Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-1031-2025
© Author(s) 2025. 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-19-1031-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Mar 2025
Research article |
| 06 Mar 2025
| 06 Mar 2025
Effects of Arctic sea-ice concentration on surface radiative fluxes in four atmospheric reanalyses
Tereza Uhlíková
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
Finnish Meteorological Institute, 00101 Helsinki, Finland
Timo Vihma
Finnish Meteorological Institute, 00101 Helsinki, Finland
Alexey Yu Karpechko
Finnish Meteorological Institute, 00101 Helsinki, Finland
Petteri Uotila
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
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A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the sensible and latent heat fluxes in the Arctic. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in SIC and in the sensitivity of the latent and sensible heat fluxes to SIC.
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Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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Janosch Michaelis, Amelie U. Schmitt, Christof Lüpkes, Jörg Hartmann, Gerit Birnbaum, and Timo Vihma
Earth Syst. Sci. Data, 14, 1621–1637, https://doi.org/10.5194/essd-14-1621-2022, https://doi.org/10.5194/essd-14-1621-2022, 2022
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A major goal of the Springtime Atmospheric Boundary Layer Experiment (STABLE) aircraft campaign was to observe atmospheric conditions during marine cold-air outbreaks (MCAOs) originating from the sea-ice-covered Arctic ocean. Quality-controlled measurements of several meteorological variables collected during 15 vertical aircraft profiles and by 22 dropsondes are presented. The comprehensive data set may be used for validating model results to improve the understanding of future trends in MCAOs.
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
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Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
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In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Nicholas L. Tyrrell, Juho M. Koskentausta, and Alexey Yu. Karpechko
Weather Clim. Dynam., 3, 45–58, https://doi.org/10.5194/wcd-3-45-2022, https://doi.org/10.5194/wcd-3-45-2022, 2022
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El Niño events are known to effect the variability of the wintertime stratospheric polar vortex. The observed relationship differs from what is seen in climate models. Climate models have errors in their average winds and temperature, and in this work we artificially reduce those errors to see how that changes the communication of El Niño events to the polar stratosphere. We find reducing errors improves stratospheric variability, but does not explain the differences with observations.
Nicholas L. Tyrrell and Alexey Yu. Karpechko
Weather Clim. Dynam., 2, 913–925, https://doi.org/10.5194/wcd-2-913-2021, https://doi.org/10.5194/wcd-2-913-2021, 2021
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Tropical Pacific sea surface temperatures (El Niño) affect the global climate. The Pacific-to-Europe connection relies on interactions of large atmospheric waves with winds and surface pressure. We looked at how mean errors in a climate model affect its ability to simulate the Pacific-to-Europe connection. We found that even large errors in the seasonal winds did not affect the response of the model to an El Niño event, which is good news for seasonal forecasts which rely on these connections.
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
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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.
Irene Erner, Alexey Y. Karpechko, and Heikki J. Järvinen
Weather Clim. Dynam., 1, 657–674, https://doi.org/10.5194/wcd-1-657-2020, https://doi.org/10.5194/wcd-1-657-2020, 2020
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In this paper we investigate the role of the tropospheric forcing in the occurrence of the sudden stratospheric warming (SSW) that took place in February 2018, its predictability and teleconnection with the Madden–Julian oscillation (MJO) by analysing the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast. The purpose of the paper is to present the results of the analysis of the atmospheric circulation before and during the SSW and clarify the driving mechanisms.
Joula Siponen, Petteri Uotila, Eero Rinne, and Steffen Tietsche
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-272, https://doi.org/10.5194/tc-2019-272, 2019
Manuscript not accepted for further review
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Long sea-ice thickness time series are needed to better understand the Arctic climate and improve its forecasts. In this study 2002–2017 satellite observations are compared with reanalysis output, which is used as initial conditions for long forecasts. The reanalysis agrees well with satellite observations, with differences typically below 1 m when averaged in time, although seasonally and in certain years the differences are large. This is caused by uncertainties in reanalysis and observations.
Kalle Nordling, Hannele Korhonen, Petri Räisänen, Muzaffer Ege Alper, Petteri Uotila, Declan O'Donnell, and Joonas Merikanto
Atmos. Chem. Phys., 19, 9969–9987, https://doi.org/10.5194/acp-19-9969-2019, https://doi.org/10.5194/acp-19-9969-2019, 2019
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We carry out long equilibrium climate simulations with two modern climate models and show that the climate model dynamic response contributes strongly to the anthropogenic aerosol response. We demonstrate that identical aerosol descriptions do not improve climate model skill to estimate regional anthropogenic aerosol impacts. Our experiment utilized two independent climate models (NorESM and ECHAM6) with an identical description for aerosols optical properties and indirect effect.
Wenfeng Huang, Bin Cheng, Jinrong Zhang, Zheng Zhang, Timo Vihma, Zhijun Li, and Fujun Niu
Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, https://doi.org/10.5194/hess-23-2173-2019, 2019
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Up to now, little has been known on ice thermodynamics and lake–atmosphere interaction over the Tibetan Plateau during ice-covered seasons due to a lack of field data. Here, model experiments on ice thermodynamics were conducted in a shallow lake using HIGHTSI. Water–ice heat flux was a major source of uncertainty for lake ice thickness. Heat and mass budgets were estimated within the vertical air–ice–water system. Strong ice sublimation occurred and was responsible for water loss during winter.
Lejiang Yu, Shiyuan Zhong, and Timo Vihma
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-38, https://doi.org/10.5194/tc-2019-38, 2019
Manuscript not accepted for further review
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Arctic sea ice cover has been decreasing in recent decades. The reason for the decrease remains unclear. In this study, we examine the contributions of the North Pacific SST anomalies to the decrease. There are global warming and Pacific Decadal Oscillation (PDO) modesof the North Pacific SST variability in boreal summer and autumn. The global warming mode explains 44.9% and 50.1% of the Arctic sea ice loss in boreal summer and autumn, respectively. There are 22.0% and 22.2% for PDO mode.
Timo Vihma, Petteri Uotila, Stein Sandven, Dmitry Pozdnyakov, Alexander Makshtas, Alexander Pelyasov, Roberta Pirazzini, Finn Danielsen, Sergey Chalov, Hanna K. Lappalainen, Vladimir Ivanov, Ivan Frolov, Anna Albin, Bin Cheng, Sergey Dobrolyubov, Viktor Arkhipkin, Stanislav Myslenkov, Tuukka Petäjä, and Markku Kulmala
Atmos. Chem. Phys., 19, 1941–1970, https://doi.org/10.5194/acp-19-1941-2019, https://doi.org/10.5194/acp-19-1941-2019, 2019
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The Arctic marine climate system, ecosystems, and socio-economic systems are changing rapidly. This calls for the establishment of a marine Arctic component of the Pan-Eurasian Experiment (MA-PEEX), for which we present a plan. The program will promote international collaboration; sustainable marine meteorological, sea ice, and oceanographic observations; advanced data management; and multidisciplinary research on the marine Arctic and its interaction with the Eurasian continent.
Laura Thölix, Alexey Karpechko, Leif Backman, and Rigel Kivi
Atmos. Chem. Phys., 18, 15047–15067, https://doi.org/10.5194/acp-18-15047-2018, https://doi.org/10.5194/acp-18-15047-2018, 2018
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We analyse the impact of water vapour (WV) on Arctic ozone loss and find the strongest impact during intermediately cold stratospheric winters when chlorine activation increases with increasing PSCs and WV. In colder winters the impact is limited because chlorine activation becomes complete at relatively low WV values, so further addition of WV does not affect ozone loss. Our results imply that improved simulations of WV are needed for more reliable projections of ozone layer recovery.
Elena Shevnina, Karoliina Pilli-Sihvola, Riina Haavisto, Timo Vihma, and Andrey Silaev
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-473, https://doi.org/10.5194/hess-2018-473, 2018
Manuscript not accepted for further review
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Projections of a potential hydropower production were evaluated in terms of probability of water resources available in the future. The future projections of annual river runoff were evaluated on average, as well as on low and high exceedance probabilities under several climate change scenarios. The main idea of the modelling method used is to simulate statistical estimators of annual river runoff (mean, variation and skewness) instead of runoff time series.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Luke G. Bennetts, Siobhan O'Farrell, and Petteri Uotila
The Cryosphere, 11, 1035–1040, https://doi.org/10.5194/tc-11-1035-2017, https://doi.org/10.5194/tc-11-1035-2017, 2017
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A numerical model is used to investigate how Antarctic sea ice concentration and volume are affected by increased melting caused by ocean-wave breakup of the ice. When temperatures are high enough to melt the ice, concentration and volume are reduced for ~ 100 km into the ice-covered ocean. When temperatures are low enough for ice growth, the concentration recovers, but the reduced volume persists.
Petteri Uotila, Doroteaciro Iovino, Martin Vancoppenolle, Mikko Lensu, and Clement Rousset
Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, https://doi.org/10.5194/gmd-10-1009-2017, 2017
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We performed ocean model simulations with new and old sea-ice components. Sea ice improved in the new model compared to the earlier one due to better model physics. In the ocean, the largest differences are confined close to the surface within and near the sea-ice zone. The global ocean circulation slowly deviates between the simulations due to dissimilar sea ice in the deep water formation regions, such as the North Atlantic and Antarctic.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
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After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Laura Thölix, Leif Backman, Rigel Kivi, and Alexey Yu. Karpechko
Atmos. Chem. Phys., 16, 4307–4321, https://doi.org/10.5194/acp-16-4307-2016, https://doi.org/10.5194/acp-16-4307-2016, 2016
P. Hari, T. Petäjä, J. Bäck, V.-M. Kerminen, H. K. Lappalainen, T. Vihma, T. Laurila, Y. Viisanen, T. Vesala, and M. Kulmala
Atmos. Chem. Phys., 16, 1017–1028, https://doi.org/10.5194/acp-16-1017-2016, https://doi.org/10.5194/acp-16-1017-2016, 2016
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This manuscript introduces a conceptual design of a global, hierarchical observation network which provides tools and increased understanding to tackle the inter-connected environmental and societal challenges that we will face in the coming decades. Each ecosystem type on the globe has its own characteristic features that need to be taken into consideration. The hierarchical network is able to tackle problems related to large spatial scales, heterogeneity of ecosystems and their complexity.
R. Pirazzini, P. Räisänen, T. Vihma, M. Johansson, and E.-M. Tastula
The Cryosphere, 9, 2357–2381, https://doi.org/10.5194/tc-9-2357-2015, https://doi.org/10.5194/tc-9-2357-2015, 2015
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We illustrate a method to measure the size distribution of a snow particle metric from macro photos of snow particles. This snow particle metric corresponds well to the optically equivalent effective radius. Our results evidence the impact of grain shape on albedo, indicate that more than just one particle metric distribution is needed to characterize the snow scattering properties at all optical wavelengths, and suggest an impact of surface roughness on the shortwave infrared albedo.
R. Döscher, T. Vihma, and E. Maksimovich
Atmos. Chem. Phys., 14, 13571–13600, https://doi.org/10.5194/acp-14-13571-2014, https://doi.org/10.5194/acp-14-13571-2014, 2014
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The article reviews progress in understanding of the Arctic sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric drivers of sea ice change. Large-scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Ocean heat fluxes are clearly impacting the ice margins. Little indication exists for a direct decisive influence of the warming ocean on the central Arctic sea ice cover.
A. Tetzlaff, C. Lüpkes, G. Birnbaum, J. Hartmann, T. Nygård, and T. Vihma
The Cryosphere, 8, 1757–1762, https://doi.org/10.5194/tc-8-1757-2014, https://doi.org/10.5194/tc-8-1757-2014, 2014
T. Vihma, R. Pirazzini, I. Fer, I. A. Renfrew, J. Sedlar, M. Tjernström, C. Lüpkes, T. Nygård, D. Notz, J. Weiss, D. Marsan, B. Cheng, G. Birnbaum, S. Gerland, D. Chechin, and J. C. Gascard
Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, https://doi.org/10.5194/acp-14-9403-2014, 2014
I. Välisuo, T. Vihma, and J. C. King
The Cryosphere, 8, 1519–1538, https://doi.org/10.5194/tc-8-1519-2014, https://doi.org/10.5194/tc-8-1519-2014, 2014
T. Nygård, T. Valkonen, and T. Vihma
Atmos. Chem. Phys., 14, 1959–1971, https://doi.org/10.5194/acp-14-1959-2014, https://doi.org/10.5194/acp-14-1959-2014, 2014
C. E. Chung, H. Cha, T. Vihma, P. Räisänen, and D. Decremer
Atmos. Chem. Phys., 13, 11209–11219, https://doi.org/10.5194/acp-13-11209-2013, https://doi.org/10.5194/acp-13-11209-2013, 2013
L. Jakobson, T. Vihma, E. Jakobson, T. Palo, A. Männik, and J. Jaagus
Atmos. Chem. Phys., 13, 11089–11099, https://doi.org/10.5194/acp-13-11089-2013, https://doi.org/10.5194/acp-13-11089-2013, 2013
A. Tetzlaff, L. Kaleschke, C. Lüpkes, F. Ament, and T. Vihma
The Cryosphere, 7, 153–166, https://doi.org/10.5194/tc-7-153-2013, https://doi.org/10.5194/tc-7-153-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Energy Balance Obs/Modelling
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation
Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt
On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic
New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements
Sunlight, clouds, sea ice, albedo, and the radiative budget: the umbrella versus the blanket
Yingzhen Zhou, Wei Li, Nan Chen, Yongzhen Fan, and Knut Stamnes
The Cryosphere, 17, 1053–1087, https://doi.org/10.5194/tc-17-1053-2023, https://doi.org/10.5194/tc-17-1053-2023, 2023
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We present a method to compute albedo (percentage of the light reflected) of the cryosphere surface using observations from optical satellite sensors. This method can be applied to sea ice, snow-covered ice, melt pond, open ocean, and mixtures thereof. Evaluation of the albedo values calculated using this approach demonstrated excellent agreement with observations. In addition, we have included a statistical comparison of the proposed method's results with those derived from other approaches.
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
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In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Einar Ólason, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 15, 1053–1064, https://doi.org/10.5194/tc-15-1053-2021, https://doi.org/10.5194/tc-15-1053-2021, 2021
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We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
Christian Katlein, Lovro Valcic, Simon Lambert-Girard, and Mario Hoppmann
The Cryosphere, 15, 183–198, https://doi.org/10.5194/tc-15-183-2021, https://doi.org/10.5194/tc-15-183-2021, 2021
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To improve autonomous investigations of sea ice optical properties, we designed a chain of multispectral light sensors, providing autonomous in-ice light measurements. Here we describe the system and the data acquired from a first prototype deployment. We show that sideward-looking planar irradiance sensors basically measure scalar irradiance and demonstrate the use of this sensor chain to derive light transmittance and inherent optical properties of sea ice.
Donald K. Perovich
The Cryosphere, 12, 2159–2165, https://doi.org/10.5194/tc-12-2159-2018, https://doi.org/10.5194/tc-12-2159-2018, 2018
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The balance of longwave and shortwave radiation plays a central role in the summer melt of Arctic sea ice. It is governed by clouds and surface albedo. The basic question is what causes more melting, sunny skies or cloudy skies. It depends on the albedo of the ice surface. For snow-covered or bare ice, sunny skies always result in less radiative heat input. In contrast, the open ocean always has, and melt ponds usually have, more radiative input under sunny skies than cloudy skies.
Cited articles
Andreas, E. L., Persson, P. O. G., Grachev, A. A., Jordan, R. E., Horst, T., Guest, P. S., and Fairall, C.: Parameterizing Turbulent Exchange over Sea Ice in Winter, J. Hydrometeorol, 11, 87–104, https://doi.org/10.1175/2009JHM1102.1, 2010. a
Boggs, P. T., Donaldson, J. T., Schnabel, R. B., and Spiegelman, C. H.: A Computational Examination of Orthogonal Distance Regression, J. Econometrics, 38, 169–201, https://doi.org/10.1016/0304-4076(88)90032-2, 1988. a
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and Bladé, I.: The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field, J. Climate, 12, 1990–2009, https://doi.org/10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2, 1999. a
Cao, Y., Liang, S., He, T., and Chen, X.: Evaluation of Four Reanalysis Surface Albedo Data Sets in Arctic Using a Satellite Product, IEEE Geosci. Remote S., 13, 384–388, https://doi.org/10.1109/LGRS.2016.2515159, 2016. a
Choi, Y.-S., Hwang, J., Ok, J., Park, D.-S. R., Su, H., Jiang, J. H., Huang, L., and Limpasuvan, V.: Effect of Arctic clouds on the ice-albedo feedback in midsummer, Int. J. Climatol., 40, 4707–4714, https://doi.org/10.1002/joc.6469, 2020. a
Devasthale, A., Sedlar, J., Tjernström, M., and Kokhanovsky, A.: A Climatological Overview of Arctic Clouds, in: Physics and Chemistry of the Arctic Atmosphere, edited by: Kokhanovsky, A. and Tomasi, C., Springer International Publishing, Chap. 5, 331–360, 2020. a
Di Biagio, C., Pelon, J., Blanchard, Y., Loyer, S., Hudson, S. R., Walden, V. P., Raut, J.-C., Kato, S., Mariage, V., and Granskog, M. A.: Toward a better surface radiation budget analysis over sea ice in the high Arctic Ocean: a comparative study between satellite, reanalysis, and local-scale observations, J. Geophys. Res.-Atmos., 126, e2020JD032555, https://doi.org/10.1029/2020JD032555, 2021. a
Duynkerke, P. and de Roode, S.: Surface energy balance and turbulence characteristics observed at the SHEBA Ice Camp during FIRE III, J. Geophys. Res.-Oceans, 106, 15313–15322, https://doi.org/10.1029/2000JD900537, 2001. 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, No. 4, ECMWF, https://doi.org/10.21957/tr5rv27xu, 2016. a
Garrett, T. and Zhao, C.: Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes, Nature, 440, 787–789, https://doi.org/10.1038/nature04636, 2006. a, b
GMAO (Global Modeling and Assimilation Office): Tavg1_2d_flx_Nx: MERRA-2 2D, 1-Hourly, Time-Averaged, Single-Level Assimilation, Single-Level Diagnostics, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/7MCPBJ41Y0K6, 2015a. a, b
GMAO (Global Modeling and Assimilation Office): Tavg1_2d_rad_Nx: MERRA-2 2D, 1-hourly, Time-Averaged, Single-Level Assimilation, Surface Flux Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/Q9QMY5PBNV1T, 2015b. a, b
GMAO (Global Modeling and Assimilation Office): Tavg1_2d_slv_Nx: MERRA-2 2D, 1-Hourly, Time-Averaged, Single-Level Assimilation, Single-Level Diagnostics, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/VJAFPLI1CSIV, 2015c. a, b
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, b, c
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., 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
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., J., M. S., 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, b, c
Japan Meteorological Agency (JMA): JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6HH6H41, 2013. a, b
Jonassen, M. O., Välisuo, I., Vihma, T., Uotila, P., Makshtas, A. P., and Launiainen, J.: Assessment of atmospheric reanalyses with independent observations in the Weddell Sea, the Antarctic, J. Geophys. Res.-Atmos., 124, 12468–12484, https://doi.org/10.1029/2019JD030897, 2019. a, b
Kwok, R.: Sea ice convergence along the Arctic coasts of Greenland and the Canadian Arctic Archipelago: Variability and extremes (1992–2014), Geophys. Res. Lett., 42, 7598–7605, https://doi.org/10.1002/2015GL065462, 2015. a
Leppäranta, M.: The Drift of Sea Ice, Springer, Berlin Heidelberg, https://doi.org/10.1007/978-3-642-04683-4, 2011. a
Liang, S., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., and Zhao, X.: Longwave Emissivity, in: Global LAnd Surface Satellite (GLASS) Products, Springer Briefs in Earth Sciences, Springer, Cham, 63–77, https://doi.org/10.1007/978-3-319-02588-9_4, 2014. a
Light, B., Smith, M. M., Perovich, D. K., Webster, M. A., Holland, M. M., Linhardt, F., Raphael, I. A., Clemens-Sewall, D., Macfarlane, A. R., Anhaus, P., and Bailey, D. A.: Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift, Elementa: Science of the Anthropocene, 10, 000103, https://doi.org/10.1525/elementa.2021.000103, 2022. a
Liu, Y., Key, J. R., Liu, Z., Wang, X., and Vavrus, S. J.: A cloudier Arctic expected with diminishing sea ice, Geophys. Res. Lett., 39, L05705, https://doi.org/10.1029/2012GL051251, 2012. a
Makshtas, A. P.: The heat budget of the Arctic ice in the winter, edited by: Andreas, E. L., Cambridge, International Glaciological Society, ISBN 0 946417 12 1, 1991. a
Morrison, A. L., Kay, J. E., Frey, W. R., Chepfer, H., and Guzman, R.: Cloud response to Arctic Sea ice loss and implications for future feedback in the CESM1 climate model, J. Geophys. Res.-Atmos., 124, 1003–1020, https://doi.org/10.1029/2018JD029142, 2019. a
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, J. Geophys. Lett., 43, 6636–6642, https://doi.org/10.1002/2016GL069330, 2016. a
Palm, S. P., Strey, S. T., Spinhirne, J., and Markus, T.: Influence of Arctic sea ice extent on polar cloud fraction and vertical structure and implications for regional climate, J. Geophys. Res., 115, D21209, https://doi.org/10.1029/2010JD013900, 2010. a
Parkinson, C. L.: Arctic sea ice coverage from 43 years of satellite passive-microwave observations, Frontiers in Remote Sensing, 3, 1021781, https://doi.org/10.3389/frsen.2022.1021781, 2022. a
Perovich, D. K., Nghiem, S. V., Markus, T., and Schweiger, A.: Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice–ocean system, J. Geophys. Res., 112, C03005, https://doi.org/10.1029/2006jc003558, 2007. a
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, https://doi.org/10.1007/s00382-011-1196-9, 2012. a, b
Persson, P. O. G., Fairall, C. W., Andreas, E. L., Guest, P. S., and Perovich, D. K.: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget, J. Geophys. Res., 107, 8045, https://doi.org/10.1029/2000JC000705, 2002. a, b
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, b
Platt, S. M., Hov, Ø., Berg, T., Breivik, K., Eckhardt, S., Eleftheriadis, K., Evangeliou, N., Fiebig, M., Fisher, R., Hansen, G., Hansson, H.-C., Heintzenberg, J., Hermansen, O., Heslin-Rees, D., Holmén, K., Hudson, S., Kallenborn, R., Krejci, R., Krognes, T., Larssen, S., Lowry, D., Lund Myhre, C., Lunder, C., Nisbet, E., Nizzetto, P. B., Park, K.-T., Pedersen, C. A., Aspmo Pfaffhuber, K., Röckmann, T., Schmidbauer, N., Solberg, S., Stohl, A., Ström, J., Svendby, T., Tunved, P., Tørnkvist, K., van der Veen, C., Vratolis, S., Yoon, Y. J., Yttri, K. E., Zieger, P., Aas, W., and Tørseth, K.: Atmospheric composition in the European Arctic and 30 years of the Zeppelin Observatory, Ny-Ålesund, Atmos. Chem. Phys., 22, 3321–3369, https://doi.org/10.5194/acp-22-3321-2022, 2022. 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
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., Chuang, H.-Y., Juang, H.-M., Sela, J., and Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010a. 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., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., 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.: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010b. a, b
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., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., 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.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D61C1TXF, 2011. a, b
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., ya Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Schweiger, A. J., Lindsay, R. W., Vavrus, S., and Francis, J. A.: Relationships between Arctic Sea Ice and Clouds during Autumn, J. Climate, 21, 4799–4810, https://doi.org/10.1175/2008JCLI2156.1, 2008. a
Senkova, A., Rontu, L., and Savijarvi, H.: Parameterization of orographic effects on surface radiation in HIRLAM, Tellus A, 59, 279–291, https://doi.org/10.1111/j.1600-0870.2007.00235.x, 2007. a
Serreze, M. C., Barrett, A. P., Stroeve, J. C., Kindig, D. N., and Holland, M. M.: The emergence of surface-based Arctic amplification, The Cryosphere, 3, 11–19, https://doi.org/10.5194/tc-3-11-2009, 2009. a
Sledd, A. and L'Ecuyer, T. S.: Emerging trends in Arctic solar absorption, Geophys. Res. Lett., 48, e2021GL095813, https://doi.org/10.1029/2021GL095813, 2021. a
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. a
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, L19501, https://doi.org/10.1029/2011GL048970, 2011. a
Taylor, P. C., Boeke, R. C., Boisvert, L. N., Feldl, N., Henry, M., Huang, Y., Langen, P. L., Liu, W., Pithan, F., Sejas, S. A., and Tan, I.: Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming, Frontiers in Earth Science, 9, 758361, https://doi.org/10.3389/feart.2021.758361, 2022. a
Uotila, P. and Uhlíková, T.: Codes used for calculations and plotting in manuscript: Effects of Arctic sea-ice concentration on surface radiative fluxes in four atmospheric reanalyses, Version v1, Zenodo [code], https://doi.org/10.5281/zenodo.11565044, 2024. a
Vihma, T.: Subgrid parameterization of surface heat and momentum fluxes over polar oceans, J. Geophys. Res., 100, 22625–22646, https://doi.org/10.1029/95JC02498, 1995. a
Vihma, T., Tisler, P., and Uotila, P.: Atmospheric forcing on the drift of Arctic sea ice in 1989–2009, Geophys. Res. Lett., 39, L02501, https://doi.org/10.1029/2011GL050118, 2012. a
Walsh, J. E., Chapman, W. L., and Portis, D. H.: Arctic Cloud Fraction and Radiative Fluxes in Atmospheric Reanalyses, J. Climate, 22, 2316–2334, https://doi.org/10.1175/2008JCLI2213.1, 2009. a
Warneke, C., Froyd, K. D., Brioude, J., Bahreini, R., Brock, C. A., Cozic, J., de Gouw, J. A., Fahey, D. W., Ferrare, R., Holloway, J. S., Middlebrook, A. M., Miller, L., Montzka, S., Schwarz, J. P., Sodemann, H., Spackman, J. R., and Stohl, A.: An important contribution to springtime Arctic aerosol from biomass burning in Russia, Geophys. Res. Lett., 37, L01801, https://doi.org/10.1029/2009GL041816, 2010. a
Wendish, M., Macke, A., Ehrlich, A., et al.: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, B. Am. Meteorol. Soc., 100, 841–871, https://doi.org/10.1175/BAMS-D-18-0072.1, 2019. a
Wilks, D. S.: “The Stippling Shows Statistically Significant Grid Points”: How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It, B. Am. Meteorol. Soc., 97, 2263–2273, https://doi.org/10.1175/BAMS-D-15-00267.1, 2016. a
Yeo, H., Kim, M.-H., Son, S.-W., Jeong, J.-H., Yoon, J.-H., Kim, B.-M., and Kim, S.-W.: Arctic cloud properties and associated radiative effects in the three newer reanalysis datasets (ERA5, MERRA-2, JRA-55): Discrepancies and possible causes, Atmos. Res., 270, 106080, https://doi.org/10.1016/j.atmosres.2022.106080, 2022. a
Short summary
To better understand the local, regional, and global impacts of the recent rapid sea-ice decline in the Arctic, one of the key issues is to quantify the effects of sea-ice concentration on the surface radiative fluxes. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in the sensitivity of the surface radiative fluxes to sea-ice concentration.
To better understand the local, regional, and global impacts of the recent rapid sea-ice decline...
