Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-957-2024
© 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-18-957-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2024
Research article |
| 01 Mar 2024
| 01 Mar 2024
Effects of Arctic sea-ice concentration on turbulent surface 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
Timo Vihma
Finnish Meteorological Institute, Helsinki, Finland
Alexey Yu Karpechko
Finnish Meteorological Institute, Helsinki, Finland
Petteri Uotila
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland
Related authors
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
EGUsphere, https://doi.org/10.5194/egusphere-2024-1759, https://doi.org/10.5194/egusphere-2024-1759, 2024
Short summary
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.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David A. Bailey, and Petteri Uotila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-213, https://doi.org/10.5194/gmd-2024-213, 2024
Preprint under review for GMD
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.
Xavier J. Levine, Ryan S. Williams, Gareth Marshall, Andrew Orr, Lise Seland Graff, Dörthe Handorf, Alexey Karpechko, Raphael Köhler, René R. Wijngaard, Nadine Johnston, Hanna Lee, Lars Nieradzik, and Priscilla A. Mooney
Earth Syst. Dynam., 15, 1161–1177, https://doi.org/10.5194/esd-15-1161-2024, https://doi.org/10.5194/esd-15-1161-2024, 2024
Short summary
Short summary
While the most recent climate projections agree that the Arctic is warming, differences remain in how much and in other climate variables such as precipitation. This presents a challenge for stakeholders who need to develop mitigation and adaptation strategies. We tackle this problem by using the storyline approach to generate four plausible and actionable realisations of end-of-century climate change for the Arctic, spanning its most likely range of variability.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Di Chen, Qizhen Sun, and Timo Vihma
EGUsphere, https://doi.org/10.5194/egusphere-2024-2359, https://doi.org/10.5194/egusphere-2024-2359, 2024
Preprint archived
Short summary
Short summary
We investigates the variations and trends in Arctic sea ice during summer and autumn, focusing on the impacts of sea surface temperature (SST) and surface air temperature (SAT). Both SST and SAT significantly influence Arctic sea ice concentration. SST affects both interannual variations and decadal trends, while SAT primarily influences interannual variations. Additionally, SAT's impact on sea ice concentration leads by seven months, due to a stronger warming trend in winter than in summer.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Yu. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzaguena, 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-1762, https://doi.org/10.5194/egusphere-2024-1762, 2024
Short summary
Short summary
Variability in the extratropical stratosphere and troposphere are coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too-weak, however downward coupling from the lower stratosphere to the near surface is too strong.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
EGUsphere, https://doi.org/10.5194/egusphere-2024-1759, https://doi.org/10.5194/egusphere-2024-1759, 2024
Short summary
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.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, Cuijuan Sui, and Bo Sun
EGUsphere, https://doi.org/10.5194/egusphere-2023-2436, https://doi.org/10.5194/egusphere-2023-2436, 2023
Preprint archived
Short summary
Short summary
In contrary to the current understanding, there can be a strong connection between ENSO and the South Atlantic Subtropical Dipole (SASD). It is highly probable that the robust inverse correlation between ENSO and SASD will persist in the future. The ENSO-SASD correlation exhibits substantial multi-decadal variability over the course of a century. The change in the ENSO-SASD relation can be linked to changes in ENSO regime and convective activities over the central South Pacific Ocean.
Tiina Nygård, Lukas Papritz, Tuomas Naakka, and Timo Vihma
Weather Clim. Dynam., 4, 943–961, https://doi.org/10.5194/wcd-4-943-2023, https://doi.org/10.5194/wcd-4-943-2023, 2023
Short summary
Short summary
Despite the general warming trend, wintertime cold-air outbreaks in Europe have remained nearly as extreme and as common as decades ago. In this study, we identify six principal cold anomaly types over Europe in 1979–2020. We show the origins of various physical processes and their contributions to the formation of cold wintertime air masses.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
Short summary
Short summary
The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
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.
Lejiang Yu, Shiyuan Zhong, Timo Vihma, Cuijuan Sui, and Bo Sun
Atmos. Chem. Phys., 23, 345–353, https://doi.org/10.5194/acp-23-345-2023, https://doi.org/10.5194/acp-23-345-2023, 2023
Short summary
Short summary
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
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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: Atmospheric Interactions
Dynamic and thermodynamic processes related to sea-ice surface melt advance in the Laptev Sea and East Siberian Sea
Attributing near-surface atmospheric trends in the Fram Strait region to regional sea ice conditions
Estimating a mean transport velocity in the marginal ice zone using ice–ocean prediction systems
Decadal changes in the leading patterns of sea level pressure in the Arctic and their impacts on the sea ice variability in boreal summer
Contributions of advection and melting processes to the decline in sea ice in the Pacific sector of the Arctic Ocean
Potential faster Arctic sea ice retreat triggered by snowflakes' greenhouse effect
Atmospheric influences on the anomalous 2016 Antarctic sea ice decay
Hongjie Liang and Wen Zhou
The Cryosphere, 18, 3559–3569, https://doi.org/10.5194/tc-18-3559-2024, https://doi.org/10.5194/tc-18-3559-2024, 2024
Short summary
Short summary
This study identifies the metric of springtime sea-ice surface melt advance in the Laptev Sea and East Siberian Sea, which can be defined on the same date each year and has the potential to be used in the practical seasonal prediction of summer sea ice cover instead of average melt onset. Detailed analysis of dynamic and thermodynamic processes related to different melt advance scenarios in this region imply considerable interannual and interdecadal variability in springtime conditions.
Amelie U. Schmitt and Christof Lüpkes
The Cryosphere, 17, 3115–3136, https://doi.org/10.5194/tc-17-3115-2023, https://doi.org/10.5194/tc-17-3115-2023, 2023
Short summary
Short summary
In the last few decades, the region between Greenland and Svalbard has experienced the largest loss of Arctic sea ice in winter. We analyze how changes in air temperature, humidity and wind in this region differ for winds that originate from sea ice covered areas and from the open ocean. The largest impacts of sea ice cover are found for temperatures close to the ice edge and up to a distance of 500 km. Up to two-thirds of the observed temperature variability is related to sea ice changes.
Graig Sutherland, Victor de Aguiar, Lars-Robert Hole, Jean Rabault, Mohammed Dabboor, and Øyvind Breivik
The Cryosphere, 16, 2103–2114, https://doi.org/10.5194/tc-16-2103-2022, https://doi.org/10.5194/tc-16-2103-2022, 2022
Short summary
Short summary
The marginal ice zone (MIZ), which is the transition region between the open ocean and the dense pack ice, is a very dynamic region comprising a mixture of ice and ocean conditions. Using novel drifters deployed in various ice conditions in the MIZ, several material transport models are tested with two operational ice–ocean prediction systems. A new general transport equation, which uses both the ice and ocean solutions, is developed that reduces the error in drift prediction for our case study.
Nakbin Choi, Kyu-Myong Kim, Young-Kwon Lim, and Myong-In Lee
The Cryosphere, 13, 3007–3021, https://doi.org/10.5194/tc-13-3007-2019, https://doi.org/10.5194/tc-13-3007-2019, 2019
Short summary
Short summary
This study compares the decadal changes of the leading patterns of sea level pressure between the early (1982–1997) and the recent (1998–2017) periods as well as their influences on the Arctic sea ice extent (SIE) variability. The correlation between the Arctic Dipole (AD) mode and SIE becomes significant in the recent period, not in the past, due to its spatial pattern change. This tends to enhance meridional wind over the Fram Strait and sea ice discharge to the Atlantic.
Haibo Bi, Qinghua Yang, Xi Liang, Liang Zhang, Yunhe Wang, Yu Liang, and Haijun Huang
The Cryosphere, 13, 1423–1439, https://doi.org/10.5194/tc-13-1423-2019, https://doi.org/10.5194/tc-13-1423-2019, 2019
Short summary
Short summary
The Arctic sea ice extent is diminishing, which is deemed an immediate response to a warmer Earth. However, quantitative estimates about the contribution due to transport and melt to the sea ice loss are still vague. This study mainly utilizes satellite observations to quantify the dynamic and thermodynamic aspects of ice loss for nearly 40 years (1979–2016). In addition, the potential impacts on ice reduction due to different atmospheric circulation pattern are highlighted.
Jui-Lin Frank Li, Mark Richardson, Wei-Liang Lee, Eric Fetzer, Graeme Stephens, Jonathan Jiang, Yulan Hong, Yi-Hui Wang, Jia-Yuh Yu, and Yinghui Liu
The Cryosphere, 13, 969–980, https://doi.org/10.5194/tc-13-969-2019, https://doi.org/10.5194/tc-13-969-2019, 2019
Short summary
Short summary
Observed summer Arctic sea ice retreat has been faster than simulated by the average CMIP5 models, most of which exclude falling ice particles from their radiative calculations.
We use controlled CESM1-CAM5 simulations to show for the first time that snowflakes' radiative effects can accelerate sea ice retreat. September retreat rates are doubled above current CO2 levels, highlighting falling ice radiative effects as a high priority for inclusion in future modelling of the Arctic.
Elisabeth Schlosser, F. Alexander Haumann, and Marilyn N. Raphael
The Cryosphere, 12, 1103–1119, https://doi.org/10.5194/tc-12-1103-2018, https://doi.org/10.5194/tc-12-1103-2018, 2018
Short summary
Short summary
The atmospheric influence on the unusually early and strong decrease in Antarctic sea ice in the austral spring 2016 was investigated using data from the global forecast model of the European Centre for Medium-range Weather Forecasts. Weather situations related to warm, northerly flow conditions in the regions with large negative anomalies in sea ice extent and area were frequent and explain to a large part the observed melting. Additionally, oceanic influences might play a role.
Cited articles
Alam, A. and Curry, J.: Determination of surface turbulent fluxes over leads in Arctic sea ice, J. Geophys. Res., 102, 3331–3343, https://doi.org/10.1029/96JC03606, 1997. a
Andreas, E. L.: Air-ice drag coefficients in the western Weddell Sea: 2. A model based on form drag and drifting snow, J. Geophys. Res., 100, 4833–4843, https://doi.org/10.1029/94JC02016, 1995. a
Andreas, E. L., Paulson, C. A., William, R. M., Lindsay, R. W., and Businger, J. A.: The turbulent heat flux from arctic leads, Bound.-Lay. Meteorol., 17, 57–91, https://doi.org/10.1007/BF00121937, 1979. a
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
Aue, L., Vihma, T., Uotila, P., and Rinke, A.: New insights into cyclone impacts on sea ice in the Atlantic sector of the Arctic Ocean in winter, Geophys. Res. Lett., 49, e2022GL100051, https://doi.org/10.1029/2022GL100051, 2022. a
Boggs, P. T., Donaldson, J. T., Schnabel, R. B., and Spiegelman, C. H.: A Computational Examination of Orthogonal Distance Regression, J. Econom., 38, 169–201, 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, 1999. a
Claussen, M.: Local advection processes in the surface layer of the marginal ice zone, Bound.-Lay. Meteorol., 54, 1–27, https://doi.org/10.1007/BF00119409, 1991. a
Cohen, J., Zhang, X., Francis, J., Jung, T., Kwok, R., Overland, J., Ballinger, T. J., Bhatt, U. S., Chen, H. W., Coumou, D., Feldstein, S., Gu, H., Handorf, D., Henderson, G., Ionita, M., Kretschmer, M., Laliberte, F., Lee, S., Linderholm, H. W., Maslowski, W., Peings, Y., Pfeiffer, K., Rigor, I., Semmler, T., Stroeve, J., Taylor, P. C., Vavrus, S., Vihma, T., Wang, S., Wendisch, M., Wu, Y., and Yoon, J.: Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather, Nat. Clim. Change, 10, 20–29, https://doi.org/10.1038/s41558-019-0662-y, 2020. a
Dai, A., Luo, D., Song, M., and Liu, J.: Arctic amplification caused by sea-ice loss under increasing CO2, Nat. Commun., 10, 121, https://doi.org/10.1038/s41467-018-07954-9, 2002. a
ECMWF: IFS Documentation CY41R2 – Part IV: Physical Processes, 4, ECMWF, https://doi.org/10.21957/tr5rv27xu, 2016. a
Elvidge, A. D., Renfrew, I. A., Edwards, J. M., Brooks, I. M., Srivastava, P., and Weiss, A. I.: Improved simulation of the polar atmospheric boundary layer by accounting for aerodynamic roughness in the parameterization of surface scalar exchange over sea ice, J. Adv. Model. Earth Sy., 15, e2022MS003305, https://doi.org/10.1029/2022MS003305, 2023. a, b
Global Modeling and Assimilation Office (GMAO): Tavg1_2d_flx_Nx: MERRA-2 2D, 1-Hourly, Time-Averaged, Single-Level Assimilation, Single-Level Diagnostics, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/7MCPBJ41Y0K6, 2015a. a, b
Global Modeling and Assimilation Office (GMAO): Tavg1_2d_slv_Nx: MERRA-2 2D, 1-hourly, Time-Averaged, Single-Level Assimilation,Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/VJAFPLI1CSIV, 2015b. a, b
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020. a
Grachev, A. A., Andreas, E. L., Fairall, C., Guest, P. S., and Persson, P. O. G.: Outlier problem in evaluating similarity functions in the stable atmospheric boundary layer, Bound.-Layer Meteorol., 144, 137–155, 2012. 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, b, c
Gultepe, I., Isaac, G. A., Williams, A., Marcotte, D., and Strawbridge, K. B.: Turbulent heat fluxes over leads and polynyas, and their effects on arctic clouds during FIRE.ACE: Aircraft observations for April 1998, Atmosphere-Ocean, 41, 15–34, https://doi.org/10.3137/ao.410102, 2003. 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., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2495, https://doi.org/10.1002/qj.3803, 2020. a, b
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, b, c
Iribarne, J. and Godson, W.: Atmospheric Thermodynamics, D. Reidel Publishing Company, 1973. a
Ishii, M., Shouji, A., Sugimoto, S., and Matsumoto, T.: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection, Int. J. Climatol., 25, 865–879, https://doi.org/10.1002/joc.1169, 2005. a
Jaiser, R., Dethloff, K., Handorf, D., and Cohen, J.: Impact of sea ice cover changes on the Northern Hemisphere atmospheric winter circulation, Tellus A, 64, 11595, https://doi.org/10.3402/tellusa.v64i0.11595, 2012. a
Japan Meteorological Agency: 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
Kobayashi, S., Ota, Y., and Harada, Y. E. A.: The JRA-55 Reanalysis: General Specifications and Basic Characteristics, J. Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/JMSJ.2015-001, 2015. a, b, c
Koster, R. D. and Suarez, M. J.: Modeling the land surface boundary in climate models as a composite of independent vegetation stands, J. Geophys. Res., 97, 2697–2715, 1992. a
Lim, W.-I., Park, H.-S., Stewart, A. L., and Seo, K.-H.: Suppression of Arctic sea ice growth in the Eurasian–Pacific seas by winter clouds and snowfall, J. Climate, 35, 669–686, https://doi.org/10.1175/JCLI-D-21-0282.1, 2022. a
Lindsay, R., Wensnahan, M., Schweiger, A., and Zhang, J.: Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic, J. Climate, 27, 2588–2606, https://doi.org/10.1175/JCLI-D-13-00014.1, 2014. a
Lüpkes, C. and Gryanik, V.: A stability-dependent parametrization of transfer coefficients for momentum and heat over polar sea ice to be used in climate models, J. Geophys. Res., 120, 552–581, https://doi.org/10.1002/2014JD022418, 2015. a, b
Lüpkes, C. and Schlünzen, K. H.: Modelling the Arctic Convective Boundary-Layer with Different Turbulence Parameterizations, Bound.-Lay. Meteorol., 79, 107–130, 1996. a
Lüpkes, C., Vihma, T., Birnbaum, G., and Wacker, U.: Influence of leads in sea ice on the temperature of the atmospheric boundary layer during polar night., Geophys. Res. Lett., 35, L03805, https://doi.org/10.1029/2007GL032461, 2008. a, b
Lüpkes, C., Vihma, T., Birnbaum, G., Dierer, S., Garbrecht, T., Gryanik, V., Gryschka, M., Hartmann, J., Heinemann, G., Kaleschke, L., Raasch, S., Savijärvi, H., Schlünzen, K., and Wacker, U.: Mesoscale modelling of the Arctic atmospheric boundary layer and its interaction with sea ice, in: Arctic Climate Change - The ACSYS Decade and Beyond, edited by: Lemke, P. and Jacobi, H.-W., vol. 43, Atmospheric and Oceanographic Sciences Library, 2012. a
Maksimovich, E. and Vihma, T.: The effect of surface heat fluxes on interannual variability in the spring onset of snow melt in the central Arctic Ocean, J. Geophys. Res.-Oceans, 117, C07012, https://doi.org/10.1029/2011JC007220, 2012. a
Malhi, Y. S.: The significance of the dual solutions for heat fluxes measured by the temperature fluctuation method in stable conditions, Bound.-Lay. Meteorol., 74, 389–396, 1995. a
Matsumoto, T., Ishii, M., Fukuda, Y., and Hirahara, S.: Sea ice data derived from microwave radiometer for climate monitoring, Proceedings of the 14th Conference on Satellite Meteorology and Oceanography, Atlanta, USA, in: Presented at the 14th Conference on Satellite Meteorology and Oceanography, https://ams.confex.com/ams/Annual2006/techprogram/paper_101105.htm (last access: 27 May 2023), 2006. a
Michaelis, J., Lüpkes, C., Schmitt, A., and Hartmann, J.: Modelling and parametrization of the convective flow over leads in sea ice and comparison with airborne observations, Q. J. Roy. Meteor. Soc., 147, 914–943, https://doi.org/10.1002/qj.3953, 2021. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., W., P., J., Hogan, E., and Killick, R. E. E. A.: An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set, J. Geophys. Res.-Atmos., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361, 2021. a
Overland, J. E., McNutt, S. L., Groves, J., Salo, S., Andreas, E. L., and Persson, P. O. G.: Regional sensible and radiative heat flux estimates for the winter arctic during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, J. Geophys. Res., 105, 14093–14102, 2000. a
Park, J.-W., Korosov, A. A., Babiker, M., Won, J.-S., Hansen, M. W., and Kim, H.-C.: Classification of sea ice types in Sentinel-1 synthetic aperture radar images, The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, 2020. a
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem, S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and role in the ice-albedo feedback, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031480, 2007. a
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, c, d
Petrich, C., Langhorne, P. J., and Haskell, T. G.: Formation and structure of refrozen cracks in land-fast first-year sea ice, J. Geophys. Res., 112, C04006, https://doi.org/10.1029/2006JC003466, 2007. a
Qiu, Y., Li, X.-M., and Guo, H.: Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution, The Cryosphere, 17, 2829–2849, https://doi.org/10.5194/tc-17-2829-2023, 2023. a
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and deformation rate of Arctic sea ice: 1979–2007, J. Geophys. Res., 114, C05013, https://doi.org/10.1029/2008JC005066, 2009. 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, 2014. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 646, 1334–1337, https://doi.org/10.1038/nature09051, 2010. 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
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res., 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008. a
Svendsen, E., Matzler, C., and Grenfell, T. C.: A model for retrieving total sea ice concentration from a spaceborne dual-polarized passive microwave instrument operating near 90 GHz, Int J. Remote Sens., 8, 1479–1487, https://doi.org/10.1080/01431168708954790, 1987. a
Taylor, K. E., Williamson, D., and Zwiers, F.: The sea surface temperature and sea-ice concentration boundary conditions for AMIP II simulations, in: PCMDI Report No. 60, https://pcmdi.llnl.gov/report/ab60.html (last access: 27 March 2023), 2000. a
Tsamados, M., Feltham, D., Petty, A., Schroeder, D., and Flocco, D.: Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model, Philos. T. Roy. Soc. A, 373, 20140167, https://doi.org/10.1098/rsta.2014.0167, 2015. a
Uhlíková, T.: Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses, Zenodo [code], https://doi.org/10.5281/zenodo.7978071, 2023. a
Uotila, P.: puotila/odrfitf902py: version 1.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7965919, 2023. a
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 reanalysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005. 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. and Pirazzini, R.: On the factors controlling the snow surface and 2-m air temperatures over the Arctic sea ice in winter, Bound.-Lay. Meteorol., 117, 73–90, 2005. a
Vihma, T., Uotila, J., and Launiainen, J.: Air-sea interaction over a thermal marine front in the Denmark Strait, J. Geophys. Res., 103, 27665–27678, https://doi.org/10.1029/98JC02415, 1998. a
Vihma, T., Jaagus, J., Jakobson, E., and Palo, T.: Meteorological conditions in the Arctic Ocean in spring and summer 2007 as recorded on the drifting ice station Tara, Geophys. Res. Lett., 35, L18706, https://doi.org/10.1029/2008GL034681, 2008. a
Walden, V. P., Hudson, S. R., Cohen, L., Murph, S. Y., and Granskog, M. A.: Atmospheric components of the surface energy budget over young sea ice: Results from the N-ICE2015 campaign., J. Geophys. Res.-Atmos., 122, 8427–8446, https://doi.org/10.1002/2016JD026091, 2017. a, b
Walsh, J. E. and Chapman, W. L.: Arctic Cloud–Radiation–Temperature Associations in Observational Data and Atmospheric Re-analyses, J. Climate, 11, 3030–3045, 1998. a
Wei, Z., Zhang, Z., Vihma, T., Wang, X., and Chen, Y.: An overview of Antarctic polynyas: sea ice production, forcing mechanisms, temporal variability and water mass formation, Adv. Polar Sci., 32, 295–311, https://doi.org/10.13679/j.advps.2021.0026, 2021. a
Wickström, S., Jonassen, M., Cassano, J. J., and Vihma, T.: Present temperature, precipitation and rain-on-snow climate in Svalbard, J. Geophys. Res.-Atmos., 125, e2019JD032155, https://doi.org/10.1029/2019JD032155, 2020. a
Woods, C. and Caballero, R.: The role of moist intrusions in winter Arctic warming and sea ice decline, J. Climate, 29, 4473–4485, https://doi.org/10.1175/JCLI-D-15-0773.1, 2016. a
Zampieri, L., Arduini, G., Holland, M., Keeley, S. P. E., Mogensen, K., Shupe, M. D., and Tietsche, S.: A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses., Mon. Weather Rev., 151, 1443–1458, https://doi.org/10.1175/MWR-D-22-0130.1, 2023. a
Short summary
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.
A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic...
