Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

Qi Shu; Fangli Qiao; Jiping Liu; Zhenya Song; Zhiqiang Chen; Jiechen Zhao; Xunqiang Yin; Yajuan Song

doi:10.1007/s13131-021-1768-4

Volume 40 Issue 10

Oct. 2021

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Qi Shu, Fangli Qiao, Jiping Liu, Zhenya Song, Zhiqiang Chen, Jiechen Zhao, Xunqiang Yin, Yajuan Song. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system[J]. Acta Oceanologica Sinica, 2021, 40(10): 65-75. doi: 10.1007/s13131-021-1768-4

Citation:

Qi Shu, Fangli Qiao, Jiping Liu, Zhenya Song, Zhiqiang Chen, Jiechen Zhao, Xunqiang Yin, Yajuan Song. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system[J]. Acta Oceanologica Sinica, 2021, 40(10): 65-75. doi: 10.1007/s13131-021-1768-4

Citation:

Qi Shu, Fangli Qiao, Jiping Liu, Zhenya Song, Zhiqiang Chen, Jiechen Zhao, Xunqiang Yin, Yajuan Song. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system[J]. Acta Oceanologica Sinica, 2021, 40(10): 65-75. doi: 10.1007/s13131-021-1768-4

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Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

doi: 10.1007/s13131-021-1768-4

Qi Shu^{1, 2, 3
,
,},
Fangli Qiao^{1, 2, 3},
Jiping Liu⁴,
Zhenya Song^{1, 2, 3},
Zhiqiang Chen⁵,
Jiechen Zhao⁶,
Xunqiang Yin^{1, 2, 3},
Yajuan Song^{1, 2, 3}

1.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China
4.
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
5.
College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
6.
National Marine Environmental Forecasting Center, Beijing 100081, China

Funds: The National Key Research and Development Program of China under contract Nos 2018YFC1407205 and 2018YFA0605901; the Basic Scientiﬁc Fund for National Public Research Institute of China (ShuXingbei Young Talent Program) under contract No. 2019S06; the National Natural Science Foundation of China under contract Nos 41821004, 42022042 and 41941012; the China-Korea Cooperation Project on Northwestern Pacific Climate Change and its Prediction.

More Information

Corresponding author: E-mail: shuqi@fio.org.cn
Received Date: 2020-10-09
Accepted Date: 2020-12-02

Available Online: 2021-09-14

Publish Date: 2021-10-30

Abstract

Abstract

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ﬁlter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction.
- FIO-ESM,
- sea ice data assimilation,
- sea ice forecast

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References(69)

References

[1]	Allard R A, Farrell S L, Hebert D A, et al. 2018. Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system. Advances in Space Research, 62(6): 1265–1280. doi: 10.1016/j.asr.2017.12.030
[2]	Barber D G, Hop H, Mundy C J, et al. 2015. Selected physical, biological and biogeochemical implications of a rapidly changing Arctic Marginal Ice Zone. Progress in Oceanography, 139: 122–150. doi: 10.1016/j.pocean.2015.09.003
[3]	Blockley E W, Peterson K A. 2018. Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness. The Cryosphere, 12(11): 3419–3438. doi: 10.5194/tc-12-3419-2018
[4]	Cavalieri D J, Parkinson C L, Gloersen P, et al. 1996. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data version 1. Boulder, CO, USA: NASA DAAC at the National Snow and Ice Data Center
[5]	Chen Zhiqiang, Liu Jiping, Song Mirong, et al. 2017. Impacts of assimilating satellite sea ice concentration and thickness on Arctic sea ice prediction in the NCEP climate forecast system. Journal of Climate, 30(21): 8429–8446. doi: 10.1175/JCLI-D-17-0093.1
[6]	Chen Hui, Yin Xunqiang, Bao Ying, et al. 2016. Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter. Science China Earth Sciences, 59(3): 484–494. doi: 10.1007/s11430-015-5187-2
[7]	Chevallier M, Salas-Mélia D. 2012. The role of sea ice thickness distribution in the Arctic sea ice potential predictability: a diagnostic approach with a coupled GCM. Journal of Climate, 25(8): 3025–3038. doi: 10.1175/JCLI-D-11-00209.1
[8]	Cohen J, Screen J A, Furtado J C, et al. 2014. Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7(9): 627–637. doi: 10.1038/ngeo2234
[9]	Collins W D, Rasch P J, Boville B A, et al. 2006. The formulation and atmospheric simulation of the Community Atmosphere Model Version 3 (CAM3). Journal of Climate, 19(11): 2144–2161. doi: 10.1175/jcli3760.1
[10]	Collow T W, Wang Wanqiu, Kumar A, et al. 2015. Improving Arctic sea ice prediction using PIOMAS initial sea ice thickness in a coupled ocean–atmosphere model. Monthly Weather Review, 143(11): 4618–4630. doi: 10.1175/MWR-D-15-0097.1
[11]	Day J J, Hawkins E, Tietsche S. 2014. Will Arctic sea ice thickness initialization improve seasonal forecast skill?. Geophysical Research Letters, 41(21): 7566–7575. doi: 10.1002/2014GL061694
[12]	Dickinson R E, Oleson K W, Bonan G, et al. 2006. The community land model and its climate statistics as a component of the community climate system model. Journal of Climate, 19(11): 2302–2324. doi: 10.1175/JCLI3742.1
[13]	Ducet N, Le Traon P Y, Reverdin G. 2000. Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. Journal of Geophysical Research: Oceans, 105(C8): 19477–19498. doi: 10.1029/2000jc900063
[14]	Fetterer F, Knowles K, Meier W, et al. 2017. Sea ice index, version 3. Boulder, CO, USA: National Snow and Ice Data Center, doi: 10.7265/N5K072F8
[15]	Fowler C, Emery W J, Maslanik J. 2004. Satellite-derived evolution of Arctic sea ice age: October 1978 to March 2003. IEEE Geoscience and Remote Sensing Letters, 1(2): 71–74. doi: 10.1109/LGRS.2004.824741
[16]	Goessling H F, Tietsche S, Day J J, et al. 2016. Predictability of the Arctic sea ice edge. Geophysical Research Letters, 43(4): 1642–1650. doi: 10.1002/2015GL067232
[17]	Hakkinen S, Proshutinsky A, Ashik I. 2008. Sea ice drift in the Arctic since the 1950s. Geophysical Research Letters, 35(19): L19704. doi: 10.1029/2008GL034791
[18]	Hebert D A, Allard R A, Metzger E J, et al. 2015. Short-term sea ice forecasting: an assessment of ice concentration and ice drift forecasts using the U.S. Navy’s Arctic Cap Nowcast/Forecast System. Journal of Geophysical Research: Oceans, 120(12): 8327–8345. doi: 10.1002/2015JC011283
[19]	Hunke E C, Lipscomb W H. 2008. CICE: the Los Alamos sea ice model. documentation and software user’s manual Version 4.0, LA-CC-06-012. Los Alamos, NM, USA: Los Alamos National Laboratory
[20]	Kimmritz M, Counillon F, Bitz C M, et al. 2018. Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model. Tellus A: Dynamic Meteorology and Oceanography, 70(1): 1–23. doi: 10.1080/16000870.2018.1435945
[21]	Kwok R, Cunningham G F. 2015. Variability of Arctic sea ice thickness and volume from CryoSat-2. Philosophical Transactions of the Royal Society A: Mathematical, 373(2045): 20140157. doi: 10.1098/rsta.2014.0157
[22]	Kwok R, Rothrock D A. 2009. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophysical Research Letters, 36(15): L15501. doi: 10.1029/2009GL039035
[23]	Laxon S W, Giles K A, Ridout A L, et al. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40(4): 732–737. doi: 10.1002/grl.50193
[24]	Lemieux J F, Beaudoin C, Dupont F, et al. 2016. The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration. Quarterly Journal of the Royal Meteorological Society, 142(695): 632–643. doi: 10.1002/qj.2526
[25]	Lindsay R, Haas C, Hendricks S, et al. 2012. Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness. Geophysical Research Letters, 39(21): L21502. doi: 10.1029/2012GL053576
[26]	Lindsay R, Schweiger A. 2015. Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations. The Cryosphere, 9(1): 269–283. doi: 10.5194/tc-9-269-2015
[27]	Lindsay R W, Zhang J. 2006. Assimilation of ice concentration in an ice–ocean model. Journal of Atmospheric and Oceanic Technology, 23(5): 742–749. doi: 10.1175/jtech1871.1
[28]	Lisæter K A, Evensen G, Laxon S. 2017. Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model. Journal of Geophysical Research: Oceans, 112(C7): C07023. doi: 10.1029/2006JC003786
[29]	Lisæter K A, Rosanova J, Evensen G. 2003. Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter. Ocean Dynamics, 53(4): 368–388. doi: 10.1007/s10236-003-0049-4
[30]	Liu Jiping, Chen Zhiqiang, Hu Yongyun, et al. 2019. Towards reliable Arctic sea ice prediction using multivariate data assimilation. Science Bulletin, 64(1): 63–72. doi: 10.1016/j.scib.2018.11.018
[31]	Liu Jiping, Curry J A, Wang Huijun, et al. 2012. Impact of declining Arctic sea ice on winter snowfall. Proceedings of the National Academy of Sciences of the United States of America, 109(11): 4074–4079. doi: 10.1073/pnas.1114910109
[32]	Maslanik J, Stroeve J, Fowler C, et al. 2011. Distribution and trends in Arctic sea ice age through spring 2011. Geophysical Research Letters, 38(13): L13502. doi: 10.1029/2011GL047735
[33]	Melia N, Haines K, Hawkins E. 2016. Sea ice decline and 21st century trans-Arctic shipping routes. Geophysical Research Letters, 43(18): 9720–9728. doi: 10.1002/2016GL069315
[34]	Mori M, Watanabe M, Shiogama H, et al. 2014. Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nature Geoscience, 7(12): 869–873. doi: 10.1038/ngeo2277
[35]	Mu Longjiang, Yang Qinghua, Losch M, et al. 2018. Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously. Quarterly Journal of the Royal Meteorological Society, 144(711): 529–538. doi: 10.1002/qj.3225
[36]	Nerger L, Hiller W. 2013. Software for ensemble-based data assimilation systems—Implementation strategies and scalability. Computers&Geosciences, 55: 110–118. doi: 10.1016/j.cageo.2012.03.026
[37]	Nghiem S V, Rigor I G, Perovich D K, et al. 2007. Rapid reduction of Arctic perennial sea ice. Geophysical Research Letters, 34(19): L19504. doi: 10.1029/2007GL031138
[38]	Olason E, Notz D. 2014. Drivers of variability in Arctic sea-ice drift speed. Journal of Geophysical Research: Oceans, 119(9): 5755–5775. doi: 10.1002/2014JC009897
[39]	Parkinson C L. 2019. A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proceedings of the National Academy of Sciences of the United States of America, 116(29): 14414–14423. doi: 10.1073/pnas.1906556116
[40]	Parkinson C L, Cavalieri D J. 2008. Arctic sea ice variability and trends, 1979–2006. Journal of Geophysical Research, 113(C7): C07003. doi: 10.1029/2007JC004558
[41]	Qiao Fangli, Song Zhenya, Bao Ying, et al. 2013. Development and evaluation of an Earth System Model with surface gravity waves. Journal of Geophysical Research: Oceans, 118(9): 4514–4524. doi: 10.1002/jgrc.20327
[42]	Qiao Fangli, Yuan Yeli, Yang Yongzeng, et al. 2004. Wave-induced mixing in the upper ocean: distribution and application to a global ocean circulation model. Geophysical Research Letters, 31(11): L11303. doi: 10.1029/2004GL019824
[43]	Reynolds R W, Smith T M, Liu Chunying, et al. 2007. Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22): 5473–5496. doi: 10.1175/2007JCLI1824.1
[44]	Ricker R, Hendricks S, Kaleschke L, et al. 2017. A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. The Cryosphere, 11(4): 1607–1623. doi: 10.5194/tc-11-1607-2017
[45]	Sakov P, Counillon F, Bertino L, et al. 2012. TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Science, 8(4): 633–656. doi: 10.5194/os-8-633-2012
[46]	Sévellec F, Fedorov A V, Liu Wei. 2017. Arctic sea-ice decline weakens the Atlantic Meridional Overturning Circulation. Nature Climate Change, 7(8): 604–610. doi: 10.1038/nclimate3353
[47]	Schweiger A, Lindsay R, Zhang Jinlun, et al. 2011. Uncertainty in modeled Arctic sea ice volume. Journal of Geophysical Research: Oceans, 116(C8): C00D06. doi: 10.1029/2011JC007084
[48]	Shu Qi, Qiao Fangli, Bao Ying, et al. 2015. Assessment of Arctic sea ice simulation by FIO-ESM based on data assimilation experiment. Haiyang Xuebao (in Chinese), 37(11): 33–40. doi: 10.3969/j.issn.0253-4193.2015.11.004
[49]	Smith R, Jones P, Briegleb B P, et al. 2010. The Parallel Ocean Program (POP) reference manual: ocean component of the community climate system model (CCSM). Boulder, CO, USA: National Centre for Atmosphere Research
[50]	Smith L C, Stephenson S R. 2013. New Trans-Arctic shipping routes navigable by midcentury. Proceedings of the National Academy of Sciences of the United States of America, 110(13): E1191–E1195. doi: 10.1073/pnas.1214212110
[51]	Song Zhenya, Shu Qi, Bao Ying, et al. 2015. The prediction on the 2015/16 El Niño event from the perspective of FIO-ESM. Acta Oceanologica Sinica, 34(12): 67–71. doi: 10.1007/s13131-015-0787-4
[52]	Stroeve J, Hamilton L C, Bitz C M, et al. 2014. Predicting September sea ice: ensemble skill of the SEARCH sea ice outlook 2008–2013. Geophysical Research Letters, 41(7): 2411–2418. doi: 10.1002/2014GL059388
[53]	Tietsche S, Notz D, Jungclaus J H, et al. 2013. Assimilation of sea-ice concentration in a global climate model–physical and statistical aspects. Ocean Science, 9(1): 19–36. doi: 10.5194/os-9-19-2013
[54]	Tilling R L, Ridout A, Shepherd A. 2016. Near-real-time Arctic sea ice thickness and volume from CryoSat-2. The Cryosphere, 10(5): 2003–2012. doi: 10.5194/tc-10-2003-2016
[55]	Tomas R A, Deser C, Sun Lantao. 2016. The role of ocean heat transport in the global climate response to projected Arctic Sea ice loss. Journal of Climate, 29(19): 6841–6859. doi: 10.1175/JCLI-D-15-0651.1
[56]	Toyoda T, Fujii Y, Yasuda T, et al. 2016. Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields. Journal of Oceanography, 72(2): 235–262. doi: 10.1007/s10872-015-0326-0
[57]	Wang Wanqiu, Chen Mingyue, Kumar A. 2013a. Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Monthly Weather Review, 141(4): 1375–1394. doi: 10.1175/MWR-D-12-00057.1
[58]	Wang Keguang, Debernard J, Sperrevik A K, et al. 2013b. A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS. Annals of Glaciology, 54(62): 8–12. doi: 10.3189/2013AoG62A138
[59]	Wayand N E, Bitz C M, Blanchard-Wrigglesworth E. 2019. A year-round subseasonal-to-seasonal sea ice prediction portal. Geophysical Research Letters, 46(6): 3298–3307. doi: 10.1029/2018GL081565
[60]	Xie Jiping, Counillon F, Bertino L, et al. 2016. Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system. The Cryosphere, 10(6): 2745–2761. doi: 10.5194/tc-10-2745-2016
[61]	Yang Qinghua, Losa S N, Losch M, et al. 2014. Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter. Journal of Geophysical Research: Oceans, 119(10): 6680–6692. doi: 10.1002/2014JC009963
[62]	Yang Qinghua, Losa S N, Losch M, et al. 2015. Assimilating summer sea-ice concentration into a coupled ice-ocean model using a LSEIK filter. Annals of Glaciology, 56(69): 38–44. doi: 10.3189/2015AoG69A740
[63]	Yang Qinghua, Losch M, Losa S N, et al. 2016. Brief communication: the challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation. The Cryosphere, 10(2): 761–774. doi: 10.5194/tc-10-761-2016
[64]	Yang Yongzeng, Qiao Fangli, Zhao Wei, et al. 2005. MASNUM ocean wave numerical model in spherical coordinates and its application. Haiyang Xuebao (in Chinese), 27(2): 1–7. doi: 10.3321/j.issn:0253-4193.2005.02.001
[65]	Yin Xunqiang. 2015. Development of assimilation module for ensemble adjustment Kalman filter and its application in ocean and climate models (in Chinese)[dissertation]. Qingdao: Ocean University of China
[66]	Zampieri L, Goessling H F, Jung T. 2018. Bright prospects for Arctic sea ice prediction on subseasonal time scales. Geophysical Research Letters, 45(18): 9731–9738. doi: 10.1029/2018GL079394
[67]	Zhang Jinlun, Ashjian C, Campbell R, et al. 2015. The influence of sea ice and snow cover and nutrient availability on the formation of massive under-ice phytoplankton blooms in the Chukchi Sea. Deep-Sea Research Part II: Topical Studies in Oceanography, 118: 122–135. doi: 10.1016/j.dsr2.2015.02.008
[68]	Zhang Jinlun, Rothrock D A. 2003. Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Monthly Weather Review, 131(5): 845–861. doi: 10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2
[69]	Zhang Jinlun, Thomas D R, Rothrock D A, et al. 2003. Assimilation of ice motion observations and comparisons with submarine ice thickness data. Journal of Geophysical Research: Oceans, 108(C6): 3170. doi: 10.1029/2001JC001041