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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Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 18, 957–976, https://doi.org/10.5194/tc-18-957-2024, https://doi.org/10.5194/tc-18-957-2024, 2024
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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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The study first estimated the summertime evaporation over lakes located in coastal Antarctica with direct (eddy-covariance) measurements collected during two austral summers (December–January) in 2017–2018 and 2019–2020. The lake evaporation was on average 1.6 mm d-1 in the ice break-up period, and it doubled in the ice free period. The bulk aerodynamic method with a site-specific transfer coefficient of moisture well reproduced the observed day-to-day variations in evaporation over lakes.
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Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
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Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
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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.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, Cuijuan Sui, and Bo Sun
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Previous studies have noted a significant relationship between the Subtropical Indian Ocean Dipole and the South Atlantic Ocean Dipole indices, but little is known about the stability of their relationship. We found a significant positive correlation between the two indices prior to the year 2000 but an insignificant correlation afterwards.
Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
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Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Elena Shevnina, Miguel Potes, Timo Vihma, Tuomas Naakka, Pankaj Ramji Dhote, and Praveen Kumar Thakur
The Cryosphere, 16, 3101–3121, https://doi.org/10.5194/tc-16-3101-2022, https://doi.org/10.5194/tc-16-3101-2022, 2022
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The evaporation over an ice-free glacial lake was measured in January 2018, and the uncertainties inherent to five indirect methods were quantified. Results show that in summer up to 5 mm of water evaporated daily from the surface of the lake located in Antarctica. The indirect methods underestimated the evaporation over the lake's surface by up to 72 %. The results are important for estimating the evaporation over polar regions where a growing number of glacial lakes have recently been evident.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
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We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
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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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
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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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.
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.
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...
