Articles | Volume 9, issue 1
Ocean Sci., 9, 19–36, 2013
https://doi.org/10.5194/os-9-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Ocean Sci., 9, 19–36, 2013
https://doi.org/10.5194/os-9-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article 15 Jan 2013
Research article | 15 Jan 2013
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects
S. Tietsche et al.
Related authors
No articles found.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
Short summary
Short summary
The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Evelien van Dijk, Johann Jungclaus, Stephan Lorenz, Claudia Timmreck, and Kirstin Krüger
Clim. Past Discuss., https://doi.org/10.5194/cp-2021-49, https://doi.org/10.5194/cp-2021-49, 2021
Preprint under review for CP
Short summary
Short summary
A double volcanic eruption in 536 and 540 AD caused one of the coldest decades during the last 2000 years. We analyze new climate model simulations from that period. We found a cooling of up to 2 °C and a sea–ice extent up to 200 km further south. Complex interactions between sea–ice and ocean circulation lead to a reduction in the northward ocean heat transport, which makes the sea-ice extend further south, which in turn leads to a surface cooling up to 20 years after the eruptions.
Oliver Gutjahr, Nils Brüggemann, Helmuth Haak, Johann H. Jungclaus, Dian A. Putrasahan, Katja Lohmann, and Jin-Song von Storch
Geosci. Model Dev., 14, 2317–2349, https://doi.org/10.5194/gmd-14-2317-2021, https://doi.org/10.5194/gmd-14-2317-2021, 2021
Short summary
Short summary
We compare four ocean vertical mixing schemes in 100-year coupled simulations with the Max Planck Institute Earth System Model (MPI-ESM1.2) and analyse their model biases. Overall, the mixing schemes modify biases in the ocean interior that vary with region and variable but produce a similar global bias pattern. We therefore cannot classify any scheme as superior but conclude that the chosen mixing scheme may be important for regional biases.
Max Thomas, James France, Odile Crabeck, Benjamin Hall, Verena Hof, Dirk Notz, Tokoloho Rampai, Leif Riemenschneider, Oliver John Tooth, Mathilde Tranter, and Jan Kaiser
Atmos. Meas. Tech., 14, 1833–1849, https://doi.org/10.5194/amt-14-1833-2021, https://doi.org/10.5194/amt-14-1833-2021, 2021
Short summary
Short summary
We describe the Roland von Glasow Air-Sea-Ice Chamber, a laboratory facility for studying ocean–sea-ice–atmosphere interactions. We characterise the technical capabilities of our facility to help future users plan and perform experiments. We also characterise the sea ice grown in the facility, showing that the extinction of photosynthetically active radiation, the bulk salinity, and the growth rate of our artificial sea ice are within the range of natural values.
Tim Rohrschneider, Johanna Baehr, Veit Lüschow, Dian Putrasahan, and Jochem Marotzke
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-124, https://doi.org/10.5194/os-2020-124, 2021
Preprint under review for OS
Short summary
Short summary
This paper presents an analysis of wind and warming sensitivity experiments in order to provide insight into the Atlantic inter-hemispheric circulation by understanding the behavior of the depth scale(s) of the AMOC. We put into question the common view that the AMOC is a nonlocal phenomenon only. The analysis of the wind experiments and the warming experiment suggests a hemisphere-dependent scaling of the strength of AMOC and temporal adjustment to global warming.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
Short summary
Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
Short summary
Short summary
Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
Short summary
Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Camilo Melo-Aguilar, J. Fidel González-Rouco, Elena García-Bustamante, Norman Steinert, Johann H. Jungclaus, Jorge Navarro, and Pedro J. Roldán-Gómez
Clim. Past, 16, 453–474, https://doi.org/10.5194/cp-16-453-2020, https://doi.org/10.5194/cp-16-453-2020, 2020
Short summary
Short summary
This study explores potential sources of bias on borehole-based temperature reconstruction from both methodological and physical factors using pseudo-proxy experiments that consider ensembles of simulations from the Community Earth System Model. The results indicate that both methodological and physical factors may have an impact on the estimation of the recent temperature trends at different spatial scales. Internal variability arises also as an important issue influencing pseudo-proxy results.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Tine Nilsen, Dmitry V. Divine, Annika Hofgaard, Andreas Born, Johann Jungclaus, and Igor Drobyshev
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-123, https://doi.org/10.5194/cp-2019-123, 2019
Revised manuscript not accepted
Short summary
Short summary
Using a set of three climate model simulations we cannot find a consistent relationship between atmospheric conditions favorable for forest fire activity in northern Scandinavia and weaker ocean circulation in the North Atlantic subpolar gyre on seasonal timescales. In the literature there is support of such a relationship for longer timescales. With the motivation to improve seasonal prediction systems, we conclude that the gyre circulation alone does not indicate forthcoming model drought.
Oliver Gutjahr, Dian Putrasahan, Katja Lohmann, Johann H. Jungclaus, Jin-Song von Storch, Nils Brüggemann, Helmuth Haak, and Achim Stössel
Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, https://doi.org/10.5194/gmd-12-3241-2019, 2019
Short summary
Short summary
We analyse how climatic mean states of the atmosphere and ocean change with increasing the horizontal model resolution of the Max Planck Institute Earth System Model (MPI-ESM1.2) and how they are affected by the representation of vertical mixing in the ocean. It is in particular a high-resolution ocean that reduces biases not only in the ocean but also in the atmosphere. The vertical mixing scheme affects the strength and stability of the Atlantic meridional overturning circulation (AMOC).
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
Short summary
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Hanna Paulsen, Tatiana Ilyina, Johann H. Jungclaus, Katharina D. Six, and Irene Stemmler
Earth Syst. Dynam., 9, 1283–1300, https://doi.org/10.5194/esd-9-1283-2018, https://doi.org/10.5194/esd-9-1283-2018, 2018
Short summary
Short summary
We use an Earth system model to study the effects of light absorption by marine cyanobacteria on climate. We find that cyanobacteria have a considerable cooling effect on tropical SST with implications for ocean and atmosphere circulation patterns as well as for climate variability. The results indicate the importance of considering phytoplankton light absorption in climate models, and specifically highlight the role of cyanobacteria due to their regulative effect on tropical SST and climate.
Masa Kageyama, Pascale Braconnot, Sandy P. Harrison, Alan M. Haywood, Johann H. Jungclaus, Bette L. Otto-Bliesner, Jean-Yves Peterschmitt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Chris Brierley, Michel Crucifix, Aisling Dolan, Laura Fernandez-Donado, Hubertus Fischer, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Daniel J. Lunt, Natalie M. Mahowald, W. Richard Peltier, Steven J. Phipps, Didier M. Roche, Gavin A. Schmidt, Lev Tarasov, Paul J. Valdes, Qiong Zhang, and Tianjun Zhou
Geosci. Model Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, https://doi.org/10.5194/gmd-11-1033-2018, 2018
Short summary
Short summary
The Paleoclimate Modelling Intercomparison Project (PMIP) takes advantage of the existence of past climate states radically different from the recent past to test climate models used for climate projections and to better understand these climates. This paper describes the PMIP contribution to CMIP6 (Coupled Model Intercomparison Project, 6th phase) and possible analyses based on PMIP results, as well as on other CMIP6 projects.
PAGES Hydro2k Consortium
Clim. Past, 13, 1851–1900, https://doi.org/10.5194/cp-13-1851-2017, https://doi.org/10.5194/cp-13-1851-2017, 2017
Short summary
Short summary
Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited due to a paucity of modern instrumental observations. We review how proxy records of past climate and climate model simulations can be used in tandem to understand hydroclimate variability over the last 2000 years and how these tools can also inform risk assessments of future hydroclimatic extremes.
Johann H. Jungclaus, Edouard Bard, Mélanie Baroni, Pascale Braconnot, Jian Cao, Louise P. Chini, Tania Egorova, Michael Evans, J. Fidel González-Rouco, Hugues Goosse, George C. Hurtt, Fortunat Joos, Jed O. Kaplan, Myriam Khodri, Kees Klein Goldewijk, Natalie Krivova, Allegra N. LeGrande, Stephan J. Lorenz, Jürg Luterbacher, Wenmin Man, Amanda C. Maycock, Malte Meinshausen, Anders Moberg, Raimund Muscheler, Christoph Nehrbass-Ahles, Bette I. Otto-Bliesner, Steven J. Phipps, Julia Pongratz, Eugene Rozanov, Gavin A. Schmidt, Hauke Schmidt, Werner Schmutz, Andrew Schurer, Alexander I. Shapiro, Michael Sigl, Jason E. Smerdon, Sami K. Solanki, Claudia Timmreck, Matthew Toohey, Ilya G. Usoskin, Sebastian Wagner, Chi-Ju Wu, Kok Leng Yeo, Davide Zanchettin, Qiong Zhang, and Eduardo Zorita
Geosci. Model Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, https://doi.org/10.5194/gmd-10-4005-2017, 2017
Short summary
Short summary
Climate model simulations covering the last millennium provide context for the evolution of the modern climate and for the expected changes during the coming centuries. They can help identify plausible mechanisms underlying palaeoclimatic reconstructions. Here, we describe the forcing boundary conditions and the experimental protocol for simulations covering the pre-industrial millennium. We describe the PMIP4 past1000 simulations as contributions to CMIP6 and additional sensitivity experiments.
Jonathan M. Gregory, Nathaelle Bouttes, Stephen M. Griffies, Helmuth Haak, William J. Hurlin, Johann Jungclaus, Maxwell Kelley, Warren G. Lee, John Marshall, Anastasia Romanou, Oleg A. Saenko, Detlef Stammer, and Michael Winton
Geosci. Model Dev., 9, 3993–4017, https://doi.org/10.5194/gmd-9-3993-2016, https://doi.org/10.5194/gmd-9-3993-2016, 2016
Short summary
Short summary
As a consequence of greenhouse gas emissions, changes in ocean temperature, salinity, circulation and sea level are expected in coming decades. Among the models used for climate projections for the 21st century, there is a large spread in projections of these effects. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate and explain this spread by prescribing a common set of changes in the input of heat, water and wind stress to the ocean in the participating models.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
Short summary
Short summary
The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
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.
Marlene Klockmann, Uwe Mikolajewicz, and Jochem Marotzke
Clim. Past, 12, 1829–1846, https://doi.org/10.5194/cp-12-1829-2016, https://doi.org/10.5194/cp-12-1829-2016, 2016
Short summary
Short summary
We study the response of the glacial AMOC to different forcings in a coupled AOGCM. The depth of the upper overturning cell remains almost unchanged in response to the full glacial forcing. This is the result of two opposing effects: a deepening due to the ice sheets and a shoaling due to the low GHG concentrations. Increased brine release in the Southern Ocean is key to the shoaling. With glacial ice sheets, a shallower cell can be simulated with GHG concentrations below the glacial level.
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, https://doi.org/10.5194/tc-10-1631-2016, 2016
Short summary
Short summary
We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
Anastasios Matsikaris, Martin Widmann, and Johann Jungclaus
Clim. Past, 12, 1555–1563, https://doi.org/10.5194/cp-12-1555-2016, https://doi.org/10.5194/cp-12-1555-2016, 2016
Short summary
Short summary
We have assimilated proxy-based (PAGES 2K) and instrumental (HadCRUT3v) observations into a General Circulation Model (MPI-ESM-CR). Assimilating instrumental data improves the performance of Data Assimilation. No skill on small spatial scales is however found for either of the two schemes. Errors in the assimilated data are therefore not the main reason for this lack of skill; continental mean temperatures cannot provide skill on small spatial scales in palaeoclimate reconstructions.
K. Lohmann, J. Mignot, H. R. Langehaug, J. H. Jungclaus, D. Matei, O. H. Otterå, Y. Q. Gao, T. L. Mjell, U. S. Ninnemann, and H. F. Kleiven
Clim. Past, 11, 203–216, https://doi.org/10.5194/cp-11-203-2015, https://doi.org/10.5194/cp-11-203-2015, 2015
Short summary
Short summary
We use model simulations to investigate mechanisms of similar Iceland--Scotland overflow (outflow from the Nordic seas) and North Atlantic sea surface temperature variability, suggested from palaeo-reconstructions (Mjell et al., 2015). Our results indicate the influence of Nordic Seas surface temperature on the pressure gradient across the Iceland--Scotland ridge, not a large-scale link through the meridional overturning circulation, is responsible for the (simulated) co-variability.
A. Matsikaris, M. Widmann, and J. Jungclaus
Clim. Past, 11, 81–93, https://doi.org/10.5194/cp-11-81-2015, https://doi.org/10.5194/cp-11-81-2015, 2015
Short summary
Short summary
We compare an off-line and an on-line ensemble-based data assimilation method, for the climate of the 17th century. Both schemes perform better than the simulations without DA, and similar skill on the continental and hemispheric scales is found. This indicates either a lack of control of the slow components in our setup or a lack of skill in the information propagation on decadal timescales. The temporal consistency of the analysis in the on-line method makes it generally more preferable.
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
S. Rysgaard, F. Wang, R. J. Galley, R. Grimm, D. Notz, M. Lemes, N.-X. Geilfus, A. Chaulk, A. A. Hare, O. Crabeck, B. G. T. Else, K. Campbell, L. L. Sørensen, J. Sievers, and T. Papakyriakou
The Cryosphere, 8, 1469–1478, https://doi.org/10.5194/tc-8-1469-2014, https://doi.org/10.5194/tc-8-1469-2014, 2014
D. Zanchettin, O. Bothe, C. Timmreck, J. Bader, A. Beitsch, H.-F. Graf, D. Notz, and J. H. Jungclaus
Earth Syst. Dynam., 5, 223–242, https://doi.org/10.5194/esd-5-223-2014, https://doi.org/10.5194/esd-5-223-2014, 2014
K. Lohmann, J. H. Jungclaus, D. Matei, J. Mignot, M. Menary, H. R. Langehaug, J. Ba, Y. Gao, O. H. Otterå, W. Park, and S. Lorenz
Ocean Sci., 10, 227–241, https://doi.org/10.5194/os-10-227-2014, https://doi.org/10.5194/os-10-227-2014, 2014
D. Notz
The Cryosphere, 8, 229–243, https://doi.org/10.5194/tc-8-229-2014, https://doi.org/10.5194/tc-8-229-2014, 2014
O. Bothe, J. H. Jungclaus, and D. Zanchettin
Clim. Past, 9, 2471–2487, https://doi.org/10.5194/cp-9-2471-2013, https://doi.org/10.5194/cp-9-2471-2013, 2013
M. Vancoppenolle, D. Notz, F. Vivier, J. Tison, B. Delille, G. Carnat, J. Zhou, F. Jardon, P. Griewank, A. Lourenço, and T. Haskell
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-3209-2013, https://doi.org/10.5194/tcd-7-3209-2013, 2013
Revised manuscript not accepted
O. Bothe, J. H. Jungclaus, D. Zanchettin, and E. Zorita
Clim. Past, 9, 1089–1110, https://doi.org/10.5194/cp-9-1089-2013, https://doi.org/10.5194/cp-9-1089-2013, 2013
J. Segschneider, A. Beitsch, C. Timmreck, V. Brovkin, T. Ilyina, J. Jungclaus, S. J. Lorenz, K. D. Six, and D. Zanchettin
Biogeosciences, 10, 669–687, https://doi.org/10.5194/bg-10-669-2013, https://doi.org/10.5194/bg-10-669-2013, 2013
Cited articles
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and DeWeaver, E.: Persistence and inherent predictability of A}rctic 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., Bitz, C. M., and Holland, M. M.: Influence of initial conditions and climate forcing on predicting A}rctic sea ice, Geophys. Res. Lett., 38, L18503, https://doi.org/10.1029/2011GL048807, 2011{b.
Bouttier, F. and Courtier, P.: Data assimilation concepts and methods – training course lecture notes, from http://www.ecmwf.int (last access: 11 January 2013), 1999.
Budikova, D.: Role of {A}rctic sea ice in global atmospheric circulation: A review, Global Planet. Change, 68, 149–163, https://doi.org/10.1016/j.gloplacha.2009.04.001, 2009.
Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H. J.: Sea Ice Concentrations from N}imbus-7 {SMMR and DMSP {SSM/I} Passive Microwave Data, from http://nsidc.org/data/nsidc-0051.html (last access: 11 January 2013), 2008.
Dulière, V. and Fichefet, T.: On the assimilation of ice velocity and concentration data into large-scale sea ice models, Ocean Sci., 3, 321–335, https://doi.org/10.5194/os-3-321-2007, 2007.
Francis, J. A. and Vavrus, S. J.: Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000, 2012.
Hibler III, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys. Oceanogr., 9, 815–846, 1979.
Holland, M. M., Bailey, D. A., and Vavrus, S.: Inherent sea ice predictability in the rapidly changing {A}rctic environment of the {C}ommunity {C}limate {S}ystem {M}odel, version 3, Clim. Dynam., 36, 1239–1253, https://doi.org/10.1007/s00382-010-0792-4, 2011.
Honda, M., Inoue, J., and Yamane, S.: Influence of low {A}rctic sea ice minima on anomalously cold {E}urasian winters, Geophys. Res. Lett., 36, L08707, https://doi.org/10.1029/2008GL037079, 2009.
Hurrell, J., Meehl, G. A., Bader, D., Delworth, T. L., Kirtman, B., and Wielicki, B.: A Unified Modeling Approach to Climate System Prediction, Bull. Amer. Meteor. Soc., 90, 1819–1832, https://doi.org/10.1175/2009BAMS2752.1, 2009.
Jungclaus, J. H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.-J., Latif, M., Marotzke, J., Mikolajewicz, U., and Roeckner, E.: Ocean Circulation and Tropical Variability in the Coupled Model ECHAM5}/{MPI-OM, J. Climate, 19, 3952–3972, 2006.
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003.
Koenigk, T., Mikolajewicz, U., Haak, H., and Jungclaus, J.: Variability of {F}ram {S}trait sea ice export: causes, impacts and feedbacks in a coupled climate model, Clim. Dynam., 26, 17–34, https://doi.org/10.1007/s00382-005-0060-1, 2006.
Koldunov, N. V., Stammer, D., and Marotzke, J.: Present-Day A}rctic Sea Ice Variability in the Coupled {ECHAM5}/{MPI-OM Model, J. Climate, 23, 2520–2543, https://doi.org/10.1175/2009JCLI3065.1, 2010.
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi, D.: Thinning and volume loss of the {A}rctic {O}cean sea ice cover: 2003–2008, J. Geophys. Res., 114, C07005, https://doi.org/10.1029/2009JC005312, 2009.
Lemke, P.: A Coupled One-Dimensional Sea Ice–Ocean Model, J. Geophys. Res., 92, 13164–13172, 1987.
Levermann, A., Mignot, J., Nawrath, S., and Rahmstorf, S.: The Role of Northern Sea Ice Cover for the Weakening of the Thermohaline Circulation under Global Warming, J. Climate, 20, 4160–4171, https://doi.org/10.1175/JCLI4232.1, 2007.
Lindsay, R. W. and Zhang, J.: Assimilation of Ice Concentration in an Ice–Ocean Model, J. Atmos. Oceanic Technol., 23, 742–749, https://doi.org/10.1175/JTECH1871.1, 2006.
Lis\ae{}ter, K. A., Rosanova, J., and Evensen, G.: Assimilation of ice concentration in a coupled ice–ocean model, using the {E}nsemble {K}alman filter, Ocean Dynam., 53, 368–388, https://doi.org/10.1007/s10236-003-0049-4, 2003.
Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M., and Röske, F.: The {M}ax-{P}lanck-{I}nstitute global ocean/sea ice model with orthogonal curvilinear coordinates, Ocean Modell., 5, 91–127, 2003.
Maykut, G. A. and Untersteiner, N.: Some Results from a Time-Dependent Thermodynamic Model of Sea Ice, J. Geophys. Res., 76, 1550–1575, 1971.
Mellor, G. L. and Kantha, L.: An ice–ocean Coupled Model, J. Geophys. Res., 94, 10937–10954, https://doi.org/10.1029/JC094iC08p10937, 1989.
Pohlmann, H., Jungclaus, J. H., Köhl, A., Stammer, D., and Marotzke, J.: Initializing Decadal Climate Predictions with the GECCO Oceanic Synthesis: Effects on the {N}orth {A}tlantic, J. Climate, 22, 3926–3938, https://doi.org/10.1175/2009JCLI2535.1, 2009.
Roeckner, E., Buml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Manzini, L. K. E., Rhodin, A., Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric general circulation model ECHAM5, Tech. Rep. 349, Max Planck Institute for Meteorology, Hamburg, 2003.
Rothrock, D. A. and Wensnahan, M.: The accuracy of sea ice drafts measured from US {N}avy submarines, J. Atmos. Oceanic Technol., 24, 1936–1949, https://doi.org/10.1175/JTECH2097.1, 2007.
Saha, S., Moorthi, S., Pan, H. L., Wu, X. R., Wang, J. D., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H. X., 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. L., Yang, R. Q., Lord, S., Van den Dool, H., Kumar, A., Wang, W. Q., Long, C., Chelliah, M., Xue, Y., Huang, B. Y., Schemm, J. K., Ebisuzaki, W., Lin, R., Xie, P. P., Chen, M. Y., Zhou, S. T., Higgins, W., Zou, C. Z., Liu, Q. H., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, Bull. Amer. Meteor. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Schweiger, A., Lindsay, R., Zhang, J. L., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled {A}rctic Sea Ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011.
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, 1976.
Serreze, M. C., Barrett, A. P., Slater, A. G., Steele, M., Zhang, J., and Trenberth, K. E.: The large-scale energy budget of the {A}rctic, J. Geophys. Res., 112, D11122, https://doi.org/10.1029/2006JD008230, 2007.
Shepherd, T. G., Arblaster, J. M., Bitz, C. M., Furevik, T., Goosse, H., Kattsov, V. M., Marshall, J., Ryabinin, V., and Walsh, J. E.: Report on WCRP Workshop on Seasonal to Multi-Decadal Predictability of Polar Climate ({B}ergen, {N}orway, 25–29 {O}ctober 2010), SPARC Newsletter, 36, 11–19, http://www.atmosp.physics.utoronto.ca/SPARC/sparcnewsletter36.pdf, 2011. \
Smith, D. M., Cusack, S., Andrew W, C., Folland, C. K., Harris, G. R., and Murphy, J. M.: Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model, Science, 317, 796–799, https://doi.org/10.1126/science.1139540, 2007.
Stark, J. D., Ridley, J., Martin, M., and Hines, A.: Sea Ice concentration and motion assimilation in a sea ice–ocean model, J. Geophys. Res., 113, C05S91, https://doi.org/10.1029/2007JC004224, 2008.
Tonboe, R. and Nielsen, E.: Global Sea Ice Concentration Reprocessing Validation Report, Tech. rep., Danish Meteorological Institute, http://osisaf.met.no/p/ice/ice_conc_reprocessed.html, 2010.
Yi, D. and Zwally, J.: Arctic Sea Ice Freeboard and Thickness, http://nsidc.org/data/nsidc-0393.html (last access: 11 January 2013), 2010.
Zhang, X. D.: Sensitivity of arctic summer Sea Ice coverage to global warming forcing: towards reducing uncertainty in arctic climate change projections, Tellus A, 62, 220–227, https://doi.org/10.1111/j.1600-0870.2010.00441.x, 2010.
