Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1157-2024
© Author(s) 2024. 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-18-1157-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sea ice cover in the Copernicus Arctic Regional Reanalysis
Development Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
Bin Cheng
Finnish Meteorological Institute, Helsinki, Finland
Viivi Kallio-Myers
Finnish Meteorological Institute, Helsinki, Finland
Related authors
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irene Erner, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022, https://doi.org/10.5194/amt-15-3075-2022, 2022
Short summary
Short summary
We investigate the boundary-layer (BL) height and surface stability in southern Finland using radiosondes, a microwave radiometer and ERA5 reanalysis. Accurately quantifying the BL height is challenging, and the diagnosed BL height can depend strongly on the method used. Microwave radiometers provide reliable estimates of the BL height but only in unstable conditions. ERA5 captures the BL height well except under very stable conditions, which occur most commonly at night during the warm season.
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.
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.
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.
Aku Riihelä, Emmihenna Jääskeläinen, and Viivi Kallio-Myers
Earth Syst. Sci. Data, 16, 1007–1028, https://doi.org/10.5194/essd-16-1007-2024, https://doi.org/10.5194/essd-16-1007-2024, 2024
Short summary
Short summary
We describe a new climate data record describing the surface albedo, or reflectivitity, of Earth's surface (called CLARA-A3 SAL). The climate data record spans over 4 decades of satellite observations, beginning in 1979. We conduct a quality assessment of the generated data, comparing them against other satellite data and albedo observations made on the ground. We find that the new data record in general matches surface observations well and is stable through time.
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.
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.
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irene Erner, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022, https://doi.org/10.5194/amt-15-3075-2022, 2022
Short summary
Short summary
We investigate the boundary-layer (BL) height and surface stability in southern Finland using radiosondes, a microwave radiometer and ERA5 reanalysis. Accurately quantifying the BL height is challenging, and the diagnosed BL height can depend strongly on the method used. Microwave radiometers provide reliable estimates of the BL height but only in unstable conditions. ERA5 captures the BL height well except under very stable conditions, which occur most commonly at night during the warm season.
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.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
Short summary
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.
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.
Cited articles
Aagaard, K. and Coachman, L. K.: The East Greenland Current North of Denmark Strait: Part I, Arctic, 21, 181–200, https://doi.org/10.14430/arctic3262, 1968. a
Anttila, K., Manninen, T., Jääskeläinen, E., and Riihelä, A.: Validation Report (VAL) CLARA-A2 Surface Albedo Product, Tech. rep., EUMETSAT Satellite Application Facility on Climate Monitoring, https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V001, 2016. a
Arduini, G., Keeley, S., Day, J. J., Sandu, I., Zampieri, L., and Balsamo, G.: On the Importance of Representing Snow Over Sea-Ice for Simulating the Arctic Boundary Layer, J. Adv. Model. Earth Sy., 14, e2021MS002777, https://doi.org/10.1029/2021MS002777, 2022. a, b
Batrak, Y.: Implementation of an Adaptive Bias-Aware Extended Kalman Filter for Sea-Ice Data Assimilation in the HARMONIE-AROME Numerical Weather Prediction System, J. Adv. Model. Earth Sy., 13, e2021MS002533, https://doi.org/10.1029/2021MS002533, 2021. a
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice, Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3, 2019. a
Bengtsson, L. and Shukla, J.: Integration of Space and In Situ Observations to Study Global Climate Change, B. Am. Meteorol. Soc., 69, 1130–1143, https://doi.org/10.1175/1520-0477(1988)069<1130:IOSAIS>2.0.CO;2, 1988. a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K.-I., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Muñoz, D. S., Subias, A., Tijm, S., Toll, V., Yang, X., and Køltzow, M. Ø.: The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System, Mon. Weather Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a, b
Boone, A.: Modeling hydrological processes in the land surface scheme ISBA: inclusion of a hydrological reservoir, ice and a snow model, Ph.D. thesis, Paul Sabatier University, 2000. a
Boone, A. and Etchevers, P.: An Intercomparison of Three Snow Schemes of Varying Complexity Coupled to the Same Land Surface Model: Local-Scale Evaluation at an Alpine Site, J. Hydrometeorol., 2, 374–394, https://doi.org/10.1175/1525-7541(2001)002<0374:AIOTSS>2.0.CO;2, 2001. a
Chen, S., Liu, J., Ding, Y., Zhang, Y., Cheng, X., and Hu, Y.: Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data, Adv. Atmos. Sci., 38, 168–186, https://doi.org/10.1007/s00376-020-0213-5, 2021. a, b
Cheng, B., Launianen, J., and Vihma, T.: Modelling of Superimposed Ice Formation and Sub-Surface Melting in the Baltic Sea, Geophysica, 39, 31–50, 2003. a
Cheng, B., Zhang, Z., Vihma, T., Johansson, M., Bian, L., Li, Z., and Wu, H.: Model experiments on snow and ice thermodynamics in the Arctic Ocean with CHINARE 2003 data, J. Geophys. Res.-Oceans, 113, C09020, https://doi.org/10.1029/2007JC004654, 2008. a
Cheng, B., Vihma, T., Palo, T., Nicolaus, M., Gerland, S., Rontu, L., Haapala, J., and Perovich, D.: Observation and modelling of snow and sea ice mass balance and its sensitivity to atmospheric forcing during spring and summer 2007 in the Central Arctic, Adv. Polar Sci., 32, 312–326, https://doi.org/10.13679/j.advps.2021.0047, 2021. a, b, c
Chung, C. E., Cha, H., Vihma, T., Räisänen, P., and Decremer, D.: On the possibilities to use atmospheric reanalyses to evaluate the warming structure in the Arctic, Atmos. Chem. Phys., 13, 11209–11219, https://doi.org/10.5194/acp-13-11209-2013, 2013. a
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.: Recent Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7, 627–637, https://doi.org/10.1038/ngeo2234, 2014. a
Comiso, J. C. and Hall, D. K.: Climate trends in the Arctic as observed from space, WIREs Climate Change, 5, 389–409, https://doi.org/10.1002/wcc.277, 2014. a
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea Ice-Albedo Climate Feedback Mechanism, J. Climate, 8, 240–247, https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2, 1995. a
Douville, H., Royer, J.-F., and Mahfouf, J.-F.: A new snow parameterization for the Météo-France climate model, Clim. Dynam., 12, 21–35, https://doi.org/10.1007/BF00208760, 1995. a, b
Dunbar, M.: Ice Regime and Ice Transport in Nares Strait, Arctic, 26, 282–291, https://doi.org/10.14430/arctic2927, 1973. a
Ebert, E. E. and Curry, J. A.: An intermediate one-dimensional thermodynamic sea ice model for investigating ice-atmosphere interactions, J. Geophys. Res.-Oceans, 98, 10085–10109, https://doi.org/10.1029/93JC00656, 1993. a
ECMWF: IFS Documentation CY41R2 – Part IV: Physical Processes, 4, ECMWF, https://doi.org/10.21957/tr5rv27xu, 2016. a
Frank, C. W., Pospichal, B., Wahl, S., Keller, J. D., Hense, A., and Crewell, S.: The added value of high resolution regional reanalyses for wind power applications, Renew. Energ., 148, 1094–1109, https://doi.org/10.1016/j.renene.2019.09.138, 2020. a
Fujiwara, M., Wright, J. S., Manney, G. L., Gray, L. J., Anstey, J., Birner, T., Davis, S., Gerber, E. P., Harvey, V. L., Hegglin, M. I., Homeyer, C. R., Knox, J. A., Krüger, K., Lambert, A., Long, C. S., Martineau, P., Molod, A., Monge-Sanz, B. M., Santee, M. L., Tegtmeier, S., Chabrillat, S., Tan, D. G. H., Jackson, D. R., Polavarapu, S., Compo, G. P., Dragani, R., Ebisuzaki, W., Harada, Y., Kobayashi, C., McCarty, W., Onogi, K., Pawson, S., Simmons, A., Wargan, K., Whitaker, J. S., and Zou, C.-Z.: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems, Atmos. Chem. Phys., 17, 1417–1452, https://doi.org/10.5194/acp-17-1417-2017, 2017. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Graham, R. M., Cohen, L., Ritzhaupt, N., Segger, B., Graversen, R. G., Rinke, A., Walden, V. P., Granskog, M. A., and Hudson, S. R.: Evaluation of Six Atmospheric Reanalyses over Arctic Sea Ice from Winter to Early Summer, J. Climate, 32, 4121–4143, https://doi.org/10.1175/JCLI-D-18-0643.1, 2019. a
Greenwald, M. and Khanna, S.: Space-Efficient Online Computation of Quantile Summaries, SIGMOD Rec., 30, 58–66, https://doi.org/10.1145/376284.375670, 2001. a
Hall, D., Key, J., Casey, K., Riggs, G., and Cavalieri, D.: Sea ice surface temperature product from MODIS, IEEE T. Geosci. Remote, 42, 1076–1087, https://doi.org/10.1109/TGRS.2004.825587, 2004. a, b
Hall, D. K. and Riggs, G.: MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km, Version 6, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD29.006, 2015a. a, b
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km, Version 6, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD29.006, 2015b. a, b
Hansen, E., Gerland, S., Granskog, M. A., Pavlova, O., Renner, A. H. H., Haapala, J., Løyning, T. B., and Tschudi, M.: Thinning of Arctic sea ice observed in Fram Strait: 1990–2011, J. Geophys. Res.-Oceans, 118, 5202–5221, https://doi.org/10.1002/jgrc.20393, 2013. a
Herrmannsdörfer, L., Müller, M., Shupe, M. D., and Rostosky, P.: Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval, Elementa: Science of the Anthropocene, 11, 00085, https://doi.org/10.1525/elementa.2022.00085, 2023. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
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 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hines, K. M., Bromwich, D. H., Bai, L., Bitz, C. M., Powers, J. G., and Manning, K. W.: Sea Ice Enhancements to Polar WRF, Mon. Weather Rev., 143, 2363–2385, https://doi.org/10.1175/MWR-D-14-00344.1, 2015. a
IHO (International Hydrographic Organization): Limits of Oceans and Seas, 3rd edition. Special Publication No. 23 (S-23), Tech. rep., International Hydrographic Organization, Monaco, 1953. a
Isaksen, K., Nordli, Ø., Ivanov, B., Køltzow, M. A. Ø., Aaboe, S., Gjelten, H. M., Mezghani, A., Eastwood, S., Førland, E., Benestad, R. E., Hanssen-Bauer, I., Brækkan, R., Sviashchennikov, P., Demin, V., Revina, A., and Karandasheva, T.: Exceptional warming over the Barents area, Sci. Rep.-UK, 12, 9371, https://doi.org/10.1038/s41598-022-13568-5, 2022. a
Itkin, P., Spreen, G., Cheng, B., Doble, M., Gerland, S., Granskog, M. A., Haapala, J., Hudson, S. R., Kaleschke, L., Nicolaus, M., Pavlov, A., Shestov, A., Steen, H., Wilkinson, J., and Helgeland, C.: N-ICE2015 buoy data, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2015.6ed9a8ca, 2015. a
Jackson, K., Wilkinson, J., Maksym, T., Meldrum, D., Beckers, J., Haas, C., and Mackenzie, D.: A Novel and Low-Cost Sea Ice Mass Balance Buoy, J. Atmos. Ocean. Tech., 30, 2676–2688, https://doi.org/10.1175/JTECH-D-13-00058.1, 2013. a
Kaiser-Weiss, A. K., Borsche, M., Niermann, D., Kaspar, F., Lussana, C., Isotta, F. A., van den Besselaar, E., van der Schrier, G., and Undén, P.: Added value of regional reanalyses for climatological applications, Environ. Res. Commun., 1, 071004, https://doi.org/10.1088/2515-7620/ab2ec3, 2019. a
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP-DOE AMIP-II reanalysis (R-2), B. Am. Math. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631, 2002. a
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017. a, b
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Solodovnik, I., Meirink, J. F., Devasthale, A., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Stein, D., Finkensieper, S., Håkansson, N., Hollmann, R., Kaiser, J., and Werscheck, M.: CLARA-A2.1: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 2.1, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002 _01, 2020. a
Koo, Y., Lei, R., Cheng, Y., Cheng, B., Xie, H., Hoppmann, M., Kurtz, N. T., Ackley, S. F., and Mestas-Nuñez, A. M.: Estimation of thermodynamic and dynamic contributions to sea ice growth in the Central Arctic using ICESat-2 and MOSAiC SIMBA buoy data, Remote Sens. Environ., 267, 112730, https://doi.org/10.1016/j.rse.2021.112730, 2021. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V., and Yi, D.: IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/G519SHCKWQV6, 2015. a, b
Kurtz, N., Studinger, M., Harbeck, J., Onana, V., and Yi, D.: IceBridge Sea Ice Freeboard, Snow Depth, and Thickness Quick Look, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/GRIXZ91DE0L9, 2016. a, b
Køltzow, M., Casati, B., Bazile, E., Haiden, T., and Valkonen, T.: An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1, Weather Forecast., 34, 959–983, https://doi.org/10.1175/WAF-D-19-0003.1, 2019. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a
Lawrence, H., Bormann, N., Sandu, I., Day, J., Farnan, J., and Bauer, P.: Use and impact of Arctic observations in the ECMWF Numerical Weather Prediction system, Q. J. Roy. Meteor. Soc., 145, 3432–3454, https://doi.org/10.1002/qj.3628, 2019. a
Lee, S.-M., Shi, H., Sohn, B.-J., Gasiewski, A. J., Meier, W. N., and Dybkjær, G.: Winter Snow Depth on Arctic Sea Ice From Satellite Radiometer Measurements (2003–2020): Regional Patterns and Trends, Geophys. Res. Lett., 48, e2021GL094541, https://doi.org/10.1029/2021GL094541, 2021a. a, b, c
Lee, S.-M., Shi, H., Sohn, B.-J., Gasiewski, A. J., Meier, W. N., and Dybkjær, G.: Monthly total freeboard and snow depth over Arctic sea ice from AMSR-E&2 and AVHRR measurements (2003–2020) (Version 3), Zenodo [data set], https://doi.org/10.5281/zenodo.5081765, 2021b. a
Lei, R., Cheng, B., Heil, P., Vihma, T., Wang, J., Ji, Q., and Zhang, Z.: Seasonal and Interannual Variations of Sea Ice Mass Balance From the Central Arctic to the Greenland Sea, J. Geophys. Res.-Oceans, 123, 2422–2439, https://doi.org/10.1002/2017JC013548, 2018. a, b
Lei, R., Cheng, B., Hoppmann, M., Zhang, F., Zuo, G., Hutchings, J. K., Lin, L., Lan, M., Wang, H., Regnery, J., Krumpen, T., Haapala, J., Rabe, B., Perovich, D. K., and Nicolaus, M.: Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020, Elementa: Science of the Anthropocene, 10, https://doi.org/10.1525/elementa.2021.000089, 2022. a
Li, N., Li, B., Lei, R., and Li, Q.: Comparison of summer Arctic sea ice surface temperatures from in situ and MODIS measurements, Acta Oceanologica Sinica, 39, 18–24, https://doi.org/10.1007/s13131-020-1644-7, 2020. a
Liao, Z., Cheng, B., Zhao, J., Vihma, T., Jackson, K., Yang, Q., Yang, Y., Zhang, L., Li, Z., Qiu, Y., and Cheng, X.: Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm, Int. J. Digit. Earth, 12, 962–979, https://doi.org/10.1080/17538947.2018.1545877, 2018. a
Lindsay, R. W., Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Arctic Sea Ice Retreat in 2007 Follows Thinning Trend, J. Climate, 22, 165–176, https://doi.org/10.1175/2008JCLI2521.1, 2009. a
Liu, J., Zhang, Z., Inoue, J., and Horton, R. M.: Evaluation of snow/ice albedo parameterizations and their impacts on sea ice simulations, Int. J. Climatol., 27, 81–91, https://doi.org/10.1002/joc.1373, 2007. a
Lucht, W., Schaaf, C., and Strahler, A.: An algorithm for the retrieval of albedo from space using semiempirical BRDF models, IEEE T. Geosci. Remote, 38, 977–998, https://doi.org/10.1109/36.841980, 2000. a
Nielsen-Englyst, P., Høyer, J. L., Kolbe, W. M., Dybkjær, G., Lavergne, T., Tonboe, R. T., Skarpalezos, S., and Karagali, I.: A combined sea and sea-ice surface temperature climate dataset of the Arctic, 1982–2021, Remote Sens. Environ., 284, 113331, https://doi.org/10.1016/j.rse.2022.113331, 2023. a, b
Parkinson, C. L., Ward, A., and King, M. D., eds.: Earth science reference handbook: a guide to NASA's Earth science program and Earth observing satellite missions, National Aeronautics and Space Administration, Washington, D.C., 2006. a
Perovich, D. and Richter-Menge, J.: Regional variability in sea ice melt in a changing Arctic, Philos. T. Royal Soc. A, 373, 20140165, https://doi.org/10.1098/rsta.2014.0165, 2015. a
Perovich, D., Richter-Menge, J., and Polashenski, C.: Observing and understanding climate change: Monitoring the mass balance, motion, and thickness of Arctic sea ice, CRREL-Dartmouth [data set], https://imb-crrel-dartmouth.org, last access: 29 February 2024. a
Pistone, K., Eisenman, I., and Ramanathan, V.: Observational determination of albedo decrease caused by vanishing Arctic sea ice, P. Natl. Acad. Sci. USA, 111, 3322–3326, https://doi.org/10.1073/pnas.1318201111, 2014. a
Pohl, C., Istomina, L., Tietsche, S., Jäkel, E., Stapf, J., Spreen, G., and Heygster, G.: Broadband albedo of Arctic sea ice from MERIS optical data, The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, 2020. a
Provost, C., Sennéchael, N., Miguet, J., Itkin, P., Rösel, A., Koenig, Z., Villacieros-Robineau, N., and Granskog, M. A.: Observations of flooding and snow-ice formation in a thinner Arctic sea-ice regime during the N-ICE2015 campaign: Influence of basal ice melt and storms, J. Geophys. Res.-Oceans, 122, 7115–7134, https://doi.org/10.1002/2016JC012011, 2017. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Renner, A. H. H., Gerland, S., Haas, C., Spreen, G., Beckers, J. F., Hansen, E., Nicolaus, M., and Goodwin, H.: Evidence of Arctic sea ice thinning from direct observations, Geophys. Res. Lett., 41, 5029–5036, https://doi.org/10.1002/2014GL060369, 2014. a
Richter-Menge, J., Perovich, D., Elder, B. C., Claffey, K., Rigor, I., and Ortmeyer, M.: Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover, Ann. Glaciol., 44, 205–210, https://doi.org/10.3189/172756406781811727, 2006. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017 (data available at: ftp://ftp.awi.de/sea_ice/product/cryosat2_smos, last access: 29 February 2024). a, b, c
Riihelä, A., Bright, R. M., and Anttila, K.: Recent strengthening of snow and ice albedo feedback driven by Antarctic sea-ice loss, Nat. Geosci., 14, 832–836, https://doi.org/10.1038/s41561-021-00841-x, 2021. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., ya Chuang, H., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Delst, P. V., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Schmith, T. and Hansen, C.: Fram Strait Ice Export during the Nineteenth and Twentieth Centuries Reconstructed from a Multiyear Sea Ice Index from Southwestern Greenland, J. Climate, 16, 2782–2791, https://doi.org/10.1175/1520-0442(2003)016<2782:FSIEDT>2.0.CO;2, 2003. a
Schyberg, H., Yang, X., Køltzow, M. A. Ø., Amstrup, B., Bakketun, Å., Bazile, E., Bojarova, J., Box, J. E., Dahlgren, P., Hagelin, S., Homleid, M., Horányi, A., Høyer, J., Johansson, Å., Killie, M. A., Körnich, H., Le Moigne, P., Lindskog, M., Manninen, T., Nielsen Englyst, P., Nielsen, K. P., Olsson, E., Palmason, B., Peralta Aros, C., Randriamampianina, R., Samuelsson, P., Stappers, R., Støylen, E., Thorsteinsson, S., Valkonen, T., Wang, Z. Q.: Arctic regional reanalysis on single levels from 1991 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.713858f6, 2020. a
Scott, K. A., Buehner, M., and Carrieres, T.: An Assessment of Sea-Ice Thickness Along the Labrador Coast From AMSR-E and MODIS Data for Operational Data Assimilation, IEEE T. Geosci. Remote, 52, 2726–2737, https://doi.org/10.1109/TGRS.2013.2265091, 2014. a
Screen, J. A. and Simmonds, I.: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification, Geophys. Res. Lett., 37, L16707, https://doi.org/10.1029/2010GL044136, 2010. a
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011. a
Serreze, M. C., Clark, M. P., and Bromwich, D. H.: Monitoring Precipitation over the Arctic Terrestrial Drainage System: Data Requirements, Shortcomings, and Applications of Atmospheric Reanalysis, J. Hydrometeorol., 4, 387–407, https://doi.org/10.1175/1525-7541(2003)4<387:MPOTAT>2.0.CO;2, 2003. a
Shi, H., Sohn, B.-J., Dybkjær, G., Tonboe, R. T., and Lee, S.-M.: Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements, The Cryosphere, 14, 3761–3783, https://doi.org/10.5194/tc-14-3761-2020, 2020. a, b, c
Solomon, A., Shupe, M. D., Svensson, G., Barton, N. P., Batrak, Y., Bazile, E., Day, J. J., Doyle, J. D., Frank, H. P., Keeley, S., Remes, T., and Tolstykh, M.: The winter central Arctic surface energy budget: A model evaluation using observations from the MOSAiC campaign, Elementa: Science of the Anthropocene, 11, 00104, https://doi.org/10.1525/elementa.2022.00104, 2023. a
Tang, C. C., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., and Dunlap, E.: The circulation, water masses and sea-ice of Baffin Bay, Prog. Oceanogr., 63, 183–228, https://doi.org/10.1016/j.pocean.2004.09.005, 2004. a
Taylor, J. P., Edwards, J. M., Glew, M. D., Hignett, P., and Slingo, A.: Studies with a flexible new radiation code. II: Comparisons with aircraft short-wave observations, Q. J. Roy. Meteor. Soc., 122, 839–861, https://doi.org/10.1002/qj.49712253204, 1996. a
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016. a
Toudal Pedersen, L., Dybkjær, G., Eastwood, S., Heygster, G., Ivanova, N., Kern, S., Lavergne, T., Saldo, R., Sandven, S., Sørensen, A., and Tonboe, R.: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25 km grid spacing, version 2.1, Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5, 2017. a
Vihma, T., Pirazzini, R., Fer, I., Renfrew, I. A., Sedlar, J., Tjernström, M., Lüpkes, C., Nygård, T., Notz, D., Weiss, J., Marsan, D., Cheng, B., Birnbaum, G., Gerland, S., Chechin, D., and Gascard, J. C.: Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review, Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, 2014. a
Vinje, T. and Kvambekk, Å. S.: Barents Sea drift ice characteristics, Polar Res. 10, 59–68, https://doi.org/10.3402/polar.v10i1.6728, 1991. a
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019. a
Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., and Sturm, M.: Interdecadal changes in snow depth on Arctic sea ice, J. Geophys. Res.-Oceans, 119, 5395–5406, https://doi.org/10.1002/2014JC009985, 2014. a
Yang, X., Nielsen, K. P., Amstrup, B., Peralta, C., Høyer, J., Nielsen Englyst, P., Schyberg, H., Homleid, M., Køltzow, M. A. Ø., Randriamampianina, R., Dahlgren, P., Støylen, E., Valkonen, T., Palmason, B., Thorsteinsson, S., Bojarova, J., Körnich, H., Lindskog, M., Box, J., and Mankoff, K.: C3S Arctic regional reanalysis – Full system documentation, Tech. rep., Danish Meteorological Institute, 2020. a
Zhou, L., Stroeve, J., Xu, S., Petty, A., Tilling, R., Winstrup, M., Rostosky, P., Lawrence, I. R., Liston, G. E., Ridout, A., Tsamados, M., and Nandan, V.: Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval, The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, 2021. a, b
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.
Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a...