The Arctic Coastal Dynamics Database: A New Classification Scheme and Statistics on Arctic Permafrost Coastlines
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- Lantuit, H., Overduin, P.P., Couture, N. et al. Estuaries and Coasts (2012) 35: 383. doi:10.1007/s12237-010-9362-6
Arctic permafrost coasts are sensitive to changing climate. The lengthening open water season and the increasing open water area are likely to induce greater erosion and threaten community and industry infrastructure as well as dramatically change nutrient pathways in the near-shore zone. The shallow, mediterranean Arctic Ocean is likely to be strongly affected by changes in currently poorly observed arctic coastal dynamics. We present a geomorphological classification scheme for the arctic coast, with 101,447 km of coastline in 1,315 segments. The average rate of erosion for the arctic coast is 0.5 m year−1 with high local and regional variability. Highest rates are observed in the Laptev, East Siberian, and Beaufort Seas. Strong spatial variability in associated database bluff height, ground carbon and ice content, and coastline movement highlights the need to estimate the relative importance of shifting coastal fluxes to the Arctic Ocean at multiple spatial scales.
Arctic coasts are likely to become one of the most impacted environments on Earth under changing climate conditions. Under most scenarios, the Arctic is predicted to experience the strongest air and sea temperature increase at the Earth’s surface (Kattsov and Källén 2005). As a result, the lengthening open water season and the increasing open water area, due to the decline of sea ice extent, will induce changes to the length of fetch and allow storms to affect the coasts later in the fall season (Anisimov et al. 2007; Atkinson 2005). These storms are thought to threaten community and industry infrastructure as well as to dramatically change sediment and nutrient pathways in the near-shore zone (Dunton and Cooper 2005). Unfortunately, Arctic coastal dynamics remain largely understudied and seldom modeled, which puts current adaptation and mitigation strategies in northern communities into jeopardy. A thorough systematic investigation of the coast at the circum–arctic scale is needed to better understand the processes that act upon it. Only then will it be possible to develop predictive models of coastal evolution.
The coast, whether in temperate or polar regions, is a complex and diverse environment, at a number of spatial scales. This complexity is difficult to capture with a systematic or rigid compartmentalizing approach. Nevertheless, classifications, whether hypothesis-driven or descriptive, have been a major instrument in the pursuit of scientific knowledge, helping to delineate natural systems and achieve economy of memory (Sokal 1974). Coastal scientists have not refrained from proceeding with formal descriptions of the structure of coastal components. As early as the nineteenth century, (notwithstanding traditional descriptions of coastal processes by indigenous people), geologists attempted to describe coastal landforms and to explain their origin and development. Classification schemes were rapidly devised, mostly based on a division of the coast into areas of similar geology and environment. A review of coastal classification efforts and history is provided by Finkl (2004). Existing coastal classifications share a common deficiency in their description of arctic coasts, especially those affected by the presence of permafrost. Typically, they classify the arctic coastal zone as one single category. We suggest that this approach does not do justice to the wide variety of coastal landforms and processes observed at the land–sea interface in the Arctic.
Historically, the lack of a classification scheme for arctic coasts can be explained by the late exploration of polar regions, by their remoteness, and by the low population density of the arctic coastal zone, limiting the economic relevance of studies in the north. The widely used classification of the coasts by Shepard (1948) divides shorelines into two categories, primary (shaped by nonmarine processes) and secondary (shaped by marine processes), but does not include sea ice in the coastal zone or the role of permafrost in thermal–mechanical erosion. Classifications based on the division between submergent and emergent coasts, such as that by Valentin (1952), also fail to mention permafrost and sea ice despite the important role of isostasy in determining geomorphology in the Arctic (Whitehouse et al. 2007). Classifications of the arctic coast exist at the national level (e.g., for Russia, Drozdov et al. 2005), but not yet at the circum-arctic scale.
Arctic permafrost coastlines represent approximately 34% of the world’s coastlines and are affected by the presence of permafrost and/or seasonal sea ice cover, resulting in unique conditions, landforms, and processes. These environments are undergoing tremendous change that results in redefined societal and environmental frameworks. Traditional use of the coast by Inuit communities in Canada and Alaska is threatened by the disappearance of sea ice (Huntington and Fox 2005). The subsequent opening of the northern sea route in Russia’s waters and of the Northwest Passage in the Canadian Archipelago will call upon local, regional, and international stakeholders to define new strategies for the use and protection of the coast (Matushenko 2000). The lack of a baseline dataset that accurately captures the physical state of the coast and includes the specificity of the Arctic hinders the development of such strategies. The urgency to develop such a dataset, based on a classification method specifically devised for the Arctic, is genuine and palpable.
This paper presents a classification scheme by the Arctic Coastal Dynamics (ACD) project, initiated by the International Permafrost Association in 1999 (Brown and Solomon 2000) and carried out on a cooperative basis starting in 2000 with the International Arctic Science Committee (Rachold et al. 2002, 2003, 2005; Rachold and Cherkasov 2004), with specific aims of establishing the rates and magnitudes of erosion and accumulation of arctic coasts and of creating an arctic coastal classification in digital form. ACD is also an affiliated project with the Land–Oceans Interactions in the Coastal Zone project, which in turn is part of both the International Geosphere-Biosphere Programme, and the International Human Dimension Programme.
Segmentation of the Coast
The central objective of the ACD classification is to assess the sensitivity and erosion potential of arctic coasts. The classification was therefore conceived as a framework broad enough to encompass existing classification schemes, while capturing fundamental information for the assessment of climate change impacts and coastal processes in relation to the specificity of arctic coasts.
To conform to the objective of the project, the compartmentalization of the coast was primarily geomorphological in nature, so that it emphasizes erosion and changes to the coastal tract. The basic concept underlying the segmentation, freely adapted from Howes et al. (1994), is that the shore zone can be subdivided and described in terms of a systematic collection of physical entities. In short, a coastline can be subdivided into smaller segments, and the features of each segment described and recorded. The method first segments the coastline into alongshore units that exhibit homogeneous forms and material types, then subdivides these segments into across-shore components, and describes them.
Division of the Arctic coast used in this paper based upon Arctic seas
Percentage of total coastline length (%)
Russian Chuckchi Sea
American Chuckchi Sea
American Beaufort Sea
Canadian Beaufort Sea
Greenland Sea and Canadian Archipelago
East Siberian Sea
For all cross-shore zones, the material was specified as shown in Appendix A. Unlithified and lithified coastal segments were differentiated in the process. For unlithified coasts, a detailed account of the grain size was provided, encompassing standard grain-size categories (gravel, sand, silt, clay). Lithified coastal sections were characterized by the geological and mineralogical nature of the exposed bedrock. For the purpose of quantifying sediment and organic carbon release to the near-shore zone, erosion and redeposition in the frontshore and offshore zone were considered to be transient phenomena; and detailed characterization of soil geotechnical and geocryological properties focused only on the backshore zone (e.g., bulk density and volumetric ground ice contents).
Cryolithology and Geochemistry
The third step consisted in populating each segment with additional characteristics including cryolithology (i.e., permafrost characteristics), geomorphology and geochemistry. A summary of the geochemical and cryolithological parameters used to characterize each segment is provided in Appendix A.
Ground ice contents were provided in terms of visible volumetric ice contents (vol%), both in a quantitative and qualitative fashion, using the classes inspired by the circum-arctic permafrost map (Brown et al. 1998). All forms of ground ice were included including massive ice bodies, wedge ice, pore ice, ice complexes (fully penetrated by large ice wedges), injection ice, buried snow bank, and buried glacier ice. For a definition of these terms, the reader is referred to Mackay (1972) and Pollard (1990). In any section, the percentage volume of ice vs. soil was determined and assigned a value of poor (0–2 vol%), low (2–20 vol%), medium (20–50 vol%), or high (>50 vol%). The estimates of ground ice content were based on a variety of sources, including field observations of large natural exposures, use of published material, boreholes and cores, geophysics (seismic, ground penetrating radar, electrical resistivity, gravity), terrain analysis from remotely sensed datasets, and to a large extent on the map of permafrost conditions for the northern hemisphere published by the International Permafrost Association (Brown et al. 1998).
To relate carbon contents to fluxes in the coastal zone, we seek to relate concentrations on a relative weight basis to fluxes observed using length, areal, or volumetric change rates. The spatial density of the flux components (initially, water/ice, sediment, and organic carbon) can only be determined if we can relate weight to volume. The classification includes data on the bulk density of the soil, which is defined as the ratio of the mass of dry solids to the bulk volume of the soil. In most cases, bulk density values were not available throughout the backshore profile. In these cases, unadjusted bulk density from the upper part of the profile was used to characterize the rest of the profile. In addition, ground ice was omitted in the computation of bulk densities, and the values used in the classification virtually referred to the density of soil particles after thaw settlement. In cases where field investigations could not be performed, the values for bulk densities were extrapolated from the circum-arctic map on permafrost conditions (Brown et al. 1998) by using the grain size of the sediment.
Since this classification also features ground ice contents and bulk densities, it is possible to combine these parameters to obtain the total amount of carbon available in a given coastal segment. Assuming that DOC contents are negligible, that POM can be considered to provide the TOC contents and that these organic carbon measurements can be averaged in a meaningful way so that calculated values are representative for the coastal segment, this value will form a best estimate.
Coastline erosion or aggradation is an ongoing process characterized by high interannual variability (Solomon et al. 1994). Characterizing shoreline position is therefore better done with long-term datasets that attenuate the seasonal and annual variability. These datasets typically report rates ranging from 0 to 20 m year−1, although based on very different time spans. Here, we report on erosion using yearly coastal erosion (and aggradation) rates based on the best datasets available, that is, the ones covering the longest time span. The rates are expressed in meters per year, and refer to the distance between the shoreline location from one year to the next in a direction essentially perpendicular to the coast. In the best-case scenario, rates of erosion compiled at high resolution (less than every 500 m) were used to populate the classification. These rates were extracted from the most recently published datasets, including data from Jorgenson and Brown (2005), Lantuit and Pollard (2008), Solomon (2005), Jones et al. (2008, 2009a, 2009b) and Lantuit et al. (2010b). They generally use remote sensing imagery from the second half of the twentieth century and sometimes cover over 50 years of coastline evolution. These datasets are restricted spatially, however, and most of the database segments were characterized using discrete measurements of erosion along the coastline that were then extrapolated to the rest of the segment. These records are generally from local scientific investigations, industry reports, ship-based observations, or local monitoring efforts. In remote areas, north of 80°N, such records were often unavailable and the erosion data was generated from maps of sea ice cover: using the average 1970–2001 sea ice extent for late September, a rate of 0 m year−1 was assigned to coasts located within the extent of the sea ice cover, and no rate was assigned for the ones located outside of this area. This is consistent with observations of erosion in the Canadian High Arctic and elsewhere (Shaw et al. 1998; Zenkovich 1985; Walker 2005).
Data Quality Assessment, Spatial Accuracy, and Metadata Standards
Data quality was assessed relative to the database specifications using a template to characterize coastal segments and through the use of metadata standards. Consistency was ensured through the cross-evaluation of neighboring seas in a series of five international workshops. To assess the quality of quantitative parameters, a data quality rating was added by the regional expert to the fields related to geomorphology (backshore elevation and dry bulk density), cryolithology (ground ice content), and geochemistry (organic carbon content). The rating was coupled to a “data quality comment” field that was left to the expert for further precision on the rating. The ratings were expressed as low, medium, or high data quality. These referred mostly to the spatial accuracy and resolution of the measurements. In short, a high quality rating was assigned to a segment when the record was extracted from more than one discrete measurement in the segment. A medium rating was assigned to segments where records were extracted from a single-field measurement or interpolated from a neighboring segment. A low rating referred to data being generated using existing maps such as the circum-arctic map of permafrost and ground ice conditions (Brown et al. 1998), or the Northern Circumpolar Soils Map (Tarnocai et al. 2002), or to data interpolated from non-neighboring segments. Because of the sparse nature of the data available for the classification, a quantitative assessment of the accuracy of the data records was deemed irrelevant at this stage.
The metadata standards were developed in a two-stage process. The geospatial framework was developed to match the ISO 19115 standard (ISO 2003), while the data itself was documented in an ad hoc systematic procedure. The ad hoc protocol to implement the metadata for the database focused primarily on the identification of the sources used to populate the field of the database. It was inherently coupled to the data quality assessment process since it also reports on the nature of the source used to create the data records. Despite the wide range of sources potentially available to the authors, a special effort was made to use consistent (i.e., best available circum-arctic) sources to populate the fields, and the number of sources used to create the classification is therefore small.
Finally, to avoid miscalculations due to the misrepresentation of the coast by nonfractal datasets such as the World Vector Shoreline (Soluri and Woodson 1990), a study conducted by Lantuit et al. (2009) investigated the potential effects of the use of linear datasets to compute fluxes of sediments and nutrients. The authors concluded that using the length of the coastline to compute these fluxes was inappropriate at best, and wrong in most cases. Since the length of the coastline varies with scale, there is no “absolute” coastline length and the range of lengths can vary greatly. They emphasized the need to use planimetric rates to increase the accuracy of the predicted fluxes. This is the method advocated in the framework of the present classification. The length of the World Vector Shoreline is used in the rest of this paper to provide some baseline statistics about the arctic coastline, but not to compute volumetric sediment and geochemical fluxes.
The resulting template provides a comprehensive, yet expandable individual geomorphological description of each of the segments, and forms the most comprehensive available dataset on coastlines at the circum-arctic scale. The database tables listed in Appendix A provide 23 characteristics which can be searched and queried to obtain statistics that relate both to traditional coastal geomorphology, geochemistry and coastal erosion as well as to arctic-specific features (geocryology). The database is published as a freely available dataset on the PANGAEA information system (Diepenbroek et al. 2002) in ISO compliant formats. In the following section, we describe selected information extracted from this dataset.
The coastline classified in this study spans five countries located along the Arctic Ocean, namely Russia, Alaska (USA), Canada, Greenland (Denmark), and Svalbard (Norway). The total length of the coastline affected by the presence of permafrost in the northern hemisphere is 407,680 km, which represents around 34% of the world coastline, while the coastline classified in this study covers 101,447 km, which represents about 25% of these coasts. The Russian coastline represents close to three quarters of that dataset, extending over ten time zones from the meridian at 31°E–169°W. Extending the dataset to include the inner waterways of the Canadian Archipelago would make the Canadian arctic coastline much longer than the Russian one. However, the current dataset only covers a smaller portion of the Canadian coastline, including the Beaufort Sea and the outer western edge of the Canadian Archipelago. In total, 1,314 segments were created along the arctic coast. The length of these segments varies greatly, mostly as a result of the level of knowledge acquired about a specific stretch of coast. Well-known coastlines were segmented in detail, while hardly accessible ones were generally summarized broadly in long segments. A general trend is seen along a latitudinal gradient, with southern coastlines segmented in more detail than northern ones. The coast of Novaya Zemlya, for instance, is made up of only a few segments, while the Alaskan Beaufort Sea coast is made up of 71. This is reflected in the mean and median lengths of the segments at the Arctic scale: the mean length of a segment is 74 km, while the median length is 28 km. This skewed distribution is further explained by the fact that 85% of the segments are below 100 km and 42% of them below 20 km. The trend is not only latitudinal but also geomorphological, in that most very long segments correspond to low coastal erosion rates and low organic carbon values.
Overview of parameters extracted from the classification and averaged by sea sector
Weighted mean backshore elevation (m a.s.l.)
Weighted mean coastal erosion rate (m year−1)
Weighted mean organic carbon content (wt.%)
Weighted mean volumetric ground ice content (vol%)
Russian Chukchi Sea
American Chuckchi Sea
American Beaufort Sea
Canadian Beaufort Sea
East Siberian Sea
The coastline in this study was classified as 65% unlithified and 35% lithified (66,386 and 35,051 km), a roughly 2:1 proportion which is similar to the one found using the International Permafrost Association permafrost map (66,208 and 35,238 km) on the same coastline (Brown et al. 1998). This ratio is difficult to compare with other coasts of the world, as most efforts to compile such statistics have focused on the ratio between sea cliffs and sedimentary coasts, and not between unlithified and lithified coasts. Emery and Kuhn (1982) indicate an 80–20% ratio for sea cliffs and sedimentary coasts, but include cohesive coasts (i.e., nonrocky) in the first category. Most of the unlithified coasts in the Arctic are characterized by the presence of excess ice (i.e., an ice volume that exceeds the total sediment pore volume). The effect of warming, and especially thawing, on soil volume and cohesion depends on ice content. Permafrost and ground ice therefore play an important role in maintaining coastal stability, but are uniquely susceptible to changes in land–ocean and land–atmosphere heat fluxes.
Cryolithology and Geochemistry
Sea sectors also differ substantially in cryolithology: Svalbard coasts contain virtually no visible ground ice with average ground ice contents of 0%. At the other end of the spectrum, the Canadian Beaufort Sea is the richest in ice, with weighted mean volumetric ground ice contents close to 30%, somewhat comparable to values (46%) along the Yukon Coastal Plain and on Richards Island, which are areas richer in ground ice. The Beaufort Sea as a whole with its Canadian and US parts is the most ice-rich, followed by the US Chukchi and the Kara Seas (Fig. 4b).
Organic carbon contents along the arctic rim are on average 2 wt.%, but are characterized by large variability, ranging from close to 0 wt.% to above 15 wt.% for some stretches of coast in the US Beaufort and Kara Seas. The Brownlow Coast, west of Kaktovik on the US Beaufort Sea coast is for instance characterized by organic contents of 15 wt.%. There exists a skew towards higher concentrations for the Beaufort Sea coast: out of the 22 segments with organic carbon contents above 10%, two only are located in the Kara Sea, four in the Canadian Beaufort Sea, and 16 on the US Beaufort Sea coast. In recent estimates of the permafrost soil organic carbon pool (Tarnocai et al. 2009) that were obtained using higher resolution data, the largest values of carbon content were retrieved from stretches of coast where high resolution sampling was performed, such as along the US Beaufort Sea (Jorgenson and Brown, 2005). The extreme values just mentioned are not representative for the arctic coast since 87% of the segments classified in this study feature organic carbon contents below 5% and 57% below 2%. Large discrepancies in organic carbon contents are not only recorded between segments, but also between sea sectors of the Arctic (Fig. 4c). Here, as mentioned above, the largest organic carbon contents are found along the US Beaufort Sea coast, with an average Corg value of 5.7%, followed closely by the US Chukchi Sea (3.8%). The Russian arctic seas feature consistent average organic carbon contents between 0.9% and 1.7%. Variations in carbon contents are primarily a function of geologic, cryologic, and climatic history. However, the size of the dataset must also be considered. In certain cases, a detailed analysis can result in lower carbon values than initial estimates (e.g., Streletskaya et al. 2009), but a recent determination of the size of the overall organic carbon pool in permafrost regions shows it to be much larger than previously thought (Tarnocai et al. 2009), partly because deeper and higher resolution data was used. This may help explain part of the discrepancy between sea sectors since, as noted above, the highest carbon contents have been retrieved from stretches of coast where high resolution sampling has been performed. This implies that estimates for other sea sectors may be underestimated.
These numbers for organic carbon provide an indication of what the flux of carbon may be due to erosion of the coastal sediments, however, they do not provide information on the lability of that carbon. Terrestrially derived organic matter generally contains a significant proportion of vascular plant material composed of molecules of relatively refractory carbon (Hedges et al. 1997). However, the cold and wet environments in which Arctic soils form serve to limit oxidation so the organic matter they contain is less degraded than it would be at lower latitudes (Shaver et al. 1992; Hobbie et al. 2000). In addition, frost churning of permafrost-affected soils moves surface carbon deeper into the colder part of the soil profile where decomposition is further restricted (Bockheim 2007), while changes in the depth of the active layer over time can have the same result (Tarnocai et al. 2002; Schuur et al. 2008). Once it is eroded, organic carbon from terrestrial sources has in some cases been shown to supply a significant proportion of the energy needs of coastal food webs (Dunton et al. 2006). Nevertheless, much of this material is likely to be buried in shelf sediments or exported off-shelf, rather than being remineralized in the water column as is the case with more labile marine carbon derived from photosynthesis. So although the fate of organic carbon can vary depending on regional physical and biological dynamics, it depends to a large extent on its source and its quality (de Haas et al. 2002; O’Brien et al. 2006; Stein and Macdonald 2004 and references therein). A characterization of this latter parameter will be the focus of future refinements of the ACD database.
Coastal erosion rates actually refer in our dataset to erosion or aggradation, being marked negative when the coast is aggradational, but the overwhelming part of the coasts referenced in our ca. 100,000 km classification is erosional. The average rate of erosion for the 61,919 km of coast for which data is available is 0.5 m year−1. As for other parameters though, the variability between segments and notably between neighboring segments is large. Solomon (2005), Lantuit and Pollard (2008), and Lantuit et al. (2009) depicted similar situations along Canadian coasts in even greater detail at the local scale. This variability is therefore not new, but it is striking in that it applies to all sea sectors referenced here (Fig. 4d). With the exception of Svalbard and the Canadian archipelago, the range of erosion rates observed in all sea sectors is large. At Drew Point, on the Alaskan coast, the rate of erosion is 8.4 m year−1 (this rate was later revised by Jones et al. (2008; 2009a; 2009b) but is used here as entered in the database) and the largest in the Arctic, but on the nearby Pogik Bay coast, the rate of erosion is only 0.3 m year−1.
Strong rates of erosion are not unique to the US Beaufort Sea: out of the ten segments with the strongest erosion rates, four are located in the Laptev Sea sector, three in the US Beaufort Sea, two in the East Siberian Sea and one in the Canadian Beaufort Sea. In total, 25 segments are characterized by rates greater than 3 m year−1 and are located mostly in the Laptev Sea (11) and in the East Siberian, US Beaufort and Canadian Beaufort seas (5, 4, and 3, respectively). The significance of the extreme rates is however limited. These 25 segments represent around 3% of the length of the coastline studied in this paper. Generally, most of the erosion rates lie between 0 and 2 m year−1: 89.2% of the segments fall in this category and 48.6% have erosion rates below 1 m year−1 (Fig. 5b).
Erosion rates are even less satisfactorily explained by backshore elevations (Fig. 7b). The height of the backshore shows a statistically insignificant correlation with low R² (0.006). The highest backshore elevations (>40 m) nevertheless, as expected, are retreating a little more slowly than cliffs with elevations of less than 10 m, probably because a larger quantity of debris must be removed before additional retreat can occur, but as a whole, and consistent with the findings of Héquette and Barnes (1990), erosion is poorly linked to backshore elevations.
The ACD classification of arctic coasts provides a comprehensive, yet intricate view of arctic coastal erosion, where no one factor compiled here emerges as the single explanatory variable for coastal erosion at the circum-arctic scale. In fact, the spatial variability of erosion emphasized in this paper is itself a product of the spatial variability of other parameters such as ground ice content or backshore elevation. In addition, it should be noted that waves and storm surges are a large, if not the largest, explanatory factor for coastal erosion along the arctic coastal rim and that storms are not considered in our classification scheme. Despite these limitations and the multiple factors influencing the pace of erosion, it is possible to extract some general regional traits related to the evolution of the coast. The recent dramatic increase of erosion reported on the US Beaufort Sea coasts (Jones et al. 2008; 2009a; 2009b; Mars and Houseknecht 2007), for instance, can be linked to the high ground ice contents and very low backshore elevations of these coasts. Both these variables limit the quantity of eroded material generated by a storm, so erosion products are quickly removed from the beach and shoreface by waves, leaving the coast vulnerable to the next storm. On other coasts around the Arctic, a lengthening open water season and the increase in storm frequency would probably not lead to increases in erosion rates as large as the ones observed in Alaska, because of the greater quantities of eroded material that must be removed from the beach and the shoreface. In turn, these coasts, while not eroding as quickly, can nevertheless deliver much more material to the near-shore zone, which, if carbon-rich can lead to the alteration of the near-shore carbon budget.
The dataset that we present is mostly a static view of the coast and functions as a baseline for future comparative investigations: erosion rates change yearly, and are undergoing trends (local, regional, or global) which are not represented here. In that sense, this database cannot be considered as a dynamic tool to look into seasonal or annual variability in coastal change. Yet, it provides a basis for such studies, including modeling studies, to constrain boundary parameters in erosion prognoses in the context of shifting climatic and environmental forcing. At the global level and in the Arctic, an acceleration or simply an increase in sea level rise (Proshutinsky et al. 2001; Church and White 2006) will alter the dynamics of erosion through higher storm surges. Relative sea level rise in the Arctic is also predicted to be approximately 0.2 m higher than the global average for the twenty first century (Meehl et al. 2007, p.813). Readjustments of the wave climate following synoptic changes in the Arctic and enhanced long-shore transport may lower that trend locally, but overall, sea level rise will lead to greater wave impact on arctic shorelines. Relative sea level rise will not be equally distributed in the Arctic. The North American arctic offshore and the shores of the Canadian Archipelago will see the strongest increase. The regional impact of this process is difficult to measure, but it could lead to greater storm surges in these sea sectors, thereby exposing ice-rich layers that were previously too high above water level to direct wave contact. Such deposits exist along coastline stretches of the Yukon Coastal Plain, in the southern Canadian Archipelago, and for long stretches of coast along the Kara and Laptev Seas.
One of the driving forces behind a potential increase in erosion is the lengthening of the open-water season, which is thought to have a much greater impact on the coasts than the increased fetch associated with disappearing sea ice, at least in the Canadian Beaufort Sea (Manson and Solomon 2007). In that sense, it is legitimate to think that erosion can and will increase where rates of erosion are already substantial, but in light of this study, one can equally assume that some dramatic changes can be expected regionally on coasts where the open water season was virtually nonexistent until now. Such is the case for the southern part of the Canadian Archipelago for instance, where a substantial portion of the coast is unconsolidated and could prove susceptible to erosion.
Permafrost and sea surface temperatures and their evolution over the next century will influence rates of erosion, though probably to a lesser extent globally than other forcing parameters mentioned above. The amount of heat required to bring permafrost temperatures to just below the melting point is often substantially less than the amount of heat required to melt interstitial or massive ground ice, and sea surface temperatures, when in contact with the shore, are likely to have a greater impact on the rate of erosion than air temperatures in providing the heat necessary to thaw ice-bonded or ice-rich sediments. However, both these driving forces are generally secondary to wave energy as shown by Aré (1988) and thought to impact mostly ice-rich coasts of the Arctic, that is, mostly those located at lower latitudes (although, as seen in the results section, the distribution of ground ice is spatially variable and it can also occur in quantity at high latitudes as well). Our results suggest that the regions most sensitive to a potential increase in permafrost and sea surface temperatures are the US Beaufort Sea, the US Chukchi Sea, the Canadian Beaufort Sea, and the Kara Sea. In most cases, this effect will be less important than the impact from coastal storms, but it could become prominent in isolated local situations such as low-lying ice-rich coasts from Alaska (Jones et al. 2009a; 2009b).
34% of the world’s coasts are affected by permafrost and therefore subject to a completely different set of processes that interact with climate drivers, as compared with temperate seas.
Arctic coasts are on average 8.4 m high, but a comparison between the different arctic seas shows that backshore elevations vary regionally between 1.5 and 14.5 m.
Ground ice contents range between 0% and 70 vol% along the Arctic coast, averaging 19 vol% with a strong regional positive bias towards the Canadian Beaufort, the US Beaufort, and the US Chukchi Seas.
Organic carbon contents in coastal exposures are on average ca. 2.0 wt.%, but vary regionally, and reach up to 5.7 wt.% on the US Beaufort Sea coast, where the most detailed data are available.
The average rate of erosion for the arctic coast is 0.5 m year−1, but erosion also varies dramatically locally and regionally with peaks above 3 m year−1 in the Laptev, East Siberian, US Beaufort, and Canadian Beaufort Seas.
Erosion appears to be driven by a multiplicity of factors that interact locally to widely varying extents.
Arctic coastlines are likely to undergo dramatic changes in a warming climate, affecting both biophysical and human systems, with countless impacts ranging from threats to infrastructure to changing biological environments affecting wildlife. Erosion is responsible for substantial fluxes of carbon and probably contaminants to the marine environment, which in turn can potentially alter the near-shore carbon cycle and affect several trophic levels. The dynamic nature of coastal erosion and its coupling with climate variables could thereby result in increasing fluxes of sediment from the coast. This dataset could help to quantify sediment, nutrient and contaminant fluxes in the future. In addition, it could include historical information on coastal erosion, based on physical data or traditional knowledge to provide it with a time dimension. In short, it opens the path for the integration over the years to come of a wider set of parameters and outputs than those presented in this paper.
This research is the result of the Arctic Coastal Dynamics project (ACD phase 1), which was an initiative of the International Permafrost Association (IPA) and continued in cooperation with the International Arctic Science Committee (IASC) under the auspices of the Land-Ocean Interactions In the Coastal Zone (LOICZ) project. The authors wish to thank their home institutions for continuing support as well as the following program and institutions in supporting this work: ArcticNet, Polar Continental Shelf Project (PCSP), National Science Foundation (NSF), and the Russian Federal Target program “Scientific and academic teaching staff of the innovative Russia”.