Articles | Volume 17, issue 5
https://doi.org/10.5194/tc-17-2157-2023
© Author(s) 2023. 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-17-2157-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2023
Research article |
| 25 May 2023
| 25 May 2023
Forcing and impact of the Northern Hemisphere continental snow cover in 1979–2014
Guillaume Gastineau
CORRESPONDING AUTHOR
UMR LOCEAN, Sorbonne Université/IRD/MNHM/CNRS, IPSL, Paris, 75005, France
Claude Frankignoul
UMR LOCEAN, Sorbonne Université/IRD/MNHM/CNRS, IPSL, Paris, 75005, France
Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
Yongqi Gao
Nansen Environmental and Remote Sensing Center and Bjerknes Center for Climate Research, Bergen 5006, Norway
deceased, 23 July 2021
Yu-Chiao Liang
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Young-Oh Kwon
Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
Annalisa Cherchi
National Research Council of Italy, Institute of the Atmospheric Science and Climate (CNR-ISAC), Bologna, Italy
Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy
Rohit Ghosh
Max Planck Institute for Meteorology, Hamburg, Germany
Department of Meteorology, University of Reading, Reading, UK
Elisa Manzini
Max Planck Institute for Meteorology, Hamburg, Germany
Daniela Matei
Max Planck Institute for Meteorology, Hamburg, Germany
Jennifer Mecking
National Oceanography Centre, Southampton, UK
Lingling Suo
Nansen Environmental and Remote Sensing Center and Bjerknes Center for Climate Research, Bergen 5006, Norway
Tian Tian
Danish Meteorological Institute, Copenhagen, Denmark
Shuting Yang
Danish Meteorological Institute, Copenhagen, Denmark
Ying Zhang
Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, People's Republic of China
Related authors
Amélie Simon, Guillaume Gastineau, Claude Frankignoul, Vladimir Lapin, and Pablo Ortega
Weather Clim. Dynam., 3, 845–861, https://doi.org/10.5194/wcd-3-845-2022, https://doi.org/10.5194/wcd-3-845-2022, 2022
Short summary
Short summary
The influence of the Arctic sea-ice loss on atmospheric circulation in midlatitudes depends on persistent sea surface temperatures in the North Pacific. In winter, Arctic sea-ice loss and a warm North Pacific Ocean both induce depressions over the North Pacific and North Atlantic, an anticyclone over Greenland, and a stratospheric anticyclone over the Arctic. However, the effects are not additive as the interaction between both signals is slightly destructive.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Omar Abdelazim Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2198, https://doi.org/10.5194/egusphere-2025-2198, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of climate projections. The system produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Tian Tian, Richard Davy, Leandro Ponsoni, and Shuting Yang
The Cryosphere, 19, 2751–2768, https://doi.org/10.5194/tc-19-2751-2025, https://doi.org/10.5194/tc-19-2751-2025, 2025
Short summary
Short summary
We introduced a modulating factor to the surface heat flux in the EC-Earth3 model to address the lack of parameterization for turbulent exchange over sea ice leads and correct the bias in Arctic sea ice. Three pairwise experiments showed that the amplified heat flux effectively reduces the overestimated sea ice, especially during cold periods, thereby improving agreement with observed and reanalysis data for sea ice area, volume, and ice edge, particularly in the North Atlantic sector.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
Short summary
Short summary
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Ting-Chen Chen, Hugues Goosse, Matthias Aengenheyster, Kristian Strommen, Christopher Roberts, Malcolm Roberts, Rohit Ghosh, Jin-Song von Storch, and Stephy Libera
EGUsphere, https://doi.org/10.5194/egusphere-2025-666, https://doi.org/10.5194/egusphere-2025-666, 2025
Short summary
Short summary
The Southern Annular Mode (SAM) is a key driver of Southern Hemisphere climate variability, but global models often overestimate its persistence in summer. Using high-resolution models, we show this bias can be reduced, along with some improvements in jet latitude and likely a better-resolved eddy-mean flow feedback. Controlled experiments reveal the potential roles of sea surface temperature biases and ocean mesoscales, underscoring the complex mechanisms shaping SAM persistence.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Harry Bryden, Jordi Beunk, Sybren Drijfhout, Wilco Hazeleger, and Jennifer Mecking
Ocean Sci., 20, 589–599, https://doi.org/10.5194/os-20-589-2024, https://doi.org/10.5194/os-20-589-2024, 2024
Short summary
Short summary
There is widespread interest in whether the Gulf Stream will decline under global warming. We analyse 19 coupled climate model projections of the AMOC over the 21st century. The model consensus is that the AMOC will decline by about 40 % due to reductions in northward Gulf Stream transport and southward deep western boundary current transport. Whilst the wind-driven Gulf Stream decreases by 4 Sv, most of the decrease in the Gulf Stream is due to a reduction of 7 Sv in its thermohaline component.
Lara Wallberg, Laura Suarez-Gutierrez, Daniela Matei, and Wolfgang A. Müller
Earth Syst. Dynam., 15, 1–14, https://doi.org/10.5194/esd-15-1-2024, https://doi.org/10.5194/esd-15-1-2024, 2024
Short summary
Short summary
European summer temperatures are influenced by mechanisms on different timescales. We find that timescales of 5 to 10 years dominate the changes in summer temperature over large parts of the continent. Further, we find that specific processes within the North Atlantic, affecting the storage and transport of heat, cause changes in the atmosphere and extremely warm European summers. Our findings could be used for better forecasts of extremely warm European summers several years ahead.
Stephen Outten, Camille Li, Martin P. King, Lingling Suo, Peter Y. F. Siew, Hoffman Cheung, Richard Davy, Etienne Dunn-Sigouin, Tore Furevik, Shengping He, Erica Madonna, Stefan Sobolowski, Thomas Spengler, and Tim Woollings
Weather Clim. Dynam., 4, 95–114, https://doi.org/10.5194/wcd-4-95-2023, https://doi.org/10.5194/wcd-4-95-2023, 2023
Short summary
Short summary
Strong disagreement exists in the scientific community over the role of Arctic sea ice in shaping wintertime Eurasian cooling. The observed Eurasian cooling can arise naturally without sea-ice loss but is expected to be a rare event. We propose a framework that incorporates sea-ice retreat and natural variability as contributing factors. A helpful analogy is of a dice roll that may result in cooling, warming, or anything in between, with sea-ice loss acting to load the dice in favour of cooling.
David Docquier, Stéphane Vannitsem, Alessio Bellucci, and Claude Frankignoul
EGUsphere, https://doi.org/10.5194/egusphere-2022-1340, https://doi.org/10.5194/egusphere-2022-1340, 2022
Preprint withdrawn
Short summary
Short summary
Understanding whether variations in ocean heat content are driven by air-sea heat fluxes or by ocean dynamics is of crucial importance to enhance climate projections. We use a relatively novel causal method to quantify interactions between ocean heat budget terms based on climate models. We find that low-resolution models overestimate the influence of ocean dynamics in the upper ocean, and that changes in ocean heat content are dominated by air-sea fluxes at high resolution.
Amélie Simon, Guillaume Gastineau, Claude Frankignoul, Vladimir Lapin, and Pablo Ortega
Weather Clim. Dynam., 3, 845–861, https://doi.org/10.5194/wcd-3-845-2022, https://doi.org/10.5194/wcd-3-845-2022, 2022
Short summary
Short summary
The influence of the Arctic sea-ice loss on atmospheric circulation in midlatitudes depends on persistent sea surface temperatures in the North Pacific. In winter, Arctic sea-ice loss and a warm North Pacific Ocean both induce depressions over the North Pacific and North Atlantic, an anticyclone over Greenland, and a stratospheric anticyclone over the Arctic. However, the effects are not additive as the interaction between both signals is slightly destructive.
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.
Fredrik Boberg, Ruth Mottram, Nicolaj Hansen, Shuting Yang, and Peter L. Langen
The Cryosphere, 16, 17–33, https://doi.org/10.5194/tc-16-17-2022, https://doi.org/10.5194/tc-16-17-2022, 2022
Short summary
Short summary
Using the regional climate model HIRHAM5, we compare two versions (v2 and v3) of the global climate model EC-Earth for the Greenland and Antarctica ice sheets. We are interested in the surface mass balance of the ice sheets due to its importance when making estimates of future sea level rise. We find that the end-of-century change in the surface mass balance for Antarctica is 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3), and for Greenland it is −290 Gt yr−1 (v2) and −1640 Gt yr−1 (v3).
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
Short summary
Short summary
This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
Short summary
Short summary
Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Huiling Zou, Yongqi Gao, Helene R. Langehaug, Lei Yu, and Dong Guo
Ocean Sci. Discuss., https://doi.org/10.5194/os-2021-16, https://doi.org/10.5194/os-2021-16, 2021
Publication in OS not foreseen
Short summary
Short summary
This work focuses on the the relationships between winter sea ice variability and thermodynamic processes in sea ice in the Bering Sea. It has been found that in the Norwegian Earth System Model, thermodynamics in sea ice plays an important role in winter sea ice variability and they can contribute over 70 % of winter sea ice mass incresea in the Bering Sea. The results can be very helpful to give a better understanding of sea ice changes in an Earth System Model.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Qiong Zhang, Ellen Berntell, Josefine Axelsson, Jie Chen, Zixuan Han, Wesley de Nooijer, Zhengyao Lu, Qiang Li, Qiang Zhang, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 1147–1169, https://doi.org/10.5194/gmd-14-1147-2021, https://doi.org/10.5194/gmd-14-1147-2021, 2021
Short summary
Short summary
Paleoclimate modelling has long been regarded as a strong out-of-sample test bed of the climate models that are used for the projection of future climate changes. Here, we document the model experimental setups for the three past warm periods with EC-Earth3-LR and present the results on the large-scale features. The simulations demonstrate good performance of the model in capturing the climate response under different climate forcings.
Emma L. Worthington, Ben I. Moat, David A. Smeed, Jennifer V. Mecking, Robert Marsh, and Gerard D. McCarthy
Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, https://doi.org/10.5194/os-17-285-2021, 2021
Short summary
Short summary
The RAPID array has observed the Atlantic meridional overturning circulation (AMOC) since 2004, but the AMOC was directly calculated only five times from 1957–2004. Here we create a statistical regression model from RAPID data, relating AMOC changes to density changes within the different water masses at 26° N, and apply it to historical hydrographic data. The resulting 1981–2016 record shows that the AMOC from 2008–2012 was its weakest since the mid-1980s, but it shows no overall decline.
Fredrik Boberg, Ruth Mottram, Nicolaj Hansen, Shuting Yang, and Peter L. Langen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-331, https://doi.org/10.5194/tc-2020-331, 2020
Manuscript not accepted for further review
Short summary
Short summary
Using the regional climate model HIRHAM5, we compare two versions (v2 and v3) of the global climate model EC-Earth for the Greenland and Antarctica ice sheets. We are interested in the surface mass balance of the ice sheets due to its importance when making estimates of the future sea level rise. We find that the end-of-century change of the surface mass balance for Antarctica is +150 Gt yr−1 (v2) and −710 Gt yr−1 (v3) and for Greenland the numbers are −210 Gt yr−1 (v2) and −1150 Gt yr−1 (v3).
Renate Anna Irma Wilcke, Erik Kjellström, Changgui Lin, Daniela Matei, Anders Moberg, and Evangelos Tyrlis
Earth Syst. Dynam., 11, 1107–1121, https://doi.org/10.5194/esd-11-1107-2020, https://doi.org/10.5194/esd-11-1107-2020, 2020
Short summary
Short summary
Two long-lasting high-pressure systems in summer 2018 led to heat waves over Scandinavia and an extended summer period with devastating impacts on both agriculture and human life. Using five climate model ensembles, the unique 263-year Stockholm temperature time series and a composite 150-year time series for the whole of Sweden, we found that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed in 2018, occurring in Sweden.
Cited articles
Baldwin, M. P. and Dunkerton, T. J.:
Propagation of the Arctic Oscillation from the stratosphere to the troposphere, J. Geophys. Res.-Atmos., 104, 30937–30946, https://doi.org/10.1029/1999JD900445, 1999.
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Blackport, R. and Screen, J. A.:
Observed statistical connections overestimate the causal effects of arctic sea ice changes on midlatitude winter climate, J. Climate, 34, 3021–3038, 2021.
Brown, R. D. and Derksen, C.:
Is Eurasian October snow cover extent increasing?, Environ. Res. Lett., 8, 024006, https://doi.org/10.1088/1748-9326/8/2/024006, 2013.
Brun, E., Six, D., Picard, G., Vionnet, V., Arnaud, L., Bazile, E., Boone, A., Bouchard, A., Genthon, C., Guidard, V., Moigne, P. L., Rabier, F., and Seity, Y.:
Snow/atmosphere coupled simulation at Dome C, Antarctica, J. Glaciol., 57, 721–736, Cambridge Core, https://doi.org/10.3189/002214311797409794, 2011.
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Sy., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019.
Cohen, J. and Entekhabi, D.:
Eurasian snow cover variability and Northern Hemisphere climate predictability, Geophys. Res. Lett., 26, 345–348, https://doi.org/10.1029/1998GL900321, 1999.
Cohen, J., Furtado, J. C., Jones, J., Barlow, M., Whittleston, D., and Entekhabi, D.:
Linking Siberian Snow Cover to Precursors of Stratospheric Variability, J. Climate, 27, 5422–5432, https://doi.org/10.1175/JCLI-D-13-00779.1, 2014.
Dai, A. and Bloecker, C. E.:
Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models, Clim. Dynam., 52, 289–306, https://doi.org/10.1007/s00382-018-4132-4, 2019.
Derksen, C. and Brown, R.:
Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections, Geophys. Res. Lett., 39, L19504, https://doi.org/10.1029/2012GL053387, 2012.
Déry, S. J. and Brown, R. D.:
Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback, Geophys. Res. Lett., 34, L22504, https://doi.org/10.1029/2007GL031474, 2007.
Deser, C., Tomas, R. A., and Peng, S.:
The Transient Atmospheric Circulation Response to North Atlantic SST and Sea Ice Anomalies, J. Climate, 20, 4751–4767, https://doi.org/10.1175/JCLI4278.1, 2007.
Deser, C., Phillips, A., Bourdette, V., and Teng, H.:
Uncertainty in climate change projections: The role of internal variability, Clim. Dynam., 38, 527–546, 2012.
Deser, C., Tomas, R. A., and Sun, L.:
The Role of Ocean–Atmosphere Coupling in the Zonal-Mean Atmospheric Response to Arctic Sea Ice Loss, J. Climate, 28, 2168–2186, https://doi.org/10.1175/JCLI-D-14-00325.1, 2015.
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.:
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Feldstein, S. B.:
The Timescale, Power Spectra, and Climate Noise Properties of Teleconnection Patterns, J. Climate, 13, 4430–4440, https://doi.org/10.1175/1520-0442(2000)013<4430:TTPSAC>2.0.CO;2, 2000.
Fletcher, C. G., Hardiman, S. C., Kushner, P. J., and Cohen, J.:
The Dynamical Response to Snow Cover Perturbations in a Large Ensemble of Atmospheric GCM Integrations, J. Climate, 22, 1208–1222, https://doi.org/10.1175/2008JCLI2505.1, 2009.
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdoìttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Salle, J.-B., Slangen, A. B. A., and Yu, Y.:
Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021.
García-Serrano, J., Frankignoul, C., Gastineau, G., and de la Cámara, A.:
On the Predictability of the Winter Euro-Atlantic Climate: Lagged Influence of Autumn Arctic Sea Ice, J. Climate, 28, 5195–5216, https://doi.org/10.1175/JCLI-D-14-00472.1, 2015.
Garfinkel, C. I., Schwartz, C., White, I. P., and Rao, J.:
Predictability of the early winter Arctic oscillation from autumn Eurasian snowcover in subseasonal forecast models, Clim. Dynam., 55, 961–974, https://doi.org/10.1007/s00382-020-05305-3, 2020.
Gastineau, G. and Frankignoul, C.:
Influence of the North Atlantic SST Variability on the Atmospheric Circulation during the Twentieth Century, J. Climate, 28, 1396–1416, https://doi.org/10.1175/JCLI-D-14-00424.1, 2015.
Gastineau, G., García-Serrano, J., and Frankignoul, C.:
The Influence of Autumnal Eurasian Snow Cover on Climate and Its Link with Arctic Sea Ice Cover, J. Climate, 30, 7599–7619, https://doi.org/10.1175/JCLI-D-16-0623.1, 2017.
Gastineau, G., Friedman, A. R., Khodri, M., and Vialard, J.: Global ocean heat content redistribution during the 1998–2012 Interdecadal Pacific Oscillation negative phase, Clim. Dynam., 53, 1187–1208, https://doi.org/10.1007/s00382-018-4387-9, 2019.
Ge, Y. and Gong, G.:
North American snow depth and climate teleconnection patterns, J. Climate, 22, 217–233, https://doi.org/10.1175/2008JCLI2124.1, 2009.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.:
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K., Marsh, D. R., Tilmes, S., Vitt, F., Bardeen, C. G., McInerny, J., Liu, H.-L., Solomon, S. C., Polvani, L. M., Emmons, L. K., Lamarque, J.-F., Richter, J. H., Glanville, A. S., Bacmeister, J. T., Phillips, A. S., Neale, R. B., Simpson, I. R., DuVivier, A. K., Hodzic, A., and Randel, W. J.: The Whole Atmosphere Community Climate Model Version 6 (WACCM6), J. Geophys. Res.-Atmos., 124, 12380–12403, https://doi.org/10.1029/2019JD030943, 2019.
Global Modeling and Assimilation Office (GMAO):
MERRA-2 tavgM_2d_lnd_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Land Surface Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA [data set], https://doi.org/10.5067/8S35XF81C28F (last access: 28 October 2022), 2015.
Gong, G., Entekhabi, D., and Cohen, J.:
Modeled Northern Hemisphere Winter Climate Response to Realistic Siberian Snow Anomalies, J. Climate, 16, 3917–3931, https://doi.org/10.1175/1520-0442(2003)016<3917:MNHWCR>2.0.CO;2, 2003.
Gulev, S. K., Thorne, P. W., Ahn, J., Dentener, F. J., Domingues, C. M., Gerland, S., Gong, D., Kaufman, D. S., Nnamchi, H. C., Quaas, J., Rivera, J. A., Sathyendranath, S., Smith, S. L., Trewin, B., von Schuckmann, K., and Vose, R. S.:
Changing State of the Climate System, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 287–422, 2021.
Guo, H., Yang, Y., Zhang, W., Zhang, C., and Sun, H.:
Attributing snow cover extent changes over the Northern Hemisphere for the past 65 years, Environmental Research Communications, 3, 061001, https://doi.org/10.1088/2515-7620/ac03c8, 2021.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.:
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow–atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change, 8, 954–963, 2018.
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 monthly averaged data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7 (last access: 8 May 2020), 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E. Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N.:
The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Honda, M., Inoue, J., and Yamane, S.:
Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters, Geophys. Res. Lett., 36, L08707, https://doi.org/10.1029/2008GL037079, 2009.
Hourdin, F., Rio, C., Grandpeix, J.-Y., Madeleine, J.-B., Cheruy, F., Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., Foujols, M.-A., Mellul, L., Traore, A.-K., Dufresne, J.-L., Boucher, O., Lefebvre, M.-P., Millour, E., Vignon, E., Jouhaud, J., Diallo, F. B., Lott, F., Gastineau, G., Caubel, A., Meurdesoif, Y., and Ghattas, J.: LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics, J. Adv. Model. Earth Sy., 12, e2019MS001892. https://doi.org/10.1029/2019MS001892, 2020.
Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., and Rosinski, J.:
A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model, J. Climate, 21, 5145–5153, https://doi.org/10.1175/2008JCLI2292.1, 2008.
King, M. P., Hell, M., and Keenlyside, N.:
Investigation of the atmospheric mechanisms related to the autumn sea ice and winter circulation link in the Northern Hemisphere, Clim. Dynam., 46, 1185–1195, https://doi.org/10.1007/s00382-015-2639-5, 2016.
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.:
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018.
Kug, J. S., Jeong, J. H., Jang, Y. S., Kim, B.-M., Folland, C. K., Min, S.-K., and Son, S.-W.: Two distinct influences of Arctic warming on cold winters over North America and East Asia, Nat. Geosci., 8, 759–762 https://doi.org/10.1038/ngeo2517, 2015.
Kushnir, Y., Robinson, W. A., Bladé, I., Hall, N. M. J., Peng, S., and Sutton, R.:
Atmospheric GCM Response to Extratropical SST Anomalies: Synthesis and Evaluation, J. Climate, 15, 2233–2256, https://doi.org/10.1175/1520-0442(2002)015<2233:AGRTES>2.0.CO;2, 2002.
Lau, N.-C.:
Interactions between Global SST Anomalies and the Midlatitude Atmospheric Circulation, B. Am. Meteorol. Soc., 78, 21–34, https://doi.org/10.1175/1520-0477(1997)078<0021:IBGSAA>2.0.CO;2, 1997.
Liang, Y.-C., Kwon, Y.-O., Frankignoul, C., Danabasoglu, G., Yeager, S., Cherchi, A., Gao, Y., Gastineau, G., Ghosh, R., Matei, D., Mecking, J. V., Peano, D., Suo, L., and Tian, T.:
Quantification of the Arctic Sea Ice-Driven Atmospheric Circulation Variability in Coordinated Large Ensemble Simulations, Geophys. Res. Lett., 47, e2019GL085397, https://doi.org/10.1029/2019GL085397, 2020.
Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J. V., Tian, T., and Zhang, Y.:
Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multimodel Large Ensemble, J. Climate, 34, 8419–8443, https://doi.org/10.1175/JCLI-D-20-0578.1, 2021.
López-Parages, J., Rodríguez-Fonseca, B., Dommenget, D., and Frauen, C.:
ENSO influence on the North Atlantic European climate: A non-linear and non-stationary approach, Clim. Dynam., 47, 2071–2084, https://doi.org/10.1007/s00382-015-2951-0, 2016.
Lü, Z., Li, F., Orsolini, Y. J., Gao, Y., and He, S.:
Understanding of European Cold Extremes, Sudden Stratospheric Warming, and Siberian Snow Accumulation in the Winter of 2017/18, J. Climate, 33, 527–545, https://doi.org/10.1175/JCLI-D-18-0861.1, 2020.
Luo, B., Luo, D., Dai, A., Simmonds, I., and Wu, L.:
The modulation of Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation on winter Eurasian cold anomaly via the Ural blocking change, Clim. Dynam., 59, 127–150, https://doi.org/10.1007/s00382-021-06119-7, 2022.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., and Moisander, M.: GlobSnow v3.0 snow water equivalent (SWE), PANGAEA, [data set] https://doi.org/10.1594/PANGAEA.911944 (last access: 15 July 2020), 2020.
Martius, O., Polvani, L. M., and Davies, H. C.:
Blocking precursors to stratospheric sudden warming events, Geophys. Res. Lett., 36, L14806, https://doi.org/10.1029/2009GL038776, 2009.
Mathieu, P.-P., Sutton, R. T., Dong, B., and Collins, M.:
Predictability of Winter Climate over the North Atlantic European Region during ENSO Events, J. Climate, 17, 1953–1974, https://doi.org/10.1175/1520-0442(2004)017<1953:POWCOT>2.0.CO;2, 2004.
Mori, M., Watanabe, M., Shiogama, H., Inoue, J., and Kimoto, M.: Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades, Nat. Geosci., 7, 869–873, https://doi.org/10.1038/ngeo2277, 2014.
Mudryk, L. R. and Derksen, C.:
CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2, [Daily B5 SWE], NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/96ltniikJ7vd (last access: 16 August 2019), 2017.
Mudryk, L. R., Derksen, C., Kushner, P., and Brown, R.: Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015.
Mudryk, L. R., Kushner, P. J., Derksen, C., and Thackeray, C.:
Snow cover response to temperature in observational and climate model ensembles, Geophys. Res. Lett., 44, 919–926, https://doi.org/10.1002/2016GL071789, 2017.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.:
Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020.
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J. Adv. Model. Earth Sy., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018.
Muñoz-Sabater, J.:
ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30 (last access: 4 April 2020), 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.:
ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nakamura, T., Yamazaki, K., Iwamoto, K., Honda, M., Miyoshi, Y., Ogawa, Y., and Ukita, J.:
A negative phase shift of the winter AO/NAO due to the recent Arctic sea-ice reduction in late autumn, J. Geophys. Res.-Atmos., 120, 3209–3227, https://doi.org/10.1002/2014JD022848, 2015.
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C. A.:
The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427, 2016.
Nishii, K.: Programs for calculationg wave-activity flux derived by Takaya and Nakamura (2001, JAS: TN01) or by Plumb (1985, JAS) [code], http://www.atmos.rcast.u-tokyo.ac.jp/nishii/programs/index.html (last access: 20 June 2022), 2022.
Ogawa, F., Keenlyside, N., Gao, Y., Koenigk, T., Yang, S., Suo, L., Wang, T., Gastineau, G., Nakamura, T., Cheung, H. N., Omrani, N.-E., Ukita, J., and Semenov, V.:
Evaluating impacts of recent Arctic sea ice loss on the Northern Hemisphere winter climate change, Geophys. Res. Lett., 45, 3255–3263, 2018.
Orsolini, Y. J., Senan, R., Balsamo, G., Doblas-Reyes, F. J., Vitart, F., Weisheimer, A., Carrasco, A., and Benestad, R. E.:
Impact of snow initialization on sub-seasonal forecasts, Clim. Dynam., 41, 1969–1982, https://doi.org/10.1007/s00382-013-1782-0, 2013.
Orsolini, Y. J., Senan, R., Vitart, F., Balsamo, G., Weisheimer, A., and Doblas-Reyes, F. J.:
Influence of the Eurasian snow on the negative North Atlantic Oscillation in subseasonal forecasts of the cold winter 2009/2010, Clim. Dynam., 47, 1325–1334, https://doi.org/10.1007/s00382-015-2903-8, 2016.
Paik, S. and Min, S. K.:
Quantifying the Anthropogenic Greenhouse Gas Contribution to the Observed Spring Snow-Cover Decline Using the CMIP6 Multimodel Ensemble, J. Climate, 33, 9261–9269, 2020.
Peings, Y.:
Ural blocking as a driver of early-winter stratospheric warmings, Geophys. Res. Lett., 46, 5460–5468, https://doi.org/10.1029/2019GL082097, 2019.
Peings, Y. and Magnusdottir, G.:
Response of the wintertime Northern Hemisphere atmospheric circulation to current and projected Arctic sea ice decline: A numerical study with CAM5, J. Climate, 27, 244–264, 2014.
Perlwitz, J. and Graf, H.-F.:
The Statistical Connection between Tropospheric and Stratospheric Circulation of the Northern Hemisphere in Winter, J. Climate, 8, 2281–2295, https://doi.org/10.1175/1520-0442(1995)008<2281:TSCBTA>2.0.CO;2, 1995.
Pincus, R., Forster, P. M., and Stevens, B.:
The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev., 9, 3447–3460, https://doi.org/10.5194/gmd-9-3447-2016, 2016.
Plumb, R. A.:
On the Three-Dimensional Propagation of Stationary Waves, J. Atmos. Sci., 42, 217–229, https://doi.org/10.1175/1520-0469(1985)042<0217:OTTDPO>2.0.CO;2, 1985.
Pulliainen, J., Aurela, M., Laurila, T., Aalto, T., Takala, M., Salminen, M., Kulmala, M., Barr, A., Heimann, M., Lindroth, A., Laaksonen, A., Derksen, C., Mäkelä, A., Markanen, T., Lemmetyinen, J., Susiluoto, J., Dengel, S., Mammarella, I., Tuovinen, J.-P., and Vesala, T.: Early snowmelt significantly enhances boreal springtime carbon uptake, P. Natl. Acad. Sci. USA, 114, 11081–11086, 2017.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.:
Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Robinson, D. A. and Estilow, T. W.:
Rutgers Northern Hemisphere 24 km Weekly Snow Cover Extent, September 1980 Onward, Version 1, NSIDC: National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.7265/zzbm-2w05 (last access: 22 July 2019), 2021.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.:
The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., Hartman, D., Marwan, N., Paluš, N., and Kurths, J.:
Identifying causal gateways and mediators in complex spatio-temporal systems, Nat. Commun., 6, 1–10, 2015.
Saito, K. and Cohen, J.:
The potential role of snow cover in forcing interannual variability of the major Northern Hemisphere mode, Geophys. Res. Lett., 30, 1302, https://doi.org/10.1029/2002GL016341, 2003.
San Liang, X.:
Unraveling the cause-effect relation between time series, Phys. Rev. E, 90, 052150, https://doi.org/10.1103/PhysRevE.90.052150, 2014.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T., Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson, L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M., Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., Williams, A.:
Skillful long-range prediction of European and North American winters, Geophys. Res. Lett., 41, 2514–2519, https://doi.org/10.1002/2014GL059637, 2014.
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
Simon, A., Frankignoul, C., Gastineau, G., and Kwon, Y. O.:
An observational estimate of the direct response of the cold-season atmospheric circulation to the Arctic Sea Ice Loss, J. Climate, 33, 3863–3882, 2020.
Smith, D. M., Eade, R., Andrews, M. B., Ayres, H., Clark, A., Chripko, S., Deser, C., Dunstone, N. J., Garcia-Serrano, J., Gastineau, G., Graff, L. S., Hardiman, S. C., He, B., Hermanson, L., Jung, T., Knight, J., Levine, X., Magnusdottir, G., Manzini, E., Matei, D., Mori, M., Msadek, R., Ortega, P., Peings, Y., Scaiffe, A. A., Screen, J. A., Seabrook, M., Semmler, T., Sigmond, M., Streffing, J., Sun, L., and Walsh, A.:
Robust but weak winter atmospheric circulation response to future Arctic sea ice loss, Nat. Commun., 13, 1–15, 2022.
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model : ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, https://doi.org/10.1002/jame.20015, 2013.
Storch, H. and Zwiers, F.: Statistical Analysis in Climate Research, Cambridge University Press, Cambridge, ISBN 9780511612336, https://doi.org/10.1017/CBO9780511612336, 1999.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.:
Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010.
Wallace, J. M. and Gutzler, D. S.:
Teleconnections in the Geopotential Height Field during the Northern Hemisphere Winter, Mon. Weather Rev., 109, 784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2, 1981.
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019.
Zhang, H., Zhang, M., and Zeng, Q.: Sensitivity of Simulated Climate to Two Atmospheric Models : Interpretation of Differences between Dry Models and Moist Models, Mon. Weather Rev., 141, 1558–1576, https://doi.org/10.1175/MWR-D-11-00367.1, 2013.
Zhong, X., Zhang, T., Kang, S., and Wang, J.:
Snow depth trends from CMIP6 models conflict with observational evidence, J. Climate, 35, 1293–1307, https://doi.org/10.1175/JCLI-D-21-0177.1, 2022.
Short summary
Snow cover variability is important for many human activities. This study aims to understand the main drivers of snow cover in observations and models in order to better understand it and guide the improvement of climate models and forecasting systems. Analyses reveal a dominant role for sea surface temperature in the Pacific. Winter snow cover is also found to have important two-way interactions with the troposphere and stratosphere. No robust influence of the sea ice concentration is found.
Snow cover variability is important for many human activities. This study aims to understand the...
