Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1155-2022
© 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/gmd-15-1155-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Feb 2022
Model description paper |
| 08 Feb 2022
| 08 Feb 2022
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
Chao-Yuan Yang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, and Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai,
Guangdong, China
Jiping Liu
CORRESPONDING AUTHOR
Department of Atmospheric and Environmental Sciences, University at
Albany, State University of New York, Albany, NY, USA
Dake Chen
School of Atmospheric Sciences, Sun Yat-sen University, and Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai,
Guangdong, China
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Cited articles
Aagaard, K.: A synthesis of the Arctic Ocean circulation, Rapp. P.-V. Reun.-Cons. Int. Explor. Mer., 188, 11–22, 1989.
Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., and Turner, A.
K.: Impact of a new sea ice thermodynamic formulation in the CESM2 sea ice
component, J. Adv. Model. Earth Sy., 12, e2020MS002154,
https://doi.org/10.1029/2020MS002154, 2020.
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic sea ice
model for climate study, J. Geophys. Res.-Oceans, 104, 15669–15677, 1999.
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R.,
Smirnova, T. G., and Manikin, G. S.: A North American hourly assimilation and
model forecast cycle: the Rapid Refresh, Mon. Weather Rev.,
144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2016.
Bennetts, L. G., O'Farrell, S., and Uotila, P.: Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model, The Cryosphere, 11, 1035–1040, https://doi.org/10.5194/tc-11-1035-2017, 2017.
Biswas, M. K., Zhang, J. A., Grell, E., Kalina, E., Newman, K., Bernardet,
L., Carson, L., Frimel, J., and Grell, G.: Evaluation of the Grell–Freitas
Convective Scheme in the Hurricane Weather Research and Forecasting (HWRF)
Model, Weather Forecast., 35, 1017–1033, 2020.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic sea ice model for climate study, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999.
Blanchard-Wrigglesworth, E. and Bushuk, M.: Robustness of Arctic sea-ice
predictability in GCMs, Clim. Dynam., 52, 5555–5566, 2018.
Blanchard-Wrigglesworth, E., Bitz, C., and Holland, M.: Influence of initial
conditions and climate forcing on predicting Arctic sea ice, Geophys.
Res. Lett., 38, L18503, https://doi.org/10.1029/2011GL048807, 2011.
Blanchard-Wrigglesworth, E., Cullather, R., Wang, W., Zhang, J., and Bitz,
C. M.: Model forecast skill and sensitivity to initial conditions in the
seasonal sea ice outlook, Geophys. Res. Lett., 42, 8042–8048,
https://doi.org/10.1002/2015GL065860, 2015.
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020.
Briegleb, B. P. and Light, B.: A Delta-Eddington multiple scattering
parameterization for solar radiation in the sea ice component of the
Community Climate System Model, NCAR Tech. Note NCAR/TN-472+STR, National
Center for Atmospheric Research, 2007.
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias
corrections of global models for regional climate simulations of high-impact
weather, Clim. Dynam., 43, 1847–1856,
https://doi.org/10.1007/s00382-013-2011-6, 2014.
Carmack, E., Polyakov, I., Padman, L., Fer, I., Hunke, E., Hutchings, J.,
Jackson, J., Kelley, D., Kwok, R., Layton, C., Melling, H., Perovich, D.,
Persson, O., Ruddick, B., Timmermans, M.-L., Toole, J., Ross, T., Vavrus,
S., and Winsor, P.: Toward Quantifying the Increasing Role of Oceanic Heat
in Sea Ice Loss in the New Arctic, B. Am. Meteorol.
Soc., 96, 2079–2105,
https://doi.org/10.1175/BAMS-D-13-00177.1, 2015.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: updated
yearly, 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 [data set],
https://doi.org/10.5067/8GQ8LZQVL0VL, 1996.
Chen, F. and Dudhia, J.: Coupling an advanced land surface–hydrology model
with the Penn State–NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, 2001.
Chevallier, M., Salas y Mélia, D., Voldoire, A., Déqué, M., and
Garric, G.: Seasonal forecasts of the pan-Arctic sea ice extent using a
GCM-based seasonal prediction system, J. Climate, 26,
6092–6104, 2013.
Colette, A., Vautard, R., and Vrac, M.: Regional climate downscaling with
prior statistical correction of the global climate forcing, Geophys. Res.
Lett., 39, L13707, https://doi.org/10.1029/2012GL052258, 2012.
Collins, W. D., Rasch, P. J., Boville, B. A., McCaa, J., Williamson, D. L., Kiehl, J. T., Briegleb, B. P., Bitz, C., Lin, S.-J., Zhang, M., and Dai, Y.: Description of the NCAR Community Atmosphere Model (3.0), No. NCAR/TN-464+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D63N21CH, 2004.
Craig, T., Hunke, E., DuVivier, A., dabail10, Damsgaard, A., JFLemieux73, Blain, P., Turner, M., mhrib, Rasmussen, T., and Jeffery, N.: CICE-Consortium/CICE: CICE version 6.0.0 (CICE6.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.1900639, 2018.
Day, J. J., Tietsche, S., Collins, M., Goessling, H. F., Guemas, V., Guillory, A., Hurlin, W. J., Ishii, M., Keeley, S. P. E., Matei, D., Msadek, R., Sigmond, M., Tatebe, H., and Hawkins, E.: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1, Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, 2016.
Ding, Y., Cheng, X., Liu, J., Hui, F., Wang, Z., and Chen, S.: Retrieval of
Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an
Ensemble-Based Deep Neural Network, Remote Sensing, 12, 2746, https://doi.org/10.3390/rs12172746, 2020.
DuVivier, A. K., Holland, M. M., Landrum, L., Singh, H. A., Bailey, D. A.,
and Maroon, E. A.: Impacts of sea ice mushy thermodynamics in the Antarctic
on the coupled Earth system, Geophys. Res. Lett., 48,
e2021GL094287, https://doi.org/10.1029/2021GL094287, 2021.
Fer, I.: Near-inertial mixing in the central Arctic Ocean, J. Phys.
Oceanogr., 44, 2031–2049, https://doi.org/10.1175/JPO-D-13-0133.1, 2014.
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.:
updated daily, Sea Ice Index, Version 3. Boulder, Colorado USA, NSIDC:
National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017.
Freitas, S. R., Grell, G. A., Molod, A., Thompson, M. A., Putman, W. M.,
Santos e Silva, C. M., and Souza, E. P.: Assessing the Grell–Freitas
convection parameterization in the NASA GEOS modeling system, J. Adv. Model.
Earth Sy., 10, 1266–1289, https://doi.org/10.1029/2017MS001251, 2018.
Freitas, S. R., Grell, G. A., and Li, H.: The Grell–Freitas (GF) convection parameterization: recent developments, extensions, and applications, Geosci. Model Dev., 14, 5393–5411, https://doi.org/10.5194/gmd-14-5393-2021, 2021.
Germe, A., Chevallier, M., y Mélia, D. S., Sanchez-Gomez, E., and
Cassou, C.: Interannual predictability of Arctic sea ice in a global climate
model: Regional contrasts and temporal evolution, Clim. Dynam.,
43, 2519–2538, 2014.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M., Doblas-Reyes, F. J., Fučkar, N. S., Germe, A., Hawkins, E., Keeley, S., Koenigk, T., Salas y Mélia, D., and Tietsche, S.: A review on Arctic sea-ice
predictability and prediction on seasonal to decadal time-scales, Q.
J. Roy. Meteorol. Soc., 142, 546–561, 2016.
Hecht, M. W., Wingate, B. A., and Kassis, P.: A better, more discriminating
test problem for ocean tracer transport, Ocean Model., 2, 1–15,
https://doi.org/10.1016/S1463-5003(00)00004-4, 2000.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Horvat, C. and Tziperman, E.: A prognostic model of the sea-ice floe size and thickness distribution, The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, 2015.
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe
size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43,
8083–8090, https://doi.org/10.1002/2016GL069742, 2016.
Huang, Y., Chou, G., Xie, Y., and Soulard, N.: Radiative control of the
interannual variability of Arctic sea ice, Geophys. Res. Lett., 46,
9899–9908, https://doi.org/10.1029/2019GL084204, 2019.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997.
IPCC: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B.,
Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp., 2007.
Itoh, M., Nishino, S., Kawaguchi, Y., and Kikuchi, T.: Barrow Canyon volume,
heat, and freshwater fluxes revealed by long-term mooring observations
between 2000 and 2008, J. Geophys. Res.-Oceans, 118, 4363–4379,
https://doi.org/10.1002/jgrc.20290, 2013.
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day,
J. J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland,
M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A.,
Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G.,
Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction
Capabilities on Daily to Seasonal Time Scales, B. Am.
Meteorol. Soc., 97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1, 2016.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch,
M.: Sea ice thickness retrieval from SMOS brightness temperatures during the
Arctic freeze-up period. Geophys. Res. Lett., 39, L05501, https://doi.org/10.1029/2012GL050916, 2012.
Kapsch, M., Graversen, R. G., Tjernström, M., and Bintanja, R.: The
Effect of Downwelling Longwave and Shortwave Radiation on Arctic Summer Sea
Ice, J. Climate, 29, 1143–1159, https://doi.org/10.1175/JCLI-D-15-0238.1, 2016.
Kay, J. E., L'Ecuyer, T., Gettelman, A., Stephens, G., and O'Dell, C.: The
contribution of cloud and radiation anomalies to the 2007 Arctic sea ice
extent minimum, Geophys. Res. Lett., 35, L08503, https://doi.org/10.1029/2008GL033451,
2008.
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.
Kirkman, C. H. IV, and Bitz, C. M.: The Effect of the Sea Ice Freshwater
Flux on Southern Ocean Temperatures in CCSM3: Deep-Ocean Warming and Delayed
Surface Warming, J. Climate, 24, 2224–2237, https://doi.org/10.1175/2010JCLI3625.1, 2011.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
Losses and coupled variability (1958–2018), Environ. Res. Lett.,
13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018.
Laxon, S., 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.
Leonard, B. and Mokhtari, S.: ULTRA-SHARP Non oscillatory Convection Schemes
for High-Speed Steady Multidimensional Flow, Technical Report, NASA, 1990.
Liang, X. and Losch, M.: On the effects of increased vertical mixing on the
Arctic Ocean and sea ice, J. Geophys. Res.-Oceans, 123,
9266–9282, https://doi.org/10.1029/2018JC014303, 2018.
Liu, J., Song, M., Horton, R., and Hu, Y.: Revisiting the potential of melt
pond fraction as a predictor for the seasonal Arctic sea ice minimum,
Environ. Res. Lett., 10, 054017,
https://doi.org/10.1088/1748-9326/10/5/054017, 2015.
Liu, J., Chen, Z., Hu, Y., Zhang, Y., Ding, Y., Cheng, X., Yang, Q., Nerger, L., Spreen, G., Horton, R., Inoue, J., Yang, C.-Y., Li, M., and Song, M.: Towards
reliable arctic sea ice prediction using multivariate data assimilation,
Sci. Bull., 64, 63–72, 2019.
Mallett, R. D. C., Stroeve, J. C., Cornish, S. B. Crawford, A. D., Lukovich,
J. V., Serreze, M. C., Barrett, A. P., Meier, W. N., Heorton, H. D. B. S.,
and Tsamados, M.: Record winter winds in 2020/21 drove exceptional Arctic
sea ice transport, Commun. Earth Environ., 2, 149, https://doi.org/10.1038/s43247-021-00221-8, 2021.
Maslanik, J. and Stroeve, J.: Near-Real-Time DMSP SSMIS Daily Polar Gridded
Sea Ice Concentrations, Version 1, Boulder, Colorado USA, NASA National Snow
and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/U8C09DWVX9LM, 1999.
McLaughlin, F. A., Carmack, E. C., Williams, W. J., Zimmerman, S., Shimada,
K., and Itoh, M.: Joint effects of boundary currents and thermohaline
intrusions on the warming of Atlantic water in the Canada Basin, 1993–2007,
J. Geophys. Res., 114, C00A12, https://doi.org/10.1029/2008JC005001, 2009.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Msadek, R., Vecchi, G., Winton, M., and Gudgel, R.: Importance of initial
conditions in seasonal predictions of Arctic sea ice extent, Geophys.
Res. Lett., 41, 5208–5215, https://doi.org/10.1002/2014GL060799,
2014.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
Naughten, K. A., Galton-Fenzi, B. K., Meissner, K. J., England, M. H.,
Brassington, G. B., Colberg, F., Hattermann, T., and Debernard, J. B.:
Spurious sea ice formation caused by oscillatory ocean tracer advection
schemes, Ocean Model., 116, 108–117, 2017.
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H., Timmermann, R., Hellmer, H. H., Hattermann, T., and Debernard, J. B.: Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4, Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, 2018.
Nerger, L. and Hiller, W.: Software for Ensemble-based Data Assimilation
Systems – Implementation Strategies and Scalability, Comput.
Geosci., 55, 110–118, https://doi.org/10.1016/j.cageo.2012.03.026, 2013.
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A unification
of ensemble square root Kalman filters, Mon. Weather Rev., 140,
2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Newton, R., Pfirman, S., Schlosser, P., Tremblay, B., Murray, M., and
Pomerance, R.: White Arctic vs. Blue Arctic: A case study of diverging
stakeholder responses to environmental change, Earth's Future, 4, 396–405,
https://doi.org/10.1002/2016EF000356, 2016.
Nicolaus, M. and Katlein, C.: Mapping radiation transfer through sea ice using a remotely operated vehicle (ROV), The Cryosphere, 7, 763–777, https://doi.org/10.5194/tc-7-763-2013, 2013.
Nicolaus M., Katlein, C., Maslanik, J., and Hendricks, S.: Changes in Arctic
sea ice result in increasing light transmittance and absorption, Geophys.
Res. Lett., 39, L24501, https://doi.org/10.1029/2012GL053738, 2012.
Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J., Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations, Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, 2016.
Ogi, M., Yamazaki, K., and Wallace, J. M.: Influence of winter and summer
surface wind anomalies on summer Arctic sea ice extent, Geophys. Res. Lett.,
37, L07701, https://doi.org/10.1029/2009GL042356, 2010.
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is
primarily driven by atmospheric temperature fluctuations, Nat. Geosci., 12,
430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019.
Padman, L. and Dillon, T. M.: Vertical heat fluxes through the Beaufort Sea
thermohaline staircase, J. Geophys. Res., 92, 10799–10806,
https://doi.org/10.1029/JC092iC10p10799, 1987.
Perovich, D., Richter-Menge, J., Jones, K., Light, B., Elder, B.,
Polashenski, C., Laroche, D., Markus, T., and Lindsay, R.: Arctic sea-ice
melt in 2008 and the role of solar heating, Ann. Glaciol., 52,
355–359, https://doi.org/10.3189/172756411795931714, 2011.
Perovich, D., Richter-Menge, J., Polashenski, C., Elder, B., Arbetter, T.,
and Brennick, O.: Sea ice mass balance observations from the North Pole
Environmental Observatory, Geophys. Res. Lett., 41, 2019–2025,
https://doi.org/10.1002/2014GL059356, 2014.
Peterson, K., Arribas, A., Hewitt, H., Keen, A., Lea, D., and McLaren, A.:
Assessing the forecast skill of Arctic sea ice extent in the GloSea4
seasonal prediction system, Clim. Dynam., 44, 147–162, 2015.
Pham, D. T.: Stochastic methods for sequential data assimilation in strongly
nonlinear systems, Mon. Weather Rev., 129, 1194–1207, 2001.
Rasch, P. J.: Conservative shape-preserving two-dimensional transport on a
spherical reduced grid, Mon. Weather Rev, 122, 1337–1350, 1994.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496, 2007.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017.
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An emergent sea ice
floe size distribution in a global coupled ocean–sea ice model, J.
Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017JC013692, 2018.
Roach, L. A., Bitz, C. M., Horvat, C., and Dean, S. M.: Advances in modeling
interactions between sea ice and ocean surface waves, J. Adv.
Model. Earth Sy., 11, 4167–4181,
https://doi.org/10.1029/2019MS001836, 2019.
Rocheta, E., Evans, J. P., and Sharma, A.: Can Bias Correction of Regional
Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall
Variability?, J. Climate, 30, 9785–9806, 2017.
Rocheta, E., Evans, J. P., and Sharma, A.: Correcting lateral boundary biases
in regional climate modelling: the effect of the relaxation zone, Clim.
Dynam., 55, 2511–2521, https://doi.org/10.1007/s00382-020-05393-1, 2020.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP climate forecast system
version 2, J. Climate, 27, 2185–2208, 2014.
Schmidt, G. A., Bitz, C. M., Mikolajewicz, U., and Tremblay, L.-B.:
Ice–ocean boundary conditions for coupled models, Ocean Model., 7, 59–74,
2004.
Serreze, M. C. and Meier, W. N.: The Arctic's sea ice cover: trends,
variability, predictability, and comparisons to the Antarctic, Ann. N.Y.
Acad. Sci., 1436, 36–53, https://doi.org/10.1111/nyas.13856, 2019.
Shchepetkin, A. F. and McWilliams, J. C.: Quasi-monotone advection schemes based
on explicit locally adaptive dissipation, Mon. Weather Rev., 126,
1541–1580, 1998.
Shchepetkin, A. F. and McWilliams, J. C.: The Regional Ocean Modeling
System: A split-explicit, free-surface, topography following coordinates
ocean model, Ocean Model., 9, 347–404, 2005.
Shi, X., Notz, D., Liu, J., Yang, H., and Lohmann, G.: Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations, Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, 2021.
Sigmond, M., Fyfe, J., Flato, G., Kharin, V., and Merryfield, W.: Seasonal
forecast skill of Arctic sea ice area in a dynamical forecast system,
Geophys. Res. Lett., 40, 529–534,
https://doi.org/10.1002/grl.50129, 2013.
Smolarkiewicz, P. K.: Multidimensional positive definite advection transport
algorithm: An overview, Int. J. Numer. Meth. Fl., 50, 1123–1144, 2006.
Song, Y. and Haidvogel, D. B.: A semi-implicit ocean circulation model using
a generalized topography-following coordinate system, J. Comp. Phys.,
115, 228–244, 1994.
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model,
J. Geophys. Res.-Oceans, 97, 17729–17738, https://doi.org/10.1029/92JC01755, 1992.
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook
2008–2013, Geophys. Res. Lett., 41, 2411–2418,
https://doi.org/10.1002/2014GL059388, 2014.
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N., Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014.
Tietsche, S., Day, J., Guemas, V., Hurlin, W., Keeley, S., Matei, D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice predictability in current global
climate models, Geophys. Res. Lett., 41, 1035–1043,
https://doi.org/10.1002/2013GL058755, 2014.
Turner, A. K. and Hunke, E. C.: Impacts of a mushy-layer thermodynamic
approach in global sea-ice simulations using the CICE sea-ice model, J.
Geophys. Res.-Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358, 2015.
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J. Geophys. Res.,
118, 2279–2294, https://doi.org/10.1002/jgrc.20171, 2013.
Turner, J. S.: Buoyancy Effects in Fluids, Cambridge University Press, 368
pp., 1973.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Mar. Res., 61, 235–265, https://doi.org/10.1357/002224003322005087, 2003.
Van den Dool, H.: Empirical Methods in Short-Term Climate Prediction, Oxford
Univ. Press, Oxford, U. K., 2006.
Wang, W., Chen, M., and Kumar, A.: Seasonal prediction of Arctic sea ice
extent from a coupled dynamical forecast system, Mon. Weather Rev.,
141, 1375–1394, 2013.
Warner, J. C., Armstrong, B., He, R., and Zambon, J.: Development of a
coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling
system, Ocean Model., 35, 230–244, 2010.
Woodgate, R. A., Aagaard, K., and Weingartner, T. J.: A year in the physical
oceanography of the Chukchi Sea: Moored measurements from autumn 1990–1991,
Deep-Sea Res. Pt. II, 52, 3116–3149,
https://doi.org/10.1016/j.dsr2.2005.10.016, 2005.
Wu, W., Lynch, A. H., and Rivers, A.: Estimating the Uncertainty in a
Regional Climate Model Related to Initial and Lateral Boundary Conditions,
J. Climate, 18, 917–933, 2005.
Yang, C.-Y., Liu, J., and Xu, S.: Seasonal Arctic sea ice prediction using a
newly developed fully coupled regional model with the assimilation of
satellite sea ice observations, J. Adv. Model. Earth
Sy., 12, e2019MS001938, https://doi.org/10.1029/2019MS001938, 2020.
Yang, C.-Y., Liu, J., and Chen, D.: The model code of Coupled Arctic Prediction System version 1.0 (CAPS v1.0) for the permanent archive in the article “An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018”, Zenodo [code], https://doi.org/10.5281/zenodo.5842668, 2022a.
Yang, C.-Y., Liu, J., and Chen, D.: The prediction data analyzed in the article: “An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018”, Zenodo [data set], https://doi.org/10.5281/zenodo.5839510, 2022b.
Zampieri, L., Goessling, H. F., and Jung, T.: Bright prospects for Arctic
sea ice prediction on subseasonal time scales, Geophys. Res. Lett.,
45, 9731–9738, https://doi.org/10.1029/2018GL079394, 2018.
Zhang, J. and Rothrock, D.: Modeling global sea ice with a thickness and
enthalpy distribution model in generalized curvilinear coordinates, Mon.
Weather Rev., 131, 845–861, 2003.
Zhang, J., Lindsay, R., Steele, M., and Schweiger, A.: What drove the
dramatic retreat of arctic sea ice during summer 2007?, Geophys. Res. Lett.,
35, L11505, https://doi.org/10.1029/2008GL034005, 2008.
Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Sea ice floe size
distribution in the marginal ice zone: Theory and numerical experiments,
J. Geophys. Res-.Oceans, 120, 3484–3498, https://doi.org/10.1002/2015JC010770, 2015.
Zhang, J., Stern, H., Hwang, B., Schweiger, A., Steele, M., Stark, M., and
Graber, H. C.: Modeling the seasonal evolution of the Arctic sea ice floe
size distribution, Elementa, 4, 126,
https://doi.org/10.12952/journal.elementa.000126, 2016.
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic...
