Articles | Volume 10, issue 5
21 Oct 2016
Research article | 21 Oct 2016
Assessment of Arctic and Antarctic sea ice predictability in CMIP5 decadal hindcasts
Chao-Yuan Yang et al.
No articles found.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887,Short summary
The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176,Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Mengzhen Qi, Yan Liu, Jiping Liu, Xiao Cheng, Yijing Lin, Qiyang Feng, Qiang Shen, and Zhitong Yu
Earth Syst. Sci. Data, 13, 4583–4601,Short summary
A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2, and the average calving rate was measured as 770.3 Gt yr-1. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. This annual iceberg calving dataset provides consistent and precise calving observations with the longest time coverage.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908,Short summary
The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Yongyun Hu, Yan Xia, Zhengyu Liu, Yuchen Wang, Zhengyao Lu, and Tao Wang
Clim. Past, 16, 199–209,Short summary
The paper shows, using climate simulations, that the Pacific–North American (PNA) teleconnection was distorted or completely broken at the Last Glacial Maximum (LGM). The results suggest that ENSO would have little direct impact on North American climates at the LGM.
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224,Short summary
Sea ice volume export through the Fram Strait has been studied using varied methods, however, mostly in winter months. Here we report sea ice volume estimates that extend over summer seasons. A recent developed sea ice thickness dataset, in which CryoSat-2 and SMOS sea ice thickness together with SSMI/SSMIS sea ice concentration are assimilated, is used and evaluated in the paper. Results show our estimate is more reasonable than that calculated by satellite data only.
Yifan Ding, Xiao Cheng, Jiping Liu, Fengming Hui, and Zhenzhan Wang
The Cryosphere Discuss.,
Preprint withdrawnShort summary
This study develops a new melt pond fraction (MPF) data set over sea ice on Arctic-wide scale, using a method of ensemble-based deep neural network. Based on the new dataset, we analyze the spatial-temporal variations of MPF on different ice types and the prediction of MPF to the Arctic sea ice extent in recent years. The new dataset may help improve the prediction of the Arctic sea ice minimum by assimilating the MPF in models.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Wei Han, Zhiheng Du, Andrew R. Mahoney, Joshua Jones, and Hajo Eicken
The Cryosphere, 13, 1233–1246,Short summary
The variability and potential trends of rain-on-snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. This study demonstrates that rain-on-snow events are a critical factor in initiating the onset of surface melt over Arctic sea ice, and onset of spring rainfall over sea ice has shifted to earlier dates since the 1970s, which may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757,
Lu Zhou, Shiming Xu, Jiping Liu, and Bin Wang
The Cryosphere, 12, 993–1012,Short summary
This work proposes a new data synergy method for the retrieval of sea ice thickness and snow depth by using colocating L-band passive remote sensing and active laser altimetry. Physical models are adopted for the retrieval, including L-band radiation model and buoyancy relationship. Covariability of snow depth and total freeboard is further utilized to mitigate resolution differences and improve retrievability. The method can be applied to future campaigns including ICESat-2 and WCOM.
Wenshou Tian, Yuanpu Li, Fei Xie, Jiankai Zhang, Martyn P. Chipperfield, Wuhu Feng, Yongyun Hu, Sen Zhao, Xin Zhou, Yun Yang, and Xuan Ma
Atmos. Chem. Phys., 17, 6705–6722,Short summary
Although the principal mechanisms responsible for the formation of the Antarctic ozone hole are well understood, the factors or processes that generate interannual variations in ozone levels in the southern high-latitude stratosphere remain under debate. This study finds that the SST variations across the East Asian marginal seas (5° S–35° N, 100–140° E) could modulate the southern high-latitude stratospheric ozone interannual changes.
Alex C. Ruane, Claas Teichmann, Nigel W. Arnell, Timothy R. Carter, Kristie L. Ebi, Katja Frieler, Clare M. Goodess, Bruce Hewitson, Radley Horton, R. Sari Kovats, Heike K. Lotze, Linda O. Mearns, Antonio Navarra, Dennis S. Ojima, Keywan Riahi, Cynthia Rosenzweig, Matthias Themessl, and Katharine Vincent
Geosci. Model Dev., 9, 3493–3515,Short summary
The Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) Advisory Board for CMIP6 was created to improve communications between communities that apply climate model output for societal benefit and the climate model centers. This manuscript describes the establishment of the VIACS Advisory Board as a coherent avenue for communication utilizing leading networks, experts, and programs; results of initial interactions during the development of CMIP6; and its potential next activities.
Dorothee C. E. Bakker, Benjamin Pfeil, Camilla S. Landa, Nicolas Metzl, Kevin M. O'Brien, Are Olsen, Karl Smith, Cathy Cosca, Sumiko Harasawa, Stephen D. Jones, Shin-ichiro Nakaoka, Yukihiro Nojiri, Ute Schuster, Tobias Steinhoff, Colm Sweeney, Taro Takahashi, Bronte Tilbrook, Chisato Wada, Rik Wanninkhof, Simone R. Alin, Carlos F. Balestrini, Leticia Barbero, Nicholas R. Bates, Alejandro A. Bianchi, Frédéric Bonou, Jacqueline Boutin, Yann Bozec, Eugene F. Burger, Wei-Jun Cai, Robert D. Castle, Liqi Chen, Melissa Chierici, Kim Currie, Wiley Evans, Charles Featherstone, Richard A. Feely, Agneta Fransson, Catherine Goyet, Naomi Greenwood, Luke Gregor, Steven Hankin, Nick J. Hardman-Mountford, Jérôme Harlay, Judith Hauck, Mario Hoppema, Matthew P. Humphreys, Christopher W. Hunt, Betty Huss, J. Severino P. Ibánhez, Truls Johannessen, Ralph Keeling, Vassilis Kitidis, Arne Körtzinger, Alex Kozyr, Evangelia Krasakopoulou, Akira Kuwata, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Claire Lo Monaco, Ansley Manke, Jeremy T. Mathis, Liliane Merlivat, Frank J. Millero, Pedro M. S. Monteiro, David R. Munro, Akihiko Murata, Timothy Newberger, Abdirahman M. Omar, Tsuneo Ono, Kristina Paterson, David Pearce, Denis Pierrot, Lisa L. Robbins, Shu Saito, Joe Salisbury, Reiner Schlitzer, Bernd Schneider, Roland Schweitzer, Rainer Sieger, Ingunn Skjelvan, Kevin F. Sullivan, Stewart C. Sutherland, Adrienne J. Sutton, Kazuaki Tadokoro, Maciej Telszewski, Matthias Tuma, Steven M. A. C. van Heuven, Doug Vandemark, Brian Ward, Andrew J. Watson, and Suqing Xu
Earth Syst. Sci. Data, 8, 383–413,Short summary
Version 3 of the Surface Ocean CO2 Atlas (www.socat.info) has 14.5 million CO2 (carbon dioxide) values for the years 1957 to 2014 covering the global oceans and coastal seas. Version 3 is an update to version 2 with a longer record and 44 % more CO2 values. The CO2 measurements have been made on ships, fixed moorings and drifting buoys. SOCAT enables quantification of the ocean carbon sink and ocean acidification, as well as model evaluation, thus informing climate negotiations.
Xianwei Wang, David M. Holland, Xiao Cheng, and Peng Gong
The Cryosphere, 10, 2043–2056,Short summary
MIT was reported to have calved subsequent to being rammed by a large iceberg. However from remote sensing, the ice fronts being rammed did not move out first which led us to detect the influence of seafloor on instability of MIT. Using Firn Air Content extracted from slightly grounded icebergs, laser altimetry, remote sensing, and seafloor topography data, grounding of the MIT caused by Mertz Bank is extracted. Mertz Bank is confirmed to control calving of the MIT at a cycle of ~70 years.
Yan Xia, Yongyun Hu, and Yi Huang
Atmos. Chem. Phys., 16, 7559–7567,Short summary
In this work, we discover a strong cloud radiative adjustment that affects the sign of the global surface temperature change in response to stratospheric ozone forcing. We believe this discovery is both interesting, in that our GCM experiments show that a global cooling can result from a warming forcing, and new, in that a strong cloud adjustment to ozone forcing, to the best of our knowledge, has not being documented before.
S. Xu, B. Wang, and J. Liu
Geosci. Model Dev., 8, 3471–3485,Short summary
This article applies Schwarz-Christoffel (SC) conformal mappings for single-connected and multiple-connected regions to the generation of general orthogonal grids for OGCMs, to achieve 1) the enlarged lat-lon proportion, 2) the removal of landmass and easier load balancing, 3) better spatial resolution on continental boundaries, and 4) alignment of grid lines to large-scale coastlines. The generated grids could be readily utilized by the majority of OGCMs that support general orthogonal grids.
D. C. E. Bakker, B. Pfeil, K. Smith, S. Hankin, A. Olsen, S. R. Alin, C. Cosca, S. Harasawa, A. Kozyr, Y. Nojiri, K. M. O'Brien, U. Schuster, M. Telszewski, B. Tilbrook, C. Wada, J. Akl, L. Barbero, N. R. Bates, J. Boutin, Y. Bozec, W.-J. Cai, R. D. Castle, F. P. Chavez, L. Chen, M. Chierici, K. Currie, H. J. W. de Baar, W. Evans, R. A. Feely, A. Fransson, Z. Gao, B. Hales, N. J. Hardman-Mountford, M. Hoppema, W.-J. Huang, C. W. Hunt, B. Huss, T. Ichikawa, T. Johannessen, E. M. Jones, S. D. Jones, S. Jutterström, V. Kitidis, A. Körtzinger, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. B. Manke, J. T. Mathis, L. Merlivat, N. Metzl, A. Murata, T. Newberger, A. M. Omar, T. Ono, G.-H. Park, K. Paterson, D. Pierrot, A. F. Ríos, C. L. Sabine, S. Saito, J. Salisbury, V. V. S. S. Sarma, R. Schlitzer, R. Sieger, I. Skjelvan, T. Steinhoff, K. F. Sullivan, H. Sun, A. J. Sutton, T. Suzuki, C. Sweeney, T. Takahashi, J. Tjiputra, N. Tsurushima, S. M. A. C. van Heuven, D. Vandemark, P. Vlahos, D. W. R. Wallace, R. Wanninkhof, and A. J. Watson
Earth Syst. Sci. Data, 6, 69–90,
J. Yang, Y. Hu, and W. R. Peltier
Clim. Past, 8, 2019–2029,
Related subject area
Arctic (e.g. Greenland)Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radarBrief communication: Preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness changeContribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020The importance of freeze–thaw cycles for lateral tracer transport in ice-wedge polygonsUncertainties in projected surface mass balance over the polar ice sheets from dynamically downscaled EC-Earth modelsPerspectives on future sea ice and navigability in the ArcticLasting impact of winds on Arctic sea ice through the ocean's memoryHolocene sea-ice dynamics in Petermann Fjord in relation to ice tongue stability and Nares Strait ice arch formationPresentation and evaluation of the Arctic sea ice forecasting system neXtSIM-FModelling the mass budget and future evolution of Tunabreen, central SpitsbergenComment on “Exceptionally high heat flux needed to sustain the Northeast Greenland Ice Stream” by Smith-Johnsen et al. (2020)Early spring subglacial discharge plumes fuel under-ice primary production at a Svalbard tidewater glacierCombined influence of oceanic and atmospheric circulations on Greenland sea ice concentrationSeasonal changes in sea ice kinematics and deformation in the Pacific sector of the Arctic Ocean in 2018/19Trends and spatial variation in rain-on-snow events over the Arctic Ocean during the early melt seasonThinning leads to calving-style changes at Bowdoin Glacier, GreenlandInter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrievalYear-round impact of winter sea ice thickness observations on seasonal forecastsEnsemble-based estimation of sea-ice volume variations in the Baffin BayThe cryostratigraphy of the Yedoma cliff of Sobo-Sise Island (Lena delta) reveals permafrost dynamics in the central Laptev Sea coastal region during the last 52 kyrPossible impacts of a 1000 km long hypothetical subglacial river valley towards Petermann Glacier in northern GreenlandSea ice drift and arch evolution in the Robeson Channel using the daily coverage of Sentinel-1 SAR data for the 2016–2017 freezing seasonBrief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcingSeasonal transition dates can reveal biases in Arctic sea ice simulationsThermokarst lake inception and development in syngenetic ice-wedge polygon terrain during a cooling climatic trend, Bylot Island (Nunavut), eastern Canadian ArcticThe Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate missionThe MOSAiC ice floe: sediment-laden survivor from the Siberian shelfSpectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice modelsThe current state and 125 kyr history of permafrost on the Kara Sea shelf: modeling constraintsNew observations of the distribution, morphology and dissolution dynamics of cryogenic gypsum in the Arctic OceanEvaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIMMultidecadal Arctic sea ice thickness and volume derived from ice ageGoing with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observationsThe Arctic sea ice extent change connected to Pacific decadal variabilityImpact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea iceEstimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content dataA distributed temperature profiling method for assessing spatial variability in ground temperatures in a discontinuous permafrost region of AlaskaGreenland Ice Sheet late-season melt: investigating multiscale drivers of K-transect eventsArctic freshwater fluxes: sources, tracer budgets and inconsistenciesInduced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errorsComparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolutionBenchmark seasonal prediction skill estimates based on regional indicesIn situ observed relationships between snow and ice surface skin temperatures and 2 m air temperatures in the ArcticNew insights into the environmental factors controlling the ground thermal regime across the Northern Hemisphere: a comparison between permafrost and non-permafrost areasDynamic ocean topography of the northern Nordic seas: a comparison between satellite altimetry and ocean modelingOn the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalysesPast and future interannual variability in Arctic sea ice in coupled climate modelsArctic sea-ice-free season projected to extend into autumnDefinition differences and internal variability affect the simulated Arctic sea ice melt seasonThe potential of sea ice leads as a predictor for summer Arctic sea ice extent
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844,Short summary
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Christian J. Taubenberger, Denis Felikson, and Thomas Neumann
The Cryosphere, 16, 1341–1348,Short summary
Outlet glaciers are projected to account for half of the total ice loss from the Greenland Ice Sheet over the 21st century. We classify patterns of seasonal dynamic thickness changes of outlet glaciers using new observations from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Our results reveal seven distinct patterns that differ across glaciers even within the same region. Future work can use our results to improve our understanding of processes that drive seasonal ice sheet changes.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123,Short summary
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Elchin E. Jafarov, Daniil Svyatsky, Brent Newman, Dylan Harp, David Moulton, and Cathy Wilson
The Cryosphere, 16, 851–862,Short summary
Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
Fredrik Boberg, Ruth Mottram, Nicolaj Hansen, Shuting Yang, and Peter L. Langen
The Cryosphere, 16, 17–33,Short summary
Using the regional climate model HIRHAM5, we compare two versions (v2 and v3) of the global climate model EC-Earth for the Greenland and Antarctica ice sheets. We are interested in the surface mass balance of the ice sheets due to its importance when making estimates of future sea level rise. We find that the end-of-century change in the surface mass balance for Antarctica is 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3), and for Greenland it is −290 Gt yr−1 (v2) and −1640 Gt yr−1 (v3).
Jinlei Chen, Shichang Kang, Wentao Du, Junming Guo, Min Xu, Yulan Zhang, Xinyue Zhong, Wei Zhang, and Jizu Chen
The Cryosphere, 15, 5473–5482,Short summary
Sea ice is retreating with rapid warming in the Arctic. It will continue and approach the worst predicted pathway released by the IPCC. The irreversible tipping point might show around 2060 when the oldest ice will have completely disappeared. It has a huge impact on human production. Ordinary merchant ships will be able to pass the Northeast Passage and Northwest Passage by the midcentury, and the opening time will advance to the next 10 years for icebreakers with moderate ice strengthening.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725,Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Henrieka Detlef, Brendan Reilly, Anne Jennings, Mads Mørk Jensen, Matt O'Regan, Marianne Glasius, Jesper Olsen, Martin Jakobsson, and Christof Pearce
The Cryosphere, 15, 4357–4380,Short summary
Here we examine the Nares Strait sea ice dynamics over the last 7000 years and their implications for the late Holocene readvance of the floating part of Petermann Glacier. We propose that the historically observed sea ice dynamics are a relatively recent feature, while most of the mid-Holocene was marked by variable sea ice conditions in Nares Strait. Nonetheless, major advances of the Petermann ice tongue were preceded by a shift towards harsher sea ice conditions in Nares Strait.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227,Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Johannes Oerlemans, Jack Kohler, and Adrian Luckman
The Cryosphere Discuss.,
Revised manuscript accepted for TCShort summary
Tunabreen is a 26-km long tidewater glacier. It is the most frequently surging glacier in Svalbard, with four documented surges in the past hundred years. We have modelled this glacier to find out how it react to future climate change. Careful calibration was done against the observed length record for the past 100 years. For a 50 m increase in the equilibrium line altitude (ELA) the length of the glacier will be shortened by 10 km after about 100 years.
Paul D. Bons, Tamara de Riese, Steven Franke, Maria-Gema Llorens, Till Sachau, Nicolas Stoll, Ilka Weikusat, Julien Westhoff, and Yu Zhang
The Cryosphere, 15, 2251–2254,Short summary
The modelling of Smith-Johnson et al. (The Cryosphere, 14, 841–854, 2020) suggests that a very large heat flux of more than 10 times the usual geothermal heat flux is required to have initiated or to control the huge Northeast Greenland Ice Stream. Our comparison with known hotspots, such as Iceland and Yellowstone, shows that such an exceptional heat flux would be unique in the world and is incompatible with known geological processes that can raise the heat flux.
Tobias Reiner Vonnahme, Emma Persson, Ulrike Dietrich, Eva Hejdukova, Christine Dybwad, Josef Elster, Melissa Chierici, and Rolf Gradinger
The Cryosphere, 15, 2083–2107,Short summary
We describe the impact of subglacial discharge in early spring on a sea-ice-covered fjord on Svalbard by comparing a site influenced by a shallow tidewater glacier with two reference sites. We found a moderate under-ice phytoplankton bloom at the glacier front, which we attribute to subglacial upwelling of nutrients; a strongly stratified surface layer; and higher light penetration. In contrast, sea ice algae biomass was limited by low salinities and brine volumes.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319,Short summary
Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341,Short summary
Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895,Short summary
Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Eef C. H. van Dongen, Guillaume Jouvet, Shin Sugiyama, Evgeny A. Podolskiy, Martin Funk, Douglas I. Benn, Fabian Lindner, Andreas Bauder, Julien Seguinot, Silvan Leinss, and Fabian Walter
The Cryosphere, 15, 485–500,Short summary
The dynamic mass loss of tidewater glaciers is strongly linked to glacier calving. We study calving mechanisms under a thinning regime, based on 5 years of field and remote-sensing data of Bowdoin Glacier. Our data suggest that Bowdoin Glacier ungrounded recently, and its calving behaviour changed from calving due to surface crevasses to buoyancy-induced calving resulting from basal crevasses. This change may be a precursor to glacier retreat.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367,Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344,Short summary
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181,Short summary
An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Sebastian Wetterich, Alexander Kizyakov, Michael Fritz, Juliane Wolter, Gesine Mollenhauer, Hanno Meyer, Matthias Fuchs, Aleksei Aksenov, Heidrun Matthes, Lutz Schirrmeister, and Thomas Opel
The Cryosphere, 14, 4525–4551,Short summary
In the present study, we analysed geochemical and sedimentological properties of relict permafrost and ground ice exposed at the Sobo-Sise Yedoma cliff in the eastern Lena delta in NE Siberia. We obtained insight into permafrost aggradation and degradation over the last approximately 52 000 years and the climatic and morphodynamic controls on regional-scale permafrost dynamics of the central Laptev Sea coastal region.
Christopher Chambers, Ralf Greve, Bas Altena, and Pierre-Marie Lefeuvre
The Cryosphere, 14, 3747–3759,Short summary
The topography of the rock below the Greenland ice sheet is not well known. One long valley appears as a line of dips because of incomplete data. So we use ice model simulations that unblock this valley, and these create a watercourse that may represent a form of river over 1000 km long under the ice. When we melt ice at the bottom of the ice sheet only in the deep interior, water can flow down the valley only when the valley is unblocked. It may have developed while an ice sheet was present.
Mohammed E. Shokr, Zihan Wang, and Tingting Liu
The Cryosphere, 14, 3611–3627,Short summary
This paper uses sequential daily SAR images covering the Robeson Channel to quantitatively study kinematics of individual ice floes with exploration of wind influence and the evolution of the ice arch at the entry of the channel. Results show that drift of ice floes within the Robeson Channel and the arch are both significantly influenced by wind. The study highlights the advantage of using the high-resolution daily SAR coverage in monitoring sea ice cover in narrow water passages.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486,Short summary
We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change. The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997,Short summary
The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Frédéric Bouchard, Daniel Fortier, Michel Paquette, Vincent Boucher, Reinhard Pienitz, and Isabelle Laurion
The Cryosphere, 14, 2607–2627,Short summary
We combine lake mapping, landscape observations and sediment core analyses to document the evolution of a thermokarst (thaw) lake in the Canadian Arctic over the last millennia. We conclude that temperature is not the only driver of thermokarst development, as the lake likely started to form during a cooler period around 2000 years ago. The lake is now located in frozen layers with an organic carbon content that is an order of magnitude higher than the usually reported values across the Arctic.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251,Short summary
The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187,Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069,Short summary
Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Anatoliy Gavrilov, Vladimir Pavlov, Alexandr Fridenberg, Mikhail Boldyrev, Vanda Khilimonyuk, Elena Pizhankova, Sergey Buldovich, Natalia Kosevich, Ali Alyautdinov, Mariia Ogienko, Alexander Roslyakov, Maria Cherbunina, and Evgeniy Ospennikov
The Cryosphere, 14, 1857–1873,Short summary
The geocryological study of the Arctic shelf remains insufficient for economic activity. The article presents a study of its evolution by methods of math modeling of heat transfer in rocks. As a result, a model of the evolution and current state of the cryolithozone of the Kara shelf was created based on ideas about the history of its geocryological development over the past 125 kyr. The modeling results are correlated to the available field data and are presented as a geocryological map.
Jutta E. Wollenburg, Morten Iversen, Christian Katlein, Thomas Krumpen, Marcel Nicolaus, Giulia Castellani, Ilka Peeken, and Hauke Flores
The Cryosphere, 14, 1795–1808,Short summary
Based on an observed omnipresence of gypsum crystals, we concluded that their release from melting sea ice is a general feature in the Arctic Ocean. Individual gypsum crystals sank at more than 7000 m d−1, suggesting that they are an important ballast mineral. Previous observations found gypsum inside phytoplankton aggregates at 2000 m depth, supporting gypsum as an important driver for pelagic-benthic coupling in the ice-covered Arctic Ocean.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746,Short summary
This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345,Short summary
This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Alice K. DuVivier, Patricia DeRepentigny, Marika M. Holland, Melinda Webster, Jennifer E. Kay, and Donald Perovich
The Cryosphere, 14, 1259–1271,Short summary
In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system changes. We analyze climate model data from a group of experiments and follow virtual sea ice floes throughout a year. The modeled sea ice conditions along possible tracks are highly variable. Observations that sample a wide range of sea ice conditions and represent the variety and diversity in possible conditions are necessary for improving climate model parameterizations over all types of sea ice.
Xiao-Yi Yang, Guihua Wang, and Noel Keenlyside
The Cryosphere, 14, 693–708,Short summary
The post-2007 Arctic sea ice cover is characterized by a remarkable increase in annual cycle amplitude, which is attributed to multiyear variability in spring Bering sea ice extent. We demonstrated that changes of NPGO mode, by anomalous wind stress curl and Ekman pumping, trigger subsurface variability in the Bering basin. This accounts for the significant decadal oscillation of spring Bering sea ice after 2007. The study helps us to better understand the recent Arctic climate regime shift.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428,Short summary
The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Elchin E. Jafarov, Dylan R. Harp, Ethan T. Coon, Baptiste Dafflon, Anh Phuong Tran, Adam L. Atchley, Youzuo Lin, and Cathy J. Wilson
The Cryosphere, 14, 77–91,Short summary
Improved subsurface parameterization and benchmarking data are needed to reduce current uncertainty in predicting permafrost response to a warming climate. We developed a subsurface parameter estimation framework that can be used to estimate soil properties where subsurface data are available. We utilize diverse geophysical datasets such as electrical resistance data, soil moisture data, and soil temperature data to recover soil porosity and soil thermal conductivity.
Emmanuel Léger, Baptiste Dafflon, Yves Robert, Craig Ulrich, John E. Peterson, Sébastien C. Biraud, Vladimir E. Romanovsky, and Susan S. Hubbard
The Cryosphere, 13, 2853–2867,Short summary
We propose a new strategy called distributed temperature profiling (DTP) for improving the estimation of soil thermal properties through the use of an unprecedented number of laterally and vertically distributed temperature measurements. We tested a DTP system prototype by moving it sequentially across a discontinuous permafrost environment. The DTP enabled high-resolution identification of near-surface permafrost location and covariability with topography, vegetation, and soil properties.
Thomas J. Ballinger, Thomas L. Mote, Kyle Mattingly, Angela C. Bliss, Edward Hanna, Dirk van As, Melissa Prieto, Saeideh Gharehchahi, Xavier Fettweis, Brice Noël, Paul C. J. P. Smeets, Carleen H. Reijmer, Mads H. Ribergaard, and John Cappelen
The Cryosphere, 13, 2241–2257,Short summary
Arctic sea ice and the Greenland Ice Sheet (GrIS) are melting later in the year due to a warming climate. Through analyses of weather station, climate model, and reanalysis data, physical links are evaluated between Baffin Bay open water duration and western GrIS melt conditions. We show that sub-Arctic air mass movement across this portion of the GrIS strongly influences late summer and autumn melt, while near-surface, off-ice winds inhibit westerly atmospheric heat transfer from Baffin Bay.
Alexander Forryan, Sheldon Bacon, Takamasa Tsubouchi, Sinhué Torres-Valdés, and Alberto C. Naveira Garabato
The Cryosphere, 13, 2111–2131,Short summary
We compare control volume and geochemical tracer-based methods of estimating the Arctic Ocean freshwater budget and find both methods in good agreement. Inconsistencies arise from the distinction between
Pacificwaters in the geochemical calculations. The definition of Pacific waters is particularly problematic due to the non-conservative nature of the nutrients underpinning the definition and the low salinity characterizing waters entering the Arctic through Bering Strait.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022,Short summary
This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679,Short summary
A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
John E. Walsh, J. Scott Stewart, and Florence Fetterer
The Cryosphere, 13, 1073–1088,Short summary
Persistence-based statistical forecasts of a Beaufort Sea ice severity index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the trends are removed from the data. This finding is consistent with the notion of a springtime “predictability barrier” that has been found in sea ice forecasts based on more sophisticated methods.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus Tonboe, Gorm Dybkjær, and Emy Alerskans
The Cryosphere, 13, 1005–1024,Short summary
The paper facilitates the construction of a satellite-derived 2 m air temperature (T2m) product for Arctic snow/ice areas. The relationship between skin temperature (Tskin) and T2m is analysed using weather stations. The main factors influencing the relationship are seasonal variations, wind speed and clouds. A clear-sky bias is estimated to assess the effect of cloud-limited satellite observations. The results are valuable when validating satellite Tskin or estimating T2m from satellite Tskin.
Olli Karjalainen, Miska Luoto, Juha Aalto, and Jan Hjort
The Cryosphere, 13, 693–707,Short summary
Using a statistical modelling framework, we examined the environmental factors controlling ground thermal regimes inside and outside the Northern Hemisphere permafrost domain. We found that climatic factors were paramount in both regions, but with varying relative importance and effect size. The relationships were often non-linear, especially in permafrost conditions. Our results suggest that these non-linearities should be accounted for in future ground thermal models at the hemisphere scale.
Felix L. Müller, Claudia Wekerle, Denise Dettmering, Marcello Passaro, Wolfgang Bosch, and Florian Seitz
The Cryosphere, 13, 611–626,Short summary
Knowledge of the dynamic ocean topography (DOT) enables studying changes of ocean surface currents. The DOT can be derived by satellite altimetry measurements or by models. However, in polar regions, altimetry-derived sea surface heights are affected by sea ice. Model representations are consistent but impacted by the underlying functional backgrounds and forcing models. The present study compares results from both data sources in order to investigate the potential for a combination of the two.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543,Short summary
The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
John R. Mioduszewski, Stephen Vavrus, Muyin Wang, Marika Holland, and Laura Landrum
The Cryosphere, 13, 113–124,Short summary
Arctic sea ice is projected to thin substantially in every season by the end of the 21st century with a corresponding increase in its interannual variability as the rate of ice loss peaks. This typically occurs when the mean ice thickness falls between 0.2 and 0.6 m. The high variability in both growth and melt processes is the primary factor resulting in increased ice variability. This study emphasizes the importance of short-term variations in ice cover within the mean downward trend.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96,Short summary
The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20,Short summary
Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757,
Bellucci, A., Haarsma, R., Gualdi, S., Athanasiadis, P. J., Caian, M., Cassou, C., Fernandez, E., Germe, A., Jungclaus, J., Kröger, J., Matei, D., Müller, W., Pohlmann, H., Salas y Melia, D., Sanchez, E., Smith, D., Terray, L., Wyser, K., and Yang, S.: An assessment of a multi-model ensemble of decadal climate predictions, Clim. Dynam., 44, 2787–2806, https://doi.org/10.1007/s00382-014-2164-y, 2015.
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Sebbari, R., and Zhang, X.: Detection and attribution of climate change: From global to regional, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Doschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, 867–952, https://doi.org/10.1017/CBO9781107415324.022, 2013.
Blanchard-Wrigglesworth, E., Armour, K., Bitz, C. M., and deWeaver, E.: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1, 2011a.
Blanchard-Wrigglesworth, E., Bitz, C. M., and Holland, M. M.: Influence of initial conditions and climate forcing on predicting Arctic sea ice, Geophys. Res. Lett., 38, L18503, https://doi.org/10.1029/2011GL048807, 2011b.
Boé, J. L., Hall, A., and Qu, X.: September sea-ice cover in the Arctic Ocean projected to vanish by 2100, Nat. Geosci., 2, 341–343, 2009.
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012, 2012.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.:. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, updated yearly, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/8GQ8LZQVL0VL, available at: http://nsidc.org/data/NSIDC-0051, last access: October 2016, 1996.
Collins, M. and Allen, M. R.: On assessing the relative roles of initial and boundary conditions in interannual to decadal climate predictability, J. Climate, 21, 3104–3109, 2002.
Comiso, J. C.: Large Decadal Decline of the Arctic Multiyear Ice Cover, J. Climate, 25, 1176–1193, 2012.
Comiso, J. C.: Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, Version 2, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA, 2000, updated 2015.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703, https://doi.org/10.1029/2007GL031972, 2008.
Comiso, J. C., Kwok, R., Martin, S., and Gordon, A. L.: Variability and trends in sea ice extent and ice production in the Ross Sea, J. Geophys. Res., 116, C04021, https://doi.org/10.1029/2010JC006391, 2011.
Cunningham, S. A., Kanzow, T., Rayner, D., Baringer, M. O., Johns, W., Marotzke, E. J., Longworth, H. R., Grant, E. M., Hirschi, J. J.-M., Beal, L. M., Meinen, C. S., and Bryden, H. L.: Temporal variability of the Atlantic meridional overturning circulation at 26.5° N, Science, 317, 935–938, 2007.
Day, J. J., Hargreaves, J. C., Annan, J. D., and Abe-Ouchi, A.: Sources of multi-decadal variability in Arctic sea ice extent, Environ. Res. Lett., 7, 034011, https://doi.org/10.1088/1748-9326/7/3/034011, 2012.
Day, J. J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41, 7566–7575, https://doi.org/10.1002/2014GL061694, 2014a.
Day, J. J., Tietsche, S., and Hawkins, E.: Pan-arctic and regional sea ice predictability: Initialization month dependence, J. Climate, 27, 4371–4390, 2014b.
Day, J. J., Tietsche, S., Collins, M., Goessling, H. F., Guemas, V., Guillory, A., Hurlin, W. J., Ishii, M., Keeley, S. P. E., Matei, D., Msadek, R., Sigmond, M., Tatebe, H., and Hawkins, E.: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1, Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, 2016.
Doblas-Reyes, F. J., Andreu-Burillo, I., Chikamoto, Y., García-Serrano, J., Guemas, V., Kimoto, M., Mochizuki, T., Rodrigues, L. R. L., and van Oldenborgh, G. J.: Initialized near-term regional climate change prediction, Nature Communications, 4, 1715, https://doi.org/10.1038/ncomms2704, 2013.
Döös, K., Nycander, J., and Coward, A. C.: Lagrangian decomposition of the Deacon Cell, J. Geophys. Res., 113, C07028, https://doi.org/10.1029/2007JC004351, 2008.
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental US, Geophys. Res. Lett., 28, 2077–2080, 2001.
Fetterer, F., Knowles, K., Meier, W., and Savoie, M.: Sea ice index, digital media, National Snow and Ice Data Center, Boulder, CO, 2002.
Fetterer, F., Knowles, K., Meier, W., and Savoie, M.: Sea ice index, digital media, National Snow and Ice Data Center, Boulder, CO, 2010.
Francis, J. A. and Vavrus, S. J.: Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000, 2012.
Germe, A., Chevallier, M., Salas y Mélia, D., Sanchez-Gomez, E., and Cassou, C.: Interannual predictability of Arctic sea ice in a global climate model: Regional contrasts and temporal evolution, Clim. Dynam., 43, 2519–2538, https://doi.org/10.1007/s00382-014-2071-2, 2014.
Goddard, L., Kumar, A., Solomon, A., Smith, D., Boer, G., Gonzalez, P., Kharin, V., Merryfield, W., Deser, C., Mason, S. J., Kirtman, B. P., Msadek, R., Sutton, R., Hawkins, E., Fricker, T., Hegerl, G., Ferro, C. A. T., Stephenson, D. B., Meehl, G. A., Stockdale, T., Burgman, R., Greene, A. M., Kushnir, Y., Newman, M., Carton, J., Fukumori, I., and Delworth, T.: A verification framework for interannual-to-decadal predictions experiments, Clim. Dynam., 40, 245–272, https://doi.org/10.1007/s00382-012-1481-2, 2013.
Goosse, H., Close, S., Dubinkina, S., Massonnet, F., Zunz, V., Vannitsem, S., Schaeybroeck, B. V., Barth, A., and Canter, M.: Understanding and predicting Antarctic sea ice variability at the decadal timescale – "PREDANTAR", available at: http://www.elic.ucl.ac.be/users/zunz/site_PREDANTAR/en-project_results.html, last access: October 2016, 2015.
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M., Doblas-Reyes, F. J., Fuckar, N. S., Germe, A., Hawkins, E., Keeley, S., Koenigk, T., Salas y Mélia, D., and Tietsche, S.: A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales, Q. J. Roy. Meteor. Soc., 142, 546–561, https://doi.org/10.1002/qj.2401, 2016.
Ham, Y.-G., Rienecker, M. M., Suarez, M. J., Vikhliaev, Y., Zhao, B., Marshak, J., Vernieres, G., and Schubert, S. D.: Decadal prediction skill in the GEOS-5 forecast system, Clim. Dynam., 42, 1–20, 2014.
Holland, M. M. and Raphael, M. N.: Twentieth century simulation of the Southern Hemisphere climate in coupled models. Part II: Sea ice conditions and variability, Clim. Dynam., 26, 229–245, 2006.
Holland, M. M., Bailey, D. A., and Vavrus, S.: Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3, Clim. Dynam., 36, 1239–1253, 2011.
Holland, M. M., Blanchard-Wrigglesworth, E., Kay, J., and Vavrus, S.: Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3, Geophys. Res. Lett., 40, 2121–2124, https://doi.org/10.1002/grl.50410, 2013.
Holland, P. R. and Kwok, R.: Wind-driven trends in Antarctic sea-ice drift, Nat. Geosci., 5, 872–875, 2012.
Ishii, M. and Kimoto, M.: Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections, J. Oceanogr., 65, 287–299, https://doi.org/10.1007/s10872-009-0027-7, 2009.
Jackson, L. C., Kahana, R., Graham, T., Ringer, M. A., Woollings, T., Mecking, J. V., and Wood, R. A.: Global and European climate impacts of a slowdown of the AMOC in a high resolution GCM, Clim. Dynam., 45, 3299–3316, https://doi.org/10.1007/s00382-015-2540-2, 2015.
Jeffries, M. O., Richter-Menge, J., and Overland, J. E. (Eds.): Arctic Report Card 2015, available at: http://www.arctic.noaa.gov/reportcard (last access: December 2015), 2015.
Jung, T., Gordon, N., Klebe, S., Bauer, P., Bromwich, D. H., Day, J., Doblas-Reyes, F., Fairall, C., Hines, K., Holland, M., Iversen, T., Lemke, P., Mills, B., Nurmi, P., Renfrew, I., Smith, G., Svensson, G., and Tolstykh, M.: WWRP Polar Prediction Project implementation plan, WWRP/PPP No. 2, http://polarprediction.net/en/documents/, last access: 29 August 2014, 2013.
Kattsov, V., Ryabinin, V., Overland, J., Serreze, M., Visbeck, M., Walsh, J., Meier, W., and Zhang, X.: Arctic sea ice change: A grand challenge of climate science, J. Glaciol., 56, 1115–1121, 2010.
Keenlyside, N. S., Latif, M., Jungclaus, J., Kornblueh, L., and Roeckner, E.: Advancing decadal-scale climate prediction in the North Atlantic sector, Nature, 453, 84–88, https://doi.org/10.1038/nature06921, 2008.
Kharin, V. V., Boer, G. J., Merryfield, W. J., Scinocca, J. F., and Lee, W.-S.: Statistical adjustment of decadal predictions in a changing climate, Geophys. Res. Lett., 39, L19705, https://doi.org/10.1029/2012GL052647, 2012.
Kim, H.-M., Webster, P. J., and Curry, J. A.: Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts, Geophys. Res. Lett., 39, L10701, https://doi.org/10.1029/2012GL051644, 2012.
Koenigk, T. and Mikolajewicz, U.: Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model, Clim. Dynam., 32, 783–798, 2009.
Koenigk, T., Mikolajewicz, U., Haak, H., and Jungclaus J.: Arctic Freshwater Export in the 20th and 21st Century, J. Geophys. Res., 112, GS04S41, https://doi.org/10.1029/2006JG000274, 2007.
Koenigk, T., Beatty, C. K., Caian, M., Döscher, R., and Wyser, K.: Potential decadal predictability and its sensitivity to sea ice albedo parameterization in a global coupled model, Clim. Dynam., 38, 2389–2408, 2012.
Kurtz, N. T. and Markus, T.: Satellite observations of Antarctic sea ice thickness and volume, J. Geophys. Res., 117, C08025, https://doi.org/10.1029/2012JC008141, 2012.
Kwok, R.: Recent changes of the Arctic Ocean sea ice motion associated with the North Atlantic Oscillation, Geophys. Res. Lett., 27, 775–778, 2000.
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008, J. Geophys. Res., 114, C07005, https://doi.org/10.1029/2009JC005312, 2009.
Lindsay, R. W. and Zhang, J.: The Thinning of Arctic Sea Ice, 1988–2003: Have We Passed a Tipping Point?, J. Climate, 18, 4879–4894, 2005.
Liu, J. and Curry, J. A.: Accelerated warming of the Southern Ocean and its impacts on the hydrological cycle and sea ice, P. Natl. Acad. Sci. USA, 107, 14987–14992, 2010.
Liu, J., Curry, J. A., and Martinson, D. G.: Interpretation of recent Antarctic sea ice variability, Geophys. Res. Lett., 31, L02205, https://doi.org/10.1029/2003GL018732, 2004.
Liu, J., Curry, J. A., Wang, H., Song, M., and Horton, R.: Impact of declining Arctic sea ice on winter snowfall, P. Natl. Acad. Sci. USA, 109, 4074–4079; Corrigendum, 109, 6781–6783, 2012.
Liu, J., Song, M., Horton, R. M., and Hu, Y.: Reducing spread in climate model projections of a September ice-free Arctic, P. Natl. Acad. Sci. USA, 110, 12571–12576, https://doi.org/10.1073/pnas.1219716110, 2013.
Liu, J. F., Yuan, X., Rind, D., and Martinson, D.: Mechanism study of the ENSO and southern high latitude climate teleconnections, Geophys. Res. Lett., 29, 1679, https://doi.org/10.1029/2002GL015143, 2002.
Lumpkin, R. and Speer, K.: Global ocean meridional overturning, J. Phys. Oceanogr., 37, 2550–2562, 2007.
Mahajan, S., Zhang, R., and Delworth, T. L.: Impact of the Atlantic Meridional Overturning Circulation (AMOC) on Arctic surface air temperature and sea-ice variability, J. Climate, 24, 6573–6581, https://doi.org/10.1175/2011JCLI4002.1, 2011.
Massonnet, F., Fichefet, T., Goosse, H., Bitz, C. M., Philippon-Berthier, G., Holland, M. M., and Barriat, P.-Y.: Constraining projections of summer Arctic sea ice, The Cryosphere, 6, 1383–1394, https://doi.org/10.5194/tc-6-1383-2012, 2012.
McCarthy, G. D., Smeed, D. A., Johns, W. E., Frajka-Williams, E., Moat, B. I., Rayner, D., Baringer, M. O., Meinen, C. S., and Bryden, H. L.: Measuring the Atlantic meridional overturning circulation at 26° N, Prog. Oceanogr., 130, 91–111, 2015.
Meehl, G. A., Goddard, L., Murphy, J., Stouffer, R. J., Boer, G., Danabasoglu, G., Dixon, K., Giorgetta, M. A., Greene, A. M., Hawkins, E., Hegerl, G., Karoly, D., Keenlyside, N., Kimoto, M., Kirtman, B., Navarra, A., Pulwarty, R., Smith, D., Stammer, D., and Stockdale, T.: Decadal Prediction, B. Am. Meteorol. Soc., 90, 1467–1485, https://doi.org/10.1175/2009BAMS2778.1, 2009.
Meehl, G. A., Hu, A., Arblaster, J. M., Fasullo, J., and Trenberth, K. E.: Externally Forced and Internally Generated Decadal Climate Variability Associated with the Interdecadal Pacific Oscillation, J. Climate, 26, 7298–7310, https://doi.org/10.1175/JCLI-D-12-00548.1, 2013
Merryfield, W. J., Lee, W.-S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., Ajayamohan, R. S., Fyfe, J. C., Tang, Y., and Polavarapu, S.: The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization, Mon. Weather Rev., 141, 2910–2945, https://doi.org/10.1175/MWR-D-12-00216.1, 2013.
Mochizuki, T., Chikamoto, T., Kimoto, M., Ishii, M., Tatebe, H., Komuro, Y., Sakamoto, T., Watanabe, M., and Mori, M.: Decadal prediction using a recent series of MIROC global climate models, J. Meteorol. Soc. Jpn., 90, 373–383, 2012.
Msadek, R., Vecchi, G. A., Winton, M., and Gudgel, R. G.: Importance of initial conditions in seasonal predictions of Arctic sea ice extent, Geophys. Res. Lett., 41, 5208–5215, https://doi.org/10.1002/2014GL060799, 2014.
Müller, W. A., Baehr, J., Haak, H., Jungclaus, J. H., Kröger, J., Matei, D., Notz, D., Pohlmann, H., von Storch, J.-S., and Marotzke, J.: Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology, Geophys. Res. Lett., 39, L22707, https://doi.org/10.1029/2012GL053326, 2012.
National Research Council: Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies, The National Academies Press, Washington, DC, 2012.
Parkinson, C. L. and Cavalieri, D. J.: Antarctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 871–880, https://doi.org/10.5194/tc-6-871-2012, 2012.
Pohlmann, H., Jungclaus, J. H., Köhl, A., Stammer, D., and Marotzke, J.: Initialized decadal climate predictions with the GECCO oceanic synthesis: Effects on the North Atlantic, J. Climate, 22, 3926–3938, 2009.
Polvani, L. M. and Smith, K. L.: Can natural variability explain observed Antarctic sea ice trends? New modeling evidence from CMIP5, Geophys. Res. Lett., 40, 3195–3199, https://doi.org/10.1002/grl.50578, 2013.
National Research Council: Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies, The National Academies Press, Washington, DC, https://doi.org/10.17226/13515, 2012.
Rigor, I. G. and Wallace, J. M.: Variations in age of Arctic sea ice and summer sea-ice extent, Geophys. Res. Lett., 31, L09401, https://doi.org/10.1029/2004GL019492, 2004.
Serreze, M. C., Holland, M. M., and Stroeve, J.: Perspectives on the Arctic's shrinking sea-ice cover, Science, 315, 1533–1536, 2007.
Sigmond, M. and Fyfe, J. C.: Has the ozone hole contributed to increased Antarctic sea ice extent?, Geophys. Res. Lett., 37, L18502, https://doi.org/10.1029/2010GL044301, 2010.
Shu, Q., Song, Z., and Qiao, F.: Assessment of sea ice simulations in the CMIP5 models, The Cryosphere, 9, 399–409, https://doi.org/10.5194/tc-9-399-2015, 2015.
Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A., and Bryden, H. L.: Observed decline of the Atlantic meridional overturning circulation 2004–2012, Ocean Sci., 10, 29–38, https://doi.org/10.5194/os-10-29-2014, 2014.
Smith, D. M., Cusack, S. A., Colman, W., Folland, C. K., Harris, G. R., and Murphy, J. M.: Improved surface temperature prediction for the coming decade from a global climate model, Science, 317, 796–799, 2007.
Smith, L. C. and Stephenson, S. R.: New Trans-Arctic shipping routes navigable by mid-century, P. Natl. Acad. Sci. USA, 110, E1191–E1195, https://doi.org/10.1073/pnas.1214212110, 2013.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501, https://doi.org/10.1029/2007GL029703, 2007.
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland, M. M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676, 2012.
Stroeve, J. C., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.: Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008–2013, Geophys. Res. Lett., 41, 2411–2418, https://doi.org/10.1002/2014GL059388, 2014a.
Stroeve, J. C., Markus, T., Boisvert, L., Miller, J., and Barrett, A.: Changes in Arctic melt season and implications for sea ice loss, Geophys. Res. Lett., 41, 1216–1225, https://doi.org/10.1002/2013gl058951, 2014b.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Predictability of large interannual Arctic sea-ice anomalies, Clim. Dynam., 41, 2511–2526, https://doi.org/10.1007/s00382-013-1698-8, 2013.
Tietsche, S., Day, J. J., Guemas, V., Hurlin, W. J., Keeley, S. P. E., Matei, D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice predictability in current global climate models, Geophys. Res. Lett., 41, 1035–1043, https://doi.org/10.1002/2013GL058755, 2014.
Turner, J., Comiso, J. C., Marshall, G. J., Lachlan-Cope, T. A., Bracegirdle, T., Maksym, T., Meredith, M. P., Wang, Z., and Orr, A.: Non-annular atmospheric circulation change induced by stratospheric ozone depletion and its role in the recent increase of Antarctic sea ice extent, Geophys. Res. Lett., 36, L08502, https://doi.org/10.1029/2009GL037524, 2009.
Turner, J., Bracegirdle, T. J., Phillips, T., Marshall, G. J., and Scott Hosking, J.: An initial assessment of Antarctic sea ice extent in the CMIP5 models, J. Climate, 26, 1473–1484, 2013.
Vera, C., Barange, M., Dube, O. P., Goddard, L., Griggs, D., Kobysheva, N., Odada, E., Parey, S., Polovina, J., Poveda, G., Seguin, B., and Trenberth, K.: Needs assessment for climate information on decadal time scales and longer, in: World Climate Conference – 3, Geneva, Switzerland, 31 August–4 September 2009, edited by: Sivakumar, M. V. K., Nyenzi, B. S., and Tyagi, A., Procedia Environmental Sciences, 1, 275–286, https://doi.org/10.1016/j.proenv.2010.09.017, 2010.
Wang, M. and Overland, J. E.: A sea ice free summer Arctic within 30 years?, Geophys. Res. Lett., 36, L07502, https://doi.org/10.1029/2009GL037820, 2009.
Wang, M. and Overland, J. E.: A sea ice free summer Arctic within 30 years: An update from CMIP5 models, Geophys. Res. Lett., 39, L18501, https://doi.org/10.1029/2012GL052868, 2012.
WCRP: Coupled Model Intercomparison Project phase 5 (CMIP5) model output, World Climate Research Programme's (WCRP) Working Group on Coupled Modeling, available at: http://cmip-pcmdi.llnl.gov/cmip5/, last access: October 2016.
Wendler, G., Chen, L., and Moore, B.: Recent sea ice increase and temperature decrease in the Bering Sea area, Alaska, Theor. Appl. Climatol., 117, 393–398, 2014.
Zhang, J.: Increasing Antarctic sea ice under warming atmospheric and oceanic conditions, J. Climate, 20, 2515–2529, 2007.
Zhang, R.: Mechanisms for low-frequency variability of summer Arctic sea ice extent, P. Natl. Acad. Sci. USA, 112, 4570–4575, https://doi.org/10.1073/pnas.1422296112, 2015.
Zhang, X.: Sensitivity of Arctic summer sea ice coverage to global warming forcing: Towards reducing uncertainty in arctic climate change projections, Tellus A, 62, 220–227, 2010.
Zhang, J. and Zhang, R.: On the Evolution of Atlantic Meridional Overturning Circulation (AMOC) Fingerprint and Implications for Decadal Predictability in the North Atlantic, Geophys. Res. Lett., 42, 5419–5426, https://doi.org/10.1002/2015GL064596, 2015.
Zhang, J., Woodgate, R., and Moritz, R.: Sea ice response to atmospheric and oceanic forcing in the Bering Sea, J. Phys. Oceanogr., 40, 1729–1747, https://doi.org/10.1175/2010JPO4323.1, 2010.
Zunz, V., Goosse, H., and Massonnet, F.: How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?, The Cryosphere, 7, 451–468, https://doi.org/10.5194/tc-7-451-2013, 2013.
Zunz, V., Goosse, H., and Dubinkina, S.: Impact of the initialisation on the predictability of the Southern Ocean sea ice at interannual to multi-decadal timescales, Clim. Dynam., 44, 2267–2286, 2015.
The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales.
The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that...