Articles | Volume 16, issue 10
https://doi.org/10.5194/tc-16-4013-2022
© Author(s) 2022. 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-16-4013-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2022
Research article |
| 07 Oct 2022
| 07 Oct 2022
Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt
Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
Edward Blockley
Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
Matthew Collins
College of Engineering, Mathematics and Physical Sciences, University
of Exeter, Stocker Road, Exeter EX4 4PY, UK
Related authors
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
Short summary
Short summary
This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
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.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, https://doi.org/10.5194/gmd-13-4845-2020, 2020
Short summary
Short summary
This study calculates sea ice energy fluxes from data produced by ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025, https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, and they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 d ahead – and an outlook of their upcoming developments.
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
Short summary
Short summary
This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
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.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
Emily A. Hill, Sebastian H. R. Rosier, G. Hilmar Gudmundsson, and Matthew Collins
The Cryosphere, 15, 4675–4702, https://doi.org/10.5194/tc-15-4675-2021, https://doi.org/10.5194/tc-15-4675-2021, 2021
Short summary
Short summary
Using an ice flow model and uncertainty quantification methods, we provide probabilistic projections of future sea level rise from the Filchner–Ronne region of Antarctica. We find that it is most likely that this region will contribute negatively to sea level rise over the next 300 years, largely as a result of increased surface mass balance. We identify parameters controlling ice shelf melt and snowfall contribute most to uncertainties in projections.
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, https://doi.org/10.5194/gmd-14-3437-2021, 2021
Short summary
Short summary
The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
Qun Liu, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis
Geosci. Model Dev., 14, 2801–2826, https://doi.org/10.5194/gmd-14-2801-2021, https://doi.org/10.5194/gmd-14-2801-2021, 2021
Short summary
Short summary
Clouds play an vital role in Earth's energy budget, and even a small change in cloud fields can have a large impact on the climate system. They also bring lots of uncertainties to climate models. Here we implement a simple diagnostic cloud scheme in order to reproduce the general radiative properties of clouds. The scheme can capture some key features of the cloud fraction and cloud radiative properties and thus provide a useful tool to explore unsolved problems relating to clouds.
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.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, https://doi.org/10.5194/gmd-13-4845-2020, 2020
Short summary
Short summary
This study calculates sea ice energy fluxes from data produced by ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Cited articles
Anderson, M., Bliss, A., and Drobot, S.: Snow Melt Onset Over Arctic Sea Ice
from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 3. Boulder,
Colorado USA, NASA National Snow and Ice Data Center Distributed Active
Archive Center, https://doi.org/10.5067/22NFZL42RMUO (last access: October 2015),
2001, updated 2012.
Barker, H. W. and Li, Z.: Improved simulation of clear-sky radiative
transfer in the CCC-GCM, J. Climate, 8, 2213–2223, 1995.
Bitz, C. M.: Some Aspects of Uncertainty in Predicting Sea Ice Thinning, in:
Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and
Implications, edited by: DeWeaver, E. T., Bitz, C. M., and Tremblay, L.-B., American
Geophysical Union, Washington, D.C., https://doi.org/10.1029/180GM06, 2008.
Bitz, C. and Lipscomb, W. H.: An energy-conserving thermodynamic model of
sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999.
Bitz, C. M. and Roe, G. H.: A Mechanism for the High Rate of Sea Ice
Thinning in the Arctic Ocean, J. Climate, 17, 3623–3632, https://doi.org/10.1175/1520-0442(2004)017<3623:AMFTHR>2.0.CO;2, 2004.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: Sea Ice
Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave
Data, Version 1., Boulder, Colorado USA, NASA
National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/8GQ8LZQVL0VL (last access: 2016), 1996, updated yearly.
Christensen, M. W., Behrangi, A., L'ecuyer, T. S., Wood, N. B., Lebsock, M.
D., and Stephens, G. L.: Arctic Observation and Reanalysis Integrated System,
A New Data Product for Validation and Climate Study, B. Am. Meteorol. Soc., 97, 907–916, https://doi.org/10.1175/BAMS-D-14-00273.1, 2016.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011.
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.,
Belsol, C., Dragani, R., Fuentes, M., Geer, A., J., Haimberger, L., Healy,
S. B., Hersbach, H., Holm, 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. Meoteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
DeWeaver, E. T., Hunke, E. C., and Holland, M. M., Sensitivity of Arctic Sea
Ice Thickness to Intermodel Variations in the Surface Energy Budget, AGU Geophysical Monograph 180: Arctic Sea Ice Decline: Observations, Projections, Mechanisms and Implications,
77–91, https://doi.org/10.1029/180GM07, 2008.
Eisenman, I., Untersteiner, N., and Wettlaufer, J. S.: On the reliability of
simulated Arctic sea ice in global climate models, Geophys. Res. Lett., 34, L10501,
https://doi.org/10.1029/2007GL029914, 2007.
Flocco, D., Feltham, D. L., Bailey, E., and Schroeder, D.: The refreezing of
melt ponds on Arctic sea ice, J. Geophys. Res.-Oceans, 120, 647–659, https://doi.org/10.1002/2014JC010140,
2015.
Graham, R. M., Cohen, L., Petty, A. A., Boisvert, L. N., Rinke, A., Hudson,
S. R., Nicolaus, M., and Granskog, M. A.: Increasing frequency and duration
of Arctic winter warming events, Geophys. Res. Lett., 44, 6974–6983,
https://doi.org/10.1002/2017GL073395, 2017.
Holland, M. and Landrum, L.: Factors affecting projected Arctic surface
shortwave heating and albedo change in coupled climate models,
Philos. T. Roy. Soc. A, 373, 20140162, https://doi.org/10.1098/rsta.2014.0162, 2015.
Holland, M. M., Serreze, M. C., and Stroeve, J.: The sea ice mass
budget of the Arctic and its future change as simulated by coupled climate
models, Clim. Dynam., 34, 185–200, https://doi.org/10.1007/s00382-008-0493-4, 2010.
Hunke, E. C. and Dukowicz, J. K.: An Elastic-Viscous-Plastic Model for Sea Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.
Jin, Z., Qiao, Y., Wang, Y., Fang, Y., and Yi, W.: A new parameterization of
spectral and broadband ocean surface albedo, Opt. Expres, 19, 26429–26443,
https://doi.org/10.1364/OE.19.026429, 2011.
Keen, A. and Blockley, E.: Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model, The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, 2018.
Keen, A., Blockley, E., Bailey, D. A., Boldingh Debernard, J., Bushuk, M., Delhaye, S., Docquier, D., Feltham, D., Massonnet, F., O'Farrell, S., Ponsoni, L., Rodriguez, J. M., Schroeder, D., Swart, N., Toyoda, T., Tsujino, H., Vancoppenolle, M., and Wyser, K.: An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models, The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, 2021.
Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, Ed., 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, https://doi.org/10.1029/2018MS001370, 2018.
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea
ice thickness in the Arctic region, Nature, 425, 947–950, https://doi.org/10.1038/nature02050,
2003.
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.
Lindsay, R. and Schweiger, A.: Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations, The Cryosphere, 9, 269–283, https://doi.org/10.5194/tc-9-269-2015, 2015.
Lindsay, R., Wensnahan, M., Schweiger, A., and Zhang, J.: Evaluation of Seven
Difference Atmospheric Reanalysis Products in the Arctic, J. Climate, 27, 2588–2606,
https://doi.org/10.1175/JCLI-D-13-00014.1, 2014.
Lindsay, R. W.: Temporal variability of the energy balance of thick Arctic
pack ice, J. Climate, 11, 313–333, 1998.
Lipscomb, W. H. and Hunke, E. C.: Modelling sea ice transport using
incremental remapping, Mon. Weather Rev., 132, 1341–1354, https://doi.org/10.1175/1520-0493(2004)132<1341:MSITUI>2.0.CO;2, 2004.
Markus, T., Stroeve, J. C., and Miller, J.: Recent changes in Arctic sea ice
melt onset, freezeup, and melt season length, J. Geophys. Res.-Oceans, 114, C12, https://doi.org/10.1029/2009JC005436, 2009.
Massonnet, F., Fichefet, T., Goosse, H., Bitz, C. M., Philippon-Berthier, G., Holland, M. M., and Barriat, P.-Y.: Constraining projections of summer Arctic sea ice, The Cryosphere, 6, 1383–1394, https://doi.org/10.5194/tc-6-1383-2012, 2012.
McPhee, M. G., Kikuchi, T., Morison, J. H., and Stanton, T. P.: Ocean-to-ice
heat flux at the North Pole environmental observatory, Geophys. Res. Lett.,
30, 2274, https://doi.org/10.1029/2003GL018580, 2003.
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent
Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17,
https://doi.org/10.1038/ngeo1332, 2012.
Notz, D.: How well must climate models agree with observations?, Philos. T. Roy. Soc. A, 373, 2052,
https://doi.org/10.1098/rsta.2014.0164, 2015.
Notz, D. and SIMIP Community: Arctic sea ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020.
Perovich, D. K., Richter-Menge, J. A., Jones, K. F., and Light, B.: Sunlight,
water, and ice: Extreme Arctic sea ice melt during the summer of 2007,
Geophys. Res. Lett., 35, L11501, https://doi.org/10.1029/2008GL034007, 2008.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the
late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
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, 2018.
Rosenblum, E. and Eisenman, I.: Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming, J. Climate, 30, 6265–6278, 2017.
Rothrock, D. A., Percival, D. B., and Wensnahan, M.: The decline in arctic
sea ice thickness: Separating the spatial, annual, and interannual
variability in a quarter century of submarine data, J. Geophys. Res., 113, C05003, https://doi.org/10.1029/2007JC004252, 2008.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06,
https://doi.org/10.1029/2011JC007084, 2011.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the U.K. Earth System
Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739,
2019.
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical
Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, https://doi.org/10.1175/1520-0485, 1976.
Shu, Q., Song, Z., and Qiao, F.: Assessment of sea ice simulations in the CMIP5 models, The Cryosphere, 9, 399–409, https://doi.org/10.5194/tc-9-399-2015, 2015.
Stammerjohn, S., Massom, R., Rind, D., and Martinson, D.: Regions of rapid
sea ice change: An inter-hemispheric seasonal comparison, Geophys. Res. Lett., 39, 6, https://doi.org/10.1029/2012GL050874, 2012.
Steele, M., Zhang, J., and Ermold, W.: Mechanisms of summer Arctic Ocean
warming, J. Geophys. Res.-Oceans, 115, C11004,
https://doi.org/10.1029/2009JC005849, 2010.
Stroeve, J. C., Holland, M. M., Meier, W. M., Scambos, T., and Serreze, M.
C.: Arctic sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, 9, https://doi.org/10.1029/2007GL029703, 2007.
Stroeve, J. C., Kattsov, V, Barrett, A., Serreze, M., Pavlova, T., Holland,
M. M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3
and observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676, 2012.
Thorndike, A. S.: A toy model linking atmospheric thermal radiation and sea
ice growth, J. Geophys. Res., 97, 9401–9410, https://doi.org/10.1029/92JC00695, 1992.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The
thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975.
Titchner, H. A. and Rayner, N. A.: The Met Office Hadley Centre sea ice and
sea surface temperature data set, version 2: 1. Sea ice concentrations, J. Geophys. Res.-Atmos.,
119, 2864–2889, https://doi.org/10.1002/2013JD020316, 2014.
Wang, X., Key, J., Kwok, R., and Zhang, J.: Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data, Remote Sens., 8, 713, https://doi.org/10.3390/rs8090713, 2016.
West, A.: Code associated with final revised paper: “Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt” (1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7058497, 2022.
West, A., Collins, M., Blockley, E., Ridley, J., and Bodas-Salcedo, A.: Induced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errors, The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, 2019.
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.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert,
D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., 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., Schiemann, 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 & GC3.1) configurations, J. Adv. Model. Earth Sy., 10, 357–380, https://doi.org/10.1002/2017MS001115, 2017.
Zhang, Y., Rossow, W. B., Lacis, A. A., Oinas, V., and Mishchenko, M. I.:
Calculation of radiative fluxes from the surface to the top of the
atmosphere based on ISCCP and other data sets: Refinements to the radiative
transfer model and the input data, J. Geophys. Res., 109, D19105, https://doi.org/10.1029/2003JD004457,
2004.
Short summary
In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
In this study we explore a method of examining model differences in ice volume by looking at the...
