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.