Articles | Volume 16, issue 3
https://doi.org/10.5194/tc-16-1107-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1107-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2022
Research article |
| 31 Mar 2022
| 31 Mar 2022
Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020
Yu Liang
Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Yuquan Road 19, Beijing
100049, China
Key Laboratory for Polar Science, MNR, Polar Research Institute of
China, Shanghai 200136, China
Haibo Bi
CORRESPONDING AUTHOR
Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Yuquan Road 19, Beijing
100049, China
Laboratory for Marine Geology, Qingdao National Laboratory for Marine
Science and Technology, Qingdao 266061, China
Haijun Huang
CORRESPONDING AUTHOR
Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Yuquan Road 19, Beijing
100049, China
Ruibo Lei
Key Laboratory for Polar Science, MNR, Polar Research Institute of
China, Shanghai 200136, China
Technology and Equipment Engineering Centre for Polar Observations,
Zhejiang University, Zhoushan 316000, China
Key Laboratory of Research on Marine Hazard Forecasting Center,
National Marine Environmental Forecasting Center, Beijing 100081, China
Bin Cheng
Polar Meteorology and Climatology, Finnish Meteorological Institute,
P.O. Box 33, Helsinki 00931, Finland
Yunhe Wang
Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Related authors
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
Short summary
Short summary
We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
The Cryosphere, 19, 3065–3087, https://doi.org/10.5194/tc-19-3065-2025, https://doi.org/10.5194/tc-19-3065-2025, 2025
Short summary
Short summary
We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations in the central Arctic Ocean. The trajectories revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study provides critical support for the planning and implementation of Lagrangian observations relying on ice floes in the central Arctic Ocean under changing environmental conditions.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Yibin Ren, Xiaofeng Li, and Yunhe Wang
Geosci. Model Dev., 18, 2665–2678, https://doi.org/10.5194/gmd-18-2665-2025, https://doi.org/10.5194/gmd-18-2665-2025, 2025
Short summary
Short summary
This study developed a transformer-based deep learning model to predict the Arctic sea ice seasonally. By integrating the sea ice thickness data into the model, the spring prediction barrier of Arctic sea ice is optimized significantly. The model achieves better skills than the typical numerical model in predicting September’s sea ice extent seasonally. The sea ice thickness data play a key role in reducing the prediction errors of the Beaufort Sea, the East Siberian Sea and the Laptev Sea.
Runzhuo Fang, Jinfeng Ding, Wenjuan Gao, Xi Liang, Zhuoqi Chen, Chuanfeng Zhao, Haijin Dai, and Lei Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-186, https://doi.org/10.5194/essd-2025-186, 2025
Preprint under review for ESSD
Short summary
Short summary
IMPMCT is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1,184 PMCs tracks, 16,630 cloud patterns, and 4,373 wind records, providing fundamental data for advancing our understanding of their development mechanisms.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Revised manuscript under review for GMD
Short summary
Short summary
In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
EGUsphere, https://doi.org/10.5194/egusphere-2025-1164, https://doi.org/10.5194/egusphere-2025-1164, 2025
Short summary
Short summary
We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li
The Cryosphere, 19, 849–868, https://doi.org/10.5194/tc-19-849-2025, https://doi.org/10.5194/tc-19-849-2025, 2025
Short summary
Short summary
We developed a new method for retrieving lake ice phenology for a lake with complex surface cover. The method is particularly useful for mixed-pixel satellite data. We implement this method on Lake Ulansu, a lake characterized by complex shorelines and aquatic plants in northwestern China. In connection with a random forest model, we reconstructed the longest lake ice phenology in China.
Yi Zhou, Xianwei Wang, Ruibo Lei, Arttu Jutila, Donald K. Perovich, Luisa von Albedyll, Dmitry V. Divine, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2821, https://doi.org/10.5194/egusphere-2024-2821, 2024
Preprint archived
Short summary
Short summary
This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
Short summary
Short summary
In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Yi Zhou, Xianwei Wang, Ruibo Lei, Luisa von Albedyll, Donald K. Perovich, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1240, https://doi.org/10.5194/egusphere-2024-1240, 2024
Preprint archived
Short summary
Short summary
This study examines how the density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, using direct observations and satellite data, we found that sea ice density decreases significantly until mid-January due to increased porosity as the ice ages, and then stabilizes until April. We then developed new models to estimate sea ice density. This advance can improve our ability to monitor changes in Arctic sea ice.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
Short summary
Short summary
We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
Short summary
Short summary
Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
Short summary
Short summary
Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
Short summary
Short summary
Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
Short summary
Short summary
The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
Short summary
Short summary
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
Short summary
Short summary
The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
Short summary
Short summary
To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Long Lin, Ruibo Lei, Mario Hoppmann, Donald K. Perovich, and Hailun He
The Cryosphere, 16, 4779–4796, https://doi.org/10.5194/tc-16-4779-2022, https://doi.org/10.5194/tc-16-4779-2022, 2022
Short summary
Short summary
Ice mass balance observations indicated that average basal melt onset was comparable in the central Arctic Ocean and approximately 17 d earlier than surface melt in the Beaufort Gyre. The average onset of basal growth lagged behind the surface of the pan-Arctic Ocean for almost 3 months. In the Beaufort Gyre, both drifting-buoy observations and fixed-point observations exhibit a trend towards earlier basal melt onset, which can be ascribed to the earlier warming of the surface ocean.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
Short summary
Short summary
We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
Short summary
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.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
Short summary
Short summary
Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Cited articles
Årthun, M. and Eldevik, T.: On anomalous ocean heat transport toward
the Arctic and associated climate predictability, J. Climate, 29,
689–704, 2016.
Årthun, M., Eldevik, T., Smedsrud, L., Skagseth, Ø., and Ingvaldsen,
R.: Quantifying the influence of Atlantic heat on Barents Sea ice
variability and retreat, J. Climate, 25, 4736–4743, 2012.
Babar, B., Graversen, R., and Boström, T.: Solar radiation estimation at
high latitudes: Assessment of the CMSAF databases, ASR and ERA5, Sol.
Energy, 182, 397–411, 2019.
Ballinger, T. J., Overland, J. E., Wang, M., Bhatt, U. S., Hanna, E., Hanssen-Bauer, I., Kim, S. J., Thoman, R. L., and Walsh, J. E.: Arctic Report Card 2020: Surface Air Temperature, National Oceanic and Atmospheric Administration, https://doi.org/10.25923/gcw8-2z06, 2020.
Baxter, I., Ding, Q., Schweiger, A., L'Heureux, M., Baxter, S., Wang, T.,
Zhang, Q., Harnos, K., Markle, B., and Topal, D.: How tropical Pacific
surface cooling contributed to accelerated sea ice melt from 2007 to 2012 as
ice is thinned by anthropogenic forcing, J. Climate, 32, 8583–8602,
2019.
Bi, H., Zhang, J., Wang, Y., Zhang, Z., Zhang, Y., Fu, M., Huang, H., and
Xu, X.: Arctic Sea Ice Volume Changes in Terms of Age as Revealed From
Satellite Observations, IEEE J. Sel. Top. Appl., 11, 2223–2237, 2018.
Bi, H., Zhang, Z., Wang, Y., Xu, X., Liang, Y., Huang, J., Liu, Y., and Fu, M.: Baffin Bay sea ice inflow and outflow: 1978–1979 to 2016–2017, The Cryosphere, 13, 1025–1042, https://doi.org/10.5194/tc-13-1025-2019, 2019.
Bi, H., Wang, Y., Liang, Y., Sun, W., Liang, X., Yu, Q., Zhang, Z., and Xu,
X.: Influences of Summertime Arctic Dipole Atmospheric Circulation on Sea
Ice Concentration Variations in the Pacific Sector of the Arctic during
Different Pacific Decadal Oscillation Phases, J. Climate, 34,
3003–3019, https://doi.org/10.1175/jcli-d-19-0843.1, 2021.
Brümmer, B., Müller, G., Affeld, B., Gerdes, R., Karcher, M., and
Kauker, F.: Cyclones over Fram Strait: impact on sea ice and variability,
Polar Res., 20, 147–152, https://doi.org/10.3402/polar.v20i2.6511, 2001.
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.:
Ubiquitous low-level liquid-containing Arctic clouds: New observations and
climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett.,
39, L20804, https://doi.org/10.1029/2012GL053385, 2012.
Cohen, L., Hudson, S. R., Walden, V. P., Graham, R. M., and Granskog, M. A.:
Meteorological conditions in a thinner Arctic sea ice regime from winter
through summer during the Norwegian Young Sea Ice expedition (N-ICE2015),
J. Geophys. Res.-Atmos., 122, 7235–7259, https://doi.org/10.1002/2016JD026034, 2017.
Comiso, J. C.: Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP
SSM/I-SSMIS, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/7Q8HCCWS4I0R,
2017.
Comiso, J. C. and Hall, D. K.: Climate trends in the Arctic as observed
from space, Wiley Interdiscip. Rev. Clim. Change, 5, 389–409, https://doi.org/10.1002/wcc.277,
2014.
Crépin, A.-S., Karcher, M., and Gascard, J.-C.: Arctic climate change,
economy and society (ACCESS): Integrated perspectives, Ambio, 46, 341–354,
2017.
Curry, J. A., Schramm, J. L., Rossow, W. B., and Randall, D.: Overview of
Arctic Cloud and Radiation Characteristics, J. Climate, 9,
1731–1764, 1996.
Deser, C. and Teng, H.: Evolution of Arctic sea ice concentration trends
and the role of atmospheric circulation forcing, 1979–2007, Geophys.
Res. Lett., 35, L02504, https://doi.org/10.1029/2007gl032023, 2008.
Deser, C., Walsh, J. E., and Timlin, M. S.: Arctic sea ice variability in
the context of recent atmospheric circulation trends, J. Climate,
13, 617–633, https://doi.org/10.1175/1520-0442(2000)013<0617:Asivit>2.0.Co;2, 2000.
Ding, Q., Schweiger, A., L'Heureux, M., Battisti, David S., Po-Chedley, S.,
Johnson, Nathaniel C., Blanchard-Wrigglesworth, E., Harnos, K., Zhang, Q.,
Eastman, R., and Steig, Eric J.: Influence of high-latitude atmospheric
circulation changes on summertime Arctic sea ice, Nat. Clim. Change, 7,
289–295, https://doi.org/10.1038/nclimate3241, 2017.
Dmitrenko, I., Gribanov, V., Volkov, D., Berezovskaya, S., and Kassens, H.:
The role of river runoff in the interannual variability of the fast-ice
extension in the Russian Arctic, Russ. Meteorol. Hydrol., 3, 65–72, 2000.
Dou, T., Xiao, C., Liu, J., Wang, Q., Pan, S., Su, J., Yuan, X., Ding, M., Zhang, F., Xue, K., Bieniek, P. A., and Eicken, H.: Trends and spatial variation in rain-on-snow events over the Arctic Ocean during the early melt season, The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, 2021.
Doyle, J., Lesins, G., Thackray, C., Perro, C., Nott, G., Duck, T., Damoah,
R., and Drummond, J.: Water vapor intrusions into the High Arctic during
winter, Geophys. Res. Lett., 38, L12806,
https://doi.org/10.1029/2011GL047493, 2011.
Dufour, A., Zolina, O., and Gulev, S. K.: Atmospheric moisture transport to
the Arctic: Assessment of reanalyses and analysis of transport components,
J. Climate, 29, 5061–5081, 2016.
English, J. M., Gettelman, A., and Henderson, G. R.: Arctic Radiative
Fluxes: Present-Day Biases and Future Projections in CMIP5 Models, J.
Climate, 28, 6019–6038, https://doi.org/10.1175/jcli-d-14-00801.1, 2015.
Gaffney, S.: Probabilistic curve-aligned clustering and prediction with
regression mixture models, University of California, Irvine, 2004.
Graversen, R. G., Mauritsen, T., Tjernström, M., Källén, E., and
Svensson, G.: Vertical structure of recent Arctic warming, Nature, 451,
53–56, 2008.
Graversen, R. G., Mauritsen, T., Drijfhout, S., Tjernström, M., and
Mårtensson, S.: Warm winds from the Pacific caused extensive Arctic
sea-ice melt in summer 2007, Clim. Dynam., 36, 2103–2112, 2011.
Gu, S., Zhang, Y., Wu, Q., and Yang, X. Q.: The linkage between Arctic sea
ice and midlatitude weather: In the perspective of energy, J.
Geophys. Res.-Atmos., 123, 11536–511550, 2018.
Hall, A.: The role of surface albedo feedback in climate, J.
Climate, 17, 1550–1568, 2004.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.:
The ERA5 global reanalysis, Q. J. Roy. Meteor.
Soc., 146, 1999–2049, 2020.
Horvath, S., Stroeve, J., Rajagopalan, B., and Jahn, A.: Arctic sea ice melt
onset favored by an atmospheric pressure pattern reminiscent of the North
American-Eurasian Arctic pattern, Clim. Dynam., 57, 1771–1787,
https://doi.org/10.1007/s00382-021-05776-y, 2021.
Huang, Y., Dong, X., Xi, B., and Deng, Y.: A survey of the atmospheric
physical processes key to the onset of Arctic sea ice melt in spring,
Clim. Dynam., 52, 4907–4922, 2019.
Jakobson, E. and Vihma, T.: Atmospheric moisture budget in the Arctic based on the ERA-40 reanalysis, Int. J. Climatol., 30,
2175–2194, 2010.
Johansson, E., Devasthale, A., Tjernström, M., Ekman, A. M. L., and
L'Ecuyer, T.: Response of the lower troposphere to moisture intrusions into
the Arctic, Geophys. Res. Lett., 44, 2527–2536, https://doi.org/10.1002/2017GL072687, 2017.
Kapsch, M.-L., Graversen, R. G., and Tjernström, M.: Springtime
atmospheric energy transport and the control of Arctic summer sea-ice
extent, Nat. Clim. Change, 3, 744–748, 2013.
Kapsch, M.-L., Skific, N., Graversen, R. G., Tjernström, M., and
Francis, J. A.: Summers with low Arctic sea ice linked to persistence of
spring atmospheric circulation patterns, Clim. Dynam., 52, 2497–2512,
https://doi.org/10.1007/s00382-018-4279-z, 2019.
Kay, J. E., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., and Cesana,
G.: Recent Advances in Arctic Cloud and Climate Research, Current Climate
Change Reports, 2, 159–169, https://doi.org/10.1007/s40641-016-0051-9, 2016.
Kim, K.-Y., Hamlington, B. D., Na, H., and Kim, J.: Mechanism of seasonal Arctic sea ice evolution and Arctic amplification, The Cryosphere, 10, 2191–2202, https://doi.org/10.5194/tc-10-2191-2016, 2016.
Komurcu, M., Storelvmo, T., Tan, I., Lohmann, U., Yun, Y., Penner, J. E.,
Wang, Y., Liu, X., and Takemura, T.: Intercomparison of the cloud water
phase among global climate models, J. Geophys. Res.-Atmos., 119, 3372–3400, 2014.
Koyama, T., Stroeve, J., Cassano, J., and Crawford, A.: Sea Ice Loss and
Arctic Cyclone Activity from 1979 to 2014, J. Climate, 30,
4735–4754, https://doi.org/10.1175/jcli-d-16-0542.1, 2017.
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021.
Kwok, R.: Exchange of sea ice between the Arctic Ocean and the Canadian
Arctic Archipelago, Geophys. Res. Lett., 33, 627–642, 2006.
Kwok, R.: Baffin Bay ice drift and export: 2002–2007, Geophys. Res.
Lett., 34, L19501, https://doi.org/10.1029/2007GL031204, 2007.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
losses and coupled variability (1958–2018), Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec,
2018.
Kwok, R. and Rothrock, D. A.: Decline in Arctic sea ice thickness from
submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, L15501, https://doi.org/10.1029/2009GL039035,
2009.
Lei, R., Tian-Kunze, X., Leppäranta, M., Wang, J., Kaleschke, L., and
Zhang, Z.: Changes in summer sea ice, albedo, and portioning of surface
solar radiation in the Pacific sector of Arctic Ocean during 1982–2009,
J. Geophys. Res.-Oceans, 121, 5470–5486, 2016.
Lei, R., Gui, D., Hutchings, J. K., Wang, J., and Pang, X.: Backward and
forward drift trajectories of sea ice in the northwestern Arctic Ocean in
response to changing atmospheric circulation, Int. J.
Climatol., 39, 4372–4391, https://doi.org/10.1002/joc.6080, 2019.
Lei, R., Gui, D., Heil, P., Hutchings, J. K., and Ding, M.: Comparisons of
sea ice motion and deformation, and their responses to ice conditions and
cyclonic activity in the western Arctic Ocean between two summers, Cold
Reg. Sci. Technol., 170, 102925, https://doi.org/10.1016/j.coldregions.2019.102925, 2020.
Liang, Y., Bi, H., Wang, Y., Huang, H., Zhang, Z., Huang, J., and Liu, Y.:
Role of Extratropical Wintertime Cyclones in Regulating the Variations of
Baffin Bay Sea Ice Export, J. Geophys. Res.-Atmos.,
126, e2020JD033616, https://doi.org/10.1029/2020JD033616, 2021.
Liu, Z. and Schweiger, A.: Synoptic conditions, clouds, and sea ice melt
onset in the Beaufort and Chukchi seasonal ice zone, J. Climate, 30,
6999–7016, 2017.
Markus, T., Stroeve, J. C., and Miller, J.: Recent changes in Arctic sea ice
melt onset, freezeup, and melt season length, J. Geophys.
Res.-Oceans, 114, C12024, https://doi.org/10.1029/2009JC005436, 2009.
Miles, M. W., Divine, D. V., Furevik, T., Jansen, E., Moros, M., and
Ogilvie, A. E.: A signal of persistent Atlantic multidecadal variability in
Arctic sea ice, Geophys. Res. Lett., 41, 463–469, 2014.
Mortin, J., Svensson, G., Graversen, R. G., Kapsch, M. L., Stroeve, J. C.,
and Boisvert, L. N.: Melt onset over Arctic sea ice controlled by
atmospheric moisture transport, Geophys. Res. Lett., 43, 6636–6642, https://doi.org/10.1002/2016GL069330, 2016.
NASA Earth Sciences: Arctic Sea Ice Melt, NASA Earth Sciences [data set], https://earth.gsfc.nasa.gov/index.php/cryo/data/arctic-sea-ice-melt, last
access: 22 February 2021.
Neu, U., Akperov, M. G., Bellenbaum, N., Benestad, R., Blender, R.,
Caballero, R., Cocozza, A., Dacre, H. F., Feng, Y., and Fraedrich, K.:
IMILAST: A community effort to intercompare extratropical cyclone detection
and tracking algorithms, B. Am. Meteorol. Soc.,
94, 529–547, 2013.
Ogi, M. and Wallace, J. M.: The role of summer surface wind anomalies in the summer Arctic sea ice extent in 2010 and 2011, Geophys. Res. Lett., 39, L09704, https://doi.org/10.1029/2012GL051330, 2012.
Ogi, M., Rysgaard, S., and Barber, D. G.: Importance of combined winter and
summer Arctic Oscillation (AO) on September sea ice extent, Environ. Res.
Lett., 11, 034019, https://doi.org/10.1088/1748-9326/11/3/034019, 2016.
Olason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift
speed, J. Geophys. Res.-Oceans, 119, 5755–5775, https://doi.org/10.1002/2014JC009897, 2015.
Overland, J., Francis, J. A., Hall, R., Hanna, E., Kim, S.-J., and Vihma,
T.: The melting Arctic and midlatitude weather patterns: Are they
connected?, J. Climate, 28, 7917–7932, 2015.
Papritz, L.: Arctic lower-tropospheric warm and cold extremes: Horizontal
and vertical transport, diabatic processes, and linkage to synoptic
circulation features, J. Climate, 33, 993–1016, 2020.
Parkinson, C. and Washington, W.: A large-scale numerical model of sea ice,
J. Geophys. Res., 84, 311–337, https://doi.org/10.1029/JC084iC01p00311, 1979.
Persson, P. O. G.: Onset and end of the summer melt season over sea ice:
Thermal structure and surface energy perspective from SHEBA, Clim.
Dynam., 39, 1349–1371, 2012.
Polar Science Center: PIOMAS Data, Polar Science Center [data set], http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid, last access: 23 March 2021.
Post, E., Bhatt, U. S., Bitz, C. M., Brodie, J. F., Fulton, T. L.,
Hebblewhite, M., Kerby, J., Kutz, S. J., Stirling, I., and Walker, D. A.:
Ecological consequences of sea-ice decline, Science, 341, 519–524, 2013.
Previdi, M., Janoski, T. P., Chiodo, G., Smith, K. L., and Polvani, L. M.:
Arctic amplification: A rapid response to radiative forcing, Geophys.
Res. Lett., 47, e2020GL089933, https://doi.org/10.1029/2020GL089933, 2020.
Rigor, I. G. and Wallace, J. M.: Variations in the age of Arctic sea-ice
and summer sea-ice extent, Geophys. Res. Lett., 31, L09401, https://doi.org/10.1029/2004GL019492, 2004.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011.
Screen, J. A. and Deser, C.: Pacific Ocean Variability Influences the Time
of Emergence of a Seasonally Ice-Free Arctic Ocean, Geophys. Res.
Lett., 46, 2222–2231, https://doi.org/10.1029/2018GL081393,
2019.
Sepp, M. and Jaagus, J.: Changes in the activity and tracks of Arctic
cyclones, Climatic Change, 105, 577–595, https://doi.org/10.1007/s10584-010-9893-7, 2011.
Serreze, M. C.: Climatological aspects of cyclone development and decay in
the Arctic, Journal of Atmosphere, 33, 1–23, https://doi.org/10.1080/07055900.1995.9649522, 1995.
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic
amplification: A research synthesis, Global Planetary Change, 77, 85–96,
2011.
Serreze, M. C., Box, J. E., Barry, R. G., and Walsh, J. E.: Characteristics
of Arctic synoptic activity, 1952–1989, Meteorol. Atmos.
Phys., 51, 147–164, 1993.
Singh, H. K., Bitz, C. M., Donohoe, A., and Rasch, P.: A source–receptor
perspective on the polar hydrologic cycle: Sources, seasonality, and
Arctic–Antarctic parity in the hydrologic cycle response to CO2 doubling,
J. Climate, 30, 9999–10017, 2017.
Smedsrud, L. H., Halvorsen, M. H., Stroeve, J. C., Zhang, R., and Kloster, K.: Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years, The Cryosphere, 11, 65–79, https://doi.org/10.5194/tc-11-65-2017, 2017.
Stramler, K., Genio, A., and Rossow, W. B.: Synoptically Driven Arctic
Winter States, J. Climate, 24, 1747–1762, 2011.
Stroeve, J., Barrett, A., Serreze, M., and Schweiger, A.: Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness, The Cryosphere, 8, 1839–1854, https://doi.org/10.5194/tc-8-1839-2014, 2014.
Thorndike, A. and Colony, R.: Sea ice motion in response to geostrophic
winds, J. Geophys. Res.-Oceans, 87, 5845–5852, 1982.
Tschudi, M., Stroeve, J., and Stewart, J.: Relating the age of Arctic sea
ice to its thickness, as measured during NASA's ICESat and IceBridge
campaigns, Remote Sensing, 8, 457, https://doi.org/10.3390/rs8060457, 2016.
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik., J.:
Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4,
NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/INAWUWO7QH7B, 2019.
Vázquez, M., Nieto, R., Drumond, A., and Gimeno, L.: Moisture transport
into the Arctic: Source-receptor relationships and the roles of atmospheric
circulation and evaporation, J. Geophys. Res.-Atmos.,
121, 13493–13509, https://doi.org/10.1002/2016JD025400, 2016.
Vázquez, M., Nieto, R., Drumond, A., and Gimeno, L.: Extreme Sea Ice
Loss over the Arctic: An Analysis Based on Anomalous Moisture Transport,
Atmosphere, 8, 32, https://doi.org/10.3390/atmos8020032, 2017.
Vihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova,
V., Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the
Arctic water cycle: A review on processes, past and future changes, and
their impacts, J. Geophys. Res.-Biogeo.,
121, 586–620, 2016.
Villamil-Otero, G. A., Zhang, J., He, J., and Zhang, X.: Role of
extratropical cyclones in the recently observed increase in poleward
moisture transport into the Arctic Ocean, Adv. Atmos. Sci.,
35, 85–94, 2018.
Wang, J., Zhang, J., Watanabe, E., Ikeda, M., Mizobata, K., Walsh, J. E.,
Bai, X., and Wu, B.: Is the Dipole Anomaly a major driver to record lows in
Arctic summer sea ice extent?, Geophys. Res. Lett., 36, L05706,
https://doi.org/10.1029/2008gl036706, 2009.
Wang, X., Key, J., Kwok, R., and Zhang, J.: Comparison of Arctic sea ice
thickness from satellites, aircraft, and PIOMAS data, Remote Sensing, 8,
713, https://doi.org/10.3390/rs8090713, 2016.
Wang, X. L., Swail, V. R., and Zwiers, F. W.: Climatology and changes of
extratropical cyclone activity: Comparison of ERA-40 with NCEP–NCAR
reanalysis for 1958–2001, J. Climate, 19, 3145–3166, 2006.
Wang, X. L., Feng, Y., Compo, G., Swail, V., Zwiers, F., Allan, R., and
Sardeshmukh, P.: Trends and low frequency variability of extra-tropical
cyclone activity in the ensemble of twentieth century reanalysis, Clim.
Dynam., 40, 2775–2800, 2013.
Wang, Y., Bi, H., Huang, H., Liu, Y., Liu, Y., Liang, X., Fu, M., and Zhang,
Z.: Satellite-observed trends in the Arctic sea ice concentration for the
period 1979–2016, Journal of Oceanology & Limnology, 37, 18–37, 2019.
Warner, J., Screen, J., and Scaife, A.: Links Between Barents-Kara Sea Ice and the Extratropical Atmospheric Circulation Explained by Internal Variability and Tropical Forcing, Geophys. Res. Lett., 47,
e2019GL085679, https://doi.org/10.1029/2019GL085679, 2020.
Wernli, H. and Papritz, L.: Role of polar anticyclones and mid-latitude
cyclones for Arctic summertime sea-ice melting, Nat. Geosci., 11,
108–113, 2018.
Wu, B. Y., Wang, J., and Walsh, J. E.: Dipole anomaly in the winter Arctic
atmosphere and its association with sea ice motion, J. Climate, 19,
210–225, https://doi.org/10.1175/Jcli3619.1, 2006.
Yin, J. H.: A consistent poleward shift of the storm tracks in simulations
of 21st century climate, Geophys. Res. Lett., 32, L18701,
https://doi.org/10.1029/2005gl023684, 2005.
Zhang, J. and Rothrock, D.: Modeling global sea ice with a thickness and
enthalpy distribution model in generalized curvilinear coordinates, Mon.
Weather Rev., 131, 845–861, 2003.
Zhang, J., Lindsay, R., Schweiger, A., and Steele, M.: The impact of an
intense summer cyclone on 2012 Arctic sea ice retreat, Geophys. Res.
Lett., 40, 720–726, https://doi.org/10.1002/grl.50190, 2013.
Zhang, R.: Mechanisms for low-frequency variability of summer Arctic sea ice
extent, P. Natl. Acad. Sci. USA, 112, 4570–4575,
2015.
Zhang, X., Walsh, J. E., Zhang, J., Bhatt, U. S., and Ikeda, M.: Climatology
and interannual variability of arctic cyclone activity: 1948–2002, J. Climate, 17, 2300–2317, https://doi.org/10.1175/1520-0442(2004)017<2300:Caivoa>2.0.Co;2, 2004.
Zhang, X., He, J., Zhang, J., Polyakov, I., Gerdes, R., Inoue, J., and Wu,
P.: Enhanced poleward moisture transport and amplified northern
high-latitude wetting trend, Nat. Clim. Change, 3, 47–51, 2013.
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.
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that...
