Towards reliable Arctic sea ice prediction using multivariate data assimilation
|
摘要
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
引文
[1]Polyak L,Alley R,Andrews J,et al.History of sea ice in the Arctic.Quat Sci Rev2010;29:1757-78.
[2]Meier W,Stroeve J,Barrett A,et al.A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present.Cryosphere 2012;6:1359-68.
[3]Cavalieri D,Parkinson C.Arctic sea ice variability and trends,1979-2010.Cryosphere 2012;6:881-9.
[4]Stroeve J,Kattsov V,Barrett A,et al.Trends in Arctic sea ice extent from CMIP5,CMIP3 and observations.Geophys Res Lett 2012;39:L16502.
[5]Kwok R,Rothrock D.Decline in Arctic sea ice thickness from submarine and ICESat records 1958-2008.Geophys Res Lett 2009;36:L15501.
[6]Kwok R,Cunningham G.Variability of Arctic sea ice thickness and volume from CryoSat-2.Phil Trans R Soc Ser A Math Phys Eng Sci 2015;373:2045.
[7]Maslanik J,Stroeve J,Fowler C,et al.Distribution and trends in Arctic sea ice age through spring 2011.Geophys Res Lett 2011;38:L13502.
[8]Comiso J.Large decadal decline of the Arctic multiyear ice cover.J Clim2012;25:1176-93.
[9]Rampal P,Weiss J,Marsan D.Positive trend in the mean speed and deformation rate of Arctic sea ice 1979-2007.J Geophys Res 2009;114:C05013.
[10]Spreen G,Kwok R,Menemenlis D.Trends in Arctic sea ice drift and role of wind forcing:1992-2009.Geophys Res Lett 2011;38:L19501.
[11]Kwok R,Spreen G,Pang S.Arctic sea ice circulation and drift speed:decadal trends and ocean currents.J Geophys Res 2013;118:2408-25.
[12]Smith L,Stephenson S.New Trans-Arctic shipping routes navigable by midcentury.Proc Natl Acad Sci USA 2013;110:E1191-5.
[13]World Weather Research Programme(WWRP)/Polar Prediction Project(PPP)implementation plan;2014 https://www.polarprediction.net/fileadmin/user_upload/www.polarprediction.net/Home/Documents/WWRP-PPP_IP_Final_12Jan2013_v1_2.pdf.
[14]Melia N,Haines K,Hawkins E.Sea ice decline and 21st century trans-Arctic shipping routes.Geophys Res Lett 2016;43:9720-8.
[15]Newton R,Pfirman S,Schlosser P,et al.White Arctic vs.Blue Arctic:a case study of diverging stakeholder responses to environmental change.Earth Future 2016;4:396-405.
[16]Stroeve J,Hamilton L,Bitz C,et al.Predicting September sea ice:ensemble skill of the SEARCH Sea Ice Outlook 2008-2013.Geophys Res Lett2014;41:2411-8.
[17]Ono J,Inoue J,Yamazaki A,et al.The impact of radiosonde data on forecasting sea-ice distribution along the Northern Sea Route during an extremely developed cyclone.J Adv Model Earth Syst 2016;8:292-303.
[18]Liu J,Curry J,Wang H,et al.Impact of declining Arctic sea ice on winter snowfall.Proc Natl Acad Sci USA 2012;109:4074-9.
[19]Francis J,Vavrus S.Evidence linking Arctic amplification to extreme weather in mid-latitudes.Geophys Res Lett 2012;39:L06801.
[20]Mori M,Watanabe M,Shiogama H,et al.Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades.Nat Geosci 2014;7:869-73.
[21]Cohen J,Screen J,Furtado J,et al.Recent Arctic amplification and extreme mid-latitude weather.Nat Geosci 2014;7:627-37.
[22]National Research Council.Linkages between Arctic warming and midlatitude weather patterns:summary of a workshop.Washington DC:The National Academies Press;2014.
[23]Overland J.Is the melting Arctic changing midlatitude weather?Phys Today2016;69:38.
[24]Overland J,Wang M.When will the summer Arctic be nearly sea ice free?Geophys Res Lett 2013;40:2097-101.
[25]Liu J,Song M,Horton R,et al.Reducing spread in climate model projections of a September ice-free Arctic.Proc Natl Acad Sci USA 2013;110:12571-6.
[26]Notz D,Stroeve J.Observed Arctic sea-ice loss directly follows anthropogenic CO2emission.Science 2016;354:747-50.
[27]Yang S,Dong W,Feng J,et al.Global warming projections using the humanearth system model BNUHESM1.0.Sci Bull 2016;61:1833-8.
[28]Zhang J,Steele M,Lindsay A,et al.Ensemble one-year predictions of arctic sea ice for the spring and summer of 2008.Geophys Res Lett 2008;35:L08502.
[29]Hebert D,Allard R,Metzger E,et al.Short-term sea ice forecasting:an assessment of ice concentration and ice drift forecasts using the US Navy’s Arctic Cap Nowcast/Forecast System.J Geophys Res 2015;120:8327-45.
[30]Buehner M,Caya A,Pogson L,et al.A new environment Canada regional ice analysis system.Atmos Ocean 2013;51:18-34.
[31]Sakov P,Counillon F,Bertino L,et al.TOPAZ4:an ocean-sea ice data assimilation system for the North Atlantic and Arctic.Ocean Sci2012;8:633-56.
[32]Saha S,Moorthi S,Wu X,et al.The NCEP climate forecast system version 2.JClim 2014;27:2185-208.
[33]Rienecker M,Suarez M,Todling R,et al.In:The GEOS-5 data assimilation system-documentation of versions 5.0.1,5.1.0,and 5.2.0.Technical report series on global modeling and data assimilation.p.27.
[34]MacLachlan C,Arribas A,Peterson K,et al.Description of GloSea5:the Met Office high resolution seasonal forecast system.Quart J Roy Meteorol Soc2015;141:1072-84.
[35]Guemas V,Blanchard-Wrigglesworth E,Chevallier M,et al.A review on Arctic sea ice predictability and prediction on seasonal to decadal time-scales.Quart J Roy Meteorol Soc 2014;142:546-61.
[36]Jung T,Gordon N,Bauer P,et al.Advancing polar prediction capabilities on daily to seasonal time scales.Bull Am Meteorol Soc 2016;97:1631-47.
[37]Ivanova N,Johannessen O,Pedersen L,et al.Retrieval of Arctic sea ice parameters by satellite passive microwave sensors:a comparison of eleven sea ice concentration algorithms.IEEE Trans Geosci Remote Sens2014;52:7233-46.
[38]Ivanova N,Pedersen L,Tonboe T,et al.Inter-comparison and evaluation of sea ice algorithms:towards further identification of challenges and optimal approach using passive microwave observations.Cryosphere2015;9:1797-817.
[39]Swift C,Fedor L,Ramseier R.An algorithm to measure sea ice concentration with microwave radiometers.J Geophys Res 1985;90:1087-99.
[40]Maslanik J,Stroeve J.Near-real-time DMSP SSMIS daily polar gridded sea ice concentrations,version.1 National Snow and Ice Data Center;1999.updated daily.
[41]Meier W,Peng G,Scott D,et al.Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record.Polar Res 2014;33:21004.
[42]Meier W,Fetterer F,Windnagel A.Near-real-time NOAA/NSIDC climate data record of passive microwave sea ice concentration,version 1,2017.
[43]Spreen G,Kaleschke L,Heygster G.Sea ice remote sensing using AMSR-E 89GHz channels.J Geophys Res 2008;113:C02S03.
[44]Tonboe R,Lavelle J,Pfeiffer R,et al.Product user manual for OSI SAF global sea ice concentration.Copenhagen:Danish Meteorological Institutes;2016.
[45]Thomas D,Rothrock D.Blending sequential scanning multichannel microwave radiometer and buoy data into a sea ice model.J Geophys Res1989;94:10907-20.
[46]Thomas D,Rothrock D.The Arctic Ocean ice balance:a Kalman smoother estimate.J Geophys Res 1993;98:10053-67.
[47]Thomas D,Martin S,Rothrock D,et al.Assimilating satellite concentration data into an Arctic sea ice mass balance model,1979-1985.J Geophys Res1996;101:20849-68.
[48]Duliere V,Fichefet T.On the assimilation of ice velocity and concentration data into large-scale sea ice models.Ocean Sci 2007;3:321-35.
[49]Lis?ter K,Rosanova J,Evensen G.Assimilation of ice concentration in a coupled ice-ocean model,using the Ensemble Kalman filter.Ocean Dyn2003;53:368-88.
[50]Van Woert M,Zou C,Meier W,et al.Forecast verification of the polar ice prediction system(PIPS)sea ice concentration fields.J Atmos Oceanic Technol2004;21:944-57.
[51]Lindsay R,Zhang J.Assimilation of ice concentration in an ice-ocean model.JAtmos Oceanic Technol 2006;23:742-9.
[52]Stark J,Ridley J,Martin M,et al.Sea ice concentration and motion assimilation in a sea ice-ocean model.J.Geophys Res 2008;113:C05S91.
[53]Wang K,Debernard J,Sperrevik A,et al.A combined optimal interpolation and nudging scheme to assimilate OSISAF sea ice concentration in ROMS.Ann Glaciol 2013;54:8-12.
[54]Yang Q,Losa N,Losch M,et al.Assimilating summer sea ice concentration into a coupled ice-ocean model using a localized SEIK filter.Ann Glaciol2015;56:38-44.
[55]Toyoda T,Fujii Y,Yasuda T,et al.Data assimilation of sea ice concentration into a global ocean-sea ice model with corrections for atmospheric forcing and ocean temperature fields.J Oceanogr 2016;72:235-62.
[56]Wang W,Chen M,Kumar A.Seasonal prediction of arctic sea ice extent from a coupled dynamical forecast system.Mon Weather Rev 2013;141:1375-94.
[57]Tietsche S,Notz D,Jungclaus J,et al.Assimilation of sea-ice concentration in a global climate model-physical and statistical aspects.Ocean Sci2013;9:19-36.
[58]Kimmritz M,Counillon F,Bitz C,et al.Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model.Tellus A Dyn Meteorol Oceanogr 2018;70:1435945.
[59]Bunzel F,Notz D,Baehr J,et al.Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration.Geophys Res Lett 2016;43:852-9.
[60]Tonboe R,Nielsen E OSI-409.In:Global sea ice concentration reprocessing validation report.Eumetsat Osi Saf Prod Rep 2010;18.p.18.
[61]Yang Q,Losch M,Losa S,et al.Brief communication:the challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation.Cryosphere2016;10:761-74.
[62]Lavergne T,S?rensen A,Kern S,et al.Version 2 of the EUMETSAT OSI SAF and ESA CCI sea ice concentration climate data records.Cryosphere Discuss 2018.https://doi.org/10.5194/tc-2018-127.
[63]Olason E,Notz D.Drivers of variability in Arctic sea-ice drift speed.J Geophys Res 2014;119:5755-75.
[64]Kwok R.Summer sea ice motion from the 18 GHz channel of AMSR-E and the exchange of sea ice between the Pacific and Atlantic sectors.Geophys Res Lett2008;35:L03504.
[65]Girard-Ardhuin F,Ezraty R.Enhanced Arctic sea ice drift estimation merging radiometer and scatterometer data.IEEE Trans Geosci Remote Sens2012;50:2639-48.
[66]Fowler C,Emery W,Tschudi M.Polar Pathfinder daily 25 km EASE-Grid sea ice motion vectors,version 2.NSIDC;2013.
[67]Kwok R,Cunningham G.Seasonal ice area and volume production of the Arctic Ocean:November 1996 through April 1997.J Geophys Res2002;107:8038.
[68]Korosov A,Rampal P.A combination of feature tracking and pattern matching with optimal parameterization for sea ice drift retrieval from SAR Data.Remote Sens 2017;9:258.
[69]Sumata H,Lavergne T,Girard-Ardhuin F,et al.An intercomparison of arctic ice drift products to deduce uncertainty estimates.J Geophys Res2014;119:4887-921.
[70]Lavergne T,Eastwood S,Teffah Z.Sea ice motion from low-resolution satellite sensors:an alternative method and its validation in the Arctic.J Geophys Res2010;115:C10032.
[71]Girard-Ardhuin F,Ezraty R,Croizé-Fillon D,et al.Sea ice drift in the Central Arctic combing Quikscat and Ssmi/I sea ice Drift Data-User’S Manual Version3.0.Brest,France:IFREMER;2008.
[72]Sumata H,Gerdes R,Kauker F,et al.Empirical error functions for monthly mean Arctic sea-ice drift.J Geophys Res 2015;120:7450-75.
[73]Meier W,Maslanik J,Fowler C.Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model.J Geophys Res2000;105:3339-56.
[74]Meier W,Maslanik J.Improved sea ice parcel trajectories in the Arctic via data assimilation.Mar Pollut Bull 2001;42:505-11.
[75]Meier W,Maslanik J.Effect of environmental conditions on observed,modeled,and assimilated sea ice motion errors.J Geophys Res2003;108:1-11.
[76]Arbetter T,Lynch A,Maslanik J,et al.Effects of data assimilation of ice motion in a basin-scale sea ice model,in ice in the environment.In:Squire V,Langhore P,editors.Proceedings of the 16th IAHR international symposium on ice.p.186-93.
[77]Dai M,Arbetter T,Meier W.Data assimilation of sea ice motion vectors:sensitivity to the parameterization of sea ice strength.Ann Glaciol2006;44:357-60.
[78]Rollenhagen K,Timmermann R,Janjic T,et al.Assimilation of sea ice motion in a finite-element sea ice model.J Geophys Res 2009;114:C05007.
[79]Zhang J,Thomas D,Rothrock D,et al.Assimilation of ice motion observations and comparisons with submarine ice thickness data.J Geophys Res2003;108:3170.
[80]Spreen G,Kwok R,Menemenlis D,et al.Sea-ice deformation in a coupled ocean-sea-ice model and in satellite remote sensing data.Cryosphere2017;11:1553-73.
[81]Rampal P,Bouillon S,Olason E,et al.neXtSIM:a new Lagrangian sea ice model.Cryosphere 2016;10:1055-73.
[82]Yang CY,Liu J,Hu Y,et al.Assessment of Arctic and Antarctic sea ice predictability in CMIP5 Decadal Hindcasts.Cryosphere 2016;10:2429-52.
[83]Nakanowatari T,Inoue J,Sato K,et al.Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice-ocean data assimilation system.Cryosphere 2018;12:2005-20.
[84]Blanchard-Wrigglesworth E,Armour K,Bitz C,et al.Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations.J Clim2011;24:231-50.
[85]Chevallier M,Salas-Melia D.The role of sea ice thickness distribution in the Arctic sea ice potential predictability:a diagnostic approach with a coupled GCM.J Clim 2012;25:3025-37.
[86]Day J,Hawkins E,Tietsche S.Will Arctic sea ice thickness initialization improve seasonal forecast skill?Geophys Res Lett 2014;41:7566-75.
[87]Kwok R.Satellite remote sensing of sea ice thickness and kinematics:a review.J Glaciol 2010;56:1129-40.
[88]Laxon S,Giles K,Ridout A,et al.CryoSat-2 estimates of Arctic sea ice thickness and volume.Geophys Res Lett 2013;40:732-7.
[89]Ricker R,Hendricks S,Helm V,et al.Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation.Cryosphere2014;8:1607-22.
[90]Armitage T,Ridout A.Arctic sea ice freeboard from AltiKa and comparison with CryoSat-2 and Operation IceBridge.Geophys Res Lett 2015;42:6724-31.
[91]Maa?N,Kaleschke L,Tian-Kunze X,et al.Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data.Cryosphere 2013;7:1971-89.
[92]Sato K,Inoue J.Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations.Clim Dyn 2018;50:289-301.
[93]Kaleschke L,Tian-Kunze X,Maa?N,et al.Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period.Geophys Res Lett 2012;39:L05501.
[94]Tian-Kunze X,Kaleschke L,Maa?N,et al.SMOS-derived thin sea ice thickness:algorithm baseline,product specifications and initial verification.Cryosphere 2014;8:997-1018.
[95]Huntemann M,Heygster G,Kaleschke L,et al.Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations.Cryosphere 2014;8:439-51.
[96]Patilea C,Heygster G,Huntemann M,et al.Combined SMAP/SMOS thin sea ice thickness retrieval.Cryosphere 2017.https://doi.org/10.5194/tc-2017-168.
[97]Schmitt A,Kaleschke L.A consistent combination of brightness temperatures from SMOS and SMAP over polar oceans for sea ice applications.Remote Sens2018;10:553.
[98]Ricker R,Hendricks S,Kaleschke L,et al.A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data.Cryosphere2017;11:1607-23.
[99]Kurtz N,Galin N,Studinger M.An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting.Cryosphere2014;8:1217-37.
[100]Kurtz N,Harbeck J.CryoSat-2 Level-4 sea ice elevation,freeboard,and thickness,version 1.National Snow and Ice Data Center;2017.
[101]Grosfeld K,Treffeisen R,Asseng J,et al.Online sea-ice knowledge and data platform.Polarforschung 2016;85:143-55.
[102]Tian-Kunze X,Kaleschke L,Maass NSMOS.Daily sea ice thickness version 3,ICDC,icdc.cen.uni-hamburg.de.Germany:University of Hamburg;2016.
[103]Lis?ter K,Evensen G,Laxon S.Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model.J Geophys Res 2007;112:C07023.
[104]Lindsay R,Haas C,Hendricks S,et al.Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness.Geophys Res Lett 2012;39:L21502.
[105]Yang Q,Losa S,Losch M,et al.Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter.J Geophys Res2014;119:6680-92.
[106]Yang Q,Losch M,Loza S,et al.Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an iceocean model.J Atmos Oceanic Technol 2016;33:397-407.
[107]Xie J,Counillon F,Bertino L,et al.Benefits of assimilating 5 thin sea ice thickness from SMOS into the TOPAZ system.Cryosphere 2016;10:2745-61.
[108]Mu L,Yang Q,Losch M,et al.Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously.Quart J Roy Meteorol Soc 2018;144:529-38.
[109]Allard R,Farrell S,Hebert D,et al.Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system.Adv Space Res2018;62:1265-80.
[110]Collow T,Wang W,Kumar A,et al.Improving Arctic sea ice prediction using PIOMAS initial sea ice thickness in a coupled ocean-atmosphere model.J Clim2015;143:4618-30.
[111]Chen Z,Liu J,Song M,et al.Impacts of assimilating satellite sea ice concentration and thickness on Arctic sea ice prediction in the NCEP climate forecast system.J Clim 2017;30:8429-46.
[112]Zhang J,Rothrock D.Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates.Mon Weather Rev2003;131:845-61.
[113]Xie J,Counillon F,Bertino L.Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis.Cryosphere Discuss 2018.https://doi.org/10.5194/tc-2018-101.
[114]Mu L,Losch M,Yang Q,et al.Arctic-wide sea-ice thickness estimates from combining satellite remote sensing data and a dynamic ice-ocean model with data assimilation during the CryoSat-2.J Geophys Res 2018.https://doi.org/10.1029/2018JC014316.
[115]Polashenski C,Perovich D,Courville Z.The mechanisms of sea ice melt pond formation and evolution.J Geophys Res 2012;117:C01001.
[116]Hunke E,Hebert D,Lecomte O.Level-ice melt ponds in the Los Alamos sea ice model,CICE.Ocean Model 2013;71:26-42.
[117]Curry J,Schramm J,Ebert E.On the sea ice albedo climate feedback mechanism.J Clim 1995;8:240-7.
[118]Perovich D,Grenfell T,Light B,et al.Trans-polar observations of the morphological properties of Arctic sea ice.J Geophys Res 2009;114:C00A04.
[119]Dou T,Xiao C,Du Z,et al.Sources,evolution and impacts of EC and OC in snow on sea ice:a measurement study in Barrow,Alaska.Sci Bull2017;62:1547-54.
[120]Nicolaus M,Katlein C,Maslanik J,et al.Changes in Arctic sea ice result in increasing light transmittance and absorption.Geophys Res Lett 2012;39:L24501.
[121]Nicolaus M,Katlein C.Mapping radiation transfer through sea ice using a remotely operated vehicle(ROV).Cryosphere 2013;7:763-77.
[122]Inoue J,Kikuchi T,Perovich D.Effect of heat transmission through melt ponds and ice on melting during summer in the Arctic Ocean.J Geophys Res2008;113:C05020.
[123]Schroder D,Feltham D,Flocco D,et al.September Arctic sea-ice minimum predicted by spring melt-pond fraction.Nat Clim Change 2014;4:353-7.
[124]Liu J,Song M,Horton R,et al.Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice minimum.Environ Res Lett2015;10:054017.
[125]R?sel A,Kaleschke L,Birnbaum G.Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network.Cryosphere2012;6:31-46.
[126]Zege E,Malinka A,Katsev I,et al.Algorithm to retrieve the melt pond fraction and the spectral albedo of Arctic summer ice from satellite optical data.Remote Sens Environ 2015;163:153-64.
[127]Istomina L,Heygster G,Huntemann M,et al.Melt pond fraction and spectral sea ice albedo retrieval from MERIS data-Part 1:validation against in situ,aerial,and ship cruise data.Cryosphere 2015;9:1551-66.
[128]Malinka A,Zege E,Istomina L,et al.Reflective properties of melt ponds on sea ice.Cryosphere 2018;12:1921-37.
[129]Andreas E,Paulson C,William R,et al.The turbulent heat flux from Arctic leads.Bound Lay Meteorol 1979;17:57-91.
[130]Andreas E,Cash B.Convective heat transfer over wintertime leads and polynyas.J Geophys Res 1999;104:25721-34.
[131]Marcq S,Weiss J.Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere.Cryosphere2012;6:143-56.
[132]Ledley T.A coupled energy balance climate-sea ice model:impact of sea ice and leads on climate.J Geophys Res 1988;93:15919-32.
[133]Zhang Y,Cheng X,Liu J,et al.The potential of sea ice leads as a predictor for seasonal Arctic sea ice extent prediction.Cryosphere 2018.https://doi.org/10.5194/tc-2018-108.
[134]R?hrs J,Kaleschke L.An algorithm to detect sea ice leads by using AMSR-Epassive microwave imagery.Cryosphere 2012;6:343-52.
[135]Willmes S,Heinemann G.Pan-Arctic lead detection from MODIS thermal infrared imagery.Ann Glaciol 2015;56:29-37.
[136]Willmes S,Heinemann G.Sea-ice wintertime lead frequencies and regional characteristics in the Arctic,2003-2015.Remote Sens 2016;8:4.
[137]Blockley E,Peterson K.Improving met office seasonal forecasts of Arctic sea ice using assimilation of CryoSat-2 thickness.Cryosphere Discuss2018;12:3419-38.
[138]Derber J,Rosati A.A global oceanic data assimilation system.J Phys Oceanogr1989;19:1333-47.
[139]Caya A,Buehner M,Carrieres T.Analysis and forecasting of sea ice conditions in three dimensional variational data assimilation and a coupled ice-ocean model.J Atmos Oceanic Technol 2010;27:353-69.
[140]Hebert D,Allard R,Metzger E,et al.Short-term sea ice forecasting:an assessment of ice concentration and ice drift forecasts using the U.S.Navy’s Arctic Cap Nowcast/ForecastSystem.J Geophys Res 2015;120:8327-45.
[141]Lemieux J,Beaudoin C,Dupont F,et al.The regional ice prediction system(RIPS):verification of forecast sea ice concentration.Quart J Roy Meteorol Soc2016;142:632-43.
[142]Evensen G.Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.J Geophys Res1994;99:10143-62.
[143]Massonnet F,Fichefetc T,Goosse H.Prospects for improved seasonal Arctic sea ice predictions from multivariate data assimilation.Ocean Model2015;88:16-25.
[144]Nerger L,Danilov S,Hiller W,et al.Using sea-level data to constrain a finiteelement primitive-equation ocean model with a local SEIK filter.Ocean Dyn2006;56:634-49.
[145]Nerger L,Janjic′T,Schr?ter J,et al.A unification of ensemble square root Kalman filters.Mon Weather Rev 2012;140:2335-45.
[146]Desroziers G,Camino J,Berre L.4DenVar:link with 4D state formulation of variational assimilation and different possible implementations.Quart J Roy Meteorol Soc 2014;14:2097-110.
[147]Lorenc A,Bowler N,Clayton A,et al.Comparison of hybrid-4denvar and hybrid-4dvar data assimilation methods for global NWP.Mon Weather Rev2015;143:212-29.
[148]Shlyaeva A,Buehner M,Caya A,et al.Towards ensemble data assimilation for the environment Canada Regional Ice Prediction System.Quart J Roy Meteorol Soc 2016;142:1090-9.
[149]Kern S,Spreen G.Uncertainties in Antarctic Sea-Ice thickness retrieval from ICESat.Ann Glaciol 2015;56:107-19.
[150]Nerger L,Hiller W.Software for ensemble-based data assimilation systemsimplementation strategies and scalability.Comp Geosci 2013;55:110-8.
[151]Fletcher S,Zupanski M.A data assimilation method for lognormally distributed observational errors.Quart J Roy Meteorol Soc 2006;132:2505-19.
[152]Fletcher S,Zupanski M.Implications and impacts of transforming lognormal variables into normal variables in VAR.Meteor Z 2007;16:755-65.
[153]Van Leeuwen P.Particle filtering in geophysical systems.Mon Weather Rev2009;137:4089-114.
[154]T?dter J,Kirchgessner P,Nerger L,et al.Assessment of a nonlinear ensemble transform filter for high-dimensional data assimilation.Mon Weather Rev2016;144:409-27.
[155]Poterjoy J,Anderson J.Efficient assimilation of simulated observations in a high-dimensional geophysical system using a localized particle filter.Mon Weather Rev 2016;144:2007-20.
[156]Zheng F,Zhu J.Coupled assimilation for an intermediated coupled ENSOprediction model.Ocean Dyn 2010;60:1061-73.
[157]Liang X,Yang Q,Nerger L,et al.Assimilating Copernicus SST data into a panArctic ice-ocean coupled model with a local SEIK filter.J Atmos Oceanic Technol 2017;34:1985-99.
[158]Inoue J,Yamazaki A,Ono J,et al.Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route.Sci Rep2015;5:16868.
[2]Meier W,Stroeve J,Barrett A,et al.A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present.Cryosphere 2012;6:1359-68.
[3]Cavalieri D,Parkinson C.Arctic sea ice variability and trends,1979-2010.Cryosphere 2012;6:881-9.
[4]Stroeve J,Kattsov V,Barrett A,et al.Trends in Arctic sea ice extent from CMIP5,CMIP3 and observations.Geophys Res Lett 2012;39:L16502.
[5]Kwok R,Rothrock D.Decline in Arctic sea ice thickness from submarine and ICESat records 1958-2008.Geophys Res Lett 2009;36:L15501.
[6]Kwok R,Cunningham G.Variability of Arctic sea ice thickness and volume from CryoSat-2.Phil Trans R Soc Ser A Math Phys Eng Sci 2015;373:2045.
[7]Maslanik J,Stroeve J,Fowler C,et al.Distribution and trends in Arctic sea ice age through spring 2011.Geophys Res Lett 2011;38:L13502.
[8]Comiso J.Large decadal decline of the Arctic multiyear ice cover.J Clim2012;25:1176-93.
[9]Rampal P,Weiss J,Marsan D.Positive trend in the mean speed and deformation rate of Arctic sea ice 1979-2007.J Geophys Res 2009;114:C05013.
[10]Spreen G,Kwok R,Menemenlis D.Trends in Arctic sea ice drift and role of wind forcing:1992-2009.Geophys Res Lett 2011;38:L19501.
[11]Kwok R,Spreen G,Pang S.Arctic sea ice circulation and drift speed:decadal trends and ocean currents.J Geophys Res 2013;118:2408-25.
[12]Smith L,Stephenson S.New Trans-Arctic shipping routes navigable by midcentury.Proc Natl Acad Sci USA 2013;110:E1191-5.
[13]World Weather Research Programme(WWRP)/Polar Prediction Project(PPP)implementation plan;2014 https://www.polarprediction.net/fileadmin/user_upload/www.polarprediction.net/Home/Documents/WWRP-PPP_IP_Final_12Jan2013_v1_2.pdf.
[14]Melia N,Haines K,Hawkins E.Sea ice decline and 21st century trans-Arctic shipping routes.Geophys Res Lett 2016;43:9720-8.
[15]Newton R,Pfirman S,Schlosser P,et al.White Arctic vs.Blue Arctic:a case study of diverging stakeholder responses to environmental change.Earth Future 2016;4:396-405.
[16]Stroeve J,Hamilton L,Bitz C,et al.Predicting September sea ice:ensemble skill of the SEARCH Sea Ice Outlook 2008-2013.Geophys Res Lett2014;41:2411-8.
[17]Ono J,Inoue J,Yamazaki A,et al.The impact of radiosonde data on forecasting sea-ice distribution along the Northern Sea Route during an extremely developed cyclone.J Adv Model Earth Syst 2016;8:292-303.
[18]Liu J,Curry J,Wang H,et al.Impact of declining Arctic sea ice on winter snowfall.Proc Natl Acad Sci USA 2012;109:4074-9.
[19]Francis J,Vavrus S.Evidence linking Arctic amplification to extreme weather in mid-latitudes.Geophys Res Lett 2012;39:L06801.
[20]Mori M,Watanabe M,Shiogama H,et al.Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades.Nat Geosci 2014;7:869-73.
[21]Cohen J,Screen J,Furtado J,et al.Recent Arctic amplification and extreme mid-latitude weather.Nat Geosci 2014;7:627-37.
[22]National Research Council.Linkages between Arctic warming and midlatitude weather patterns:summary of a workshop.Washington DC:The National Academies Press;2014.
[23]Overland J.Is the melting Arctic changing midlatitude weather?Phys Today2016;69:38.
[24]Overland J,Wang M.When will the summer Arctic be nearly sea ice free?Geophys Res Lett 2013;40:2097-101.
[25]Liu J,Song M,Horton R,et al.Reducing spread in climate model projections of a September ice-free Arctic.Proc Natl Acad Sci USA 2013;110:12571-6.
[26]Notz D,Stroeve J.Observed Arctic sea-ice loss directly follows anthropogenic CO2emission.Science 2016;354:747-50.
[27]Yang S,Dong W,Feng J,et al.Global warming projections using the humanearth system model BNUHESM1.0.Sci Bull 2016;61:1833-8.
[28]Zhang J,Steele M,Lindsay A,et al.Ensemble one-year predictions of arctic sea ice for the spring and summer of 2008.Geophys Res Lett 2008;35:L08502.
[29]Hebert D,Allard R,Metzger E,et al.Short-term sea ice forecasting:an assessment of ice concentration and ice drift forecasts using the US Navy’s Arctic Cap Nowcast/Forecast System.J Geophys Res 2015;120:8327-45.
[30]Buehner M,Caya A,Pogson L,et al.A new environment Canada regional ice analysis system.Atmos Ocean 2013;51:18-34.
[31]Sakov P,Counillon F,Bertino L,et al.TOPAZ4:an ocean-sea ice data assimilation system for the North Atlantic and Arctic.Ocean Sci2012;8:633-56.
[32]Saha S,Moorthi S,Wu X,et al.The NCEP climate forecast system version 2.JClim 2014;27:2185-208.
[33]Rienecker M,Suarez M,Todling R,et al.In:The GEOS-5 data assimilation system-documentation of versions 5.0.1,5.1.0,and 5.2.0.Technical report series on global modeling and data assimilation.p.27.
[34]MacLachlan C,Arribas A,Peterson K,et al.Description of GloSea5:the Met Office high resolution seasonal forecast system.Quart J Roy Meteorol Soc2015;141:1072-84.
[35]Guemas V,Blanchard-Wrigglesworth E,Chevallier M,et al.A review on Arctic sea ice predictability and prediction on seasonal to decadal time-scales.Quart J Roy Meteorol Soc 2014;142:546-61.
[36]Jung T,Gordon N,Bauer P,et al.Advancing polar prediction capabilities on daily to seasonal time scales.Bull Am Meteorol Soc 2016;97:1631-47.
[37]Ivanova N,Johannessen O,Pedersen L,et al.Retrieval of Arctic sea ice parameters by satellite passive microwave sensors:a comparison of eleven sea ice concentration algorithms.IEEE Trans Geosci Remote Sens2014;52:7233-46.
[38]Ivanova N,Pedersen L,Tonboe T,et al.Inter-comparison and evaluation of sea ice algorithms:towards further identification of challenges and optimal approach using passive microwave observations.Cryosphere2015;9:1797-817.
[39]Swift C,Fedor L,Ramseier R.An algorithm to measure sea ice concentration with microwave radiometers.J Geophys Res 1985;90:1087-99.
[40]Maslanik J,Stroeve J.Near-real-time DMSP SSMIS daily polar gridded sea ice concentrations,version.1 National Snow and Ice Data Center;1999.updated daily.
[41]Meier W,Peng G,Scott D,et al.Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record.Polar Res 2014;33:21004.
[42]Meier W,Fetterer F,Windnagel A.Near-real-time NOAA/NSIDC climate data record of passive microwave sea ice concentration,version 1,2017.
[43]Spreen G,Kaleschke L,Heygster G.Sea ice remote sensing using AMSR-E 89GHz channels.J Geophys Res 2008;113:C02S03.
[44]Tonboe R,Lavelle J,Pfeiffer R,et al.Product user manual for OSI SAF global sea ice concentration.Copenhagen:Danish Meteorological Institutes;2016.
[45]Thomas D,Rothrock D.Blending sequential scanning multichannel microwave radiometer and buoy data into a sea ice model.J Geophys Res1989;94:10907-20.
[46]Thomas D,Rothrock D.The Arctic Ocean ice balance:a Kalman smoother estimate.J Geophys Res 1993;98:10053-67.
[47]Thomas D,Martin S,Rothrock D,et al.Assimilating satellite concentration data into an Arctic sea ice mass balance model,1979-1985.J Geophys Res1996;101:20849-68.
[48]Duliere V,Fichefet T.On the assimilation of ice velocity and concentration data into large-scale sea ice models.Ocean Sci 2007;3:321-35.
[49]Lis?ter K,Rosanova J,Evensen G.Assimilation of ice concentration in a coupled ice-ocean model,using the Ensemble Kalman filter.Ocean Dyn2003;53:368-88.
[50]Van Woert M,Zou C,Meier W,et al.Forecast verification of the polar ice prediction system(PIPS)sea ice concentration fields.J Atmos Oceanic Technol2004;21:944-57.
[51]Lindsay R,Zhang J.Assimilation of ice concentration in an ice-ocean model.JAtmos Oceanic Technol 2006;23:742-9.
[52]Stark J,Ridley J,Martin M,et al.Sea ice concentration and motion assimilation in a sea ice-ocean model.J.Geophys Res 2008;113:C05S91.
[53]Wang K,Debernard J,Sperrevik A,et al.A combined optimal interpolation and nudging scheme to assimilate OSISAF sea ice concentration in ROMS.Ann Glaciol 2013;54:8-12.
[54]Yang Q,Losa N,Losch M,et al.Assimilating summer sea ice concentration into a coupled ice-ocean model using a localized SEIK filter.Ann Glaciol2015;56:38-44.
[55]Toyoda T,Fujii Y,Yasuda T,et al.Data assimilation of sea ice concentration into a global ocean-sea ice model with corrections for atmospheric forcing and ocean temperature fields.J Oceanogr 2016;72:235-62.
[56]Wang W,Chen M,Kumar A.Seasonal prediction of arctic sea ice extent from a coupled dynamical forecast system.Mon Weather Rev 2013;141:1375-94.
[57]Tietsche S,Notz D,Jungclaus J,et al.Assimilation of sea-ice concentration in a global climate model-physical and statistical aspects.Ocean Sci2013;9:19-36.
[58]Kimmritz M,Counillon F,Bitz C,et al.Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model.Tellus A Dyn Meteorol Oceanogr 2018;70:1435945.
[59]Bunzel F,Notz D,Baehr J,et al.Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration.Geophys Res Lett 2016;43:852-9.
[60]Tonboe R,Nielsen E OSI-409.In:Global sea ice concentration reprocessing validation report.Eumetsat Osi Saf Prod Rep 2010;18.p.18.
[61]Yang Q,Losch M,Losa S,et al.Brief communication:the challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation.Cryosphere2016;10:761-74.
[62]Lavergne T,S?rensen A,Kern S,et al.Version 2 of the EUMETSAT OSI SAF and ESA CCI sea ice concentration climate data records.Cryosphere Discuss 2018.https://doi.org/10.5194/tc-2018-127.
[63]Olason E,Notz D.Drivers of variability in Arctic sea-ice drift speed.J Geophys Res 2014;119:5755-75.
[64]Kwok R.Summer sea ice motion from the 18 GHz channel of AMSR-E and the exchange of sea ice between the Pacific and Atlantic sectors.Geophys Res Lett2008;35:L03504.
[65]Girard-Ardhuin F,Ezraty R.Enhanced Arctic sea ice drift estimation merging radiometer and scatterometer data.IEEE Trans Geosci Remote Sens2012;50:2639-48.
[66]Fowler C,Emery W,Tschudi M.Polar Pathfinder daily 25 km EASE-Grid sea ice motion vectors,version 2.NSIDC;2013.
[67]Kwok R,Cunningham G.Seasonal ice area and volume production of the Arctic Ocean:November 1996 through April 1997.J Geophys Res2002;107:8038.
[68]Korosov A,Rampal P.A combination of feature tracking and pattern matching with optimal parameterization for sea ice drift retrieval from SAR Data.Remote Sens 2017;9:258.
[69]Sumata H,Lavergne T,Girard-Ardhuin F,et al.An intercomparison of arctic ice drift products to deduce uncertainty estimates.J Geophys Res2014;119:4887-921.
[70]Lavergne T,Eastwood S,Teffah Z.Sea ice motion from low-resolution satellite sensors:an alternative method and its validation in the Arctic.J Geophys Res2010;115:C10032.
[71]Girard-Ardhuin F,Ezraty R,Croizé-Fillon D,et al.Sea ice drift in the Central Arctic combing Quikscat and Ssmi/I sea ice Drift Data-User’S Manual Version3.0.Brest,France:IFREMER;2008.
[72]Sumata H,Gerdes R,Kauker F,et al.Empirical error functions for monthly mean Arctic sea-ice drift.J Geophys Res 2015;120:7450-75.
[73]Meier W,Maslanik J,Fowler C.Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model.J Geophys Res2000;105:3339-56.
[74]Meier W,Maslanik J.Improved sea ice parcel trajectories in the Arctic via data assimilation.Mar Pollut Bull 2001;42:505-11.
[75]Meier W,Maslanik J.Effect of environmental conditions on observed,modeled,and assimilated sea ice motion errors.J Geophys Res2003;108:1-11.
[76]Arbetter T,Lynch A,Maslanik J,et al.Effects of data assimilation of ice motion in a basin-scale sea ice model,in ice in the environment.In:Squire V,Langhore P,editors.Proceedings of the 16th IAHR international symposium on ice.p.186-93.
[77]Dai M,Arbetter T,Meier W.Data assimilation of sea ice motion vectors:sensitivity to the parameterization of sea ice strength.Ann Glaciol2006;44:357-60.
[78]Rollenhagen K,Timmermann R,Janjic T,et al.Assimilation of sea ice motion in a finite-element sea ice model.J Geophys Res 2009;114:C05007.
[79]Zhang J,Thomas D,Rothrock D,et al.Assimilation of ice motion observations and comparisons with submarine ice thickness data.J Geophys Res2003;108:3170.
[80]Spreen G,Kwok R,Menemenlis D,et al.Sea-ice deformation in a coupled ocean-sea-ice model and in satellite remote sensing data.Cryosphere2017;11:1553-73.
[81]Rampal P,Bouillon S,Olason E,et al.neXtSIM:a new Lagrangian sea ice model.Cryosphere 2016;10:1055-73.
[82]Yang CY,Liu J,Hu Y,et al.Assessment of Arctic and Antarctic sea ice predictability in CMIP5 Decadal Hindcasts.Cryosphere 2016;10:2429-52.
[83]Nakanowatari T,Inoue J,Sato K,et al.Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice-ocean data assimilation system.Cryosphere 2018;12:2005-20.
[84]Blanchard-Wrigglesworth E,Armour K,Bitz C,et al.Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations.J Clim2011;24:231-50.
[85]Chevallier M,Salas-Melia D.The role of sea ice thickness distribution in the Arctic sea ice potential predictability:a diagnostic approach with a coupled GCM.J Clim 2012;25:3025-37.
[86]Day J,Hawkins E,Tietsche S.Will Arctic sea ice thickness initialization improve seasonal forecast skill?Geophys Res Lett 2014;41:7566-75.
[87]Kwok R.Satellite remote sensing of sea ice thickness and kinematics:a review.J Glaciol 2010;56:1129-40.
[88]Laxon S,Giles K,Ridout A,et al.CryoSat-2 estimates of Arctic sea ice thickness and volume.Geophys Res Lett 2013;40:732-7.
[89]Ricker R,Hendricks S,Helm V,et al.Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation.Cryosphere2014;8:1607-22.
[90]Armitage T,Ridout A.Arctic sea ice freeboard from AltiKa and comparison with CryoSat-2 and Operation IceBridge.Geophys Res Lett 2015;42:6724-31.
[91]Maa?N,Kaleschke L,Tian-Kunze X,et al.Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data.Cryosphere 2013;7:1971-89.
[92]Sato K,Inoue J.Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations.Clim Dyn 2018;50:289-301.
[93]Kaleschke L,Tian-Kunze X,Maa?N,et al.Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period.Geophys Res Lett 2012;39:L05501.
[94]Tian-Kunze X,Kaleschke L,Maa?N,et al.SMOS-derived thin sea ice thickness:algorithm baseline,product specifications and initial verification.Cryosphere 2014;8:997-1018.
[95]Huntemann M,Heygster G,Kaleschke L,et al.Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations.Cryosphere 2014;8:439-51.
[96]Patilea C,Heygster G,Huntemann M,et al.Combined SMAP/SMOS thin sea ice thickness retrieval.Cryosphere 2017.https://doi.org/10.5194/tc-2017-168.
[97]Schmitt A,Kaleschke L.A consistent combination of brightness temperatures from SMOS and SMAP over polar oceans for sea ice applications.Remote Sens2018;10:553.
[98]Ricker R,Hendricks S,Kaleschke L,et al.A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data.Cryosphere2017;11:1607-23.
[99]Kurtz N,Galin N,Studinger M.An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting.Cryosphere2014;8:1217-37.
[100]Kurtz N,Harbeck J.CryoSat-2 Level-4 sea ice elevation,freeboard,and thickness,version 1.National Snow and Ice Data Center;2017.
[101]Grosfeld K,Treffeisen R,Asseng J,et al.Online sea-ice knowledge and data platform
[102]Tian-Kunze X,Kaleschke L,Maass NSMOS.Daily sea ice thickness version 3,ICDC,icdc.cen.uni-hamburg.de.Germany:University of Hamburg;2016.
[103]Lis?ter K,Evensen G,Laxon S.Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model.J Geophys Res 2007;112:C07023.
[104]Lindsay R,Haas C,Hendricks S,et al.Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness.Geophys Res Lett 2012;39:L21502.
[105]Yang Q,Losa S,Losch M,et al.Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter.J Geophys Res2014;119:6680-92.
[106]Yang Q,Losch M,Loza S,et al.Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an iceocean model.J Atmos Oceanic Technol 2016;33:397-407.
[107]Xie J,Counillon F,Bertino L,et al.Benefits of assimilating 5 thin sea ice thickness from SMOS into the TOPAZ system.Cryosphere 2016;10:2745-61.
[108]Mu L,Yang Q,Losch M,et al.Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously.Quart J Roy Meteorol Soc 2018;144:529-38.
[109]Allard R,Farrell S,Hebert D,et al.Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system.Adv Space Res2018;62:1265-80.
[110]Collow T,Wang W,Kumar A,et al.Improving Arctic sea ice prediction using PIOMAS initial sea ice thickness in a coupled ocean-atmosphere model.J Clim2015;143:4618-30.
[111]Chen Z,Liu J,Song M,et al.Impacts of assimilating satellite sea ice concentration and thickness on Arctic sea ice prediction in the NCEP climate forecast system.J Clim 2017;30:8429-46.
[112]Zhang J,Rothrock D.Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates.Mon Weather Rev2003;131:845-61.
[113]Xie J,Counillon F,Bertino L.Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis.Cryosphere Discuss 2018.https://doi.org/10.5194/tc-2018-101.
[114]Mu L,Losch M,Yang Q,et al.Arctic-wide sea-ice thickness estimates from combining satellite remote sensing data and a dynamic ice-ocean model with data assimilation during the CryoSat-2.J Geophys Res 2018.https://doi.org/10.1029/2018JC014316.
[115]Polashenski C,Perovich D,Courville Z.The mechanisms of sea ice melt pond formation and evolution.J Geophys Res 2012;117:C01001.
[116]Hunke E,Hebert D,Lecomte O.Level-ice melt ponds in the Los Alamos sea ice model,CICE.Ocean Model 2013;71:26-42.
[117]Curry J,Schramm J,Ebert E.On the sea ice albedo climate feedback mechanism.J Clim 1995;8:240-7.
[118]Perovich D,Grenfell T,Light B,et al.Trans-polar observations of the morphological properties of Arctic sea ice.J Geophys Res 2009;114:C00A04.
[119]Dou T,Xiao C,Du Z,et al.Sources,evolution and impacts of EC and OC in snow on sea ice:a measurement study in Barrow,Alaska.Sci Bull2017;62:1547-54.
[120]Nicolaus M,Katlein C,Maslanik J,et al.Changes in Arctic sea ice result in increasing light transmittance and absorption.Geophys Res Lett 2012;39:L24501.
[121]Nicolaus M,Katlein C.Mapping radiation transfer through sea ice using a remotely operated vehicle(ROV).Cryosphere 2013;7:763-77.
[122]Inoue J,Kikuchi T,Perovich D.Effect of heat transmission through melt ponds and ice on melting during summer in the Arctic Ocean.J Geophys Res2008;113:C05020.
[123]Schroder D,Feltham D,Flocco D,et al.September Arctic sea-ice minimum predicted by spring melt-pond fraction.Nat Clim Change 2014;4:353-7.
[124]Liu J,Song M,Horton R,et al.Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice minimum.Environ Res Lett2015;10:054017.
[125]R?sel A,Kaleschke L,Birnbaum G.Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network.Cryosphere2012;6:31-46.
[126]Zege E,Malinka A,Katsev I,et al.Algorithm to retrieve the melt pond fraction and the spectral albedo of Arctic summer ice from satellite optical data.Remote Sens Environ 2015;163:153-64.
[127]Istomina L,Heygster G,Huntemann M,et al.Melt pond fraction and spectral sea ice albedo retrieval from MERIS data-Part 1:validation against in situ,aerial,and ship cruise data.Cryosphere 2015;9:1551-66.
[128]Malinka A,Zege E,Istomina L,et al.Reflective properties of melt ponds on sea ice.Cryosphere 2018;12:1921-37.
[129]Andreas E,Paulson C,William R,et al.The turbulent heat flux from Arctic leads.Bound Lay Meteorol 1979;17:57-91.
[130]Andreas E,Cash B.Convective heat transfer over wintertime leads and polynyas.J Geophys Res 1999;104:25721-34.
[131]Marcq S,Weiss J.Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere.Cryosphere2012;6:143-56.
[132]Ledley T.A coupled energy balance climate-sea ice model:impact of sea ice and leads on climate.J Geophys Res 1988;93:15919-32.
[133]Zhang Y,Cheng X,Liu J,et al.The potential of sea ice leads as a predictor for seasonal Arctic sea ice extent prediction.Cryosphere 2018.https://doi.org/10.5194/tc-2018-108.
[134]R?hrs J,Kaleschke L.An algorithm to detect sea ice leads by using AMSR-Epassive microwave imagery.Cryosphere 2012;6:343-52.
[135]Willmes S,Heinemann G.Pan-Arctic lead detection from MODIS thermal infrared imagery.Ann Glaciol 2015;56:29-37.
[136]Willmes S,Heinemann G.Sea-ice wintertime lead frequencies and regional characteristics in the Arctic,2003-2015.Remote Sens 2016;8:4.
[137]Blockley E,Peterson K.Improving met office seasonal forecasts of Arctic sea ice using assimilation of CryoSat-2 thickness.Cryosphere Discuss2018;12:3419-38.
[138]Derber J,Rosati A.A global oceanic data assimilation system.J Phys Oceanogr1989;19:1333-47.
[139]Caya A,Buehner M,Carrieres T.Analysis and forecasting of sea ice conditions in three dimensional variational data assimilation and a coupled ice-ocean model.J Atmos Oceanic Technol 2010;27:353-69.
[140]Hebert D,Allard R,Metzger E,et al.Short-term sea ice forecasting:an assessment of ice concentration and ice drift forecasts using the U.S.Navy’s Arctic Cap Nowcast/ForecastSystem.J Geophys Res 2015;120:8327-45.
[141]Lemieux J,Beaudoin C,Dupont F,et al.The regional ice prediction system(RIPS):verification of forecast sea ice concentration.Quart J Roy Meteorol Soc2016;142:632-43.
[142]Evensen G.Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.J Geophys Res1994;99:10143-62.
[143]Massonnet F,Fichefetc T,Goosse H.Prospects for improved seasonal Arctic sea ice predictions from multivariate data assimilation.Ocean Model2015;88:16-25.
[144]Nerger L,Danilov S,Hiller W,et al.Using sea-level data to constrain a finiteelement primitive-equation ocean model with a local SEIK filter.Ocean Dyn2006;56:634-49.
[145]Nerger L,Janjic′T,Schr?ter J,et al.A unification of ensemble square root Kalman filters.Mon Weather Rev 2012;140:2335-45.
[146]Desroziers G,Camino J,Berre L.4DenVar:link with 4D state formulation of variational assimilation and different possible implementations.Quart J Roy Meteorol Soc 2014;14:2097-110.
[147]Lorenc A,Bowler N,Clayton A,et al.Comparison of hybrid-4denvar and hybrid-4dvar data assimilation methods for global NWP.Mon Weather Rev2015;143:212-29.
[148]Shlyaeva A,Buehner M,Caya A,et al.Towards ensemble data assimilation for the environment Canada Regional Ice Prediction System.Quart J Roy Meteorol Soc 2016;142:1090-9.
[149]Kern S,Spreen G.Uncertainties in Antarctic Sea-Ice thickness retrieval from ICESat.Ann Glaciol 2015;56:107-19.
[150]Nerger L,Hiller W.Software for ensemble-based data assimilation systemsimplementation strategies and scalability.Comp Geosci 2013;55:110-8.
[151]Fletcher S,Zupanski M.A data assimilation method for lognormally distributed observational errors.Quart J Roy Meteorol Soc 2006;132:2505-19.
[152]Fletcher S,Zupanski M.Implications and impacts of transforming lognormal variables into normal variables in VAR.Meteor Z 2007;16:755-65.
[153]Van Leeuwen P.Particle filtering in geophysical systems.Mon Weather Rev2009;137:4089-114.
[154]T?dter J,Kirchgessner P,Nerger L,et al.Assessment of a nonlinear ensemble transform filter for high-dimensional data assimilation.Mon Weather Rev2016;144:409-27.
[155]Poterjoy J,Anderson J.Efficient assimilation of simulated observations in a high-dimensional geophysical system using a localized particle filter.Mon Weather Rev 2016;144:2007-20.
[156]Zheng F,Zhu J.Coupled assimilation for an intermediated coupled ENSOprediction model.Ocean Dyn 2010;60:1061-73.
[157]Liang X,Yang Q,Nerger L,et al.Assimilating Copernicus SST data into a panArctic ice-ocean coupled model with a local SEIK filter.J Atmos Oceanic Technol 2017;34:1985-99.
[158]Inoue J,Yamazaki A,Ono J,et al.Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route.Sci Rep2015;5:16868.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |