The shading in the arrows denotes the relative contributions of white ice and melt ponds. Adapted from Nicolaus et al. (2012).

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Figure 5.8 A time series of sea-ice age in March from 1985 to the present and maps of sea ice age in March 1985 and March 2015 (Perovich et al., 2015).

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Figure 5.9 Evolution of albedo over first-year ice and multi-year ice (based on Perovich and Polashenski, 2012).

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Figure 5.10 Spatial trend in drift speed of Arctic sea ice. Background colors: Linear trend in winter (October to May) sea ice speed for the period 1992–2009. Vectors: changes in sea ice motion for the same period. Data: sea ice drift from SSM/I (Special Sensor Microwave Imager) 85 GHz channels (JPL). Based on Spreen et al. (2011).

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Figure 5.11 Development of a sea-ice algal bloom during the winter-spring-summer transition. The transition from winter to early spring is driven by light limitation and from spring to summer by increasing temperatures and ice melt (based on Leu et al., 2015).

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Figure 5.12 Average number of species of sea ice flora and under-ice fauna identified in the Beaufort Gyre (1975–1981 and 1997–2000) and at the North Pole (2007–2011 and 2015). Data for 1975–1981 from Melnikov (1989), 1997–2000 from Melnikov et al. (2002) and 2007–2011 and 2015 from Melnikov et al. (2017).

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Figure 5.13 Frost flowers on sea ice showing a refrozen lead with new flowers; (A) an individual flower, (B) a collection of frost flowers and (C) a thermal infra-red image of a single flower (based on Barber et al., 2014b).

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Figure 5.14 A conceptual model of the proposed life history of Apherusa glacialis, a common Arctic sea-ice amphipod, illustrating the intimate connection between ice-associated fauna and Arctic Ocean currents (based on Berge et al., 2012). 

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Figure 5.15 Percentage change in pan-Arctic occurrence of autumn blooms between 1998–2001 versus 2007–2012 (adapted from Ardyna et al., 2014).
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Figure 6.1 Arctic glacier cumulative total mass change and sea level equivalence (SLE) (Wouters et al., 2008; updated by B. Wouters, Utrecht University). Uncertainty ranges represent the standard deviation of least squares fits plus uncertainties in the corrections for glacial isostatic adjustment.
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Figure 6.2 Sea level pressure (hPa) 1 June to 31 August 2013. Persistent low atmospheric surface pressure between Arctic Canada and Greenland brought cold air to the region, suppressing melting. The same circulation regime also favored the delivery of relatively warm and moist air to Svalbard, driving its melt rates to twice that of the previous four decades (Jason Box and NCEP/NCAR Reanalysis).
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Figure 6.3 Sea level pressure (hPa) 1 June to 31 August 2012. An abnormally persistent atmospheric circulation pattern, brought record melting to western Greenland and the Canadian Arctic at the same time bringing abnormally cold air to Svalbard (Jason Box and NCEP/NCAR Reanalysis).
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Figure 6.4 Net change in ice mass balance for Storglaciären (see photo), the glacier with the World’s longest continuous distributed mass balance record (Peter Jansson, Stockholm University).
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Figure 6.5 ICESat repeat-tracks with average rates of change in elevation for the period October 2003 to October 2009 for glaciers in the Russian High Arctic. Average elevation change rates are given for each of the three named regions (Moholdt et al., 2012).

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Figure 6.6 Siberian glacier locations (Maria Ananicheva). Glaciers 7, 8 and 9 not shown.
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Figure 6.7 Englacial and subglacial meltwater emerges at tidewater glacier fronts, forcing a heat exchange between the ice front and relatively warm ocean waters. In cases where water depth exceeds 350 m, entrainment of water of 4–5°C can enhance melt rates (after Chauché et al., 2014).

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Figure 6.8 Ocean currents delivering heat linked with glacier destabilization (after Straneo and Heimbach, 2013).
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Figure 6.9 Aerial oblique view on 12 August 2012 of meltwater channeling in the surface snow and firn of the southwestern Greenland ice sheet lower accumulation area at 1800 m elevation, made possible by the development of thick impermeable ice layers in successive previous high melt years.
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Figure 6.10 Flooding washing out around the Watson river bridge foundations and over the Kangerlussuaq West Greenland water supply pipeline on 11 July 2012.
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Figure 6.11 Linear trends in annual surface temperature since AD 1901 (after Rahmstorf et al., 2015).

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Figure 6.12 Greenland net snow accumulation after Box et al. (2013) based on a statistical reconstruction from 123 ice cores versus the RACMO2.1 model. Also shown is the result from the MAR regional climate model driven by the NOAA 20th Century Reanalysis (Compo et al., 2011) and the ECMWF ERA-20C (Fettweis et al., 2016).

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Figure 6.13 Time series of the total Greenland ice sheet surface mass balance (SMB), consisting of the balance between mass inputs from snowfall and mass loss from meltwater runoff, simulated by MARv3.5.2 forced by NCEP-NCARv1 over the period 1948–2014 (Fettweis, University of Liège, Belgium).
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Figure 6.14 Greenland all-ice total ice mass balance from June 2003 to August 2015 according to satellite gravity measurements (after Barletta et al., 2013).

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Figure 6.15 Rates of change in the mass of the Greenland Ice Sheet and its surrounding glaciers from 2003 to 2009, based on data from Zwally et al. (2011), Bolch et al. (2013), Gardner et al. (2013), and Colgan et al. (2015b).

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Figure 6.16 Greenland ice sheet total mass balance (after Box and Colgan 2013 and Kjeldsen et al., 2015), derived by modelling ice discharge as a function of runoff and ice discharge (data from Rignot et al., 2008 and Enderlin et al., 2014). The correspondence with GRACE satellite-based Greenland ice mass change estimates is also shown.
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Figure 6.17 Cumulative Greenland ice sheet contribution to global mean sea level for the period 1840–2012; the dashed line indicates acceleration of loss from climate warming (after Box and Colgan, 2013).

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Figure 6.18 Cumulative area change of 45 among the widest Greenland tidewater glaciers (after Jensen et al., 2016 and Box and Hansen, 2015).

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Figure 6.19 The massive (245 km2) summer 2010 calving event at the floating Petermann Glacier ice shelf in northwestern Greenland (see Figure 6.22 for location).
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Figure 6.20 Ice velocity map (magnitude, in logarithmic scale) of the Greenland ice sheet derived from SAR data of the EU Copernicus Sentinel-1A satellite, plus an inset showing the Northeastern Greenland ice stream (NEGIS) ice velocity, merging data acquired from 15 October 2014 to 15 October 2016 (data visualization by M. Hermann after Nagler et al., 2015).
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Figure 6.21 Surface meltwater draining into the Greenland ice sheet.
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Figure 6.22 Bed elevation of the Greenland ice sheet color-coded up to 2000 m, with submarine areas in blue. Details of the large-scale map for
(a) Petermann, Steensby and Ryder Gletscher, (b) Hayes Gletscher, Allison Gletscher and Illullip Sermia, (c) Upernavik Isstrøm and Nunatakassaap Sermia, (d) Marie Sophie Gletscher, Academy Gletscher and Hagen Bræ, (e) F. Graae, Charcot and Daugaard-Jensen, and (f ) Kangerlussuaq Gletscher; glaciers within each of the six categories are listed in clockwise order. The white contour line delineates the limit of land ice. The mass conservation method is employed for the glaciers. Kriging is used to map the interior regions (after Morlighem et al., 2014).

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Figure 6.23 Cross-section of the Russell Glacier catchment showing the locations of GPS sites relative to the mean 1990–2011 equilibrium line altitude (ELA) of 1553 m above sea level (after Doyle et al., 2014).
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Figure 6.24 Variation in relative sea level (RSL) due to the gravitational and Earth rotational effects of ice mass losses from different sources for the period 2000–2008; Greenland only, Antarctica only, Alaska only, and mountain glaciers and ice caps in the Arctic, Patagonia and Rockies. The thick green contour indicates the global average eustatic RSL for each source (Bamber and Riva, 2010).
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Figure 6.25 Aerial view of sediment-rich waters in front of the Humboldt glacier in northwest Greenland, July 2009.
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Figure 7.1 Domain of the Arctic Freshwater System (AFD) showing four Arctic Ocean gateways, eleven gyral circulation patterns, major ocean currents, and the All Arctic Regions (AAR) definition of the terrestrial contributing area shown in white (Prowse et al. 2015a).
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Figure 7.2 Schematic representation of lower (850 hPa) and upper (250 hPa) troposphere storm tracks (after Hoskins and Hodges, 2002).
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Figure 7.3 Four definitions for the terrestrial contributing area (TCA) part of the Arctic freshwater system: All Arctic Regions (AAR), Arctic Ocean Basin (AOB), Arctic Climate System Study (ACSYS), Arctic Ocean River Basin (AORB), and flow volume versus contributing area for the various TCAs including the Mackenzie, Ob, Lena, and Yenisey Basins (MOLY) and Global Runoff Data Centre (GRDC) (Prowse et al., 2015a).

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Figure 7.4 Schematic representation of the high-latitude freshwater system as introduced by Prowse et al. (2015a). Moisture is transported from the subtropical and tropical Atlantic Ocean to the Pacific Ocean via the Trade Winds over Central America (thick grey arrow). The sub-Arctic front separates the thermally stratified sub-Arctic oceans (darker blue) from the salt-stratified northern ocean (lighter blue) in both the Atlantic and Pacific Oceans. Moisture is transported from the Pacific and Atlantic Oceans to the Arctic catchment basins by the mid-latitude (Westerlies) storm tracks (thick blue arrows), and subsequently drains into the Arctic Ocean (thick white arrows) where it spreads initially within the Riverine Coastal Domain (dashed green arrows). Warm, salty Atlantic-origin waters (thin red arrows) enter the Arctic Ocean through Fram Strait (the Fram Strait Branch) and the Barents Sea Opening (the Barents Sea Branch) and circulate within the Arctic Basins as subsurface, cyclonic, topographically steered boundary currents along the continental margin and ridge system. Internally modified Atlantic waters exit the Arctic Ocean southward through Fram Strait along eastern Greenland. Cooler and fresher Pacific-origin waters (thin blue arrows) enter the Arctic Ocean through Bering Strait and exit through the Canadian Arctic Archipelago and Fram Strait along eastern Greenland. Within the Arctic Ocean a topological distinction is made between inflow, interior, and outflow shelves (cf. Carmack and Wassmann, 2006; Bluhm et al., 2015).

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Figure 7.5 Schematic representations of Arctic Ocean circulation: Surface circulation of the Arctic Ocean as shown by dynamic topography (20/400 db); mid-water halocline sources, flows and associated fronts (blue shows Pacific-origin waters, maroon shows Atlantic-origin waters, thick maroon line depicts the front between them) (after McLaughlin et al., 1996); the Arctic Circumpolar Boundary Current system derived from Atlantic water inflows (after Rudels et al., 1999; Aksenov et al., 2011); and deep-water exchange (Aagaard et al., 1985). BG is the Beaufort Gyre, BSB is the Barents Sea Branch, FSB is the Fram Strait Branch, GG is the Greenland Gyre, NAC is the Norwegian-Atlantic Current, TPD is the Transpolar Drift. (Bluhm et al., 2015).

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Figure 7.6 Liquid freshwater content (i.e., thickness of freshwater anomaly relative to a reference salinity of 34.8) from the PHC (Polar Science Center Hydrographic Climatology) 3.0 climatology (Steele et al., 2001) and trajectories of sea ice computed using 2000–2010 satellite ice velocities from the Polar Pathfinder Sea Ice Motion data (Fowler et al., 2013). The small red dots indicate the starting points of the trajectories, which last two years.

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Figure 7.7 Schematic illustration of the freshwater budget for the Arctic Ocean for the period (nominally) 1980–2000, the decade of the 2000s, and the difference between them. In each diagram, the principal freshwater sources are precipitation minus evaporation (P-E), runoff, and flow through Bering Strait. The principal sinks are outflow through Davis and Fram Straits. The freshwater reservoirs are split between liquid freshwater (with the Beaufort Gyre shown in the circle) and sea ice (seasonal and multiyear). The area of each shape is proportional to the corresponding freshwater flux or reservoir volume (see the white box for scale). (Haine et al., 2015).

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Figure 7.8 Cumulative area by latitude (left) and cumulative precipitation by latitude (right) for the All Arctic Regions (AAR) contributing area and the four major river basins (MOLY; Mackenzie, Ob, Lena, Yenisey) (Prowse et al., 2015a).

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Figure 7.9 Major ecoregions within the All Arctic Regions (AAR) contributing area and cumulative area by latitude for each ecoregion (Prowse et al., 2015a).

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Figure 7.10 Major hydro-physiographic regions within the All Arctic Regions (AAR) contributing area (Bring et al., 2016). 

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Figure 7.11 Interactions and feedbacks from three general types of ecosystem shift in Arctic terrestrial and freshwater environments: tundra to shrub/forest, terrestrial to aquatic, and thermokarst lakes to drained lakes (Wrona et al., 2016 – adapted from Karlsson et al., 2011).

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Figure 7.12 Linkages and possible effects of vegetation change on Arctic terrestrial and aquatic biogeochemical and ecological processes (Wrona et al., 2016 – adapted from Myers-Smith et al., 2011).

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Figure 7.13 Schematic representation of Earth system model processes. Within a hierarchy of models, process models may focus on a single element of this schematic (such as permafrost), component models isolate a particulate component (such as the ocean or atmosphere) subject to prescribed boundary conditions, and fully coupled models include interactions across components. Copyright UCAR. Illustration courtesy of Warren Washington, NCAR (as shown in Lique et al., 2016).

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Figure 7.14 Schematic illustration of the Arctic freshwater budget. In addition to water fluxes (represented by arrows), water is also stored in the form of low-salinity ocean water, sea ice, glaciers, groundwater, and permafrost. The blue region indicates the average March sea-ice extent for the period 1980–1989 from satellite passive microwave data (Lique et al., 2016).

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Figure 7.15 Expanded domain of the Arctic freshwater system showing four Arctic Ocean gateways, eleven gyral circulation patterns, major ocean currents including the newly identified pathway originating from the Laurentian Great Lakes/St. Lawrence River system – the final trajectory of which remains to be determined (identified by question mark), and the related expanded terrestrial-contributing area (shown in white) (Prowse et al., 2015b).

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Figure 7.16 Based on Figure 7.3, the newly defined Arctic Freshwater Domain (AFD) is shown with the Laurentian Great Lakes / St Lawrence River system noted in Figure 7.15 (here outlined in red) added to the All Arctic Regions (AAR) definition shown in yellow (after Prowse and Flegg, 2000a); plus an update of the flow volume by contributing area relationship (after Prowse and Flegg, 2000b), which includes in the upper right, the largest area and flow volume TCA - the newly defined AFD (Prowse et al., 2015b).

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Figure 7.17 Schematic representation of the nearshore hydro-ecological regime, which includes illustrations of ocean-to-land moisture transport, precipitation on land, thawing permafrost with a deepening active layer, surface and groundwater flows and geochemical flux, transport to the riverine coastal domain, and subsequent trajectory to the broader ocean environment (after Prowse et al., 2015b).
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Figure 8.1 Simplified representation of those Arctic carbon fluxes that may be influenced by sea-ice retreat (Parmentier et al., 2013). Current best estimates of sink/source strengths are shown in TgC/y, where available. The uncertainty ranges of the terrestrial fluxes are shown in brackets. The arrows do not indicate the strength of each flux.

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Figure 8.2 Net oxygen (O2) production by benthic diatoms in Nuuk fjord (Greenland) in daytime at 22 m water depth in October and 7 m water depth in February. The light level required to induce net O2 production was between 2 and 4 μmol quanta/m2/s (Attard et al., 2014).
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Figure 8.3 Gross benthic primary production (GPP) as derived from benthic oxygen (O2) exchange as a function of integrated daily light availability (photosynthetically active radiation; PAR) at a range of Arctic and subarctic settings, outliers in square brackets (Attard et al., 2016).

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Figure 8.4 Mechanisms by which carbon may be lost from Arctic soils in permafrost areas (excluding fire). Area 1 has a spatially homogenous increase in active layer thickness and Area 2 has spatially heterogeneous permafrost thaw driven by differences in soil ice content (after van Huissteden and Dolman, 2013).

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Figure 8.5 Carbon distribution of northern permafrost soils derived from the Northern Circumpolar Soil Database (Hugelius et al., 2013).
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Figure 8.6 Methane flux map for the Mackenzie River Delta derived from airborne eddy covariance measurements.
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Figure 8.7 Satellite image showing very turbid waters along the East Siberian Coast where coastal erosion delivers large quantities of sediment to the shelf sea. Image acquired from the Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) on-board GeoEye’s OrbView-2 satellite on 24 August 2000.
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Figure 8.8 Correlation between terrestrial methane (CH4) emissions and sea ice for May–October over the period 1981–2010. The color of each grid cell indicates the strength of the correlation between modeled CH4 emissions and sea-ice concentration within a radius of 2000 km from the grid cell. The linear trend in sea-ice concentration is shown to indicate areas of high retreat (Parmentier et al., 2015).
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Figure 9.1 Schematic overview of cryospheric processes that influence absolute and relative sea level.
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Figure 9.2 Relative sea level variations due to the gravitational and Earth rotational effects of ice mass losses from different sources for the period 2000–2008 (Bamber and Riva, 2010). The thick green contour indicates the sum of the fingerprints from different sources.
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Figure 9.3 Comparison of Arctic sea level rates 2004–2010 with other global sea level components. ‘Scandinavian glaciers’ includes both Iceland and Svalbard. ‘Other glaciers’ includes, for example, Patagonian, Himalayan, African, and Indonesian land ice sources.

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Figure 9.4 Global sea-level rise sources with Arctic land ice contributions highlighted relative to Antarctic land ice, thermal expansion and terrestrial water storage during the 2004–2010 period. ‘Scandinavian glaciers’ includes both Iceland and Svalbard. ‘Other glaciers’ includes, for example, Patagonian, Himalayan, African, and Indonesian land ice sources. The sources are sorted from largest to smallest.
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Figure 9.5 Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5 including Extended Concentration Pathways (ECPs) after Meinshausen et al. (2011). The graphic indicates an approximate trajectory for the Paris Agreement. RCP8.5 is conceptualized as relatively slow adoption of low-carbon energy systems and weak international cooperation in reducing carbon emissions. RCP4.5, by contrast, is conceptualized as rapid development and implementation of low-carbon technology under strong international cooperation.
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Figure 9.6 An uncertainty budget that sums sea level uncertainties described in Section 9.3.
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Figure 9.7 Historical (1850–2005) and projected (2006–2100) terms influencing global mean sea level under the RCP4.5 and the RCP8.5 scenarios. Sea-level rise expressed relative to 2006. Arctic land ice contributions highlighted relative to Antarctic land ice, thermal expansion and terrestrial water storage contributions to the global budget. ‘Scandinavian glaciers’ includes both Iceland and Svalbard. ‘Other glaciers’ includes, for example, Patagonian, Himalayan, African, and Indonesian land ice sources.
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Figure 9.8 Sea level component summed difference: RCP8.5 minus RCP4.5. ‘Scandinavian glaciers’ includes both Iceland and Svalbard. ‘Other glaciers’ includes, for example, Patagonian, Himalayan, African, and Indonesian land ice sources.
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Figure 9.9 Annual sea level contribution, averaged per decade between 1850–2100, from the Arctic land ice and other regions simulated by Marzeion et al. (2012) under RCP4.5 and RCP8.5.

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Figure 9.10 Projected land ice loss (expressed in millimeters sea level equivalence; SLE) from different Arctic regions in 2030 and 2080 under the RCP4.5 and RCP8.5 scenarios, based on the 2006–2100 sea level budget described in Section 9.4. The relatively small mainland Eurasian glaciers do not appear here owing to incomplete assessment.
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