High interannual variability of sea ice thickness in the Arctic region.
TL;DR: An eight-year time-series of Arctic ice thickness is used, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65° N to 81.5° N, which reveals a high-frequency interannual variability in mean ArcticIce thickness that is dominated by changes in the amount of summer melt, rather than byChanges in circulation.
Abstract: Possible future changes in Arctic sea ice cover and thickness, and consequent changes in the ice-albedo feedback, represent one of the largest uncertainties in the prediction of future temperature rise1,2. Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate models3,4. Numerical simulations suggest that Arctic ice thickness varies primarily on decadal timescales3,5,6 owing to changes in wind and ocean stresses on the ice7,8,9,10, but observations have been unable to provide a synoptic view of sea ice thickness, which is required to validate the model results3,6,9. Here we use an eight-year time-series of Arctic ice thickness, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65° N to 81.5° N. Our data reveal a high-frequency interannual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt11, rather than by changes in circulation. Our results suggest that a continued increase in melt season length would lead to further thinning of Arctic sea ice.
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01 Jan 2007
TL;DR: Contributing Authors: J.H. Box, D.O. Robinson, Ian Joughin, S. Smith, and D.W. Walsh.
Abstract: Contributing Authors: J. Box (USA), D. Bromwich (USA), R. Brown (Canada), J.G. Cogley (Canada), J. Comiso (USA), M. Dyurgerov (Sweden, USA), B. Fitzharris (New Zealand), O. Frauenfeld (USA, Austria), H. Fricker (USA), G. H. Gudmundsson (UK, Iceland), C. Haas (Germany), J.O. Hagen (Norway), C. Harris (UK), L. Hinzman (USA), R. Hock (Sweden), M. Hoelzle (Switzerland), P. Huybrechts (Belgium), K. Isaksen (Norway), P. Jansson (Sweden), A. Jenkins (UK), Ian Joughin (USA), C. Kottmeier (Germany), R. Kwok (USA), S. Laxon (UK), S. Liu (China), D. MacAyeal (USA), H. Melling (Canada), A. Ohmura (Switzerland), A. Payne (UK), T. Prowse (Canada), B.H. Raup (USA), C. Raymond (USA), E. Rignot (USA), I. Rigor (USA), D. Robinson (USA), D. Rothrock (USA), S.C. Scherrer (Switzerland), S. Smith (Canada), O. Solomina (Russian Federation), D. Vaughan (UK), J. Walsh (USA), A. Worby (Australia), T. Yamada (Japan), L. Zhao (China)
962 citations
Cites background from "High interannual variability of sea..."
...Laxon et al. (2003) estimated average arctic sea ice thickness over the cold months (October–March) for 1993 to 2001 from satellite-borne radar altimeter measurements....
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TL;DR: The HadGEM2 family of configurations as discussed by the authors includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry.
Abstract: . We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.
837 citations
TL;DR: In this article, the authors used new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, to generate estimates of ice volume for the winters of 2010/11 and 2011/12.
Abstract: [1] Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods.
664 citations
Cites background or methods or result from "High interannual variability of sea..."
...[23] Data from CS-2 show a pattern of ice thickness similar to that observed in previous satellite data and submarine climatologies [Bourke and Mclaren, 1992; Kwok et al., 2009; Laxon et al., 2003]....
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...We then use CS-2 to compute ice volumes for the winters of 2010/11 and 2011/12 and compare the data with earlier ICESat volume estimates and with more recent PIOMAS simulations....
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...[17] In common with earlier satellite radar altimeters CS-2 sea ice thickness estimates exclude open water [Laxon et al., 2003]....
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...…principle of CS-2 is similar to previous satellite radar altimeters carried on the European Space Agency (ESA) European Remote Sensing (ERS) [Laxon et al., 2003] and Envisat [Giles et al., 2008] satellites, the mission concept of CS-2 and the synthetic aperture radar (SAR)/Interferometric…...
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...While changes in sea ice extent affect the albedo, changes in sea ice volume reflect changes in the heat budget of the Arctic and the exchanges of fresh water between sea ice and the ocean....
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TL;DR: A review of the local and remote effects of the sea ice decline on weather and climate is presented in this paper, where 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.
Abstract: The 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 tempera- ture, 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.
645 citations
Cites background from "High interannual variability of sea..."
...As a result, the freeze-up starts later, which contributes to sea ice thinning in the following year (Laxon et al. 2003; Notz 2009)....
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...Liu et al. (2012) further pointed out that, in addition to effects on winter mean statistics, the autumnal sea ice decline also increases the frequency of occurrence of strong weather events, such as snow storms and cold-air outbreaks....
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TL;DR: In this paper, the authors describe the CryoSat satellite mission, due for launch in 2005, whose aim is to accurately determine the trends in Earth's continental and marine ice fields.
539 citations
Cites background or methods from "High interannual variability of sea..."
...Laboratory experiments (Beaven, 1995) and comparisons with in situ thickness observations (Laxon et al., 2003) show that when snow is present on the ice, the radar ranges to the snow–ice, rather than the air–snow, interface, and the freeboard is then the difference between the measured ice…...
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...Taking the sea-ice figure first, Laxon et al. (2003) have shown that at a basin scale (3 · 106 km2), Arctic sea-ice has an inter-annual variability of 25 cm, almost completely determined by the melt season length....
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...1 kg m 3 respectively (Laxon et al., 2003), and the snow load from measured climatology (Warren et al....
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...Even more recently Laxon et al. (2003) have used ERS altimetry to provide the first synoptic measurements of Arctic sea-ice thickness (Fig....
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...…and the assumption of hydrostatic equilibrium: test ¼ 1 qocean qice ðqoceanfest þ msÞ. ð41Þ Ocean and ice densities have been taken as the constants qocean = 1022.9 kg m 3 and qice = 915.1 kg m 3 respectively (Laxon et al., 2003), and the snow load from measured climatology (Warren et al., 1999)....
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References
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TL;DR: In this article, the authors present an overview of the climate system and its dynamics, including observed climate variability and change, the carbon cycle, atmospheric chemistry and greenhouse gases, and their direct and indirect effects.
Abstract: Summary for policymakers Technical summary 1. The climate system - an overview 2. Observed climate variability and change 3. The carbon cycle and atmospheric CO2 4. Atmospheric chemistry and greenhouse gases 5. Aerosols, their direct and indirect effects 6. Radiative forcing of climate change 7. Physical climate processes and feedbacks 8. Model evaluation 9. Projections of future climate change 10. Regional climate simulation - evaluation and projections 11. Changes in sea level 12. Detection of climate change and attribution of causes 13. Climate scenario development 14. Advancing our understanding Glossary Index Appendix.
13,366 citations
"High interannual variability of sea..." refers background in this paper
...Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate model...
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TL;DR: In this article, the authors investigated the response of a climate model to a gradual increase or decrease of atmospheric carbon dioxide in a general circulation model of the coupled atmosphere-ocean-land surface system with global geography and seasonal variation of insulation.
Abstract: This study investigates the response of a climate model to a gradual increase or decrease of atmospheric carbon dioxide The model is a general circulation model of the coupled atmosphere-ocean-land surface system with global geography and seasonal variation of insulation To offset the bias of the coupled model toward settling into an unrealistic state, the fluxes of heat and water at the ocean-atmosphere interface are adjusted by amounts that vary with season and geography but do not change from one year to the next Starting from a quasi-equilibrium climate, three numerical time integrations of the coupled model are performed with gradually increasing, constant, and gradually decreasing concentration of atmospheric carbon dioxide It is noted that the simulated response of sea surface temperature is very slow over the northern North Atlantic and the Circumpolar Ocean of the Southern Hemisphere where vertical mixing of water penetrates very deeply However, in most of the Northern Hemisphere an
1,056 citations
"High interannual variability of sea..." refers background in this paper
..., relies on global climate models properly reproducing changes in ice thicknes...
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TL;DR: In this paper, a comparison of sea-ice draft data acquired on submarine cruises between 1993 and 1997 with similar data acquired between 1958 and 1976 indicates that the mean ice draft at the end of the melt season has decreased by about 1.3 m in most of the deep water portion of the Arctic Ocean, from 3.1 m in 1958-1976 to 1.8 m in the 1990s.
Abstract: Comparison of sea-ice draft data acquired on submarine cruises between 1993 and 1997 with similar data acquired between 1958 and 1976 indicates that the mean ice draft at the end of the melt season has decreased by about 1.3 m in most of the deep water portion of the Arctic Ocean, from 3.1 m in 1958–1976 to 1.8 m in the 1990s. The decrease is greater in the central and eastern Arctic than in the Beaufort and Chukchi seas. Preliminary evidence is that the ice cover has continued to become thinner in some regions during the 1990s.
995 citations
"High interannual variability of sea..." refers background in this paper
...Simulations of Arctic ice cover covering the past four decades have been used to argue that observed thin ic...
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TL;DR: In this paper, the authors used a two-dimensional quadratic function to represent the geographical and seasonal variation of snow depth, and fitted it to all data for a particular month, irrespective of year.
Abstract: Snow depth and density were measured at Soviet drifting stations on multiyear Arctic sea ice. Measurements were made daily at fixed stakes at the weather station and once- or thrice-monthly at 10-m intervals on a line beginning about 500 m from the station buildings and extending outward an additional 500 or 1000 m. There were 31 stations, with lifetimes of 1‐7 yr. Analyses are performed here for the 37 years 1954‐91, during which time at least one station was always reporting. Snow depth at the stakes was sometimes higher than on the lines, and sometimes lower, but no systematic trend of snow depth was detected as a function of distance from the station along the 1000-m lines that would indicate an influence of the station. To determine the seasonal progression of snow depth for each year at each station, priority was given to snow lines if available; otherwise the fixed stakes were used, with an offset applied if necessary. The ice is mostly free of snow during August. Snow accumulates rapidly in September and October, moderately in November, very slowly in December and January, then moderately again from February to May. This pattern is exaggerated in the Greenland‐Ellesmere sector, which shows almost no net accumulation from November to March. The Chukchi region shows a steadier accumulation throughout the autumn, winter, and spring. The average snow depth of the multiyear ice region reaches a maximum of 34 cm (11 g cm22) in May. The deepest snow is just north of Greenland and Ellesmere Island, peaking in early June at more than 40 cm, when the snow is already melting north of Siberia and Alaska. The average snow density increases with time throughout the snow accumulation season, averaging 300 kg m23, with little geographical variation. Usually only two stations were in operation in any particular year, so there is insufficient information to obtain the geographical pattern of interannual variations. Therefore, to represent the geographical and seasonal variation of snow depth, a two-dimensional quadratic function is fitted to all data for a particular month, irrespective of year. Interannual anomalies for each month of each year are obtained relative to the long-term mean snow depth for the geographical location of the station operating in that particular year. The computed interannual variability (IAV) of snow depth in May is 6 cm, but this is larger than the true IAV because of inadequate geographical sampling. Weak negative trends of snow depth are found for all months. The largest trend is for May, the month of maximum snow depth, a decrease of 8 cm over 37 yr, apparently due to a reduction in accumulation-season snowfall.
442 citations
TL;DR: In this paper, the response of Arctic sea ice to these atmospheric changes has been studied with a thickness distribution sea-ice model coupled to an ocean model, showing that during a period of high NAO, 1989-96, the model shows a substantial reduction of ice advection into the eastern Arctic from the Canada Basin, and an increase of ice export through Fram Strait, both of which tend to deplete thick ice in the eastern arctic Ocean and enhance it in the western Arctic, in an uneven dipolar pattern we call the East-West Arctic Anomaly Pattern (EWA
Abstract: It is well established that periods of high North Atlantic oscillation (NAO) index are characterized by a weakening of the surface high pressure and surface anticyclone in the Beaufort Sea and the intensification of the cyclonic circulation in the eastern Arctic Ocean. The response of Arctic sea ice to these atmospheric changes has been studied with a thickness distribution sea-ice model coupled to an ocean model. During a period of high NAO, 1989–96, the model shows a substantial reduction of ice advection into the eastern Arctic from the Canada Basin, and an increase of ice export through Fram Strait, both of which tend to deplete thick ice in the eastern Arctic Ocean and enhance it in the western Arctic, in an uneven dipolar pattern we call the East–West Arctic Anomaly Pattern (EWAAP). From the period 1979–88 with a lower-NAO index to the period 1988–96 with a high-NAO index, the simulated ice volume in the eastern Arctic drops by about a quarter, while that in the western Arctic increases by ...
215 citations