Articles | Volume 15, issue 1
https://doi.org/10.5194/tc-15-325-2021
© Author(s) 2021. 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-15-325-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2021
Research article |
| 26 Jan 2021
| 26 Jan 2021
Year-round impact of winter sea ice thickness observations on seasonal forecasts
Beena Balan-Sarojini
CORRESPONDING AUTHOR
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Steffen Tietsche
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Michael Mayer
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Magdalena Balmaseda
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Patricia de Rosnay
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Tim Stockdale
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Frederic Vitart
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Related authors
No articles found.
Susanna Winkelbauer, Isabella Winterer, Michael Mayer, Yao Fu, and Leopold Haimberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4093, https://doi.org/10.5194/egusphere-2025-4093, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Ocean reanalyses combine models and observations to reconstruct past ocean conditions. We evaluate their performance against detailed measurements from the subpolar North Atlantic at the OSNAP section. While reanalyses capture long-term averages and broad circulation patterns, they miss some more regional features and variability. This highlights both their value and their limitations, stressing the need for improved observations and higher-resolution models.
John R. Albers, Matthew Newman, Magdalena A. Balmaseda, William Sweet, Yan Wang, and Tongtong Xu
Ocean Sci., 21, 1761–1785, https://doi.org/10.5194/os-21-1761-2025, https://doi.org/10.5194/os-21-1761-2025, 2025
Short summary
Short summary
Providing early warning of coastal flooding is an emerging priority for the National Oceanic and Atmospheric Administration. We assess whether current operational forecast models can provide the basis for predicting the risks of higher-than-normal coastal sea level values up to 6 weeks in advance. For many United States coastal locations, models have sufficient prediction skill to be used as the basis for the development of a high tide flooding prediction system on subseasonal timescales.
Blanca Ayarzagüena, Amy H. Butler, Peter Hitchcock, Chaim I. Garfinkel, Zac D. Lawrence, Wuhan Ning, Philip Rupp, Zheng Wu, Hilla Afargan-Gerstman, Natalia Calvo, Álvaro de la Cámara, Martin Jucker, Gerbrand Koren, Daniel De Maeseneire, Gloria L. Manney, Marisol Osman, Masakazu Taguchi, Cory Barton, Dong-Chang Hong, Yu-Kyung Hyun, Hera Kim, Jeff Knight, Piero Malguzzi, Daniele Mastrangelo, Jiyoung Oh, Inna Polichtchouk, Jadwiga H. Richter, Isla R. Simpson, Seok-Woo Son, Damien Specq, and Tim Stockdale
EGUsphere, https://doi.org/10.5194/egusphere-2025-3611, https://doi.org/10.5194/egusphere-2025-3611, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Sudden Stratospheric Warmings (SSWs) are known to follow a sustained wave dissipation in the stratosphere, which depends on both the tropospheric and stratospheric states. However, the relative role of each state is still unclear. Using a new set of subseasonal to seasonal forecasts, we show that the stratospheric state does not drastically affect the precursors of three recent SSWs, but modulates the stratospheric wave activity, with impacts depending on SSW features.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
Short summary
UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
Xue Feng, Matthew J. Widlansky, Tong Lee, Ou Wang, Magdalena A. Balmaseda, Hao Zuo, Gregory Dusek, William Sweet, and Malte F. Stuecker
Ocean Sci., 21, 1663–1676, https://doi.org/10.5194/os-21-1663-2025, https://doi.org/10.5194/os-21-1663-2025, 2025
Short summary
Short summary
Forecasting sea level changes months in advance along the Gulf Coast and East Coast of the United States is challenging. Here, we present a method that uses past ocean states to forecast future sea levels, while assuming no knowledge of how the atmosphere will evolve other than its typical annual cycle near the ocean's surface. Our findings indicate that this method improves sea level outlooks for many locations along the Gulf Coast and East Coast, especially south of Cape Hatteras.
Alexey Yu. Karpechko, Amy H. Butler, and Frederic Vitart
EGUsphere, https://doi.org/10.5194/egusphere-2025-2556, https://doi.org/10.5194/egusphere-2025-2556, 2025
Short summary
Short summary
We study how the knowledge of future tropical and stratospheric conditions could improve forecasts in winter remotely, via teleconnections, 3–6 weeks ahead. We find that the tropics improve forecasts of sea level pressure in subtropics, Europe, and North America. The stratosphere improves forecasts in high latitudes and Europe. Improvements are small for temperature and precipitation. Larger forecast ensembles than usually available for research are needed to predict teleconnection signals.
Fiona Raphaela Spuler, Marlene Kretschmer, Magdalena Alonso Balmaseda, Yevgeniya Kovalchuk, and Theodore G. Shepherd
EGUsphere, https://doi.org/10.5194/egusphere-2024-4115, https://doi.org/10.5194/egusphere-2024-4115, 2025
Short summary
Short summary
Large-scale atmospheric dynamics modulate the occurrence of extreme events and can be leveraged to improve their predictability. In this paper, we introduce a generative machine learning method to identify dynamical drivers of a relevant impact variable in the form of targeted circulation regimes. Applying the method to study extreme precipitation over Morocco, we show that these regimes are more predictive of the impact while maintaining their own predictability and physical consistency.
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.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian M. Grams
Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, https://doi.org/10.5194/nhess-24-2995-2024, 2024
Short summary
Short summary
Extreme rainfall is the leading weather-related source of damages in Europe, but it is still difficult to predict on long timescales. A recent example of this was the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allows us to better understand and predict such events.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
Short summary
Short summary
Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Eric de Boisséson and Magdalena Alonso Balmaseda
Ocean Sci., 20, 265–278, https://doi.org/10.5194/os-20-265-2024, https://doi.org/10.5194/os-20-265-2024, 2024
Short summary
Short summary
Marine heatwaves are long periods of extremely warm ocean surface temperatures. Predicting such events a few months in advance would help decision-making to mitigate their impacts on marine ecosystems. This work investigates how well operational seasonal forecasts can predict marine heatwaves. Results show that such events can be predicted a few months in advance in the tropics but that extending the predictability skill to other regions will require additional work on the forecast models.
Johannes Mayer, Leopold Haimberger, and Michael Mayer
Earth Syst. Dynam., 14, 1085–1105, https://doi.org/10.5194/esd-14-1085-2023, https://doi.org/10.5194/esd-14-1085-2023, 2023
Short summary
Short summary
This study investigates the temporal stability and reliability of winter-month trends of air–sea heat fluxes from ERA5 forecasts over the North Atlantic basin for the period 1950–2019. Driving forces of trends and the impact of modes of climate variability and analysis increments on air–sea heat fluxes are investigated. Finally, a new and independent estimate of the Atlantic Meridional Overturning Circulation weakening is provided and associated with a decrease in air–sea heat fluxes.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
Short summary
Short summary
This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
Short summary
Short summary
We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Magdalena Fritz, Michael Mayer, Leopold Haimberger, and Susanna Winkelbauer
Ocean Sci., 19, 1203–1223, https://doi.org/10.5194/os-19-1203-2023, https://doi.org/10.5194/os-19-1203-2023, 2023
Short summary
Short summary
The interaction between the Indonesian Throughflow (ITF) and regional climate phenomena indicates the high relevance for monitoring the ITF. Observations remain temporally and spatially limited; hence near-real-time monitoring is only possible with reanalyses. We assess how well ocean reanalyses depict the intensity of the ITF via comparison to observations. The results show that reanalyses agree reasonably well with in situ observations; however, some aspects require higher-resolution products.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
Short summary
Short summary
Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Wei Li, Jie Chen, Lu Li, Yvan J. Orsolini, Yiheng Xiang, Retish Senan, and Patricia de Rosnay
The Cryosphere, 16, 4985–5000, https://doi.org/10.5194/tc-16-4985-2022, https://doi.org/10.5194/tc-16-4985-2022, 2022
Short summary
Short summary
Snow assimilation over the Tibetan Plateau (TP) may influence seasonal forecasts over this region. To investigate the impacts of snow assimilation on the seasonal forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer in 2018. The results show that snow assimilation can improve seasonal forecasts over the TP through the interaction between land and atmosphere.
Jonathan J. Day, Sarah Keeley, Gabriele Arduini, Linus Magnusson, Kristian Mogensen, Mark Rodwell, Irina Sandu, and Steffen Tietsche
Weather Clim. Dynam., 3, 713–731, https://doi.org/10.5194/wcd-3-713-2022, https://doi.org/10.5194/wcd-3-713-2022, 2022
Short summary
Short summary
A recent drive to develop seamless forecasting systems has culminated in the development of weather forecasting systems that include a coupled representation of the atmosphere, ocean and sea ice. Before this, sea ice and sea surface temperature anomalies were typically fixed throughout a given forecast. We show that the dynamic coupling is most beneficial during periods of rapid ice advance, where persistence is a poor forecast of the sea ice and leads to large errors in the uncoupled system.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
Short summary
Short summary
This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
Short summary
Short summary
We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Cited articles
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.:
Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean
modeling system, Adv. Space Res., 62, 1265–1280, 2018. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., and Zuo, H.:
Towards improved sea ice initialization and forecasting with the IFS,
https://doi.org/10.21957/mt6m6rpwt,
2019. a, b
Balmaseda, M., Hernandez, F., Storto, A., Palmer, M., Alves, O., Shi, L.,
Smith, G., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D., Boyer, T.,
Chang, Y.-S., Chepurin, G., Ferry, N., Forget, G., Fujii, Y., Good, S.,
Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A., Lee, T.,
Martin, M., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K., Parent, L.,
Peterson, K., Tang, Y., Yin, Y., Vernieres, G., Wang, X., Waters, J., Wedd,
R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J.-F., Dupont, F., Kuragano,
T., Kamachi, M., Awaji, T., Caltabiano, A., Wilmer-Becker, K., and Gaillard,
F.: The Ocean Reanalyses Intercomparison Project (ORA-IP), J.
Oper. Oceanogr., 8, s80–s97, https://doi.org/10.1080/1755876X.2015.1022329,
2015. a
Balmaseda, M. A., Ferranti, L., Molteni, F., and Palmer, T. N.: Impact of 2007
and 2008 Arctic ice anomalies on the atmospheric circulation: Implications
for long-range predictions, Q. J. Roy. Meteor.
Soc., 136, 1655–1664, https://doi.org/10.1002/qj.661, 2010. a
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses
induced by the missing snow over Arctic sea-ice, Nat. Commun., 10,
1–8, 2019. a
Beesley, J., Bretherton, C., Jakob, C., Andreas, E., Intrieri, J., and Uttal,
T.: A comparison of cloud and boundary layer variables in the ECMWF forecast
model with observations at Surface Heat Budget of the Arctic Ocean (SHEBA)
ice camp, J. Geophys. Res.-Atmos., 105,
12337–12349, 2000. a
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and DeWeaver, E.:
Persistence and inherent predictability of Arctic sea ice in a GCM ensemble
and observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1,
2011. a
Blanchard-Wrigglesworth, E., Barthélemy, A., Chevallier, M., Cullather, R.,
Fučkar, N., Massonnet, F., Posey, P., Wang, W., Zhang, J., Ardilouze,
C., Bitz, C. M., Vernieres, G., Wallcraft, A., and Wang, M.: Multi-model
seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and
regional scales, Clim. Dynam., 49, 1399–1410,
https://doi.org/10.1007/s00382-016-3388-9, 2017. a
Blockley, E. W. and Peterson, K. A.: Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. a, b, c
Bunzel, F., Notz, D., and Tietsche, S.: Definition of a new set of observation
based metrics relevant for regional applications, SPICES Deliverable, D8.2
Report, Max Planck Institute for Meteorology, Hamburg, Germany, 2017. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii, Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S, Ferry, N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina, S., Tang, Y., Tsujino, H., and Wang, X.:
Intercomparison of the Arctic sea ice cover in global ocean–sea ice
reanalyses from the ORA-IP project, Clim. Dynam., 49, 1107–1136, 2017. a
CS2SMOS: AWI, available at:
ftp://ftp.awi.de/sea_ice/product/cryosat2_smos/, last access: 21 January 2020. a
Day, J. J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness
initialization improve seasonal forecast skill?, Geophys. Res.
Lett., 41, 7566–7575, https://doi.org/10.1002/2014GL061694, 2014. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N.,
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
DelSole, T. and Tippett, M. K.: Forecast Comparison Based on Random Walks,
Mon. Weather Rev., 144, 615–626, https://doi.org/10.1175/MWR-D-15-0218.1,
2016. a, b, c, d
ERA5: Copernicus C3S,
available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form, last access: 21 January 2020. a
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys.
Res., 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a
Goessling, H. F. and Jung, T.: A probabilistic verification score for
contours: Methodology and application to Arctic ice-edge forecasts,
Q. J. Roy. Meteor. Soc., 144, 735–743,
https://doi.org/10.1002/qj.3242,
2018. a
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.:
Predictability of the Arctic sea ice edge, Geophys. Res. Lett.,
43, 1642–1650, https://doi.org/10.1002/2015GL067232, 2016. a
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M., Doblas-Reyes, F. J., Fučkar, N. S., Germe, A., Hawkins, E., Keeley, S., Koenigk, T., Melia, D. S., and Tietsche, S.: A review on Arctic sea-ice predictability
and prediction on seasonal to decadal time-scales, Q. J.
Roy. Meteor. Soc., 142, 546–561, 2016. a
Haas, C., Hendricks, S., Eicken, H., and Herber, A.: Synoptic airborne
thickness surveys reveal state of Arctic sea ice cover, Geophys. Res.
Lett., 37, L09501, https://doi.org/10.1029/2010GL042652, 2010. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz‐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., 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., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hibler III, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys.
Oceanogr., 9, 815–846, 1979. a
Hogan, R., Ahlgrimm, M., Balsamo, G., Beljaars, A., Berrisford, P., Bozzo, A.,
Pe, F. D. G., Forbes, R., Haiden, T., Lang, S., Mayer, M., Polichtchouk, I.,
Sandu, I., Vitart, F., and Wedi, N.: Radiation in numerical weather
prediction, ECMWF Technical Memorandum, https://doi.org/10.21957/2bd5dkj8x, 2017. a
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019. a, b
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.:
Sea ice thickness retrieval from SMOS brightness temperatures during the
Arctic freeze-up period, Geophys. Res. Lett., 39, L05501,
https://doi.org/10.1029/2012GL050916, 2012. a
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface irradiances
of edition 4.0 clouds and the Earth’s radiant energy system (CERES) energy
balanced and filled (EBAF) data product, J. Climate, 31, 4501–4527,
2018. a
Kwok, R. and Rothrock, D. A.: Decline in Arctic sea ice thickness from
submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, L15501,
https://doi.org/10.1029/2009GL039035, 2009. a
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice
thickness in the Arctic region, Nature, 425, 947–950, 2003. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield,
R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic
sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737,
https://doi.org/10.1002/grl.50193, 2013. a
Lindsay, R. W., Zhang, J., Schweiger, A. J., and Steele, M. A.: Seasonal
predictions of ice extent in the Arctic Ocean, J. Geophys. Res., 113,
C02023, https://doi.org/10.1029/2007JC004259,
2008. a
Madec, G.: NEMO ocean engine, Tech. rep., Institut Pierre-Simon Laplace
(IPSL), Zenodo,
https://doi.org/10.5281/zenodo.1464817, 2008. a
Mayer, M., Haimberger, L., Pietschnig, M., and Storto, A.: Facets of Arctic
energy accumulation based on observations and reanalyses 2000–2015,
Geophys. Res. Lett., 43, 10–420, 2016. a
Meier, W. N., Hovelsrud, G. K., Van Oort, B. E., Key, J. R., Kovacs, K. M., Michel, C., Haas, C., Granskog, M. A., Gerland, S., Perovich, D. K., Makshtas, A., and Reist, J. D.:
Arctic sea ice in transformation: A review of recent observed changes and
impacts on biology and human activity, Rev. Geophys., 52, 185–217,
2014. a
Mogensen, K., Balmaseda, M. A., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
Tech. Rep. 668, European Centre for Medium-Range Weather Forecasts, Reading, UK, 2012. a
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. a
Mu, L., Yang, Q., Losch, M., Losa, S. N., Ricker, R., Nerger, L., and Liang,
X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS
sea ice thickness data simultaneously, Q. J. Roy.
Meteor. Soc., 144, 529–538, https://doi.org/10.1002/qj.3225, 2018. a
ORAS5: ECMWF, available at:
https://icdc.cen.uni-hamburg.de/thredds/catalog/ftpthredds/EASYInit/oras5/r1x1/catalog.html, last access: 21 January 2020. a
OSI-401-b: EUMETSAT OSI SAF, available at:
http://www.osi-saf.org/?q=content/global-sea-ice-concentration-ssmis, last access: 21 January 2020. a
Overland, J. E., Dethloff, K., Francis, J. A., Hall, R. J., Hanna, E., Kim,
S.-J., Screen, J. A., Shepherd, T. G., and Vihma, T.: Nonlinear response of
mid-latitude weather to the changing Arctic, Nat. Clim. Change, 6, 992–999,
2016. a
Pohl, C., Istomina, L., Tietsche, S., Jäkel, E., Stapf, J., Spreen, G., and Heygster, G.: Broadband albedo of Arctic sea ice from MERIS optical data, The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, 2020. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017. a, b, c, d
Ruggieri, P., Buizza, R., and Visconti, G.: On the link between Barents-Kara
sea ice variability and European blocking, J. Geophys. Res.-Atmos., 121, 5664–5679, 2016. a
Sallila, H., Farrell, S. L., McCurry, J., and Rinne, E.: Assessment of contemporary satellite sea ice thickness products for Arctic sea ice, The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, 2019. a
Scarlat, R. C., Spreen, G., Heygster, G., Huntemann, M., Paţilea, C.,
Toudal Pedersen, L., and Saldo, R.: Sea Ice and Atmospheric Parameter
Retrieval From Satellite Microwave Radiometers: Synergy of AMSR2 and SMOS
Compared With the CIMR Candidate Mission, J. Geophys. Res.-Oceans, 125, 3, https://doi.org/10.1029/2019JC015749, 2020. a
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical
Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, 1976. a
Sigmond, M., Fyfe, J. C., Flato, G. M., Kharin, V. V., and Merryfield, W. J.:
Seasonal forecast skill of Arctic sea ice area in a dynamical forecast
system, Geophys. Res. Lett., 40, 529–534, https://doi.org/10.1002/grl.50129,
2013. a
Stockdale, T., Alonso-Balmaseda, M., Johnson, S., Ferranti, L., Molteni, F.,
Magnusson, L., Tietsche, S., Vitart, F., Decremer, D., Weisheimer, A.,
Roberts, C. D., Balsamo, G., Keeley, S., Mogensen, K., Zuo, H., Mayer, M.,
and Monge-Sanz, B.: SEAS5 and the future evolution of the long-range forecast
system, ECMWF Technical Memorandum, https://doi.org/10.21957/z3e92di7y, 2018. a, b, c
Tang, Y. M., Balmaseda, M. A., Mogensen, K. S., Keeley, S. P. E., and Janssen,
P. E. A. M.: Sensitivity of sea ice thickness to observational constraints
on sea ice concentration, Tech. Rep. 707, European Centre for Medium-Range
Weather Forecasts, Reading, 2013. a
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N., Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014. a, b
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects, Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, 2013. a, b, c
Tietsche, S., Day, J. J., Guemas, V., Hurlin, W. J., Keeley, S. P. E., Matei,
D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic
sea-ice predictability in current global climate models, Geophys.
Res. Lett., 41, 1035–1043, https://doi.org/10.1002/2013GL058755,
2014. a
Tietsche, S., Balmaseda, M. A., Zuo, H., and Mogensen, K.: Arctic sea ice in
the global eddy-permitting ocean reanalysis ORAP5, Clim. Dynam.,
https://doi.org/10.1007/s00382-015-2673-3, 2015. a, b
Tietsche, S., Alonso-Balmaseda, M., Rosnay, P., Zuo, H., Tian-Kunze, X., and Kaleschke, L.: Thin Arctic sea ice in L-band observations and an ocean reanalysis, The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, 2018.
a
Tonboe, R., Lavelle, J., Pfeiffer, R., and Howe, E.: Product User Manual for
OSI SAF Global Sea Ice Concentration (Product OSI-401-b), Danish Meteorological Institute, Copenhagen, Denmark, 2017. a
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A.,
Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker,
F., Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D.,
Peterson, K. A., Sadikni, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and
Zhang, Z.: An assessment of ten ocean reanalyses in the polar regions,
Clim. Dynam., 52, 1613–1650, 2019. a, b, c
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018. a
Zampieri, L., Goessling, H. F., and Jung, T.: Predictability of Antarctic sea
ice edge on subseasonal time scales, Geophys. Res. Lett., 46,
9719–9727, 2019. a
Zuo, H., Balmaseda, M. A., and Mogensen, K.: The new eddy-permitting ORAP5
ocean reanalysis: description, evaluation and uncertainties in climate
signals, Clim. Dynam., 49, 791–811, https://doi.org/10.1007/s00382-015-2675-1,
2017. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019. a, b, c
Short summary
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal...
