Articles | Volume 15, issue 11
https://doi.org/10.5194/tc-15-5099-2021
© 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-15-5099-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2021
Research article |
| 09 Nov 2021
| 09 Nov 2021
The contribution of melt ponds to enhanced Arctic sea-ice melt during the Last Interglacial
Rachel Diamond
CORRESPONDING AUTHOR
Ice Dynamics and Palaeoclimate Team, British Antarctic Survey, Cambridge, UK
Department of Physics, Imperial College London, London, UK
Louise C. Sime
Ice Dynamics and Palaeoclimate Team, British Antarctic Survey, Cambridge, UK
David Schroeder
Department of Meteorology, University of Reading, Reading, UK
Maria-Vittoria Guarino
Ice Dynamics and Palaeoclimate Team, British Antarctic Survey, Cambridge, UK
Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, UK
Related authors
No articles found.
Sentia Goursaud Oger, Louise C. Sime, and Max Holloway
Clim. Past, 20, 2539–2560, https://doi.org/10.5194/cp-20-2539-2024, https://doi.org/10.5194/cp-20-2539-2024, 2024
Short summary
Short summary
Antarctic ice cores provide information about past temperatures. Here, we run new climate model simulations, including stable water isotopes for the historical period. Across one-third of Antarctica, there is no strong connection between isotopes and temperature and a weak connection for most of the rest of Antarctica. This disconnect between isotopes and temperature is largely driven by changes in Antarctic sea ice. Our results are helpful for temperature reconstructions from ice core records.
John Slattery, Louise C. Sime, Francesco Muschitiello, and Keno Riechers
Clim. Past, 20, 2431–2454, https://doi.org/10.5194/cp-20-2431-2024, https://doi.org/10.5194/cp-20-2431-2024, 2024
Short summary
Short summary
Dansgaard–Oeschger events are a series of abrupt past climate change events during which the atmosphere, sea ice, and ocean in the North Atlantic underwent rapid changes. One current topic of interest is the order in which these different changes occurred, which remains unknown. In this work, we find that the current best method used to investigate this topic is subject to substantial bias. This implies that it is not possible to reliably determine the order of the different changes.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Qinggang Gao, Emilie Capron, Louise C. Sime, Rachael H. Rhodes, Rahul Sivankutty, Xu Zhang, Bette L. Otto-Bliesner, and Martin Werner
EGUsphere, https://doi.org/10.5194/egusphere-2024-1261, https://doi.org/10.5194/egusphere-2024-1261, 2024
Short summary
Short summary
Marine sediment and ice core records suggest a warmer Southern Ocean and Antarctica at the early last interglacial, ~127 thousand years ago. However, when only forced by orbital parameters and greenhouse gas concentrations during that period, state-of-the-art climate models do not reproduce the magnitude of warming. Here we show that much of the warming at southern mid-to-high latitudes can be reproduced by a UK climate model HadCM3 with a 3000-year freshwater forcing over the North Atlantic.
Alexander T. Archibald, Bablu Sinha, Maria Russo, Emily Matthews, Freya Squires, N. Luke Abraham, Stephane Bauguitte, Thomas Bannan, Thomas Bell, David Berry, Lucy Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Ben I. Moat, Katie Read, Chris Reed, Malcolm Roberts, Reinhard Schiemann, David Schroeder, Tim Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Ming-Xi Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-405, https://doi.org/10.5194/essd-2023-405, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Here we present an overview of the data generated as part of the North Atlantic Climate System Integrated Studies (ACSIS) programme which are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA, www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC, bodc.ac.uk). ACSIS data cover the full North Atlantic System comprising: the North Atlantic Ocean, the atmosphere above it including its composition, Arctic Sea Ice and the Greenland Ice Sheet.
Qinggang Gao, Louise C. Sime, Alison J. McLaren, Thomas J. Bracegirdle, Emilie Capron, Rachael H. Rhodes, Hans Christian Steen-Larsen, Xiaoxu Shi, and Martin Werner
The Cryosphere, 18, 683–703, https://doi.org/10.5194/tc-18-683-2024, https://doi.org/10.5194/tc-18-683-2024, 2024
Short summary
Short summary
Antarctic precipitation is a crucial component of the climate system. Its spatio-temporal variability impacts sea level changes and the interpretation of water isotope measurements in ice cores. To better understand its climatic drivers, we developed water tracers in an atmospheric model to identify moisture source conditions from which precipitation originates. We find that mid-latitude surface winds exert an important control on moisture availability for Antarctic precipitation.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Rebecca Caitlin Frew, Daniel Feltham, David Schroeder, and Adam William Bateson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-91, https://doi.org/10.5194/tc-2023-91, 2023
Preprint under review for TC
Short summary
Short summary
As summer Arctic sea ice extent has retreated, the marginal ice zone (MIZ) has been widening and making up an increasing percentage of the summer sea ice. The MIZ is projected to become a larger percentage of the summer ice cover, as the Arctic transitions to ice free summers. Using a sea ice model we find that the processes and timing of sea ice loss differ in the MIZ to the rest of the sea cover.
Irene Malmierca-Vallet, Louise C. Sime, and the D–O community members
Clim. Past, 19, 915–942, https://doi.org/10.5194/cp-19-915-2023, https://doi.org/10.5194/cp-19-915-2023, 2023
Short summary
Short summary
Greenland ice core records feature Dansgaard–Oeschger (D–O) events, abrupt warming episodes followed by a gradual-cooling phase during mid-glacial periods. There is uncertainty whether current climate models can effectively represent the processes that cause D–O events. Here, we propose a Marine Isotopic Stage 3 (MIS3) baseline protocol which is intended to provide modelling groups investigating D–O oscillations with a common framework.
Louise C. Sime, Rahul Sivankutty, Irene Vallet-Malmierca, Agatha M. de Boer, and Marie Sicard
Clim. Past, 19, 883–900, https://doi.org/10.5194/cp-19-883-2023, https://doi.org/10.5194/cp-19-883-2023, 2023
Short summary
Short summary
It is not known if the Last Interglacial (LIG) experienced Arctic summers that were sea ice free: models show a wide spread in LIG Arctic temperature and sea ice results. Evaluation against sea ice markers is hampered by few observations. Here, an assessment of 11 climate model simulations against summer temperatures shows that the most skilful models have a 74 %–79 % reduction in LIG sea ice. The measurements of LIG areas indicate a likely mix of ice-free and near-ice-free LIG summers.
Maria Vittoria Guarino, Louise C. Sime, Rachel Diamond, Jeff Ridley, and David Schroeder
Clim. Past, 19, 865–881, https://doi.org/10.5194/cp-19-865-2023, https://doi.org/10.5194/cp-19-865-2023, 2023
Short summary
Short summary
We investigate the response of the atmosphere, ocean, and ice domains to the release of a large volume of glacial meltwaters thought to have occurred during the Last Interglacial period. We show that the signal that originated in the North Atlantic travels over great distances across the globe. It modifies the ocean gyre circulation in the Northern Hemisphere as well as the belt of westerly winds in the Southern Hemisphere, with consequences for Antarctic sea ice.
Xavier Crosta, Karen E. Kohfeld, Helen C. Bostock, Matthew Chadwick, Alice Du Vivier, Oliver Esper, Johan Etourneau, Jacob Jones, Amy Leventer, Juliane Müller, Rachael H. Rhodes, Claire S. Allen, Pooja Ghadi, Nele Lamping, Carina B. Lange, Kelly-Anne Lawler, David Lund, Alice Marzocchi, Katrin J. Meissner, Laurie Menviel, Abhilash Nair, Molly Patterson, Jennifer Pike, Joseph G. Prebble, Christina Riesselman, Henrik Sadatzki, Louise C. Sime, Sunil K. Shukla, Lena Thöle, Maria-Elena Vorrath, Wenshen Xiao, and Jiao Yang
Clim. Past, 18, 1729–1756, https://doi.org/10.5194/cp-18-1729-2022, https://doi.org/10.5194/cp-18-1729-2022, 2022
Short summary
Short summary
Despite its importance in the global climate, our knowledge of Antarctic sea-ice changes throughout the last glacial–interglacial cycle is extremely limited. As part of the Cycles of Sea Ice Dynamics in the Earth system (C-SIDE) Working Group, we review marine- and ice-core-based sea-ice proxies to provide insights into their applicability and limitations. By compiling published records, we provide information on Antarctic sea-ice dynamics over the past 130 000 years.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
Short summary
Short summary
Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Erin L. McClymont, Michael J. Bentley, Dominic A. Hodgson, Charlotte L. Spencer-Jones, Thomas Wardley, Martin D. West, Ian W. Croudace, Sonja Berg, Darren R. Gröcke, Gerhard Kuhn, Stewart S. R. Jamieson, Louise Sime, and Richard A. Phillips
Clim. Past, 18, 381–403, https://doi.org/10.5194/cp-18-381-2022, https://doi.org/10.5194/cp-18-381-2022, 2022
Short summary
Short summary
Sea ice is important for our climate system and for the unique ecosystems it supports. We present a novel way to understand past Antarctic sea-ice ecosystems: using the regurgitated stomach contents of snow petrels, which nest above the ice sheet but feed in the sea ice. During a time when sea ice was more extensive than today (24 000–30 000 years ago), we show that snow petrel diet had varying contributions of fish and krill, which we interpret to show changing sea-ice distribution.
Matthew Chadwick, Claire S. Allen, Louise C. Sime, Xavier Crosta, and Claus-Dieter Hillenbrand
Clim. Past, 18, 129–146, https://doi.org/10.5194/cp-18-129-2022, https://doi.org/10.5194/cp-18-129-2022, 2022
Short summary
Short summary
Algae preserved in marine sediments have allowed us to reconstruct how much winter sea ice was present around Antarctica during a past time period (130 000 years ago) when the climate was warmer than today. The patterns of sea-ice increase and decrease vary between different parts of the Southern Ocean. The Pacific sector has a largely stable sea-ice extent, whereas the amount of sea ice in the Atlantic sector is much more variable with bigger decreases and increases than other regions.
Janica C. Bühler, Carla Roesch, Moritz Kirschner, Louise Sime, Max D. Holloway, and Kira Rehfeld
Clim. Past, 17, 985–1004, https://doi.org/10.5194/cp-17-985-2021, https://doi.org/10.5194/cp-17-985-2021, 2021
Short summary
Short summary
We present three new isotope-enabled simulations for the last millennium (850–1850 CE) and compare them to records from a global speleothem database. Offsets between the simulated and measured oxygen isotope ratios are fairly small. While modeled oxygen isotope ratios are more variable on decadal timescales, proxy records are more variable on (multi-)centennial timescales. This could be due to a lack of long-term variability in complex model simulations, but proxy biases cannot be excluded.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
Short summary
Short summary
We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
Short summary
Short summary
The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Bette L. Otto-Bliesner, Esther C. Brady, Anni Zhao, Chris M. Brierley, Yarrow Axford, Emilie Capron, Aline Govin, Jeremy S. Hoffman, Elizabeth Isaacs, Masa Kageyama, Paolo Scussolini, Polychronis C. Tzedakis, Charles J. R. Williams, Eric Wolff, Ayako Abe-Ouchi, Pascale Braconnot, Silvana Ramos Buarque, Jian Cao, Anne de Vernal, Maria Vittoria Guarino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina A. Morozova, Kerim H. Nisancioglu, Ryouta O'ishi, David Salas y Mélia, Xiaoxu Shi, Marie Sicard, Louise Sime, Christian Stepanek, Robert Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, https://doi.org/10.5194/cp-17-63-2021, 2021
Short summary
Short summary
The CMIP6–PMIP4 Tier 1 lig127k experiment was designed to address the climate responses to strong orbital forcing. We present a multi-model ensemble of 17 climate models, most of which have also completed the CMIP6 DECK experiments and are thus important for assessing future projections. The lig127ksimulations show strong summer warming over the NH continents. More than half of the models simulate a retreat of the Arctic minimum summer ice edge similar to the average for 2000–2018.
Irene Malmierca-Vallet, Louise C. Sime, Paul J. Valdes, and Julia C. Tindall
Clim. Past, 16, 2485–2508, https://doi.org/10.5194/cp-16-2485-2020, https://doi.org/10.5194/cp-16-2485-2020, 2020
Josephine R. Brown, Chris M. Brierley, Soon-Il An, Maria-Vittoria Guarino, Samantha Stevenson, Charles J. R. Williams, Qiong Zhang, Anni Zhao, Ayako Abe-Ouchi, Pascale Braconnot, Esther C. Brady, Deepak Chandan, Roberta D'Agostino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, Ryouta O'ishi, Bette L. Otto-Bliesner, W. Richard Peltier, Xiaoxu Shi, Louise Sime, Evgeny M. Volodin, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 16, 1777–1805, https://doi.org/10.5194/cp-16-1777-2020, https://doi.org/10.5194/cp-16-1777-2020, 2020
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) is the largest source of year-to-year variability in the current climate, but the response of ENSO to past or future changes in climate is uncertain. This study compares the strength and spatial pattern of ENSO in a set of climate model simulations in order to explore how ENSO changes in different climates, including past cold glacial climates and past climates with different seasonal cycles, as well as gradual and abrupt future warming cases.
Charles J. R. Williams, Maria-Vittoria Guarino, Emilie Capron, Irene Malmierca-Vallet, Joy S. Singarayer, Louise C. Sime, Daniel J. Lunt, and Paul J. Valdes
Clim. Past, 16, 1429–1450, https://doi.org/10.5194/cp-16-1429-2020, https://doi.org/10.5194/cp-16-1429-2020, 2020
Short summary
Short summary
Computer simulations of the geological past are an important tool to improve our understanding of climate change. We present results from two simulations using the latest version of the UK's climate model, the mid-Holocene (6000 years ago) and Last Interglacial (127 000 years ago). The simulations reproduce temperatures consistent with the pattern of incoming radiation. Model–data comparisons indicate that some regions (and some seasons) produce better matches to the data than others.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
Short summary
Short summary
It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Quentin Dalaiden, Hugues Goosse, François Klein, Jan T. M. Lenaerts, Max Holloway, Louise Sime, and Elizabeth R. Thomas
The Cryosphere, 14, 1187–1207, https://doi.org/10.5194/tc-14-1187-2020, https://doi.org/10.5194/tc-14-1187-2020, 2020
Short summary
Short summary
Large uncertainties remain in Antarctic surface temperature reconstructions over the last millennium. Here, the analysis of climate model outputs reveals that snow accumulation is a more relevant proxy for surface temperature reconstructions than δ18O. We use this finding in data assimilation experiments to compare to observed surface temperatures. We show that our continental temperature reconstruction outperforms reconstructions based on δ18O, especially for East Antarctica.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, https://doi.org/10.5194/tc-14-403-2020, 2020
Short summary
Short summary
The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Maria-Vittoria Guarino, Louise C. Sime, David Schroeder, Grenville M. S. Lister, and Rosalyn Hatcher
Geosci. Model Dev., 13, 139–154, https://doi.org/10.5194/gmd-13-139-2020, https://doi.org/10.5194/gmd-13-139-2020, 2020
Short summary
Short summary
When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. Here, we investigate the behaviour of the Preindustrial simulation prepared by the UK Met Office for the forthcoming CMIP6 under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
Short summary
Short summary
This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
Short summary
Short summary
This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, https://doi.org/10.5194/gmd-11-713-2018, 2018
Short summary
Short summary
The sea ice component of the Met Office coupled climate model, HadGEM3-GC3.1, is presented and evaluated. We determine that the mean state of the sea ice is well reproduced for the Arctic; however, a warm sea surface temperature bias over the Southern Ocean results in a low Antarctic sea ice cover.
Louise C. Sime, Dominic Hodgson, Thomas J. Bracegirdle, Claire Allen, Bianca Perren, Stephen Roberts, and Agatha M. de Boer
Clim. Past, 12, 2241–2253, https://doi.org/10.5194/cp-12-2241-2016, https://doi.org/10.5194/cp-12-2241-2016, 2016
Short summary
Short summary
Latitudinal shifts in the Southern Ocean westerly wind jet could explain large observed changes in the glacial to interglacial ocean CO2 inventory. However there is considerable disagreement in modelled deglacial-warming jet shifts. Here multi-model output is used to show that expansion of sea ice during the glacial period likely caused a slight poleward shift and intensification in the westerly wind jet. Issues with model representation of the winds caused much of the previous disagreement.
Daniela Flocco, Daniel L. Feltham, David Schroeder, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-118, https://doi.org/10.5194/tc-2016-118, 2016
Preprint withdrawn
Short summary
Short summary
Melt ponds form over the sea ice cover in the Arctic and impact the surface albedo inducing a positive feedback leading to further melting.
While they refreeze, ponds delay basal sea ice growth in Autumn impacting the internal sea ice temperature and therefore its basal growth rate. By using a numerical model we estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October.
Related subject area
Discipline: Sea ice | Subject: Climate Interactions
Forced and internal components of observed Arctic sea-ice changes
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
Analyzing links between simulated Laptev Sea sea ice and atmospheric conditions over adjoining landmasses using causal-effect networks
Clouds damp the radiative impacts of polar sea ice loss
Jakob Simon Dörr, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills
The Cryosphere, 17, 4133–4153, https://doi.org/10.5194/tc-17-4133-2023, https://doi.org/10.5194/tc-17-4133-2023, 2023
Short summary
Short summary
The Arctic sea-ice cover is retreating due to climate change, but this retreat is influenced by natural (internal) variability in the climate system. We use a new statistical method to investigate how much internal variability has affected trends in the summer and winter Arctic sea-ice cover using observations since 1979. Our results suggest that the impact of internal variability on sea-ice retreat might be lower than what climate models have estimated.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
Short summary
Short summary
This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
Zoé Rehder, Anne Laura Niederdrenk, Lars Kaleschke, and Lars Kutzbach
The Cryosphere, 14, 4201–4215, https://doi.org/10.5194/tc-14-4201-2020, https://doi.org/10.5194/tc-14-4201-2020, 2020
Short summary
Short summary
To better understand the connection between sea ice and permafrost, we investigate how sea ice interacts with the atmosphere over the adjacent landmass in the Laptev Sea region using a climate model. Melt of sea ice in spring is mainly controlled by the atmosphere; in fall, feedback mechanisms are important. Throughout summer, lower-than-usual sea ice leads to more southward transport of heat and moisture, but these links from sea ice to the atmosphere over land are weak.
Ramdane Alkama, Patrick C. Taylor, Lorea Garcia-San Martin, Herve Douville, Gregory Duveiller, Giovanni Forzieri, Didier Swingedouw, and Alessandro Cescatti
The Cryosphere, 14, 2673–2686, https://doi.org/10.5194/tc-14-2673-2020, https://doi.org/10.5194/tc-14-2673-2020, 2020
Short summary
Short summary
The amount of solar energy absorbed by Earth is believed to strongly depend on clouds. Here, we investigate this relationship using satellite data and 32 climate models, showing that this relationship holds everywhere except over polar seas, where an increased reflection by clouds corresponds to an increase in absorbed solar radiation at the surface. This interplay between clouds and sea ice reduces by half the increase of net radiation at the surface that follows the sea ice retreat.
Cited articles
Årthun, M., Eldevik, T., and Smedsrud, L. H.: The role of Atlantic heat
transport in future Arctic winter sea ice loss, J. Climate, 32,
3327–3341, 2019. a
Auclair, G. and Tremblay, L. B.: The role of ocean heat transport in rapid sea
ice declines in the Community Earth System Model Large Ensemble, J.
Geophys. Res.-Oceans, 123, 8941–8957, 2018. a
Berger, A. and Loutre, M.-F.: Insolation values for the climate of the last 10
million years, Quaternary Sci. Rev., 10, 297–317, 1991. a
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of
sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677,
1999. a
CAPE members: Last Interglacial Arctic warmth confirms polar amplification of
climate change, Quaternary Sci. Rev., 25, 1383–1400, 2006. a
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.: The community climate system model version 3 (CCSM3), J. Climate, 19, 2122–2143, 2006. a, b, c, d
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea ice-albedo climate feedback
mechanism, J. Climate, 8, 240–247, 1995. a
Deser, C. and Teng, H.: Evolution of Arctic sea ice concentration trends and
the role of atmospheric circulation forcing, 1979–2007, Geophys. Res.
Lett., 35, L02504, https://doi.org/10.1029/2007GL032023, 2008. a, b, c
Diamond, R. and Guarino, M. V.: HadGEM3-GC3.1-LL lig127k and piControl processed outputs, [data set], available at: http://gws-access.jasmin.ac.uk/public/pmip4/HADGEM3_LIG_PI/PUBLIC_DATA/ last access: 20 September 2021. a
Eicken, H., Grenfell, T., Perovich, D., Richter-Menge, J., and Frey, K.:
Hydraulic controls of summer Arctic pack ice albedo, J. Geophys. Res.-Oceans, 109, C08007, https://doi.org/10.1029/2003JC001989, 2004. a, b, c
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. a
Fischer, H., Meissner, K. J., Mix, A. C., Abram, N. J., Austermann, J., Brovkin, V., Capron, E., Colombaroli, D., Daniau, A. L., Dyez, K. A., Felis, T., Finkelstein, S. A., Jaccard, S. L., McClymont, E. L., Rovere, A., Sutter, J., Wolff, E. W., Affolter, S., Bakker, P., Ballesteros-Cánovas, J. A., Barbante, C., Caley, T., Carlson, A. E., Churakova, O., Cortese, G., Cumming, B. F., Davis, B. A. S., De Vernal, A., Emile-Geay, J., Fritz, S. C., Gierz, P., Gottschalk, J., Holloway, M. D., Joos, F., Kucera, M., Loutre, M. F., Lunt, D. J., Marcisz, K., Marlon, J. R., Martinez, P., Masson-Delmotte, V., Nehrbass-Ahles, C., Otto-Bliesner, B. L., Raible, C. C., Risebrobakken, B., Sánchez Goñi, M. F., Saleem Arrigo, J., Sarnthein, M., Sjolte, J., and Valdes, P. J.:
Palaeoclimate constraints on the impact of 2 C anthropogenic warming and
beyond, Nat. Geosci., 11, 474–485, https://doi.org/10.1038/s41561-018-0146-0, 2018. a
Flocco, D., Feltham, D. L., and Turner, A. K.: Incorporation of a physically
based melt pond scheme into the sea ice component of a climate model, J. Geophys. Res.-Oceans, 115, C08012, https://doi.org/10.1029/2009JC005568, 2010. a
Flocco, D., Schroeder, D., Feltham, D. L., and Hunke, E. C.: Impact of melt
ponds on Arctic sea ice simulations from 1990 to 2007, J. Geophys.
Res.-Oceans, 117, C09032, https://doi.org/10.1029/2012JC008195, 2012. a, b, c
Guarino, M.-V., Sime, L. C., Schroeder, D., Lister, G. M. S., and Hatcher, R.: Machine dependence and reproducibility for coupled climate simulations: the HadGEM3-GC3.1 CMIP Preindustrial simulation, Geosci. Model Dev., 13, 139–154, https://doi.org/10.5194/gmd-13-139-2020, 2020a. a
Guarino, M.-V., Sime, L. C., Schröeder, D., Malmierca-Vallet, I., Rosenblum, E., Ringer, M., Ridley, J., Feltham, D., Bitz, C., Steig, E. J., Wolff, E., Stroeve, J., and Sellar, A.: Sea-ice-free Arctic during the Last Interglacial supports fast future
loss, Nature Climate Change, 1–5, 2020b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x
Gupta, S. K., Darnell, W. L., and Wilber, A. C.: A parameterization for
longwave surface radiation from satellite data: Recent improvements, J.
Appl. Meteorol., 31, 1361–1367, 1992. a
Holland, M. M., Bitz, C. M., and Tremblay, B.: Future abrupt reductions in the
summer Arctic sea ice, Geophys. Res. Lett., 33, L23503, https://doi.org/10.1029/2006GL028024, 2006. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Tech. Rep. 5, Intergovernmental Panel on Climate Change,
Cambridge, United Kingdom and New York, NY, USA,
https://doi.org/10.1017/CBO9781107415324, 2013. a
Kageyama, M., Sime, L. C., Sicard, M., Guarino, M.-V., de Vernal, A., Stein, R., Schroeder, D., Malmierca-Vallet, I., Abe-Ouchi, A., Bitz, C., Braconnot, P., Brady, E. C., Cao, J., Chamberlain, M. A., Feltham, D., Guo, C., LeGrande, A. N., Lohmann, G., Meissner, K. J., Menviel, L., Morozova, P., Nisancioglu, K. H., Otto-Bliesner, B. L., O'ishi, R., Ramos Buarque, S., Salas y Melia, D., Sherriff-Tadano, S., Stroeve, J., Shi, X., Sun, B., Tomas, R. A., Volodin, E., Yeung, N. K. H., Zhang, Q., Zhang, Z., Zheng, W., and Ziehn, T.: A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences, Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Kantha, L. H. and Clayson, C. A.: Numerical Models of Oceans and Oceanic Processes, vol. 66, Academic Press, San Diego, 2000. a
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal
Arctic sea ice loss, J. Geophys. Res.-Atmos., 114, D18204,
https://doi.org/10.1029/2009JD011773, 2009. a
Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, E., Stringer, M., Hill, R., Graham, T., Ridley, J., Blaker, A., Calvert, D., Copsey, D., Ellis, R., Hewitt, H., Hyder, P., Ineson, S., Mulcahy, J., Siahaan, A., and Walton, J.: The Low-Resolution
Version of HadGEM3 GC3. 1: Development and Evaluation for Global Climate,
J. Adv. Model. Earth Sy., 10, 2865–2888, 2018. a
Kwok, R., Cunningham, G., Wensnahan, M., Rigor, I., Zwally, H., and Yi, D.:
Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008,
J. Geophys. Res.-Oceans, 114, C07005, https://doi.org/10.1029/2009JC005312, 2009. a, b
Lunt, D. J., Abe-Ouchi, A., Bakker, P., Berger, A., Braconnot, P., Charbit, S., Fischer, N., Herold, N., Jungclaus, J. H., Khon, V. C., Krebs-Kanzow, U., Langebroek, P. M., Lohmann, G., Nisancioglu, K. H., Otto-Bliesner, B. L., Park, W., Pfeiffer, M., Phipps, S. J., Prange, M., Rachmayani, R., Renssen, H., Rosenbloom, N., Schneider, B., Stone, E. J., Takahashi, K., Wei, W., Yin, Q., and Zhang, Z. S.: A multi-model assessment of last interglacial temperatures, Clim. Past, 9, 699–717, https://doi.org/10.5194/cp-9-699-2013, 2013. a
Lüthje, M., Feltham, D., Taylor, P., and Worster, M.: Modeling the
summertime evolution of sea-ice melt ponds, J. Geophys. Res.-Oceans, 111, C02001, https://doi.org/10.1029/2004JC002818, 2006. a
Madec, G. and NEMO System Team: NEMO ocean engine, Scientific Notes of Climate Modelling Center, 27, ISSN 1288-1619, Institut Pierre-Simon Laplace (IPSL), Paris, 2015. a
Maslanik, J., Drobot, S., Fowler, C., Emery, W., and Barry, R.: On the Arctic
climate paradox and the continuing role of atmospheric circulation in
affecting sea ice conditions, Geophys. Res. Lett., 34, L03711, https://doi.org/10.1029/2006GL028269, 2007. a
Maykut, G. A. and Perovich, D. K.: The role of shortwave radiation in the
summer decay of a sea ice cover, J. Geophys. Res.-Oceans, 92,
7032–7044, 1987. a
Menary, M. B., Kuhlbrodt, T., Ridley, J., Andrews, M. B., Dimdore-Miles, O. B., Deshayes, J., Eade, R., Gray, L., Ineson, S., Mignot, J., Roberts, C. D., Robson, J., Wood, R. A., and Xavier, P.:
Preindustrial Control Simulations With HadGEM3-GC3. 1 for CMIP6, J.
Adv. Model. Earth Sy., 10, 3049–3075, https://doi.org/10.1029/2018MS001495, 2018. a, b
Niemelä, S., Räisänen, P., and Savijärvi, H.: Comparison of
surface radiative flux parameterizations: Part I: Longwave radiation,
Atmos. Res., 58, 1–18, 2001. a
Otto-Bliesner, B. L., Rosenbloom, N., Stone, E. J., McKay, N. P., Lunt, D. J.,
Brady, E. C., and Overpeck, J. T.: How warm was the last interglacial? New
model–data comparisons, Philos. T. R. Soc. A, 371, 20130097, https://doi.org/10.1098/rsta.2013.0097, 2013. a, b, c
Otto-Bliesner, B. L., Braconnot, P., Harrison, S. P., Lunt, D. J., Abe-Ouchi, A., Albani, S., Bartlein, P. J., Capron, E., Carlson, A. E., Dutton, A., Fischer, H., Goelzer, H., Govin, A., Haywood, A., Joos, F., LeGrande, A. N., Lipscomb, W. H., Lohmann, G., Mahowald, N., Nehrbass-Ahles, C., Pausata, F. S. R., Peterschmitt, J.-Y., Phipps, S. J., Renssen, H., and Zhang, Q.: The PMIP4 contribution to CMIP6 – Part 2: Two interglacials, scientific objective and experimental design for Holocene and Last Interglacial simulations, Geosci. Model Dev., 10, 3979–4003, https://doi.org/10.5194/gmd-10-3979-2017, 2017. a, b, c
Otto-Bliesner, B. L., Brady, E. C., Zhao, A., Brierley, C. M., Axford, Y., Capron, E., Govin, A., Hoffman, J. S., Isaacs, E., Kageyama, M., Scussolini, P., Tzedakis, P. C., Williams, C. J. R., Wolff, E., Abe-Ouchi, A., Braconnot, P., Ramos Buarque, S., Cao, J., de Vernal, A., Guarino, M. V., Guo, C., LeGrande, A. N., Lohmann, G., Meissner, K. J., Menviel, L., Morozova, P. A., Nisancioglu, K. H., O'ishi, R., Salas y Mélia, D., Shi, X., Sicard, M., Sime, L., Stepanek, C., Tomas, R., Volodin, E., Yeung, N. K. H., Zhang, Q., Zhang, Z., and Zheng, W.: Large-scale features of Last Interglacial climate: results from evaluating the lig127k simulations for the Coupled Model Intercomparison Project (CMIP6)–Paleoclimate Modeling Intercomparison Project (PMIP4), Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, 2021. a, b
Parkinson, C. L. and Comiso, J. C.: On the 2012 record low Arctic sea ice
cover: Combined impact of preconditioning and an August storm, Geophys.
Res. Lett., 40, 1356–1361, 2013. a
Perovich, D., Grenfell, T., Light, B., and Hobbs, P.: Seasonal evolution of the
albedo of multiyear Arctic sea ice, J. Geophys. Res.-Oceans,
107, 8044, https://doi.org/10.1029/2000JC000438, 2002a. a, b, c
Perovich, D., Tucker III, W., and Ligett, K.: Aerial observations of the
evolution of ice surface conditions during summer, J. Geophys. Res.-Oceans, 107, 8048, https://doi.org/10.1029/2000JC000449, 2002b. a
Perovich, D. K. and Tucker, W. B.: Arctic sea-ice conditions and the
distribution of solar radiation during summer, Ann. Glaciol., 25,
445–450, 1997. a
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem,
S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas,
1979–2005: Attribution and role in the ice-albedo feedback, Geophys.
Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC
HadGEM3-GC31-LL model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set]
https://doi.org/10.22033/ESGF/CMIP6.419, 2018a. a
Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G. L., West, A. E., and Schroeder, D.: The sea ice model component of HadGEM3-GC3.1, Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, 2018b. a, b, c
Scott, F. and Feltham, D.: A model of the three-dimensional evolution of Arctic
melt ponds on first-year and multiyear sea ice, J. Geophys. Res.-Oceans, 115, C12064, https://doi.org/10.1029/2010JC006156, 2010. a
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification:
A research synthesis, Global and planetary change, 77, 85–96, 2011. a
Sime, L., Wolff, E., Oliver, K., and Tindall, J.: Evidence for warmer
interglacials in East Antarctic ice cores, Nature, 462, 342–345, 2009. a
Skyllingstad, E. D., Paulson, C. A., and Perovich, D. K.: Simulation of melt
pond evolution on level ice, J. Geophys. Res.-Oceans, 114, C12019, https://doi.org/10.1029/2009JC005363,
2009. a
Smith, D. M., Screen, J. A., Deser, C., Cohen, J., Fyfe, J. C., García-Serrano, J., Jung, T., Kattsov, V., Matei, D., Msadek, R., Peings, Y., Sigmond, M., Ukita, J., Yoon, J.-H., and Zhang, X.: The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP6: investigating the causes and consequences of polar amplification, Geosci. Model Dev., 12, 1139–1164, https://doi.org/10.5194/gmd-12-1139-2019, 2019. a
Steele, M., Zhang, J., and Ermold, W.: Mechanisms of summertime upper Arctic
Ocean warming and the effect on sea ice melt, J. Geophys. Res.-Oceans, 115, C11004, https://doi.org/10.1029/2009JC005849, 2010. a
Stuecker, M. F., Bitz, C. M., Armour, K. C., Proistosescu, C., Kang, S. M., Xie, S.-P., Kim, D., McGregor, S., Zhang, W., Zhao, S., Cai, W., Dong, Y., and Jin, F.: Polar
amplification dominated by local forcing and feedbacks, Nat. Clim.
Change, 8, 1076–1081, 2018. a
Taylor, P. and Feltham, D.: A model of melt pond evolution on sea ice, J. Geophys. Res.-Oceans, 109, C12007, https://doi.org/10.1029/2004JC002361, 2004. a
Untersteiner, N.: On the mass and heat budget of Arctic sea ice, Arch. Meteor. Geophy. A, 12, 151–182, 1961. a
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. K., Moritz, R. E., Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R., Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A., Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe, M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Scott Pegau, W., Stanton, T. P., and Grenfeld, T. C.: Surface heat budget of the Arctic Ocean, B. Am.
Meteorol. Soc., 83, 255–276, 2002. a
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017. a, b
Wang, J., Zhang, J., Watanabe, E., Ikeda, M., Mizobata, K., Walsh, J. E., Bai,
X., and Wu, B.: Is the Dipole Anomaly a major driver to record lows in Arctic
summer sea ice extent?, Geophys. Res. Lett., 36, L05706, https://doi.org/10.1029/2008GL036706, 2009. a, b, c
West, A. E., McLaren, A. J., Hewitt, H. T., and Best, M. J.: The location of the thermodynamic atmosphere–ice interface in fully coupled models – a case study using JULES and CICE, Geosci. Model Dev., 9, 1125–1141, https://doi.org/10.5194/gmd-9-1125-2016, 2016. a
Williams, C. J. R., Guarino, M.-V., Capron, E., Malmierca-Vallet, I., Singarayer, J. S., Sime, L. C., Lunt, D. J., and Valdes, P. J.: CMIP6/PMIP4 simulations of the mid-Holocene and Last Interglacial using HadGEM3: comparison to the pre-industrial era, previous model versions and proxy data, Clim. Past, 16, 1429–1450, https://doi.org/10.5194/cp-16-1429-2020, 2020. a
Williams, K., Copsey, D., Blockley, E., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schlemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office
global coupled model 3.0 and 3.1 (GC3. 0 and GC3. 1) configurations, J.
Adv. Model. Earth Sy., 10, 357–380, 2018. a, b
Short summary
The Hadley Centre Global Environment Model version 3 (HadGEM3) is the first coupled climate model to simulate an ice-free summer Arctic during the Last Interglacial (LIG), 127 000 years ago, and yields accurate Arctic surface temperatures. We investigate the causes and impacts of this extreme simulated ice loss and, in particular, the role of melt ponds.
The Hadley Centre Global Environment Model version 3 (HadGEM3) is the first coupled climate...
