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Deep Learning Shows Promise for seasonal Prediction of Antarctic sea ice in a Rapid Decline Scenario

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Advances in Atmospheric Sciences 2024年第8期41卷 1569-1573页

作者： Xiaoran DONG Yafei NIE Jinfei WANG Hao LUO Yuchun GAO Yun WANG Jiping LIU Dake CHEN Qinghua YANG School of Atmospheric Sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Earth and Climate Center Earth and Life InstituteUniversitécatholique de LouvainLouvain-la-Neuve 1348Belgium Frontier Science Center for Deep Ocean Multispheres and Earth System(FDOMES) Physical Oceanography LaboratoryOcean University of ChinaQingdao 266100China

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the sea ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

关键词： deep learning Antarctic sea ice seasonal prediction

sea ice characteristics between the middle Weddell sea and the Prydz Bay,Antarctica during the austral summer of 2003

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Acta Oceanologica Sinica 2005年第2期24卷 9-15页

作者： TANGShulin KANGJiancheng ZHOUShangzhe LIZhijun KeyLaboratoryofWesternChina＇sEnvironmentalSystemsofMinistryofEducation LanzhouUniversityLanzhou730000China／／GeographyDepartmentShanghaiNormalUniversityShanghai200234China KeyLaboratoryofWesternChina＇sEnvironmentalSystemsofMinistryofEducation LanzhouUniversityLanzhou730000China／／SouthChinaNormalUniversityGuangzhou510000China StateKeyLaboratoryofCoastalandOffshoreEngineering DalianUniversityofTechnologyDalian116024China KeyLaboratoryofWesternChina＇sEnvironmentalSystemsofMinistryofEducation LanzhouUniversityLanzhou730000China／／GeographyDepartmentShanghaiNormalUniversityShanghai200234China

The antarctic sea ice was investigated upon five occasions between January 4 and February 15, 2003. The investigations included: (1) estimation of sea ice distribution by ship-based observations between the middle Weddell sea and the Prydz Bay; (2) estimation of sea ice distribution by aerial photography in the Prydz Bay; (3) direct measurements of fast ice thickness and snow cover, as well as ice core sampling in Nella Fjord; (4) estimation of melting sea ice distribution near the Zhongshan Station; and (5) observation of sea ice early freeze near the Zhongshan Station. On average, sea ice covered 14.4% of the study area. The highest sea ice concentration (80%) was observed in the Weddell sea. First-year ice was dominant (99.7%~99.8%). sea ice distributions in the Prydz Bay were more variable due to complex inshore topography, proximity of the Larsemann Hills, and/or grounded icebergs. The average thickness of landfast ice in Nella Fjord was 169.5 cm. Wind-blown snow redistribution plays an important role in affecting the ice thickness in Nella Fjord. Preliminary freezing of sea ice near the Zhongshan Station follows the first two phases of the pancake cycle.

关键词： Antarctica sea ice characteristics

sea ice thickness estimation in the Bohai sea using geostationary ocean color imager data

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Acta Oceanologica Sinica 2016年第7期35卷 105-112页

作者： LIU Wensong SHENG Hui ZHANG Xi Shool of Geosciences China University of Petroleum (East China) First Institute of Oceanography State Oceanic Administration

A method to estimate the thickness of the sea ice of the Bohai sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models （Lebedev and Zubov）, and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation （r2〉0.66） with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.

关键词： sea ice thickness geostationary ocean color imager Bohai sea

Reconstruction of autumn sea ice extent changes since AD1289 in the Barents-Kara sea, Arctic

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Science China Earth Sciences 2018年第9期61卷 1279-1291页

作者： Qi ZHANG Cunde XIAO Minghu DING Tingfeng DOU Institute of Polar Meteorology Chinese Academy of Meteorological Sciences State Key Laboratory of Earth Surface and Resources Ecology Beijing Normal University College of Resources and Environment University of Chinese Academy of Sciences

Using high-resolution ice core and tree ring proxies for sea ice extent （SIE）, we reconstructed a robust time series of autumn SIE over the Barents-Kara （B-K） sector of the Arctic from AD 1289-1993. After intercomparing the results and statistical parameters using the ordinary least squares regression （OLSR）, the principle component regression （PCR） and the partial least squares regression （PLSR） methods, SIE time series were synthesized into a more robust series using the weighted average method, which used the explained variances as weights. The results showed that from the end of the 13th century to the end of 18th century, the autumn B-K SIE was large, with large variations and a slightly expanding trend overall. This reflected significant multideeadal oscillations under the Little ice Age （LIA） background. The B-K SIE began to decrease at the end of the 18th century, and a shrinking trend became significant during the second half of the 19th century, which lasted into the 1930s- 1940s. The 1930s-1940s was a period with a relatively low SIE in the B-K sea, and the SIE had a short period of expansion from the 1940s-1970s. However, the B-K SIE has continuously and significantly shrank since the 1970s. The reduction in the B-K SIE since the end of the 18th century has been unprecedented in both duration and speed over the last 700 years. The B-K SIE has retreated significantly since the 1970s, with a speed 6.18 times greater than the former mean retreating speed. The industrial revolution may be a dominant factor in this result. The Arctic SIE in recent years may be the lowest it has been over the last millennium.

关键词： Arctic sea ice ice core Tree rings Paleoclimatology Global warming

Sources, evolution and impacts of EC and OC in snow on sea ice： a measurement study in Barrow, Alaska

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Science Bulletin 2017年第22期62卷 1547-1554页

作者： Tingfeng Dou Cunde Xiao Zhiheng Du J. J. Schauer Hong Ren Baozhu Ge Aihong Xie Jihua Tan Pingqing Fu Yuanxun Zhang College of Resources and Environment University of Chinese Academy of Sdences Beijing 100049 China State Key Laboratory of Cryospheric Sciences Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou 730000 China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beijing 100875 China Environmental Chemistry and Technology Program University of Wisconsin-Madison Madison Wl 53706 USA state Key Laboratory of Atmospheric Boundary Layer Physics-an-d Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Huairou Eco-Environmental Observatory Chinese Academy of Sciences Beijing 101408 China Institute of Surface-Earth System Science Tianjin University Tianjin 300072 China

Based on the field measurements in Barrow, Alaska within the period of April-May 2015, we investigate the sources and variations of elemental carbon(EC) and organic carbon(OC) in the surface layer of snowpack on sea ice, and estimate their effects on the sea ice albedo. Results show that the snow OC in Barrow are from natural sources(e.g. terrestrial higher plants and micro-organisms) mainly, as well as biomass burning(e.g. forest fires and straw combustion) as an important part. Both EC and OC can accumulate at the snow surface with snow melt. The variations in EC and OC and liquid water content in the snow layer are well consistent during the snow-melting period. A higher rate of snow melt implied a more efficient enrichment of EC and OC. In the last phase of snow melt, the concentration increased to a maximum of 16.2 ng/g for EC and 128 ng/g for OC, which is ~10 times larger than those before snow melt onset. Except for the dominant influence of melt amplification mechanism, the variation in concentrations of EC and OC could be disturbed by the air temperature fluctuation and snowfall. Our study indicates that the lightabsorbing impurities contributed 1.6%-5.1% to the reduction in sea ice albedo with melt during the measurement period. The significant period oflight-absorbing impurities influencing on sea ice albedo begins with the rapid melting of overlying snow and ends before the melt ponds formed widely, which lasted for about 10 days in Barrow, 2015.

关键词： Arctic EC OC sea ice Albedo Barrow

EXPERIMENTS OF sea ice SIMULATION

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Journal of Hydrodynamics 2005年第6期17卷 686-692页

作者： LIU Xi-ying State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG） Institute of Atmospheric Physics （IAP） Chinese Academy of Sciences Beijing 100029 China

As a substitute for the original displaced pole grids, a simple rotated spherical coordinate system was intro duced into the Community sea ice Model version 4（CSIM4）, which is a component of the Community Climate System Model（CCSM） of the American National Center of Atmospheric Researeh（NCAR）, to deal with the ＂pole problems＂. In the new coordinates, both the geographical North Pole and South Pole lie in the model equator and grid sizes near the polar region are more uniform. With reanalysis dataset of American National Centers for Environment Prediction （NCEP） and Levitus dataset without considering sub-mixed layer heat flux, the model was integrated for 100 years with thermodynamics *** involved only in the former 49 years and both dynamic and thermodynamic processes involved in the left time. Inner consistency of model results was checked with no contradiction found. The results of last 10 years＇ model output were analyzed and it is shown that the simulated sea ice seasonal variation is rational whereas sea ice extent in the Barcnts sea in winter is larger than that of observation, Numerical experiment on influence of sub-mixed layer heat flux was also carried out and it is shown that the sub-mixed layer heat flux can modulate seasonal variation of sea ice greatly. As a model component, the sea ice model with rotated spherical coordinates was coupled with other models （the oceanic general cir culation model is the LASG/IAP Climate System Ocean Model （LICOM） with reduced grid, other models are components of NCAR CCSM2） forming a climate system model and its preliminary results were also given briefly.

关键词： sea ice numerical simulation climate system model

NUMERICAL SIMULATIONS OF sea ice WITH DIFFERENT ADVECTION SCHEMES

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Journal of Hydrodynamics 2011年第3期23卷 372-378页

作者： LIU Xi-ying State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Institute of Meteorology PLA University of Science and Technology Nanjing 211101 China

Numerical simulations are carried out for sea ice with four different advection schemes to study their effects on the simulation results. The sea ice model employed here is the sea ice Simulator （SIS） of the Geophysical Fluid Dynamics Laboratory （GFDL） Modular Ocean Model version 4b （MOM4b） and the four advection schemes are, the upwind scheme originally used in the SIS, the Multi-Dimensional Positive Advection （MDPA） scheme, the Incremental Remapping Scheme （IRS） and the Two Step Shape Preserving （TSSP） scheme. The latter three schemes are newly introduced. To consider the interactions between sea ice and ocean, a mixed layer ocean model is introduced and coupled to the SIS. The coupled model uses a tri-polar coordinate with 120x65 grids, covering the whole earth globe, in the horizontal plane. Simulation results in the northern high latitudes are analyzed. In all simulations, the model reproduces the seasonal variation of sea ice in the northern high latitudes well. Compared with the results from the observation, the sea ice model produces some extra sea ice coverage in the Greenland sea and Barents sea in winter due to the exclusion of ocean current effects and the smaller simulated sea ice thickness in the Arctic basin. There are similar features among the results obtained with the introduced three advection schemes. The simulated sea ice thickness with the three newly introduced schemes are all smaller than that of the upwind scheme and the simulated sea ice velocities of movement are all smaller than that of the upwind scheme. There are more similarities shared in the results obtained with the MPDA and TSSP schemes.

关键词： advection scheme sea ice simulation

Modeling ocean wave propagation under sea ice covers

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Acta Mechanica Sinica 2015年第1期31卷 1-15页

作者： Xin Zhao Hayley H. Shen Sukun Cheng Department of Civil and Environmental Engineering Clarkson University

Operational ocean wave models need to work globally, yet current ocean wave models can only treat ice covered regions crudely. The purpose of this paper is to provide a brief overview of ice effects on wave propagation and different research methodology used in studying these effects. Based on its proximity to land or sea, sea ice can be classified as： landfast ice zone, shear zone, and the marginal ice zone. All ice covers attenuate wave energy. Only long swells can penetrate deep into an ice cover. Being closest to open water, wave propagation in the marginal ice zone is the most complex to model. The physical appearance of sea ice in the marginal ice zone varies. Grease ice, pancake ice, brash ice, floe aggregates, and continuous ice sheet may be found in this zone at different times and locations. These types of ice are formed under different thermalmechanical forcing. There are three classic models that describe wave propagation through an idealized ice cover： mass loading, thin elastic plate, and viscous layer models. From physi cal arguments we may conjecture that mass loading model is suitable for disjoint aggregates of ice floes much smaller than the wavelength, thin elastic plate model is suitable for a con tinuous ice sheet, and the viscous layer model is suitable for grease ice. For different sea ice types we may need different wave ice interaction models. A recently proposed viscoelas tic model is able to synthesize all three classic models into one. Under suitable limiting conditions it converges to the three previous models. The complete theoretical framework for evaluating wave propagation through various ice covers need to be implemented in the operational ocean wave models. In this review, we introduce the sea ice types, previous wave ice interaction models, wave attenuation mechanisms,the methods to calculate wave reflection and transmission between different ice covers, and the effect of ice floe breaking on shaping the sea ice morphology. Labora

关键词： Ocean waves sea ice Cryospheric change

Arctic sea ice distribution in summer based on aerial photos

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Acta Oceanologica Sinica 2005年第6期24卷 62-67页

作者： LU Peng LI Zhijun ZHANG Zhanhai DONG Xilu State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian 116024 China State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian 116024 China Polar Research Institute of China Shanghai 200129 China School of Electronics and Information Engineering Dalian University of Technology Dalian 116024 China

On the basis of aerial photos of sea ice in the Second Chinese NationalArctic Research Expdition during July and September 2003 in the area of 74.11°～79.56°N and144.17°～169.95°W, image processing techniques are used to acquire some geometric parameters offloes, such as fractal dimension, roundness and mean caliper diameter. Latitudinal variation ofmorphology of arctic ice floes is then obtained, after comparing fractal dimension and roundnesswhich represent geometry of floe edge, the latter is found to show a more obvious tendency withlatitude but still not enough to describe floe abrasion. Then mean caliper diameter of ice floe isused as characteristic size to analyze floe size distribution, the result reveals that cumulativeprobabilities of floe sizes agree well with a power-law function, and distribution dimension isgenerally in the range of 1.05～1.25 and slightly increases as the latitude increases. Howeverslight curvatures are still observed in the plots of cumulative probabilities as in formerresearches, which can be attributed to both the limitation of sampling area and effect ofthermodynamic process.

关键词： sea ice arctic ocean image processing fractals