Advanced Search
Article Contents

Record Low Sea-Ice Concentration in the Central Arctic during Summer 2010


doi: 10.1007/s00376-017-7066-6

  • The Arctic sea-ice extent has shown a declining trend over the past 30 years. Ice coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1450 years. In the summer of 2010, a very low sea-ice concentration (SIC) appeared at high Arctic latitudes——even lower than that of surrounding pack ice at lower latitudes. This striking low ice concentration——referred to here as a record low ice concentration in the central Arctic (CARLIC)——is unique in our analysis period of 2003-15, and has not been previously reported in the literature. The CARLIC was not the result of ice melt, because sea ice was still quite thick based on in-situ ice thickness measurements. Instead, divergent ice drift appears to have been responsible for the CARLIC. A high correlation between SIC and wind stress curl suggests that the sea ice drift during the summer of 2010 responded strongly to the regional wind forcing. The drift trajectories of ice buoys exhibited a transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector, which appeared to benefit the CARLIC in 2010. Under these conditions, more solar energy can penetrate into the open water, increasing melt through increased heat flux to the ocean. We speculate that this divergence of sea ice could occur more often in the coming decades, and impact on hemispheric SIC and feed back to the climate.
    摘要: 在过去30年, 北极海冰范围正在经历减退的趋势. 海冰覆盖范围在2007和2012年达到历史最低值. 这一变化是过去1450年以来唯一的减退事件. 在2010年夏季, 在北极高纬度海域出现非常低的海冰密集度, 甚至比较低纬度的海冰密集度还要低. 这个显著低的海冰密集度在我们研究的2003-2015年间只出现这唯一的一次, 我们将其称为北极中央区极低海冰密集度(CARLIC). 研究结果表明, CARLIC不是海冰融化导致的, 因为发生时海冰还很厚;是海冰的辐散运动导致了CARLIC. 海冰密集度与风应力旋度之间有很好的相关, 表明海冰漂移特性的改变很好地响应局地风强迫. 在大西洋扇区, 海冰的漂移轨迹呈现穿极流的特征, 而在太平洋扇区体现为一个向加拿大北极群岛方向的漂流, 这种漂流方向的不一致有利于形成2010年的CARLIC现象. 在这种海冰减少的条件下, 更多的太阳辐射能进入海洋, 加剧了海冰的融化. 我们推测, 由于海冰在不断减少, 海冰的辐散在未来可能更加频繁, 这种现象还会发生, 并对更大范围的海冰和北极气候产生显著影响.
  • 加载中
  • Asplin M. G., J. V. Lukovich, and D. G. Barber, 2009: Atmospheric forcing of the Beaufort Sea Ice Gyre: Surface pressure climatology and sea ice motion,J. Geophys. Res.,114,C00A06, https://doi.org/10.1029/2008JC005127.10.1029/2008JC00512707d78b173f67ce3c9f8e8d74aee33162http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008JC005127%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2008JC005127/fullThe Beaufort Gyre (BG) typically rotates anticyclonically and exerts an important control on Arctic Sea ice dynamics. Previous studies have shown reversals in the BG to rotate cyclonically during summer months and, in recent decades, throughout the annual cycle. In this investigation, we explore the synoptic climatology of atmospheric forcing and its relationship to sea ice motion and BG reversals. A catalog of daily synoptic weather types is generated for the Beaufort Sea Region covering the period 1979 to 2006 using NCEP/NCAR reanalysis mean sea level pressure data, principle components, and k-means cluster analyses. Mean synoptic type frequency, persistence, and duration values are calculated for each synoptic type and contrasted between the summer and winter seasons. Daily synoptic types are linked to changes in sea ice vorticity by using correlation analysis on lagged sea ice vorticity data. Lag correlations are found between synoptic types and sea ice vorticity smoothed over a 12-week running mean and show that cyclonic types, which promote southerly or easterly atmospheric circulation over the southern Beaufort Sea, commonly precede summer reversals. Furthermore, significant seasonal within-type variability in sea ice vorticity is detected within the synoptic types illustrating the importance of seasonal variability on these processes.
    Babb D., R. J. Galley, M. G. Asplin, J. V. Lukovich, and D. G. Barber, 2013: Multiyear sea ice export through the Bering Strait during winter 201112.J. Geophys. Res.,118,5489-5503, https://doi.org/10.1002/jgrc.20383.10.1002/jgrc.203830ed35786182640a17d36c944a6d789d6http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjgrc.20383%2Fabstracthttp://doi.wiley.com/10.1002/jgrc.20383Six ice beacons deployed in the Beaufort Sea during August 2011 tracked the anomalous export of multiyear sea ice from the Chukchi Sea through the Bering Strait to the Bering Sea between November 2011 and May 2012. These are the first observations in 34 years of ice beacon export through the Bering Strait. Using 34 years of passive microwave derived ice motion fields, we find that during 2011-2012 southward ice motion in the Chukchi Sea persisted for a record six of seven months and that sea ice speeds were significantly faster than the long term mean. The combination of increased ice speeds and reduced likelihood of ice arch development through the strait culminated in the record export of 13.5 x 10(3) km(2) of sea ice through the Bering Strait. Monthly sea level pressure fields, dominated by an Aleutian Low and Siberian High, show anomalies in December and January played a role in initiating this event and forced multiyear ice into the southern Chukchi Sea. However, these variations were small and typical of this area, yet we find no evidence of a similar export event in the last 34 years even though the forcing was similar to the climatology. This leads us to attribute this event to a change in the responsiveness of the Arctic ice pack to typical forcing mechanisms.
    Barber, D. G., Coauthors, 2009: Perennial pack ice in the southern Beaufort Sea was not as it appeared in the summer of 2009,Geophys. Res. Lett.,36,L24501, https://doi.org/10.1029/2009GL041434.10.1029/2009GL041434332560aa9950911d79245102d35be2fchttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2009GL041434%2Ffullhttp://doi.wiley.com/10.1029/2009GL041434In September 2009 we observed a much different sea icescape in the Southern Beaufort Sea than anticipated, based on remotely sensed products. Radarsat derived ice charts predicted 7 to 9 tenths multi-year (MY) or thick first-year (FY) sea ice throughout most of the Southern Beaufort Sea in the deep water of the Canada Basin. In situ observations found heavily decayed, very small remnant MY and FY floes interspersed with new ice between floes, in melt ponds, thaw holes and growing over negative freeboard older ice. This icescape contained approximately 25% open water, predominantly distributed in between floes or in thaw holes connected to the ocean below. Although this rotten ice regime was quite different that the expected MY regime in terms of ice volume and strength, their near-surface physical properties were found to be sufficiently alike that their radiometric and scattering characteristics were almost identical.
    Barber, D. G., Coauthors, 2012: Change and variability in sea ice during the 2007-2008 Canadian International Polar Year Program.Climatic Change,115,115-133, https://doi.org/10.1007/s10584-012-0477-6.10.1007/s10584-012-0477-6932b444edf0f5d455146c1d5cf14d4cbhttp%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs10584-012-0477-6http://link.springer.com/10.1007/s10584-012-0477-6Abstract2012) we present an overview of the consequences of this observed change and variability on ecosystem function, climatically relevant gas exchange, habitats of primary and apex predators, and impacts on northern peoples. Sea ice-themed research projects within the fourth IPY were designed to be among the most diverse international science programs. They greatly enhanced the exchange of Inuit knowledge and scientific ideas across nations and disciplines. This interdisciplinary and cultural exchange helped to explain and communicate the impacts of a transition of the Arctic Ocean and ecosystem to a seasonally ice-free state, the commensurate replacement of perennial with annual sea ice types and the causes and consequences of this globally significant metamorphosis. This paper presents a synthesis of scientific sea ice research and traditional knowledge results from Canadian-led IPY projects between 2007 and 2009. In particular, a summary of sea ice trends, basin-wide and regional, is presented in conjunction with Inuit knowledge of sea ice, gathered from communities in northern Canada. We focus on the recent observed changes in sea ice and discuss some of the causes of this change including atmospheric and oceanic forcing of both dynamic and thermodynamic forcing on the ice. Pertinent results include: 1) In the Amundsen Gulf, at the western end of the Northwest Passage, open water persists longer than normal and winter sea ice is thinner and more mobile. 2) Large areas of summer sea ice are becoming heavily decayed during summer and can be broken up by long-period waves being generated in the now extensive open water areas of the Chukchi Sea. 3) Cyclones play an important role in flaw leads攔egions of open water between pack ice and land-fast ice. They delay the formation of new ice and the growth of multi-year ice. 4) Feedbacks involving the increased period of open water, long-period wave generation, increased open-ocean roughness, and the precipitation of autumn snow are all partially responsible for the observed reduction in multiyear sea ice. 5) The atmosphere is observed as remaining generally stable throughout the winter, preventing vertical entrainment of moisture above the surface.
    Belchansky G. I., D. C. Douglas, and N. G. Platonov, 2005: Spatial and temporal variations in the age structure of Arctic sea ice,Geophys. Res. Lett.,32,L18504, https://doi.org/10.1029/2005GL023976.10.1029/2005GL02397651731aec1a678b171b2f3096d2def7a7http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005GL023976%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2005GL023976/fullSpatial and temporal variations in the age structure of Arctic sea ice are investigated using a new reverse chronology algorithm that tracks ice-covered pixels to their location and date of origin based on ice motion and concentration data. The Beaufort Gyre tends to harbor the oldest (>10 years old) sea ice in the western Arctic while direct ice advection pathways toward the Transpolar Drift Stream maintain relatively young (10 years old (10+ year age class) were observed during 1989-2003. Since the mid-1990s, losses to the 10+ year age class lacked compensation by recruitment due to a prior depletion of all mature (6-10 year) age classes. Survival of the 1994 and 1996-1998 sea ice generations reestablished most mature age classes, and thereby the potential to increase extent of the 10+ year age class during the mid-2000s.
    Comiso J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in the Arctic sea ice cover,Geophys. Res. Lett.,35,L01703, https://doi.org/10.1029/2007GL031972.10.1029/2007GL031972343feb606e7415f45d25b40e917085b6http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL031972%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2007GL031972/pdfSatellite data reveal unusually low Arctic sea ice coverage during the summer of 2007, caused in part by anomalously high temperatures and southerly winds. The extent and area of the ice cover reached minima on 14 September 2007 at 4.1 10kmand 3.6 10km, respectively. These are 24% and 27% lower than the previous record lows, both reached on 21 September 2005, and 37% and 38% less than the climatological averages. Acceleration in the decline is evident as the extent and area trends of the entire ice cover (seasonal and perennial ice) have shifted from about -2.2 and -3.0% per decade in 1979-1996 to about -10.1 and -10.7% per decade in the last 10 years. The latter trends are now comparable to the high negative trends of -10.2 and -11.4% per decade for the perennial ice extent and area, 1979-2007.
    Fowler C., 2008: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors. National Snow and Ice Data Center, Boulder, Colorado USA. Digital Media.
    Galley R. J., B. G. T. Else, S. J. Prinsenberg, and D. G. Barber, 2013: Summer sea ice concentration, motion, and thickness near areas of proposed offshore oil and gas development in the Canadian Beaufort Sea-2009. Arctic, 66( 2), 105- 116.
    Hibler III, W. D., 1979: A dynamic thermodynamic sea ice model. J. Phys. Oceanogr., 9, 815-846,https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0,CO;2.10.1175/1520-0485(1979)0092.0.CO;275fd3747b9af4a3996c9e769300bfad2http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F80014070432%2Fhttp://journals.ametsoc.org/doi/abs/10.1175/1520-0485%281979%29009%3C0815%3AADTSIM%3E2.0.CO%3B2Abstract A numerical model for the simulation of sea ice circulation and thickness over a seasonal cycle is presented. This model is used to investigate the effects of ice dynamics on Arctic ice thickness and air-sea heat flux characteristics by carrying out several numerical simulations over the entire Arctic Ocean region. The essential idea in the model is to couple the dynamics to the ice thickness characteristics by allowing the ice interaction to become stronger as the ice becomes thicker and/or contains a lower areas percentage of thin ice. The dynamics in turn causes high oceanic heat losses in regions of ice divergence and reduced heat losses in regions of convergence. TO model these effects consistently the ice is considered to interact in a plastic manner with the plastic strength chosen to depend on the ice thickness and concentration. The thickness and concentration, in turn, evolve according to continuity equations which include changes in ice mass and percent of open water due to advection, ice deformation and thermodynamic effects. For the standard experiment an integration of eight years in length is performed at one day timesteps and 125 km resolution in order to obtain a cyclic equilibrium. A zero ice strength condition is used at the Greenland-Spitsbergen passage to allow natural outflow or inflow. Several other shorter experiments, including a case without open water effects, are also run for comparison. Input fields consist of observed time varying geostrophic winds over a one year period, fixed geostrophic ocean currents, and geographically invariant ice growth rates dependent on ice thickness and season. Many of the observed features of the circulation and thickness of the Arctic ice cover are reproduced by the model. The average annual drift shows the classic anticyclonic ice flow in the Beaufort Sea together with a transpolar drift of ice from the Siberian coast toward the Greenland Sea. In addition, the nonlinear plastic rheology allows the formation of a shear zone (velocity discontinuity) from time to time off the North Slope of Alaska. The average rate of ice export out of the basin is 0.1 Sv in reasonable agreement with observational estimates. Geographical ice thickness contours show ice in excess of 6 m along the Canadian Archipelago with thicknesses decreasing to 2 m near the Siberian coast. The form of these contours is in good agreement with that estimated from submarine sonar data and aerial ridge surveys. In summer a low compactness region of up to 50% open water builds up off the Alaskan and Siberian coasts, in general agreement with satellite-derived ice concentration charts. Further from shore, smaller, but still significant, amounts (10%) of open water also form in summer. An important, less verifiable characteristic is that the annual net ice production is dominated by the North Slope and Siberian nearshore regions where, on the average, offshore advection creates open water and thinner ice. Overall the simulation results suggest that lateral heat transport due to ice motion is of the same order of magnitude as vertical air-sea heat fluxes.
    Holland, M. M., C. M. Bitz, E. C. Hunke, W. H. Lipscomb, J. L. Schramm, 2006: Influence of the sea ice thickness distribution on polar climate in CCSM3.J. Climate,19(11),2398-2414, https://doi.org/10.1175/JCLI3751.1.10.1175/JCLI3751.14d5345a88277467427f1244680234b57http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006JCli...19.2398Hhttp://journals.ametsoc.org/doi/abs/10.1175/JCLI3751.1The sea ice simulation of the Community Climate System Model version 3 (CCSM3) T42-gx1 and T85-gx1 control simulations is presented and the influence of the parameterized sea ice thickness distribution (ITD) on polar climate conditions is examined. This includes an analysis of the change in mean climate conditions and simulated sea ice feedbacks when an ITD is included. It is found that including a representation of the subgrid-scale ITD results in larger ice growth rates and thicker sea ice. These larger growth rates represent a higher heat loss from the ocean ice column to the atmosphere, resulting in warmer surface conditions. Ocean circulation, most notably in the Southern Hemisphere, is also modified by the ITD because of the influence of enhanced high-latitude ice formation on the ocean buoyancy flux and resulting deep water formation. Changes in atmospheric circulation also result, again most notably in the Southern Hemisphere. There are indications that the ITD also modifies simulated sea ice09“related feedbacks. In regions of similar ice thickness, the surface albedo changes at 2XCO2 conditions are larger when an ITD is included, suggesting an enhanced surface albedo feedback. The presence of an ITD also modifies the ice thickness09“ice strength relationship and the ice thickness09“ice growth rate relationship, both of which represent negative feedbacks on ice thickness. The net influence of the ITD on polar climate sensitivity and variability results from the interaction of these and other complex feedback processes.
    Kadko D., P. Swart, 2004: The source of the high heat and freshwater content of the upper ocean at the SHEBA site in the Beaufort Sea in 1997,J. Geophys. Res.,109,C01022, https://doi.org/10.1029/2002JC001734.10.1029/2002JC001734c98bf13db6270ef9907b0e1f0da61a55http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2002JC001734%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2002JC001734/citedbyMeasurements of Be and oxygen isotope (O/O) ratios from the 1997-1998 SHEBA experiment were used to trace the source of the high heat and freshwater content of the upper ocean observed during the initial occupation of the SHEBA site in October 1997. The evidence suggests that the heating resulted from local input primarily through extended lead coverage in the late spring and summer of 1997 with no requirement of advective input. The freshening was derived from a large ice melt (1.2 m) that was consistent with the thin ice and extensive melt pond coverage (by then frozen) observed at the site. However, a significant contribution to the freshwater budget (0.8 m) included enhanced input from river runoff during the melt season. This obviates the requirement for an unrealistically large ice melt (藴2 m) to account for the freshwater content of the mixed layer, and would have increased the stratification stability of the upper ocean that in turn would have promoted local heating. The question then arises, however, as to the fate of the significant upper ocean heat at SHEBA in the fall 1997 which resulted from an active heating season. Similar evaluation of the fall 1998 SHEBA site indicate that the ice melt was comparable to that of 1997, but the riverine input and stored water column heat were less than in the previous year.
    Kalnay, E., Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77(4), 437-471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0,CO;2.9bfeacc7ab553b364e43408563ad850bhttp%3A%2F%2Fintl-icb.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F1520-0477%281996%290772.0.CO%3B2%26amp%3Blink_type%3DDOI年度引用
    Kay J. E., M. M. Holland , and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world,Geophys. Res. Lett.,38,L15708, https://doi.org/10.1029/2011GL048008.10.1029/2011GL048008164cb215a0acd03d352352536859f23ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011GL048008%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2011GL048008/fullA climate model (CCSM4) is used to investigate the influence of anthropogenic forcing on late 20th century and early 21st century Arctic sea ice extent trends. On all timescales examined (2-50++ years), the most extreme negative observed late 20th century trends cannot be explained by modeled natural variability alone. Modeled late 20th century ice extent loss also cannot be explained by natural causes alone, but the six available CCSM4 ensemble members exhibit a large spread in their late 20th century ice extent loss. Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability explains approximately half of the observed 1979-2005 September Arctic sea ice extent loss. In a warming world, CCSM4 shows that multi-decadal negative trends increase in frequency and magnitude, and that trend variability on 2-10 year timescales increases. Furthermore, when internal variability counteracts anthropogenic forcing, positive trends on 2-20 year timescales occur until the middle of the 21st century.
    Kinnard C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. de Vernal, and L. G. Thompson, 2011: Reconstructed changes in Arctic sea ice over the past 1,450 years. Nature, 479, 509-512, Https://doi.org/10.1038/nature10581.10.1038/nature1058122113692a661d02bec203aafb67070df718fadd5http%3A%2F%2Fwww.nature.com%2Fnature%2Fjournal%2Fv479%2Fn7374%2Fnature10581%2Fmetricshttp://www.nature.com/nature/journal/v479/n7374/nature10581/metricsArctic sea ice extent is now more than two million square kilometres less than it was in the late twentieth century, with important consequences for the climate, the ocean and traditional lifestyles in the Arctic. Although observations show a more or less continuous decline for the past four or five decades, there are few long-term records with which to assess natural sea ice variability. Until now, the question of whether or not current trends are potentially anomalous has therefore remained unanswerable. Here we use a network of high-resolution terrestrial proxies from the circum-Arctic region to reconstruct past extents of summer sea ice, and show that-although extensive uncertainties remain, especially before the sixteenth century-both the duration and magnitude of the current decline in sea ice seem to be unprecedented for the past 1,450 years. Enhanced advection of warm Atlantic water to the Arctic seems to be the main factor driving the decline of sea ice extent on multidecadal timescales, and may result from nonlinear feedbacks between sea ice and the Atlantic meridional overturning circulation. These results reinforce the assertion that sea ice is an active component of Arctic climate variability and that the recent decrease in summer Arctic sea ice is consistent with anthropogenically forced warming.
    Kumar, A., Coauthors, 2010: Contribution of sea ice loss to Arctic amplification,Geophys. Res. Lett.,37,L21701, https://doi.org/10.1029/2010GL045022.10.1029/2010GL045022839e22a6dd0175cb443a8e41b6517af4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2010GL045022%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2010GL045022/pdfAtmospheric climate models are subjected to the observed sea ice conditions during 2007 to estimate the regionality, seasonality, and vertical pattern of temperature responses to recent Arctic sea ice loss. It is shown that anomalous sea ice conditions accounted for virtually all of the estimated Arctic amplification in surface-based warming over the Arctic Ocean, and furthermore they accounted for a large fraction of Arctic amplification occurring over the high-latitude land between 60ºN and the Arctic Ocean. Sea ice loss did not appreciably contribute to observed 2007 land temperature warmth equatorward of 60ºN. Likewise, the observed warming of the free atmosphere attributable to sea ice loss is confined to Arctic latitudes, and is vertically confined to the lowest 1000 m. The results further highlight a strong seasonality of the temperature response to the 2007 sea ice loss. A weak signal of Arctic amplification in surface based warming is found during boreal summer, whereas a dramatically stronger signal is shown to develop during early autumn that persisted through December even as sea ice coverage approached its climatological values in response to the polar night.
    Kwok R., G. Spreen, and S. Pang, 2013: Arctic sea ice circulation and drift speed: Decadal trends and ocean currents.J. Geophys. Res.,118,2408-2425, https://doi.org/10.1002/jgrc.20191.10.1002/jgrc.20191ae5542a4cba74a368c8d8f452f118b16http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjgrc.20191%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1002/jgrc.20191/abstractWe examine the basinwide trends in sea ice circulation and drift speed and highlight the changes between 1982 and 2009 in connection to regional winds, multiyear sea ice coverage, ice export, and the thinning of the ice cover. The polarity of the Arctic Oscillation (AO) is used as a backdrop for summarizing the variance and shifts in decadal drift patterns. The 28-year circulation fields show a net strengthening of the Beaufort Gyre and the Transpolar Drift, especially during the last decade. The imprint of the arctic dipole anomaly on the mean summer circulation is evident (2001-2009) and enhances summer ice area export at the Fram Strait. Between 2001 and 2009, the large spatially averaged trends in drift speeds (winter: +23.6%/decade, summer: +17.7%/decade) are not explained by the much smaller trends in wind speeds (winter: 1.46%/decade, summer: -3.42%/decade). Notably, positive trends in drift speed are found in regions with reduced multiyear sea ice coverage. Over 90% of the Arctic Ocean has positive trends in drift speed and negative trends in multiyear sea ice coverage. The increased responsiveness of ice drift to geostrophic wind is consistent with a thinner and weaker seasonal ice cover and suggests large-scale changes in the air-ice-ocean momentum balance. The retrieved mean ocean current field from decadal-scale average ice motion captures a steady drift from Siberia to the Fram Strait, an inflow north of the Bering Strait, and a westward drift along coastal Alaska. This mean current is comparable to geostrophic currents from satellite-derived dynamic topography.
    Lei R. B., Z. H. Zhang, I. Matero, B. Cheng, Q. Li, and W. F. Huang, 2012: Reflection and transmission of irradiance by snow and sea ice in the central Arctic Ocean in summer 2010,Polar Research,31,17325, v31i0. 17325.https://doi.org/10.3402/polar.10.3402/polar.v31i0.17325ab6f3246f4a2f70de6ecbdf5419262c8http%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.3402%2Fpolar.v31i0.17325https://www.tandfonline.com/doi/full/10.3402/polar.v31i0.17325Reflection and transmission of irradiance by the combined snow and sea ice layer were measured at an ice camp (ca. 10 days) and several short-term stations (ca. 2 h) established in the western sector of the Arctic Ocean above 80#x00B0;N during the 2010 summer. These measurements were made with an intention to quantify the apparent optical properties of snow and sea ice, and to evaluate their roles in the mass balance of snow-covered sea ice in the High Arctic. The integrated 350#x2013;920 nm albedo ranged from 0.54 to 0.88, and was primarily dependent on the geophysical properties of snow, but not those of sea ice. This implies that all snow cover was still optically thick, even though snow melting had commenced at all measurement sites. For sea ice about 1.66 m thick and covered by 2.5#x2013;8.5 cm of snow at the ice camp, the integrated 350#x2013;920 nm transmittance ranged from 0.017 to 0.065. Rapid snow melting resulting from an event of slight drizzle doubled the available solar irradiance under the ice (from ca. 3.6 to 7.2 W#x00B7;m#x2212;2), which further accelerated ice-bottom decay. During the measurement at the camp, the temporally averaged incident solar irradiance at 320#x2013;950 nm was 110.6#x00B1;33.6 W#x00B7;m#x2212;2, 29.2#x00B1;2.9% of which was absorbed by snow and sea ice and utilized to melt snow and sea ice. The melting of snow and sea ice had a distinctly greater effect on the spectral reflection and transmission for the near-infrared spectrum than for the ultraviolet and visible spectra.
    Lemke, P., Coauthors, 2007: Observations: Changes in snow, ice and frozen ground. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Soloon et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, USA, 339- 383.
    Leppäranta, M., 2005: The Drift of Sea Ice. Springer-Verlag, 266 pp.
    Lindsay R. W., J. Zhang, 2005: The thinning of Arctic sea ice,1988-2003: have we passed a tipping point? J. Climate,18(22),4879-4894,1175/JCLI3587. 1.https://doi.org/10.10.1063/1.3653854867bcc21e4302a16da0b57692779a766http%3A%2F%2Fscitation.aip.org%2Fjournals%2Fdoc%2FPHTOAD-ft%2Fvol_64%2Fiss_4%2F36_1.shtmlhttp://scitation.aip.org/journals/doc/PHTOAD-ft/vol_64/iss_4/36_1.shtmlThe surplus heat needed to explain the loss of Arctic sea ice during the past few decades is on the order of 1 W/m 2 . Observing, attributing, and predicting such a small amount of energy remain daunting problems.
    Martin T., R. Gerdes, 2007: Sea ice drift variability in Arctic Ocean Model Intercomparison Project models and observations,J. Geophys. Res.,112,C04S10, https://doi.org/10.1029/2006JC003617.10.1029/2006JC0036173c458a94ed770793e6f61f68e814914ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006JC003617%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2006JC003617/pdfDrift is a prominent parameter characterizing the Arctic sea ice cover that has a deep impact on the climate system. Hence, it is a key issue to both, the remote sensing as well as the modeling community, to provide reliable sea ice drift fields. This study focuses on the comparison of sea ice drift results from different sea ice-ocean coupled models and the validation with observational data in the period 19792001. The models all take part in the Arctic Ocean Model Intercomparison Project (AOMIP) and the observations are mainly based on satellite imagery. According to speed distributions one class of models has a mode at drift speeds around 3 cm/s and a short tail towards higher speeds. Another class shows a more even frequency distribution with large probability of drift speeds of 10 to 20 cm/s. Observations clearly agree better with the first class of model results. Reasons for these differences are manifold and lie in discrepancies of wind stress forcing as well as sea ice model characteristics and sea ice-ocean coupling. Moreover, we investigated the drift patterns of anticyclonic and cyclonic wind-driven regimes. The models are capable of producing realistic drift pattern variability. The winter of 1994/95 stands out because of its maximum in Fram Strait ice export. Although export estimates of some models agree with observations, the corresponding inner Arctic drift pattern is not reproduced. The reason for this is found in the wind forcing as well as in differences in ocean velocities.
    Maslanik J. A., J. Stroeve, C. Fowler, and W. Emery, 2011: Distribution and trends in Arctic sea ice age through spring 2011,Geophys. Res. Lett.,38,L13502, https://doi.org/10.1029/2011GL047735.10.1029/2011GL047735f55ad301b895ca4b22e29f85dbec421chttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011GL047735%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2011GL047735/fullAnalysis of a satellite-derived record of sea ice age for 1980 through March 2011 shows continued net decrease in multiyear ice coverage in the Arctic Ocean, with particularly extensive loss of the oldest ice types. The fraction of total ice extent made up of multiyear sea ice in March decreased from about 75% in the mid 1980s to 45% in 2011, while the proportion of the oldest ice declined from 50% of the multiyear ice pack to 10%. These losses in the oldest ice now extend into the central Arctic Ocean and adjacent to the Canadian Archipelago; areas where the ice cover was relatively stable prior to 2007 and where long-term survival of sea ice through summer is considered to be most likely. Following record-minimum multiyear ice coverage in summer 2008, the total multiyear ice extent has increased to amounts consistent with the negative trend from 2001-2006, with an increasing proportion of older ice types. This implies some ability for the ice pack to recover from extreme conditions. This recovery has been weakest in the Beaufort Sea and Canada Basin though, with multiyear ice coverage decreasing by 83% from 2002 to 2009 in the Canada Basin, and with more multiyear ice extent now lost in the Pacific sector than elsewhere in the Arctic Ocean.
    Meier W. N., 2005: Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in Arctic peripheral seas.IEEE Transactions on Geoscience and Remote Sensing,43(8),1324-1337, 2005. 846151.https://doi.org/10.1109/TGRS.10.1109/TGRS.2005.846151a342aec0608fb122345cec8b5a7cf12dhttp%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F1433030%2Fhttp://ieeexplore.ieee.org/document/1433030/An accurate representation of sea ice concentration is valuable to operational ice analyses, process studies, model inputs, and detection of long-term climate change. Passive microwave imagery, such as from the Special Sensor Microwave/Imager (SSM/I), are particularly valuable for monitoring of sea ice conditions because of their daily, basin-scale coverage under all sky conditions. SSM/I-derived sea ice concentration estimates using four common algorithms [Bootstrap (BT), Cal/Val (CV), NASA Team (NT), and NASA Team 2 (N2)] are compared with concentrations computed from Advanced Very High Resolution Radiometer (AVHRR) visible and infrared imagery. Comparisons are made over approximately an eight-month period in three regions of the Arctic and focus on areas near the ice edge where differences between the algorithms are likely to be most apparent. The results indicate that CV and N2 have the smallest mean error relative to AVHRR. CV tends to overestimate concentration, while the other three algorithms underestimate concentration. NT has the largest underestimation of nearly 10% on average and much higher in some instances. In most cases, mean errors of the SSM/I algorithm were significantly different from each other at the 95% significance level. The BT algorithm has the lowest error standard deviation, but none of the considered algorithms was found to have statistically significantly different error standard deviations in most cases. This indicates that spatial resolution is likely a limiting factor of SSM/I in regions near the ice edge in that none of the algorithms satisfactorily resolve mixed pixels. Statistical breakdowns by season, region, ice conditions, and AVHRR scene generally agree with the overall results. Representative case studies are presented to illustrate the statistical results.
    Nghiem S. V., I. G. Rigor, D. K. Perovich, P. Clemente-Colón, J. W. Weatherly, and G. Neumann, 2007: Rapid reduction of Arctic perennial sea ice,Geophys. Res. Lett.,34,L19504, https://doi.org/10.1029/2007GL031138.10.1029/2007GL031138e521f70cca9da03a13a2f4af13a27160http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL031138%2Fpdfhttp://doi.wiley.com/10.1029/2007GL031138The extent of Arctic perennial sea ice, the year-round ice cover, was significantly reduced between March 2005 and March 2007 by 1.08 10km, a 23% loss from 4.69 10kmto 3.61 10km, as observed by the QuikSCAT/SeaWinds satellite scatterometer (QSCAT). Moreover, the buoy-based Drift-Age Model (DM) provided long-term trends in Arctic sea-ice age since the 1950s. Perennial-ice extent loss in March within the DM domain was noticeable after the 1960s, and the loss became more rapid in the 2000s when QSCAT observations were available to verify the model results. QSCAT data also revealed mechanisms contributing to the perennial-ice extent loss: ice compression toward the western Arctic, ice loading into the Transpolar Drift (TD) together with an acceleration of the TD carrying excessive ice out of Fram Strait, and ice export to Baffin Bay. Dynamic and thermodynamic effects appear to be combining to expedite the loss of perennial sea ice.
    Olason E., D. Notz, 2014: Drivers of variability in Arctic sea-ice drift speed.J. Geophys. Res.,119,5755-5775, https://doi.org/10.1002/2014JC009897.10.1002/2014JC009897c627039f6ace043b5e65f781f8264dfbhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JC009897%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/2014JC009897/pdfAbstract We explore the main drivers of seasonal and long-term variations in basin-scale Arctic sea-ice drift speed. To do so, we examine the relationship between the observed time-varying area-mean ice drift speed in the central Arctic and observed thickness and concentration as well as surface wind stress. Drift speeds are calculated from the positions of drifting buoys, thickness is based on submarine observations, concentration on satellite observations, and the wind stress comes from a global reanalysis. We find that seasonal changes in drift speed are correlated primarily with changes in concentration when concentration is low and with changes in thickness otherwise. The correlation between drift speed and concentration occurs because changing concentration changes how readily the ice responds to the synoptic-scale forcing of the atmosphere. Drift speed is correlated with neither concentration nor thickness in April and May. We show this behavior to be correlated with a decrease in the localization of deformation. This indicates that the increase in drift speed is caused by newly formed fractures not refreezing, leading to an overall reduced ice-cover strength without a detectable change in ice concentration. We show that a strong long-term trend exists in months of relatively low ice concentration. Using our analysis of the seasonal cycle, we show that the trend in concentration drives a significant portion of the drift-speed trend, possibly reinforced by a trend in cyclone activity. Hence, the trend in drift speed in this period is primarily caused by increased synoptic-scale movement of the ice pack.
    Overland, J. E., M. Y. Wang, 2007: Future regional Arctic sea ice declines,Geophys. Res. Lett.,34,L17705, https://doi.org/10.1029/2007GL030808.10.1029/2007GL030808d8237de19cbe18c7b1dd6b4daf4cfb93http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL030808%2Fpdfhttp://doi.wiley.com/10.1029/2007GL030808Because animals and humans respond to seasonally and regionally varying climates, it is instructive to assess how much confidence we can have in regional projections of sea ice from the 20 models provided through the International Panel on Climate Change Fourth Assessment Report (AR4) process (IPCC 2007). Based on the selection of a subset models that closely simulate observed regional ice concentrations for 1979-1999, we find considerable evidence for loss of sea ice area of greater than 40% by 2050 in summer for the marginal seas of the Arctic basin. This conclusion is supported by consistency in the selection of the same models across different regions, and the importance of thinning ice and increased open water at mid-century to the rate of ice loss. With less confidence, we find that the Bering, Okhotsk and Barents Seas have a similar 40% loss of sea ice area by 2050 in winter. Baffin Bay/Labrador shows little change compared to current conditions. These seasonal ice zones have large interannual/decadal variability in addition to trends. Large model-to-model differences were seen for the Kara/Laptev Seas and East Greenland. With a careful evaluation process, AR4 sea ice projections have some utility for use in assessing potential impacts over large Arctic subregions for a 2020-2050 time horizon.
    Overland, J. E., M. Y. Wang, 2013: When will the summer Arctic be nearly sea ice free? Geophys.Res. Lett.,40,2097-2101, https://doi.org/10.1002/grl.50316.10.1002/grl.5031650025e8e79b58acde831b9bb641a2bechttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50316%2Ffullhttp://doi.wiley.com/10.1002/grl.50316The observed rapid loss of thick multiyear sea ice over the last 7years and the September 2012 Arctic sea ice extent reduction of 49% relative to the 1979-2000 climatology are inconsistent with projections of a nearly sea ice-free summer Arctic from model estimates of 2070 and beyond made just a few years ago. Three recent approaches to predictions in the scientific literature are as follows: (1) extrapolation of sea ice volume data, (2) assuming several more rapid loss events such as 2007 and 2012, and (3) climate model projections. Time horizons for a nearly sea ice-free summer for these three approaches are roughly 2020 or earlier, 203010years, and 2040 or later. Loss estimates from models are based on a subset of the most rapid ensemble members. It is not possible to clearly choose one approach over another as this depends on the relative weights given to data versus models. Observations and citations support the conclusion that most global climate model results in the CMIP5 archive are too conservative in their sea ice projections. Recent data and expert opinion should be considered in addition to model results to advance the very likely timing for future sea ice loss to the first half of the 21st century, with a possibility of major loss within a decade or two.
    Perovich D. K., T. C. Grenfell, J. A. Richter-Menge, B. Light, W. B. Tucker III, and H. Eicken, 2003: Thin and thinner: Sea ice mass balance measurements during SHEBA,J. Geophys. Res.,108(C3),8050, https://doi.org/10.1029/2001JC001079.10.1029/2001JC00107989fa855b9932e0708e1f6a53e5daa440http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2001JC001079%2Ffullhttp://doi.wiley.com/10.1029/2001JC001079ABSTRACT 1] As part of a large interdisciplinary study of the Surface Heat Budget of the Arctic Ocean (SHEBA), we installed more than 135 ice thickness gauges to determine the sea ice mass balance. While installing these gauges during the fall of 1997, we found that much of the multiyear ice cover was only 1 m thick, considerably thinner than expected. Over the course of the yearlong field experiment we monitored the mass balance for a wide variety of ice types, including first-year ice, ponded ice, unponded ice, multiyear ice, hummocks, new ridges, and old ridges. Initial ice thicknesses for these sites ranged from 0.3 to 8 m, and snow depths varied from a few centimeters to more than a meter. However, for all of their differences and variety, these thickness gauges sites shared a common trait: at every site, there was a net thinning of the ice during the SHEBA year. The thin ice found in October 1997 was even thinner in October 1998. The annual cycle of ice thickness was also similar at all sites. There was a steady increase in thickness through the winter that gradually tapered off in the spring. This was followed by a steep drop off in thickness during summer melt and another tapering in late summer and early fall as freeze-up began. Maximum surface melting was in July, while bottom ablation peaked in August. Combining results from the sites, we found an average winter growth of 0.51 m and a summer melt of 1.26 m, which consisted of 0.64 m of surface melt and 0.62 m of bottom melt. There was a weak trend for thicker ice to have less winter growth and greater net loss for the year; however, ice growth was also impacted by the snow depth. Considerable variability was observed between sites in both accretion and ablation. The total accretion during the 9-month growth season ranged from zero for thick ridged ice to more than a meter for young ice. Ponds tended to have a large amount of surface melting, while ridges had considerable bottom ablation.
    Perovich D. K., J. A. Richter-Menge, K. F. Jones, and B. Light, 2008: Sunlight,water, and ice: Extreme Arctic sea ice melt during the summer of 2007.Geophys. Res. Lett.,35,L11501, https://doi.org/10.1029/2008GL034007.10.1029/2008GL034007cc68114b793d28ed8d2d75fe81ff85dfhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008GL034007%2Ffullhttp://doi.wiley.com/10.1029/2008GL034007The summer extent of the Arctic sea ice cover, widely recognized as an indicator of climate change, has been declining for the past few decades reaching a record minimum in September 2007. The causes of the dramatic loss have implications for the future trajectory of the Arctic sea ice cover. Ice mass balance observations demonstrate that there was an extraordinarily large amount of melting on the bottom of the ice in the Beaufort Sea in the summer of 2007. Calculations indicate that solar heating of the upper ocean was the primary source of heat for this observed enhanced Beaufort Sea bottom melting. An increase in the open water fraction resulted in a 500% positive anomaly in solar heat input to the upper ocean, triggering an ice-albedo feedback and contributing to the accelerating ice retreat.
    Raddatz R. L., R. J. Galley, L. M. Cand lish, M. G. Asplin, and D. G. Barber, 2013: Integral profile estimates of latent heat flux under clear skies at an unconsolidated sea-ice surface. Atmos.-Ocean,, 51( 4), 239- 248.10.1080/07055900.2013.785383http://www.tandfonline.com/doi/abs/10.1080/07055900.2013.785383
    Rigor I., 2002: IABP drifting buoy pressure,temperature, position, and interpolated ice velocity.Compiled by the Polar Science Center,Applied Physics Laboratory,University of Washington, Seattle, in association with NSIDC. National Snow and Ice Data Center, Boulder, CO, .https://dx.doi.org/10.7265/N53X84K7f04be9ff4c4d0f07656b8878417e7984http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F291451496_IABP_drifting_buoy_pressure_temperature_position_interpolated_ice_velocityhttp://www.researchgate.net/publication/291451496_IABP_drifting_buoy_pressure_temperature_position_interpolated_ice_velocityAbstract Arctic Ocean buoy data products are 12-hourly interpolated pressure, temperature, position, and ice velocity grids. Buoys deployed on ice floes measure atmospheric pressure and temperature at ocean surface; an average of 20 buoys are in service at any time. Grid is 2 minutes longitude by 10 minutes latitude. The current pressure and temperature analysis use the synoptic buoy observations supplemented by the NMC surface fields. The buoy ice velocity fields are analyzed from 24-hour displacements. These procedures are outlined in the 1982 buoy report. The pressure fields have been reanalyzed in 1996 to correct a coding error in the calculation of the second derivative, dpp/dxy; these data are available now. Temperature fields for 1979-1986 have been analyzed using the 1982 procedure, but a new analysis combining synoptic buoy data and land station observations will be available in early 1997. As of July 1997 gridded data are available for 1979-1996. Data are ASCII (tabular); format is described in annual data reports. An online guide is available for this data set. Data are collected and processed as part of the International Arctic Buoy Program at the University of Washington, Polar Science Center, and are available via FTP. The URL for this data set is http://www-nsidc.colorado.edu/NSIDC/Catalog/ENTRIES/G00791.html. Contact: NSIDC, CIRES, Campus Box 449, University of Colorado, Boulder, CO 80309-0449, USA; tel: (303) 492-6199: fax: (303) 492-2468; e-mail: nsidc@@@kryos.colorado.edu; internet: http://www-nsidc.colorado.edu/ (source: Global Change Master Directory, http://gcmd.nasa.gov).
    Spreen G., L. Kaleschke, and G. Heygster, 2008: Sea ice remote sensing using AMSR-E 89-GHz channels,J. Geophys. Res.,113,C02S03, https://doi.org/10.1029/2005JC003384.10.1029/2005JC0033848974bd5f0cffd1fc1c3c1370332ee6e8http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005JC003384%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2005JC003384/pdfRecent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002-2006) between ASI and NASA-Team 2 are below -2 卤 8.8%, and between ASI and Bootstrap are 1.7 卤 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.
    Stroeve J., M. M. Holland , W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice decline: Faster than forecast,Geophys. Res. Lett.,34,L09501, https://doi.org/10.1029/2007GL029703.10.1029/2007GL029703fc00b9549d32b299476a36eaa2e07a43http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL029703%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2007GL029703/fullFrom 1953 to 2006, Arctic sea ice extent at the end of the melt season in September has declined sharply. All models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) show declining Arctic ice cover over this period. However, depending on the time window for analysis, none or very few individual model simulations show trends comparable to observations. If the multi-model ensemble mean time series provides a true representation of forced change by greenhouse gas (GHG) loading, 33-38% of the observed September trend from 1953-2006 is externally forced, growing to 47-57% from 1979-2006. Given evidence that as a group, the models underestimate the GHG response, the externally forced component may be larger. While both observed and modeled Antarctic winter trends are small, comparisons for summer are confounded by generally poor model performance.
    Tietsche S., D. Notz, J. H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Arctic summer sea ice,Geophys. Res. Lett.,38,L02707, https://doi.org/10.1029/2010GL045698.10.1029/2010GL045698367784b6b1ae046538a7848f404a0707http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2010GL045698%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2010GL045698/pdfWe examine the recovery of Arctic sea ice from prescribed ice-free summer conditions in simulations of 21st century climate in an atmosphere–ocean general circulation model. We find that ice extent recovers typically within two years. The excess oceanic heat that had built up during the ice-free summer is rapidly returned to the atmosphere during the following autumn and winter, and then leaves the Arctic partly through increased longwave emission at the top of the atmosphere and partly through reduced atmospheric heat advection from lower latitudes. Oceanic heat transport does not contribute significantly to the loss of the excess heat. Our results suggest that anomalous loss of Arctic sea ice during a single summer is reversible, as the ice–albedo feedback is alleviated by large-scale recovery mechanisms. Hence, hysteretic threshold behavior (or a “tipping point”) is unlikely to occur during the decline of Arctic summer sea-ice cover in the 21st century.
    Tschudi M. A., J. C. Stroeve, and J. S. Stewart, 2016: Relating the age of Arctic Sea ice to its thickness,as measured during NASA's ICESat and IceBridge campaigns.Remote Sensing,8,457, https://doi.org/10.3390/rs8060457.10.3390/rs8060457ae787fdaab71dc51d7f986667bea6c86http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F303597522_Relating_the_Age_of_Arctic_Sea_Ice_to_its_Thickness_as_Measured_during_NASA%27s_ICESat_and_IceBridge_Campaignshttp://www.mdpi.com/2072-4292/8/6/457Recent satellite observations yield estimates of the distribution of sea ice thickness across the entire Arctic Ocean. While these sensors were only placed in operation within the last few years, information from other sensors may assist us with estimating the distribution of sea ice thickness in the Arctic beginning in the 1980s. A previous study found that the age of sea ice is correlated to sea ice thickness from 2003 to 2006, but an extension of the temporal analysis is needed to better quantify this relationship and its variability from year to year. Estimates of the ice age/thickness relationship may allow the thickness record to be extended back to 1985, the beginning of our ice age dataset. Comparisons of ice age and thickness estimates derived from both ICESat (2004–2008) and IceBridge (2009–2015) reveal that the relationship between age and thickness differs between these two campaigns, due in part to the difference in area of coverage. Nonetheless, sea ice thickness and age exhibit a direct relationship when compared on pan-Arctic or regional spatial scales.
    Vihma T., 2014: Effects of Arctic sea ice decline on weather and climate: A review.Surveys in Geophysics,35,1175-1214, https://doi.org/10.1007/s10712-014-9284-0.10.1007/s10712-014-9284-077da958a10209d7918a429ac5f8df01chttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10712-014-9284-0http://link.springer.com/10.1007/s10712-014-9284-0The areal extent, concentration and thickness of sea ice in the Arctic Ocean and adjacent seas have strongly decreased during the recent decades, but cold, snow-rich winters have been common over mid-latitude land areas since 2005. A review is presented on studies addressing the local and remote effects of the sea ice decline on weather and climate. It is evident that the reduction in sea ice cover has increased the heat flux from the ocean to atmosphere in autumn and early winter. This has locally increased air temperature, moisture, and cloud cover and reduced the static stability in the lower troposphere. Several studies based on observations, atmospheric reanalyses, and model experiments suggest that the sea ice decline, together with increased snow cover in Eurasia, favours circulation patterns resembling the negative phase of the North Atlantic Oscillation and Arctic Oscillation. The suggested large-scale pressure patterns include a high over Eurasia, which favours cold winters in Europe and northeastern Eurasia. A high over the western and a low over the eastern North America have also been suggested, favouring advection of Arctic air masses to North America. Mid-latitude winter weather is, however, affected by several other factors, which generate a large inter-annual variability and often mask the effects of sea ice decline. In addition, the small sample of years with a large sea ice loss makes it difficult to distinguish the effects directly attributable to sea ice conditions. Several studies suggest that, with advancing global warming, cold winters in mid-latitude continents will no longer be common during the second half of the twenty-first century. Recent studies have also suggested causal links between the sea ice decline and summer precipitation in Europe, the Mediterranean, and East Asia.
    Wang X. Y., J. P. Zhao, 2012: Seasonal and inter-annual variations of the primary types of the Arctic sea-ice drifting patterns. Advances in Polar Science 23(3): 72-81, 2012. 00072.https://doi.org/10.3724/SP.J.1085.10.3724/SP.J.1085.2012.0007205de79ce4d944d8332e507ea4e65c67chttp%3A%2F%2Fwww.cqvip.com%2FQK%2F86655A%2F201202%2F42674119.htmlhttp://www.cqvip.com/QK/86655A/201202/42674119.htmlMonthly mean sea ice motion vectors and monthly mean sea level pressure (SLP) for the period of 1979-2006 are analyzed to investigate the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary sea-ice drifting types in the Arctic Ocean: Beaufort Gyre+Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Symmetric Drift. These four drifting types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre+Transpolar Drift are spring and summer, while the Symmetric Drift trends to prevail in winter, especially in February. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arctic Oscillation (AO) index, with a correlation coefficient of -0.54 and 0.54 (both significant with the confident level of 99%), respectively. When the AO index stays in a high positive (negative) condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise (clockwise) drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.
    Xie H., R. Lei, C. Ke, H. Wang, Z. Li, J. Zhao, and S. F. Ackley, 2013: Summer sea ice characteristics and morphology in the Pacific Arctic sector as observed during the CHINARE 2010 cruise.The Cryosphere,7,1057-1072, .http://doi.org/10.5194/tc-7-1057-201310.5194/tc-7-1057-2013a67dbafb1efdeaf1555b9a1a775573b0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013tcry....7.1057xhttp://www.the-cryosphere.net/7/1057/2013/In the summer of 2010, atmosphere-ice-ocean interaction was studied aboard the icebreaker R/V Xuelong during the Chinese National Arctic Research Expedition (CHINARE), in the sea ice zone of the Pacific Arctic sector between 150 degrees W and 180 degrees W up to 88.5 degrees N. The expedition lasted from 21 July to 28 August and comprised of ice observations and measurements along the cruise track, 8 short-term stations and one 12-day drift station. Ship-based observations of ice thickness and concentration are compared with ice thickness measured by an electromagnetic induction device (EM31) mounted off the ship's side and ice concentrations obtained from AMSR-E. It is found that the modal thickness from ship-based visual observations matches well with the modal thickness from the mounted EM31. A grid of 8 profiles of ice thickness measurements (four repeats) was conducted at the 12-day drift station in the central Arctic (similar to 86 degrees 50' N-87 degrees 20' N) and an average melt rate of 2 cm day(-1), primarily bottom melt, was found. As compared with the 2005 data from the Healy/Oden Trans-Arctic Expedition (HOTRAX) for the same sector but similar to 20 days later (9 August to 10 September), the summer 2010 was first-year ice dominant (vs. the multi-year ice dominant in 2005), 70% or less in mean ice concentration (vs. 90% in 2005), and 94-114 cm in mean ice thickness (vs. 150 cm in 2005). Those changes suggest the continuation of ice thinning, less concentration, and younger ice for the summer sea ice in the sector since 2007 when a record minimum sea ice extent was ob-served. Overall, the measurements provide a valuable dataset of sea ice morphological properties over the Arctic Pacific Sector in summer 2010 and can be used as a benchmark for measurements of future changes.
    Zhao, J. P, T. Li, S. G. Zhang, Y. T. Jiao, 2009: The shortwave solar radiation energy absorbed by packed sea ice in the central Arctic.Advances in Earth Science,24(2),34-42, . (in Chinese with English abstract)https://doi.org/10.3321/j.issn:1001-8166.2009.01.004b922ffab76a3fa0c530d522e4b9c8418http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DXJZ200901006.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-DXJZ200901006.htmThe solar energy is the main energy source to melt sea ice in the Arctic.The solar energy absorbed by the packed ice in the central Arctic is studied in this paper based on the optical observations of the Third Chinese Arctic Expedition on an ice camp during the period of August 21-27,2008.The transmission,albedo,and the absorption rates of the sea ice and their variation with ice thickness are calculated from the observed data.On average,the absorption rate of sea ice for shortwave solar radiation is about 16%,meanwhile,about 77% of the incident energy is reflected back to the space.A three-day optical observation was conducted to determine the amount of the arriving solar radiation.Although the solar radiation arriving on the upper atmosphere was still strong in August,but about 57% of them was reduced by the atmosphere,as the coverage of cloud and fog caused obvious absorption to the shortwave radiation.Therefore,the heat flux absorbed by sea ice was only 10.2 W/m2,corresponding to the heat in melting 2.6 mm ice per day or 1 m ice within 380 days.It means that the weak heat flux did not provide sufficient heat to melt the sea ice there.Therefore,the packed ice still covers the central Arctic Ocean even though the ice coverage becomes nearly the minimum in the whole Arctic.However,the result also indicated that some other factors,if appeared,could cause the increased melting of the packed ice,such as the decrease of cloud and fog,the total melting of snow layer,the reduction of ice thickness,and the increase of the ponds which could especially endanger the permanent packed ice.In the future,it is possible for the sea ice in central Arctic to collapse if more heat is absorbed under the condition different to that of the summer of 2008.
  • [1] LI Yunying, YU Rucong, XU Youping, ZHOU Tianjun, 2005: AREM Simulations of Cloud Features over Eastern China in February 2001, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 260-270.  doi: 10.1007/BF02918515
    [2] Odd Helge OTTER, Helge DRANGE, 2004: A Possible Feedback Mechanism Involving the Arctic Freshwater,the Arctic Sea Ice, and the North Atlantic Drift, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 784-801.  doi: 10.1007/BF02916375
    [3] WU Bingyi, Mark A. JOHNSON, 2010: Distinct Modes of Winter Arctic Sea Ice Motion and Their Associations with Surface Wind Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 211-229.  doi: 10.1007/s00376-009-8179-3
    [4] WU Bingyi, ZHANG Renhe, Bin WANG, 2009: On the Association between Spring Arctic Sea Ice Concentration and Chinese Summer Rainfall: A Further Study, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 666-678.  doi: 10.1007/s00376-009-9009-3
    [5] GAO Yongqi, SUN Jianqi, LI Fei, HE Shengping, Stein SANDVEN, YAN Qing, ZHANG Zhongshi, Katja LOHMANN, Noel KEENLYSIDE, Tore FUREVIK, SUO Lingling, 2015: Arctic Sea Ice and Eurasian Climate: A Review, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 92-114.  doi: 10.1007/s00376-014-0009-6
    [6] Zhuozhuo Lü, Shengping HE, Fei LI, Huijun WANG, 2019: Impacts of the Autumn Arctic Sea Ice on the Intraseasonal Reversal of the Winter Siberian High, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 173-188.  doi: 10.1007/s00376-017-8089-8
    [7] Fei ZHENG, Yue SUN, Qinghua YANG, Longjiang MU, 2021: Evaluation of Arctic Sea-ice Cover and Thickness Simulated by MITgcm, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 29-48.  doi: 10.1007/s00376-020-9223-6
    [8] Michael KELLEHER, James SCREEN, 2018: Atmospheric Precursors of and Response to Anomalous Arctic Sea Ice in CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 27-37.  doi: 10.1007/s00376-017-7039-9
    [9] Hoffman H. N. CHEUNG, Noel KEENLYSIDE, Nour-Eddine OMRANI, Wen ZHOU, 2018: Remarkable Link between Projected Uncertainties of Arctic Sea-Ice Decline and Winter Eurasian Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 38-51.  doi: 10.1007/s00376-017-7156-5
    [10] Wu Bingyi, Wang Jia, 2002: Possible Impacts of Winter Arctic Oscillation on Siberian High, the East Asian Winter Monsoon and Sea-Ice Extent, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 297-320.  doi: 10.1007/s00376-002-0024-x
    [11] Yu Rucong, Jin Xiangze, Zhang Xuehong, 1995: Design and Numerical Simulation of an Arctic Ocean Circulation and Thermodynamic Sea-Ice Model, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 289-310.  doi: 10.1007/BF02656978
    [12] Shengzhe CHEN, Jiping LIU, Yifan DING, Yuanyuan ZHANG, Xiao CHENG, Yongyun HU, 2021: Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 168-186.  doi: 10.1007/s00376-020-0213-5
    [13] Zhu Congwen, Chen Longxun, Nobuo Yamazaki, 1999: The Interdecadal Variation Characteristics of Arctic Sea Ice Cover-ENSO-East Asian Monsoon and Their Interrelationship at Quasi-Four Years Time Scale, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 641-652.  doi: 10.1007/s00376-999-0038-8
    [14] Ni Yunqi, Zhang Qin, Li Yuedong, 1991: A Numerical Study of the Mechanism for the Effect of Northern Winter Arctic Ice Cover on the Global Short-Range Climate Evolution, ADVANCES IN ATMOSPHERIC SCIENCES, 8, 489-498.  doi: 10.1007/BF02919271
    [15] Jiefan YANG, Hengchi LEI, Tuanjie HOU, 2017: Observational Evidence of High Ice Concentration in a Shallow Convective Cloud Embedded in Stratiform Cloud over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 509-520.  doi: 10.1007/s00376-016-6079-x
    [16] Qinghua YANG, Jiping LIU, Matti LEPPÄRANTA, Qizhen SUN, Rongbin LI, Lin ZHANG, Thomas JUNG, Ruibo LEI, Zhanhai ZHANG, Ming LI, Jiechen ZHAO, Jingjing CHENG, 2016: Albedo of Coastal Landfast Sea Ice in Prydz Bay, Antarctica: Observations and Parameterization, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 535-543.  doi: 10.1007/s00376-015-5114-7
    [17] Yan XIA, Yongyun HU, Jiping LIU, Yi HUANG, Fei XIE, Jintai LIN, 2020: Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 505-514.  doi: 10.1007/s00376-019-8251-6
    [18] Xinrong WU, Shaoqing ZHANG, Zhengyu LIU, 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 193-207.  doi: 10.1007/s00376-015-5099-2
    [19] Liu Chunlei, Yao Keya, 1996: Calculation of Ice Crystal Diffraction, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 340-348.  doi: 10.1007/BF02656851
    [20] XU Shiming, SONG Mirong, LIU Jiping, WANG Bin, LI Lijuan, HUANG Wenyu, LIU Li, XIA Kun, XUE Wei, PU Ye, DONG Li, SHEN Si, HU Ning, LIU Mimi, and SUN Wenqi, 2013: Simulation of Sea Ice in FGOALS-g2: Climatology and Late 20th Century Changes, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 658-673.  doi: 10.1007/s00376-013-2158-4

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 24 March 2017
Manuscript revised: 04 August 2017
Manuscript accepted: 02 September 2017
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Record Low Sea-Ice Concentration in the Central Arctic during Summer 2010

  • 1. Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, 266100, China
  • 2. Centre for Earth Observation Science, Faculty of Environment Earth and Resources, University of Manitoba, Winnipeg, Manitoba , R3T 2N2, Canada
  • 3. National Marine Environmental Forecasting Center, China, Beijing, 100081, China
  • 4. Department of Geological Sciences, University of Texas at San Antonio, San Antonio, Texas 78284, U.S.A.

Abstract: The Arctic sea-ice extent has shown a declining trend over the past 30 years. Ice coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1450 years. In the summer of 2010, a very low sea-ice concentration (SIC) appeared at high Arctic latitudes——even lower than that of surrounding pack ice at lower latitudes. This striking low ice concentration——referred to here as a record low ice concentration in the central Arctic (CARLIC)——is unique in our analysis period of 2003-15, and has not been previously reported in the literature. The CARLIC was not the result of ice melt, because sea ice was still quite thick based on in-situ ice thickness measurements. Instead, divergent ice drift appears to have been responsible for the CARLIC. A high correlation between SIC and wind stress curl suggests that the sea ice drift during the summer of 2010 responded strongly to the regional wind forcing. The drift trajectories of ice buoys exhibited a transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector, which appeared to benefit the CARLIC in 2010. Under these conditions, more solar energy can penetrate into the open water, increasing melt through increased heat flux to the ocean. We speculate that this divergence of sea ice could occur more often in the coming decades, and impact on hemispheric SIC and feed back to the climate.

摘要: 在过去30年, 北极海冰范围正在经历减退的趋势. 海冰覆盖范围在2007和2012年达到历史最低值. 这一变化是过去1450年以来唯一的减退事件. 在2010年夏季, 在北极高纬度海域出现非常低的海冰密集度, 甚至比较低纬度的海冰密集度还要低. 这个显著低的海冰密集度在我们研究的2003-2015年间只出现这唯一的一次, 我们将其称为北极中央区极低海冰密集度(CARLIC). 研究结果表明, CARLIC不是海冰融化导致的, 因为发生时海冰还很厚;是海冰的辐散运动导致了CARLIC. 海冰密集度与风应力旋度之间有很好的相关, 表明海冰漂移特性的改变很好地响应局地风强迫. 在大西洋扇区, 海冰的漂移轨迹呈现穿极流的特征, 而在太平洋扇区体现为一个向加拿大北极群岛方向的漂流, 这种漂流方向的不一致有利于形成2010年的CARLIC现象. 在这种海冰减少的条件下, 更多的太阳辐射能进入海洋, 加剧了海冰的融化. 我们推测, 由于海冰在不断减少, 海冰的辐散在未来可能更加频繁, 这种现象还会发生, 并对更大范围的海冰和北极气候产生显著影响.

1. Introduction
  • Arctic sea-ice extent has been on the decline since the late 1970s (Lindsay and Zhang, 2005; Lemke et al., 2007), and has recently been assessed to be unique over at least a 1450-year period, including the relatively warm Medieval Period (Kinnard et al., 2011). The summer sea-ice extent underwent a decreasing trend in the past 30 years, at a rate of more than 70 000 km2 yr-1 (Stroeve et al., 2007), and reached historic minima in summer 2007 (Comiso et al., 2008) and in 2012 (Overland and Wang, 2013). At this rate, a seasonally ice-free Arctic is expected sometime over the next few decades, according to a variety of models (e.g., Overland and Wang, 2007, 2013; Tietsche et al., 2011; Kay et al., 2011). The current metamorphosis from a multiyear-ice-dominated to a first-year-ice-dominated Arctic marine system appears to be a key feature of a warming planet (Lemke et al., 2007). With the reduction in multiyear ice, first-year ice has increased significantly in spatial extent and seasonal periodicity (Maslanik et al., 2011; Tschudi et al., 2016). The thinner ice and low sea-ice concentration (SIC) allow more absorption of solar radiation, which can increase the heat content of the ocean surface mixed layer (Perovich et al., 2008), and feeds back positively to more ice melt and increased energy and mass exchange across the ocean-sea-ice-atmosphere interface (Holland et al., 2006; Kumar et al., 2010, Raddatz et al., 2013).

    Figure 1.  Survey routes of Xue Long during its 2010 Arctic cruise. The blue line in (a) is the northward route and the red line the return route. (b) Navigation speed of the ship from 26 July to 6 August 2010, starting from Station S26 (72°42.04N, 153°33.12W) and ending at Station BN11 (86°04.85N, 176°05.88W). The red lines are latitudinally averaged navigation speed

    The rapid decline in sea-ice extent and concentration has mostly been reported in terms of the significant change in the marginal ice zone (MIZ), which is a transitional zone between open ocean and pack ice (e.g., Stroeve et al., 2007). When the sea ice retreats and ice thickness reduces rapidly, the main feature of the ice concentration is the spread of the MIZ. The MIZ was historically a narrow zone, but has recently become much larger in extent. The low SIC area has become so wide that it may be becoming hard to distinguish the division between the MIZ and pack ice, throwing this historical definition into question. The perennial ice-extent loss is mainly caused by: ice advection toward the Canadian Archipelago coast; ice loading into the Transpolar Drift; acceleration of the Transpolar Drift carrying ice out of Fram Strait; and ice export to Baffin Bay through the Nares Strait (Nghiem et al., 2007).

    Within the region north of 80°N——hereinafter referred to as the central Arctic——the SIC has remained high this century, with multiyear ice. However, the sea-ice age in the central Arctic has clearly responded to global warming. Most multiyear ice of age greater than ten years has been replaced by much younger ice (Belchansky et al., 2005). The loss of the oldest ice is even more extreme, with ice of greater than five years reaching a minimum in 2010 of just 6% of the 1983-2002 mean (Maslanik et al., 2011), illustrating that central Arctic ice has also been affected by this warming.

    Figure 2.  Distribution of SIC in the Arctic Ocean during August 2010 from AMSR-E data. The white circle around the pole is defined here as the "North Pole blind zone", due to satellite orbital geometry.

    Here, we show the appearance of a record low ice concentration in the central Arctic (CARLIC) in summer 2010 based on field observations and satellite remote sensing. Large areas of open water appeared in high latitude areas, north of 85°N, resulting in a SIC that was actually lower than that of the surrounding pack ice at lower latitudes. This strikingly low ice concentration in the central Arctic is unique in our analysis period of 2003-15, and has not been previously reported in the literature. The remarkable opening of the ice appears not to have been produced by local melt, but rather by sea-ice divergence. The objectives of this paper are to present new evidence for an observed reduction in SIC at high latitudes in the central high Arctic basin, and to ascertain what role regional-scale climate processes played in the observed processes.

    Figure 3.  Interannual features of low SIC in central Arctic. Daily average SIC north of 85°N from 1 August to 30 September from 2003 to 2015.

2. Record low ice concentration zone
  • Very low ice concentration was first identified by the navigation speed of the Chinese R/V ship Xue Long. The ship started its northward journey from station S26 (72°42.04N, 153°33.12W) on 27 July along the blue line in Fig. 1a. Ten days later, on 6 August, the ship arrived at Station BN11 (86°04.85N, 176°05.88W), from where the ship started ice camp measurements drifting with sea ice for 12 days. Ice conditions strongly influenced the navigation speed as the ship is an ice-strengthened ship, not an icebreaker. The ship traveled across a low SIC zone between 72.5°N and 75.6°N, with an average speed of 8.6 knots. Between 75.6°N and 82.6°N, the average navigation speed reduced to 6.5 knots, traveling in flat first-year ice of high concentration. From 82.6°N northward, the SIC decreased quickly and large areas of open water frequently appeared. The ship then traveled with an average navigation speed of more than 8.2 knots (Fig. 1b).

    The spatial distribution of SIC could be determined from 89-GHz Advanced Microwave Scanning Radiometer-EOS (AMSR-E) daily satellite microwave data at 6.25-km resolution (http://www.iup.uni-bremen.de:8084/amsr/amsre.html). AMSR-E stopped working on 4 December 2011. Its successor——Advanced Microwave Scanning Radiometer 2 (AMSR2)——started to provide data from 18 May 2012. The SIC data are retrieved using the ARTIST Sea Ice algorithm (Spreen et al., 2008). The effects of melt ponds, wet snow and atmospheric water vapor can degrade SIC estimates, but SIC from AMSR-E reliably reflects the relative spatial difference of SIC (Meier, 2005). For convenience, we express SIC as a fraction of unity 0-1.

    As shown in Fig. 2, a region with very low SIC centered at (83°N, 180°W) appeared on 31 July. It extended to a larger area up to the orbital "North Pole blind zone" of satellite coverage in the following 15 days. Although the low SIC area disappeared in the satellite record between 18 and 25 August, it subsequently reappeared, with the SIC reaching its minimum on 6 September.

    The variation in low SIC in the central Arctic can be seen in the daily averaged SIC (ASIC), \begin{equation} \label{eq1} {\rm ASIC}(t)=\frac{1}{S}\iint_S{C(x,y,t)dxdy} , \ \ (1)\end{equation} where C(x,y,t) is the SIC at each grid point, and S is the area of the zone circled between a latitude (here, it is taken as 85°N) and the blind zone of satellite coverage (about 88.25°N). The variation in ASIC during 2003-15 from 1 August to 30 September is plotted in Fig. 3. It is shown that the ASIC was very high (>0.9) in the central Arctic in most years. The lower ASICs appeared in 2007, 2010, 2012 and 2013. The minimum ASICs of the central Arctic dropped to about 0.87 (2007), 0.85 (2012) and 0.86 (2013). However, in 2010, the ASIC dropped abruptly to about 0.78, though the ice coverage of the whole Arctic was more than that in 2007 and 2012. The very low SIC in the central Arctic appeared in late July and existed continuously until late September. Figure 3 shows that the ASIC in early September was even lower than that encountered by the ship in early August 2010. As of the publication of this article, the record low of SIC in 2010 in the central Arctic has not been broken.

    Figure 4.  Distribution of ice concentration in the Arctic Ocean with the lowest daily average SIC north of 85°N in (a) 2007 and (b) 2010.

    Here, we define a useful integral to express the interannual difference in SIC in the central Arctic: \begin{equation} \label{eq2} {\rm AOW}_{2{\rm M}}=\frac{1}{T}\int_0^T[1-{\rm ASIC}(t)]dt ,\ \ (2) \end{equation} where T is equal to 61 days within August and September, and AOW 2M is a two-month averaged area of open water. Higher values of AOW 2M indicate a joint effect of lower SIC and/or longer duration of the low ASIC. The AOW 2M for the central Arctic surrounded by 85°N in Fig. 4 clearly shows that the averaged area of open water reached its maximum in 2010 because of a longer-lasting low SIC. The AOW 2M in 2007 was the second lowest ASIC this century (Fig. 4). In 2012 and 2013, the minimum ASICs were close to that in 2007, but the AOW 2M in these years was much lower than that of 2007.

    The two plots of SICs using the regional minimum days of 5 September 2007 and 6 September 2010 are plotted in Figs. 5a and b, respectively, to compare their differences. It is clear that the low overall concentration in 2007 was caused by an extreme retreat of the ice edge; whereas, within the pack ice, the concentration remained high (Fig. 5a). The low concentration in 2010 was different, as it formed as an opening within the pack ice (Fig. 5b). It can be seen from Fig. 5 that in 2010 not only did the SIC north of 85°N decline, but the overall SIC in the Atlantic sector was also significantly reduced.

    Figure 5.  Two-month averaged area of open water in the central Arctic surrounded by 85°N and the blind zone from 2003 to 2015.

3. Discussion on the driving factors of CARLIC
  • Based on optical measurements, (Zhao et al., 2009) studied the sea-ice melt rate in the central Arctic. They found that only 0.33 cm d-1 of sea ice could be melted by absorption of solar radiation, even with the strong solar insolation in August. The solar radiation penetrating open water in the summer is the main heat source to the ocean surface mixed layer, as widely addressed by previous studies (e.g., Kadko and Swart, 2004). Work by (Perovich et al., 2008) showed the strong influence of heat in the ocean surface mixed layer on the reduction of sea ice in the Beaufort Sea, while questioning whether the receipt of surface radiation would be able to melt ice at higher latitudes. Repeated in-situ ice thickness measurements by an electromagnetic induction (EM31) were conducted during the 12-day ice camp in 2010 started from (86°04.85N, 176°05.88W). The sea ice surrounding the ice camp was first-year ice, based on ice-core analysis (Lei et al., 2012). The measurements of ice thickness along four repeated profiles suggested an average melt rate of 2 cm d-1, primarily bottom melt (Xie et al., 2013). The melting rate was 2.5 times greater than the average ∼0.8 cm d-1 during a similar period of the SHEBA experiments in 1998, even though SHEBA was at much lower latitudes (70°-80°N) (Perovich et al., 2003). The high melt rate occurred due to the heat absorption through the large area of open water. However, even with this rate, it would still need 100 days to melt a 2-m-thick layer of ice. Therefore, the sea-ice melt could not be solely responsible for the record low SIC at the end of July. Instead, the rapid opening and closing shift of the low SIC region suggests that the divergence/convergence of ice drift might be more relevant to the CARLIC.

    The transfer of momentum from the atmosphere to the ice is critical to sea-ice drift (Martin and Gerdes, 2007). Ignoring the geopotential gradient and nonlinear interaction, the equation of ice motion as a continuum is (Leppäranta, 2005) \begin{equation} \label{eq3} \rho h\left(\frac{\partial{v}}{\partial t}+f{k}\times{v}\right)=\nabla\cdot{\sigma}+{\tau}_{\rm a}+{\tau}_{rm w} , \ \ (3)\end{equation} where v is ice velocity; ρ is sea ice density; h is ice thickness; f is the Coriolis parameter; σ is the two-dimensional internal ice stress; τ a and τ w are the wind and water stresses acting on the upper and bottom surfaces of sea ice. Provided ρ and h are locally homogeneous, the divergence of ice drift, D, and relative vorticity, ζ, $$ D=\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y};\zeta=\frac{\partial v}{\partial x}-\frac{\partial u}{\partial y} , $$ can be obtained by taking the curl and divergence for both sides of Eq. (4): \begin{equation} \label{eq4} \begin{array}{rcl} \rho h\left(\dfrac{\partial\zeta}{\partial t}+fD\right)&=&\nabla\times\nabla\cdot{\sigma}+{\rm curl}{\tau}_{\rm a}+{\rm curl}{\tau}_{\rm w}\\[3mm] \rho h\left(\dfrac{\partial D}{\partial t}-f\zeta\right)&=&\nabla\left(\nabla\cdot{\sigma}\right)+{\rm div}{\tau}_{\rm a}+{\rm div}{\tau}_{{\rm w}} \end{array} . \ \ (4)\end{equation} Then, the equation for the divergence of ice drift is \begin{eqnarray} \label{eq5} \rho h\left(\dfrac{\partial^2D}{\partial t^2}+f^2D\right)&=&f[\nabla\times(\nabla\cdot{\sigma})+({\rm curl}{\tau}_{\rm a}+{\rm curl}{\tau}_{\rm w})]\nonumber\\ &&+\dfrac{\partial}{\partial t}[\nabla(\nabla\cdot{\sigma})+({\rm div}{\tau}_{\rm a}+{\rm div}{\tau}_{\rm w})] .\ \ (5) \end{eqnarray} Considering that the time scale of ice drifting is larger than the inertial period, the two-order temporal derivative is small, and the variation of the divergence of the stresses is negligible, through analyzing the order of magnitude, D can be approximately expressed by \begin{equation} \label{eq6} D\approx\frac{1}{\rho fh}[\nabla\times(\nabla\cdot{\sigma})+({\rm curl}{\tau}_{\rm a}+{\rm curl}{\tau}_{\rm w})] . \ \ (6)\end{equation} The equation of ice concentration is as follows (Hibler, 1979): \begin{equation} \label{eq7} \frac{\partial C}{\partial t}=-\left(\dfrac{\partial uC}{\partial x}+\frac{\partial vC}{\partial y}\right)+S_A+\varepsilon ,\ \ (7) \end{equation} where SA is related to ice growth, and ε represents diffusion terms. Because the new ice formation is negligible in this season, and the diffusion and advection are both small, the relationship between the divergence of sea-ice drift and concentration becomes \begin{equation} \label{eq8} \frac{\partial C}{\partial t}=-CD .\ \ (8) \end{equation} Replacing D with a spatially averaged version in the region north of 85°N, and substituting Eq. (9) into Eq. (2), we obtain \begin{equation} \label{eq9} \frac{1}{{\rm ASIC}(t)}\frac{d{\rm ASIC}(t)}{dt}\!\approx\!-\frac{1}{\rho fhS}\!\!\iint_S\!\!(\nabla\!\times\!(\nabla\!\cdot{\sigma})\!+\!{\rm curl}{\tau}_{\rm a}\!+\!{\rm curl}{\tau}_{\rm w})ds, \ \ (9)\end{equation} where S is the area north of 85°N, and the left-hand side of Eq. (10) is the relative rate of variation of ASIC. Because the internal ice stress and the drag stress of water are unknown, ASIC(t) cannot be obtained from Eq. (10). The response of the variation in SIC depends on the SIC itself. When sea ice is dense, the sea ice responds weakly to the wind stress curl, as the internal stress σ arising from the interaction of different parts of ice floe balance most of the wind forcing. Otherwise, when the sea ice is sparse, the ice drift becomes more responsive to wind (Kwok et al., 2013; Olason and Notz, 2014). The water drag stress for sea ice usually responds to ice drift. The curl of wind stress in this equation is the only forcing factor, and the other terms are response factors and are expected to respond to the wind in different ways.

    We calculated the daily averaged wind stress curl (AWSC) north of 85°N in August and September 2010 using daily wind velocity data from NCEP Reanalysis 1 (Kalnay et al., 1996). The relative rate of variation of ASIC was calculated using the daily ASIC. The total correlation coefficient between ASIC and AWSC was -0.54, at much higher than the 99.5% confidence level. It can be seen from Fig. 6a that ASIC responds well to each event with high averaged wind stress curl. It verifies that the wind stress curl is one of the most important factors in producing CARLIC.

    Although the relative rate of variation of ASIC correlated well with wind stress curl, the response of ASIC itself to wind stress curl was related to the degree of sea-ice concentrating, as shown in Fig. 6b. From early August, a positive AWSC lasted for a couple of weeks, driving the ASIC decline from 0.97 to its first minimum of 0.89. Then, a seven-day negative AWSC acted on the area to cause a convergence of the ice-drift field, and the ASIC recovered to 0.96. After this, a three-week period dominated by positive AWSC occurred. The ASIC declined again and reached its minimum of 0.78 on 6 September. A rapid increase in the ASIC occurred again, responding to the strong negative trend of AWSC since 13 September, and all of the open water in the central Arctic closed over this period. The positive AWSC occurred once again after 20 September, but the ASIC did not respond to it anymore because freezing of sea ice had begun at these high latitudes.

    Figure 6.  Influence of wind stress curl on SIC. The green bars present the time series of negative averaged wind stress curl (AWSC) north of 85°N. (a) Correlation of AWSC and relative rate of variation of ASIC (blue dots). The correlation coefficient is -0.54. (b) Comparison of AWSC and ASIC (red dots).

    Figure 7.  Averaged wind stress curl in August each year.

    The AWSCs of August in recent years are plotted in Fig. 7. Since 2007, the wind stress curl in the central Arctic was negative, except in 2010. This may explain why the CARLIC only occurred in 2010. During the sea-ice minimum in 2007, the amount of multiyear ice was reduced in the Pacific sector of the Arctic Ocean, and replaced with an increased areal extent of first-year ice (Barber et al., 2012). With positive wind stress curl, the ASIC in 2007 reached the second low record. In 2012 summer, the sea-ice cover of the Arctic reached its recorded minimum, but the ice concentration in the central Arctic was still high. This means that the wind stress curl did not drive the occurrence of low SIC in the central Arctic after 2010.

    Figure 8.  Ice-drift pattern in 2010. (a) Drift trajectories of ice buoys in the Arctic Ocean from 1 January to 30 September 2010. Red dots are the start positions of each buoy. The dashed purple line is the division of the two drifting groups. (b) Averaged SLP of April-August 2010.

    However, in 2003 and 2006, positive wind stress curls with magnitudes much larger than those in 2007 and 2010 were found, but no CARLIC events occurred. Therefore, it seems that the wind forcing is not the only factor in generating CARLIC. A reasonable explanation is that the SIC in 2003 and 2006 was higher (see Fig. 3), responding poorly to wind forcing. The response of the ice to wind forcing has recently become more pronounced, since in summer the Arctic is now dominated by first-year ice types, smaller floe sizes and decreased concentration (e.g., Asplin et al., 2009), allowing for increased ice speeds within the gyres (Galley et al., 2013).

    Besides the regional wind forcing by positive wind stress curl, the ice drift patterns on the basin scale might also contribute to the CARLIC. The trajectories of the buoys from the International Arctic Buoy Program (IABP; Rigor, 2002) from 1 January to 30 September 2010 (Fig. 8a) were clustered into two regions, separated by the dashed purple line: one group went toward the Fram Strait within the Transpolar Drift, and the other went eastward into the Beaufort Gyre. No buoy went across the dashed line in the first 10 months of 2010, which means that the ice flowing out of the Arctic was partly compensated for by export from the Laptev Sea and western sector. The lack of full compensation favored a low ice concentration.

    The averaged SLP field of April to August 2010 (Fig. 8b) also matches the drift patterns identified from the IABP buoys. This pattern drives the production of a divergence in the central Arctic, which is quite similar to the double-gyre ice drift pattern reported by (Wang and Zhao, 2012). Using Polar Pathfinder monthly 25-km EASE-Grid Sea Ice Motion Vectors (Fowler, 2008), (Wang and Zhao, 2012) divided the ice drift pattern into four main types: Transpolar Drift plus Beaufort Gyre (TPDBG; 38% of total occurrence), anticyclonic (15%), cyclonic (16%), and double-gyre (15%). The TPDBG type is the typical ice drift pattern in the Arctic, with the highest occurrence. The double-gyre drift type is quite similar to the TPDBG type, except that the sea ice in the central Arctic drifts to the Canadian Archipelago, not to the Fram Strait——quite similar to the typical characteristic of the drifting pattern in 2010. Although the double-gyre drift pattern benefits divergence in the central Arctic, its occurrence usually lasts a short time. However, in 2010, the double-gyre pattern at the basin scale lasted more than 10 months, which might have been the immediate cause of the CARLIC.

4. Summary and conclusions
  • A record low concentration of sea ice and large area of open water in the central Arctic during summer 2010 is reported in this paper. The lowest averaged SIC north of 85°N reached as low as 0.78, becoming the sparsest than at any time in the historical record. We conclude that, in this particular case, the low SIC was caused more by ice divergence than by in-situ melt, based on the temporally resolved measurement of ice melt at a nearby ice camp. A high correlation between SIC and wind stress curl is revealed to address the contribution of regional wind forcing on the divergence of ice drift. The high correlation coefficient suggests that regional wind forcing might have been a key driving factor of the sea-ice drift in summer 2010. However, in 2003 and 2006, the magnitude of the wind stress curls were much larger than those in 2007 and 2010, but no CARLIC events occurred, because the heavy ice seems to have prevented the occurrence of low SIC, as the region was still dominated then by multiyear ice forms. The drift trajectories of ice buoys (IABP) depicted a divergent transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector. This feature illustrates a double-gyre drift pattern, cyclonic in the Transpolar Drift and anticyclonic in the Beaufort Gyre, which resulted in ice divergence in the central Arctic. In 2010, this drift pattern resulted in high concentrations of sea ice at lower latitudes, which decreased within the high latitudes toward the pole (Fig. 1). This pattern was also observed in 2009 (Barber et al., 2009), and again in 2012 (Babb et al., 2013), while the occurrence in 2010 lasted more than 10 months and the persistent divergence drove CARLIC.

    An important question relates to the frequency of future occurrences of CARLIC-type events in the central Arctic, which is difficult to project given its rare occurrence and our limited knowledge about their formation and maintenance. Based on our results, however, a long-lasting positive wind stress curl favors the occurrence of CARLIC, which might occur again when such a wind condition reappears. This divergence of ice drift in the central Arctic might be a significant feature of sea-ice rapid decline at high latitudes in the future, due to the preconditioning that the open water would have on increasing melt from an enhanced ocean surface mixed layer temperature. The physical significance of CARLIC is that more solar energy can penetrate into open water, which can in turn enhance the ice melt and feed back to the atmosphere (Vihma, 2014). The large area of open water in the ice pack potentially has substantial biological implications as well. Further investigation is necessary to reveal the climatic significance of this double-gyre pattern, and its coupling to sea-ice motion and melt.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return