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Journal articles on the topic 'Sea ice; Arctic'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Blockley, Edward W., and K. Andrew Peterson. "Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness." Cryosphere 12 (October 30, 2018): 3419–38. https://doi.org/10.5194/tc-12-3419-201.

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Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere&nd

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2

Zhang, Yuanyuan, Xiao Cheng, Jiping Liu, and Fengming Hui. "The potential of sea ice leads as a predictor for summer Arctic sea ice extent." Cryosphere 12, no. 12 (2018): 3747–57. http://dx.doi.org/10.5194/tc-12-3747-2018.

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Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area

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3

Nehiem, Son, and G. Neumann. "Arctic sea ice change." IOP Conference Series: Earth and Environmental Science 6, no. 1 (2009): 012012. http://dx.doi.org/10.1088/1755-1307/6/1/012012.

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4

Mikolajewicz, Uwe, Dmitry V. Sein, Daniela Jacob, Torben Königk, Ralf Podzun, and Tido Semmler. "Simulating Arctic sea ice variability with a coupled regional atmosphere-ocean-sea ice model." Meteorologische Zeitschrift 14, no. 6 (2005): 793–800. http://dx.doi.org/10.1127/0941-2948/2005/0083.

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5

Cocetta, Francesco, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino. "Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses." Cryosphere 18, no. 10 (2024): 4687–702. http://dx.doi.org/10.5194/tc-18-4687-2024.

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Abstract. The recent development of data-assimilating reanalyses of the global ocean and sea ice enables a better understanding of the polar region dynamics and provides gridded descriptions of sea ice variables without temporal and spatial gaps. Here, we study the spatiotemporal variability of the Arctic sea ice area and thickness using the Global ocean Reanalysis Ensemble Product (GREP) produced and disseminated by the Copernicus Marine Service (CMS). GREP is compared and validated against the state-of-the-art regional reanalyses PIOMAS and TOPAZ, as well as observational datasets of sea ice

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6

Dekker, Evelien, Richard Bintanja, and Camiel Severijns. "Nudging the Arctic Ocean to Quantify Sea Ice Feedbacks." Journal of Climate 32, no. 8 (2019): 2381–95. http://dx.doi.org/10.1175/jcli-d-18-0321.1.

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AbstractWith Arctic summer sea ice potentially disappearing halfway through this century, the surface albedo and insulating effects of Arctic sea ice will decrease considerably. The ongoing Arctic sea ice retreat also affects the strength of the Planck, lapse rate, cloud, and surface albedo feedbacks together with changes in the heat exchange between the ocean and the atmosphere, but their combined effect on climate sensitivity has not been quantified. This study presents an estimate of all Arctic sea ice related climate feedbacks combined. We use a new method to keep Arctic sea ice at its pre

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7

Li, Dawei, Rong Zhang, and Thomas Knutson. "Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Models." Journal of Climate 31, no. 3 (2018): 1205–26. http://dx.doi.org/10.1175/jcli-d-16-0617.1.

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Abstract In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decl

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8

Simon, Amélie, Guillaume Gastineau, Claude Frankignoul, Clément Rousset, and Francis Codron. "Transient Climate Response to Arctic Sea Ice Loss with Two Ice-Constraining Methods." Journal of Climate 34, no. 9 (2021): 3295–310. http://dx.doi.org/10.1175/jcli-d-20-0288.1.

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AbstractThe impact of Arctic sea ice loss on the ocean and atmosphere is investigated focusing on a gradual reduction of Arctic sea ice by 20% of the annual mean, occurring within 30 years, starting from present-day conditions. Two ice-constraining methods are explored to melt Arctic sea ice in a coupled climate model, while keeping present-day conditions for external forcing. The first method uses a reduction of sea ice albedo, which modifies the incoming surface shortwave radiation. The second method uses a reduction of thermal conductivity, which changes the heat conduction flux inside ice.

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9

Brennan, M. Kathleen, and Gregory J. Hakim. "Reconstructing Arctic Sea Ice over the Common Era Using Data Assimilation." Journal of Climate 35, no. 4 (2022): 1231–47. http://dx.doi.org/10.1175/jcli-d-21-0099.1.

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Abstract Arctic sea ice decline in recent decades has been dramatic; however, few long-term records of Arctic sea ice exist to put such a decline in context. Here we employ an ensemble Kalman filter data assimilation approach to reconstruct Arctic sea ice concentration over the last two millennia by assimilating temperature-sensitive proxy records with ensembles drawn from last millennium climate model simulations. We first test the efficacy of this method using pseudoproxy experiments. Results show good agreement between the target and reconstructed total Arctic sea ice extent (R2 value and c

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10

Stroeve, Julienne, Allan Frei, James McCreight, and Debjani Ghatak. "Arctic sea-ice variability revisited." Annals of Glaciology 48 (2008): 71–81. http://dx.doi.org/10.3189/172756408784700699.

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AbstractThis paper explores spatial and temporal relationships between variations in Arctic sea-ice concentration (summer and winter) and near-surface atmospheric temperature and atmospheric pressure using multivariate statistical techniques. Trend, empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses are used to identify spatial patterns associated with covariances and correlations between these fields. Results show that (1) in winter, the Arctic Oscillation still explains most of the variability in sea-ice concentration from 1979 to 2006; and (2) in summer, a d

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11

Detlef, Henrieka, Matt O'Regan, Christian Stranne, et al. "Seasonal sea-ice in the Arctic's last ice area during the Early Holocene." Communications Earth & Environment 4, no. 1 (2023): 1–11. https://doi.org/10.1038/s43247-023-00720-w.

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According to climate models, the Lincoln Sea, bordering northern Greenland and Canada, will be the final stronghold of perennial Arctic sea-ice in a warming climate. However, recent observations of prolonged periods of open water raise concerns regarding its long-term stability. Modelling studies suggest a transition from perennial to seasonal sea-ice during the Early Holocene, a period of elevated global temperatures around 10,000 years ago. Here we show marine proxy evidence for the disappearance of perennial sea-ice in the southern Lincoln Sea during the Early Holocene, which suggests a wid

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12

Li, Linghan, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph. "Impact of atmospheric rivers on Arctic sea ice variations." Cryosphere 18, no. 1 (2024): 121–37. http://dx.doi.org/10.5194/tc-18-121-2024.

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Abstract. Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25∘ × 0.25∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid se

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13

Grunseich, Gary, and Bin Wang. "Arctic Sea Ice Patterns Driven by the Asian Summer Monsoon." Journal of Climate 29, no. 24 (2016): 9097–112. http://dx.doi.org/10.1175/jcli-d-16-0207.1.

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Abstract The fluctuation of Arctic sea ice concentration (SIC) has been associated with changes in ocean circulation, ecology, and Northern Hemisphere climate. Prediction of sea ice melting patterns is of great societal interest, but such prediction remains difficult because the factors controlling year-to-year sea ice variability remain unresolved. Distinct monsoon–Arctic teleconnections modulate summer Arctic SIC largely by changing wind-forced sea ice transport. East Asian monsoon rainfall produces a northward-propagating meridional Rossby wave train extending into the Siberian Arctic. The

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14

Berge, J., Ø. Varpe, M. A. Moline, et al. "Retention of ice-associated amphipods: possible consequences for an ice-free Arctic Ocean." Biology Letters 8, no. 6 (2012): 1012–15. http://dx.doi.org/10.1098/rsbl.2012.0517.

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Recent studies predict that the Arctic Ocean will have ice-free summers within the next 30 years. This poses a significant challenge for the marine organisms associated with the Arctic sea ice, such as marine mammals and, not least, the ice-associated crustaceans generally considered to spend their entire life on the underside of the Arctic sea ice. Based upon unique samples collected within the Arctic Ocean during the polar night, we provide a new conceptual understanding of an intimate connection between these under-ice crustaceans and the deep Arctic Ocean currents. We suggest that downward

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15

Wang, Jia, and Moto Ikeda. "Arctic oscillation and Arctic sea-ice oscillation." Geophysical Research Letters 27, no. 9 (2000): 1287–90. http://dx.doi.org/10.1029/1999gl002389.

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16

Mu, Longjiang, Xi Liang, Qinghua Yang, Jiping Liu, and Fei Zheng. "Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017." Journal of Glaciology 65, no. 253 (2019): 813–21. http://dx.doi.org/10.1017/jog.2019.55.

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AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather

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17

Martin, Torge, and Thomas Martin. "Anomalies of Sea-ice transports in the Arctic." Annals of Glaciology 44 (2006): 310–16. http://dx.doi.org/10.3189/172756406781811826.

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AbstractIn the Arctic, Sea-ice motion and ice export are prominent processes and good indicators of Arctic climate System variability. Sea-ice drift is Simulated using a dynamic–thermodynamic Sea-ice model, validated with retrievals from SsM/I Satellite observations. Both datasets agree well in reproducing the main Arctic drift patterns. In order to Study inner Arctic transports and ice volume anomalies, the Arctic Ocean is Split by ten boundaries, Separating the central Arctic Ocean from the Nordic and marginal Seas. It is found that the already dominant Sea-ice export through Fram Strait has

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18

Zhang, Qiaoqiao, Hao Luo, Chao Min, Yongwu Xiu, Qian Shi, and Qinghua Yang. "Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model." Remote Sensing 15, no. 10 (2023): 2537. http://dx.doi.org/10.3390/rs15102537.

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Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a compariso

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19

He, Zhaoxiangrui, Aiguo Dai, Brian E. J. Rose, and Mathias Vuille. "Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013." Journal of Climate 37, no. 17 (2024): 4481–506. http://dx.doi.org/10.1175/jcli-d-23-0520.1.

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Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The inte

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20

Semenov, V. A., and M. Latif. "The early twentieth century warming and winter Arctic sea ice." Cryosphere Discussions 6, no. 3 (2012): 2037–57. http://dx.doi.org/10.5194/tcd-6-2037-2012.

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Abstract. The Arctic featured the strongest surface warming over the globe during the recent decades, and the temperature increase was accompanied by a rapid decline in sea ice extent. However, little is known about Arctic sea ice change during the Early Twentieth Century Warming (ETCW) during 1920–1940, also a period of a strong surface warming, both globally and in the Arctic. Here, we investigate the sensitivity of Arctic winter surface air temperature (SAT) to sea ice during 1875–2008 by means of simulations with an atmospheric general circulation model (AGCM) forced by estimates of the ob

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21

Kim, Kwang-Yul, Benjamin D. Hamlington, Hanna Na, and Jinju Kim. "Mechanism of seasonal Arctic sea ice evolution and Arctic amplification." Cryosphere 10, no. 5 (2016): 2191–202. http://dx.doi.org/10.5194/tc-10-2191-2016.

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Abstract. Sea ice loss is proposed as a primary reason for the Arctic amplification, although the physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-Interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice loss in the Arctic Ocean and the Arctic amplification. While sea ice loss is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains thin in winter only in the Barents–Kara seas. Excessive

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22

Koldunov, Nikolay V., Armin Köhl, Nuno Serra, and Detlef Stammer. "Sea ice assimilation into a coupled ocean–sea ice model using its adjoint." Cryosphere 11, no. 5 (2017): 2265–81. http://dx.doi.org/10.5194/tc-11-2265-2017.

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Abstract. Satellite sea ice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–sea ice model covering the period of 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months, values of sea ice extent (SIE) integrated over the

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23

Sewall, J. O. "Model resolution influence on simulated sea ice decline." Cryosphere Discussions 2, no. 5 (2008): 759–76. http://dx.doi.org/10.5194/tcd-2-759-2008.

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Abstract. Satellite observations and model predictions of recent and future Arctic sea ice decline have raised concerns over the timing and potential impacts of a seasonally ice-free Arctic Ocean. Model predictions of seasonally ice-free Arctic conditions are, however, highly variable. Here I present results from fourteen climate system models from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset that indicate modeled Arctic sea ice sensitivity to increased atmospheric CO2 forcing is strongly correlated with ice/ocean mod

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24

Semenov, V. A., and M. Latif. "The early twentieth century warming and winter Arctic sea ice." Cryosphere 6, no. 6 (2012): 1231–37. http://dx.doi.org/10.5194/tc-6-1231-2012.

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Abstract. The Arctic has featured the strongest surface warming over the globe during the recent decades, and the temperature increase has been accompanied by a rapid decline in sea ice extent. However, little is known about Arctic sea ice change during the early twentieth century warming (ETCW) during 1920–1940, also a period of a strong surface warming, both globally and in the Arctic. Here, we investigate the sensitivity of Arctic winter surface air temperature (SAT) to sea ice during 1875–2008 by means of simulations with an atmospheric general circulation model (AGCM) forced by estimates

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25

Chatterjee, Sourav, Tido Semmler, James Screen, et al. "Atmosphere–Ocean–Sea Ice Feedbacks Sustain Recent Barents Sea Ice Loss despite Cooler Atlantic Water Inflow." Journal of Climate 37, no. 24 (2024): 6519–32. http://dx.doi.org/10.1175/jcli-d-24-0020.1.

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Abstract Winter sea ice cover has declined faster in the northern Barents Sea (NBS) than in the rest of the Arctic Ocean. One of the key elements controlling sea ice extent in the NBS is the inflow of warm and saline Atlantic water (AW) through the Barents Sea Opening. We show that despite a pronounced decadal variability in the AW temperature with a cooling trend since the mid-2000s, sea ice in the NBS continues to decline. We find that the sea ice decline is partly caused by reduced oceanic heat loss in the southern Barents Sea (SBS) and subsequent transport of warmer AW downstream to the NB

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26

Han, Lu, Haihua Chen, Lei Guan, and Lele Li. "Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic." Remote Sensing 15, no. 3 (2023): 678. http://dx.doi.org/10.3390/rs15030678.

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Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are

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27

Showstack, Randy. "Arctic sea ice minimum extent." Eos, Transactions American Geophysical Union 93, no. 40 (2012): 388. http://dx.doi.org/10.1029/2012eo400005.

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28

Balcerak, Ernie. "Evaluating Arctic sea ice predictions." Eos, Transactions American Geophysical Union 95, no. 32 (2014): 292. http://dx.doi.org/10.1002/2014eo320018.

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29

Zhang, Shuyu, Thian Yew Gan, and Andrew B. G. Bush. "Variability of Arctic Sea Ice Based on Quantile Regression and the Teleconnection with Large-Scale Climate Patterns." Journal of Climate 33, no. 10 (2020): 4009–25. http://dx.doi.org/10.1175/jcli-d-19-0375.1.

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AbstractUnder global warming, Arctic sea ice has declined significantly in recent decades, with years of extremely low sea ice occurring more frequently. Recent studies suggest that teleconnections with large-scale climate patterns could induce the observed extreme sea ice loss. In this study, a probabilistic analysis of Arctic sea ice was conducted using quantile regression analysis with covariates, including time and climate indices. From temporal trends at quantile levels from 0.01 to 0.99, Arctic sea ice shows statistically significant decreases over all quantile levels, although of differ

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30

Abe, Manabu, Toru Nozawa, Tomoo Ogura, and Kumiko Takata. "Effect of retreating sea ice on Arctic cloud cover in simulated recent global warming." Atmospheric Chemistry and Physics 16, no. 22 (2016): 14343–56. http://dx.doi.org/10.5194/acp-16-14343-2016.

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Abstract. This study investigates the effect of sea ice reduction on Arctic cloud cover in historical simulations with the coupled atmosphere–ocean general circulation model MIROC5. Arctic sea ice has been substantially retreating since the 1980s, particularly in September, under simulated global warming conditions. The simulated sea ice reduction is consistent with satellite observations. On the other hand, Arctic cloud cover has been increasing in October, with about a 1-month lag behind the sea ice reduction. The delayed response leads to extensive sea ice reductions because the heat and mo

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31

Petty, Alek A., Julienne C. Stroeve, Paul R. Holland, et al. "The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows." Cryosphere 12, no. 2 (2018): 433–52. http://dx.doi.org/10.5194/tc-12-433-2018.

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Abstract. The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to

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32

Wang, Xuanji, Yinghui Liu, Jeffrey R. Key, and Richard Dworak. "A New Perspective on Four Decades of Changes in Arctic Sea Ice from Satellite Observations." Remote Sensing 14, no. 8 (2022): 1846. http://dx.doi.org/10.3390/rs14081846.

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Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations—the extended AVHRR Polar Pathfinder (APP-x)—trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at a rate of −3.24 cm per year, resulting in an approximate 52% reduction in thickness from 2.35 m in

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Matthews, Jessica L., Ge Peng, Walter N. Meier, and Otis Brown. "Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections." Remote Sensing 12, no. 5 (2020): 807. http://dx.doi.org/10.3390/rs12050807.

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Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and

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Liu, Yihan, Hao Luo, Chao Min, Qiong Chen, and Qinghua Yang. "Changes in the Arctic Traffic Occupancy and Their Connection to Sea Ice Conditions from 2015 to 2020." Remote Sensing 16, no. 7 (2024): 1157. http://dx.doi.org/10.3390/rs16071157.

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Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all

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Kopec, Ben G., Xiahong Feng, Fred A. Michel, and Eric S. Posmentier. "Influence of sea ice on Arctic precipitation." Proceedings of the National Academy of Sciences 113, no. 1 (2015): 46–51. http://dx.doi.org/10.1073/pnas.1504633113.

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Global climate is influenced by the Arctic hydrologic cycle, which is, in part, regulated by sea ice through its control on evaporation and precipitation. However, the quantitative link between precipitation and sea ice extent is poorly constrained. Here we present observational evidence for the response of precipitation to sea ice reduction and assess the sensitivity of the response. Changes in the proportion of moisture sourced from the Arctic with sea ice change in the Canadian Arctic and Greenland Sea regions over the past two decades are inferred from annually averaged deuterium excess (d

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Yang, Qinghua, Svetlana N. Losa, Martin Losch, et al. "Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter." Annals of Glaciology 56, no. 69 (2015): 38–44. http://dx.doi.org/10.3189/2015aog69a740.

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AbstractThe decrease in summer sea-ice extent in the Arctic Ocean opens shipping routes and creates potential for many marine operations. For these activities accurate predictions of sea-ice conditions are required to maintain marine safety. In an attempt at Arctic sea-ice prediction, the summer of 2010 is selected to implement an Arctic sea-ice data assimilation (DA) study. The DA system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter to assimilate Spec

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Yang, Lu, Hongli Fu, Xiaofan Luo, and Xuefeng Zhang. "Reconstruction of Arctic Sea Ice Thickness and Its Impact on Sea Ice Forecasting in the Melting Season." Journal of Atmospheric and Oceanic Technology 41, no. 7 (2024): 685–704. http://dx.doi.org/10.1175/jtech-d-23-0049.1.

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Abstract Generally, sea ice prediction skills can be improved by assimilating available observations of the sea ice concentration (SIC) and sea ice thickness (SIT) into a numerical forecast model to update the initial conditions. However, due to inadequate daily SIT satellite observations in the Arctic melting season, the SIC fields in forecast models are usually directly updated, which causes mismatch of SIC and SIT in dynamics and affects the model prediction accuracy. In this study, a statistically based bivariate regression model of SIT (BRMT) is tentatively established based on the grid r

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38

Polyakov, Igor V., Michael Mayer, Steffen Tietsche, and Alexey Yu Karpechko. "Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice." Journal of Climate 35, no. 9 (2022): 2849–65. http://dx.doi.org/10.1175/jcli-d-21-0463.1.

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Abstract The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (grow

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Wang, Yuan, Jonathan H. Jiang, Hui Su, et al. "Elucidating the Role of Anthropogenic Aerosols in Arctic Sea Ice Variations." Journal of Climate 31, no. 1 (2017): 99–114. http://dx.doi.org/10.1175/jcli-d-17-0287.1.

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AbstractObservations show that the Arctic sea ice cover has been shrinking at an unprecedented rate since the 1970s. Even though the accumulation of greenhouse gases in the atmosphere has been closely linked with the loss of Arctic sea ice, the role of atmospheric aerosols in past and future Arctic climate change remains elusive. Using a state-of-the-art fully coupled climate model, the authors assess the equilibrium responses of the Arctic sea ice to the different aerosol emission scenarios and investigate the pathways by which aerosols impose their influence in the Arctic. These sensitivity

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Sun, Xiaoyu, Tingting Lv, Qizhen Sun, et al. "Analysis of Spatiotemporal Variations and Influencing Factors of Sea Ice Extent in the Arctic and Antarctic." Remote Sensing 15, no. 23 (2023): 5563. http://dx.doi.org/10.3390/rs15235563.

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The 44 years (1979–2022) of satellite-derived sea ice extent in the Arctic and Antarctic reveals the details and new trends in the process of polar sea ice coverage changes. The speed of Arctic sea ice extent reduction and the interannual difference significantly increased after 2004. Trend analysis suggests that the Arctic Ocean may experience an ice-free period around 2060. The maximum anomaly of Arctic sea ice extent has gradually transitioned from September to October, indicating a trend of prolonged melting period. The center of gravity of sea ice in the Arctic Ocean is biased towards the

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Zhang, Rong. "Mechanisms for low-frequency variability of summer Arctic sea ice extent." Proceedings of the National Academy of Sciences 112, no. 15 (2015): 4570–75. http://dx.doi.org/10.1073/pnas.1422296112.

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Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centen

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Liang, Xi, Chengyan Liu, Lejiang Yu, et al. "Impact of Local Atmospheric Intraseasonal Variability on Mean Sea Ice State in the Arctic Ocean." Journal of Climate 35, no. 5 (2022): 1559–75. http://dx.doi.org/10.1175/jcli-d-21-0376.1.

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Abstract The Arctic atmosphere shows significant variability on intraseasonal time scales of 10–90 days. The intraseasonal variability in the Arctic sea ice is clearly related to that in the Arctic atmosphere. It is well known that the Arctic mean sea ice state is governed by the local mean atmospheric state. However, the response of the Arctic mean sea ice state to the local atmospheric intraseasonal variability is unclear. The Arctic atmospheric intraseasonal variability exists in both the thermodynamical and dynamical variables. Based on a sea ice–ocean coupled simulation with a quantitativ

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Bradley, Raymond S., and John H. England. "The Younger Dryas and the Sea of Ancient Ice." Quaternary Research 70, no. 1 (2008): 1–10. http://dx.doi.org/10.1016/j.yqres.2008.03.002.

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AbstractWe propose that prior to the Younger Dryas period, the Arctic Ocean supported extremely thick multi-year fast ice overlain by superimposed ice and firn. We re-introduce the historical term paleocrystic ice to describe this. The ice was independent of continental (glacier) ice and formed a massive floating body trapped within the almost closed Arctic Basin, when sea-level was lower during the last glacial maximum. As sea-level rose and the Barents Sea Shelf became deglaciated, the volume of warm Atlantic water entering the Arctic Ocean increased, as did the corresponding egress, driving

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Forchhammer, Mads. "Sea-ice induced growth decline in Arctic shrubs." Biology Letters 13, no. 8 (2017): 20170122. http://dx.doi.org/10.1098/rsbl.2017.0122.

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Measures of increased tundra plant productivity have been associated with the accelerating retreat of the Arctic sea-ice. Emerging studies document opposite effects, advocating for a more complex relationship between the shrinking sea-ice and terrestrial plant productivity. I introduce an autoregressive plant growth model integrating effects of biological and climatic conditions for analysing individual ring-width growth time series. Using 128 specimens of Salix arctica , S. glauca and Betula nana sampled across Greenland to Svalbard, an overall negative effect of the retreating June sea-ice e

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DeRepentigny, Patricia, L. Bruno Tremblay, Robert Newton, and Stephanie Pfirman. "Patterns of Sea Ice Retreat in the Transition to a Seasonally Ice-Free Arctic." Journal of Climate 29, no. 19 (2016): 6993–7008. http://dx.doi.org/10.1175/jcli-d-15-0733.1.

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Abstract The patterns of sea ice retreat in the Arctic Ocean are investigated using two global climate models (GCMs) that have profound differences in their large-scale mean winter atmospheric circulation and sea ice drift patterns. The Community Earth System Model Large Ensemble (CESM-LE) presents a mean sea level pressure pattern that is in general agreement with observations for the late twentieth century. The Community Climate System Model, version 4 (CCSM4), exhibits a low bias in its mean sea level pressure over the Arctic region with a deeper Icelandic low. A dynamical mechanism is pres

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Blockley, Edward W., and K. Andrew Peterson. "Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness." Cryosphere 12, no. 11 (2018): 3419–38. http://dx.doi.org/10.5194/tc-12-3419-2018.

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Abstract. Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–s

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Hezel, P. J., T. Fichefet, and F. Massonnet. "Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs." Cryosphere 8, no. 4 (2014): 1195–204. http://dx.doi.org/10.5194/tc-8-1195-2014.

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Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the representative concentration pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum followi

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Hezel, P. J., T. Fichefet, and F. Massonnet. "Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs." Cryosphere Discussions 8, no. 1 (2014): 1383–406. http://dx.doi.org/10.5194/tcd-8-1383-2014.

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Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following th

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Dörr, Jakob Simon, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills. "Forced and internal components of observed Arctic sea-ice changes." Cryosphere 17, no. 9 (2023): 4133–53. http://dx.doi.org/10.5194/tc-17-4133-2023.

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Abstract. The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in t

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Holland, Marika M., and Donald Perovich. "Sea Ice Summer Camp: Bringing Together Sea Ice Modelers and Observers to Advance Polar Science." Bulletin of the American Meteorological Society 98, no. 10 (2017): 2057–59. http://dx.doi.org/10.1175/bams-d-16-0229.1.

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Abstract Arctic sea ice has undergone significant change with large reductions in thickness and areal extent over the historical record. Numerical models project sea ice loss to continue for the foreseeable future, with the possibility of September ice-free conditions later this century. Understanding the mechanisms behind ice loss and its consequences for the larger Arctic and global systems is important if we are to anticipate and plan for the future. Meeting this challenge requires the collective and collaborative insights of scientists investigating the system from numerous perspectives. O

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