Antarctic sea ice and ENSO event

The characteristic low-frequency oscillation of the sea surface temperature anomaly (SSTA) of ENSO related regions, Nino 1 + 2, Nino 3, Nino 4 and Nino West, and the Southern Oscillation index (SOI) is analyzed with the method of maximum entropy spectrum. Antarctic sea ice is divided into 4 regions, i. e. East Antarctic is Region Ⅰ (0°-120° E), the region dominated by Ross Sea ice is Region Ⅱ (120° E-120° W), the region dominated by Ross Sea ice is Region Ⅲ (120° W-0°), and the whole Antarctic sea ice area is Region Ⅳ. Also, the month-to-month correlation series of the sea ice with ENSO from contemporary to 5-years lag is calculated. The optimum correlation period is selected from the series. The characteristics and the rules obtained are as follows.1. There are a common 4-years main period of the SSTA of Ninos 1 + 2,3 and 4, a rather strong 4-years secondary period and a quasi-8-years main period of that of Nino West. There are also 1. 5 and 2 to 3-years secondary periods of that of all 4 Nin     

A global coupled sea ice–ocean model

A new sea ice model, GELATO, was developed at Centre National de Recherches Météorologiques (CNRM) and coupled with OPA global ocean model. The sea ice model includes elastic–viscous–plastic rheology, redistribution of ice floes of different thicknesses, and it also takes into account leads, snow cover and snow ice formation. Climatologies of atmospheric surface parameters are used to perform a 20-year global ocean–sea ice simulation, in order to compute surface heat fluxes from diagnosed sea ice or ocean surface temperature. A surface salinity restoring term is applied only to ocean grid cells with no sea ice to avoid significant surface salinity drifts, but no correction of sea surface temperature is introduced. In the Arctic the use of an ocean model substantially improves the representation of sea ice, and particularly of the ice edge in all seasons, as advection of heat and salt can be more accurately accounted for than in the case of, for example, a sea ice–ocean mixed layer model. In contrast, in the Antarctic, a region where ocean convective processes bear a much stronger influence in shaping sea ice characteristics, a better representation of convection and probably of sea ice (for example, of frazil sea ice, brine rejection) would be needed to improve the simulation of the annual cycle of the sea ice cover. The effect of the inclusion of several ice categories in the sea ice model is assessed by running a sensitivity experiment in which only one category of sea ice is considered, along with leads. In the Arctic, such an experiment clearly shows that a multicategory sea ice model better captures the position of the sea ice edge and yields much more realistic sea ice concentrations in most of the region, which is in agreement with results from Bitz et al. [J. Geophys. Res. 106 (C2) (2001) 2441–2463].     

Uncertainty and joint probability of sea ice loads

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Numerical simulation for dynamical processes of sea ice

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The study of sea ice classification by radar observation

In order to mornitor the floating sea ice along the northeast coast of the Liaodong Gulf, since the winter of 1986, the Sea Ice Research Division of the Institute of Marine Enironmental Protection, State Oceanic Administration, has been making tests on the classification of sea ice in the Liaodong Gulf with radar at the Ice-survey Station at Bayuquan (Fig. 1 ).     

Arctic sea ice distribution in summer based on aerial photos

1Introduction TheArcticOceanisoneoftheimportantcold sourcesontheearth,whichaffectsglobalclimateand oceancirculationseriously.Itsinteractionwithglobal climatesystemisrepresentedbyseaice,whichisthe mainfeatureonthesurfaceoftheArcticOcean(Aa- gaard,etal.,1989).Firstly,seaiceplaysapivotalrole intheheatandmassbalanceonthesurfaceoftheArc- ticOcean.Seaicenotonlyobstructstheheatexchange betweenatmosphereandocean,butalsoreflectsthe mostofthelocalsolarradiationbacktotheatmo- spherebecauseofitshighalb…     

The variation features of the Antarctic sea ice (Ⅱ)

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A study on remote sensing models of sea ice thickness by microwave radiometry

Extrapolating from the propagation theories of electromagnetic waves in a layered medium, a three-layer medium model is deduced in this paper by using microwave radiometric remote sensing technology which is suitable to first-year sea ice condition of the northern part of China seas. Comparison with in situ data indicates that for microwave wavelength of 10 cm, the coherent model gives a quite good fit result for the thickness of sea ice less than 20 cm, and the incoherent model also works well for thickness within 20 to 40 cm. Based on three theoretical models, the inversion soft ware from microwave remote sensing data for calculating the thickness of sea ice can be set up. The relative complex dielectrical constants of different types of sea ice in the Liaodong Gulf calculated by using these theoretical models and measurement data are given in this paper. The extent of their values is (0. 5-4. 0)-j(0. 07~0. 19).     

Statistical study on the spatial-temporal distribution features of the arctic sea ice extent

On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.     

Satellite-observed trends in the Arctic sea ice concentration for the period 1979–2016

Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land(S-FJL),and the passage between Franz Josef Land and Severnaya Zemlya(FJL-SZ).     

Recent satellite-derived sea ice volume flux through the Fram Strait: 2011–2015

The Fram Strait(FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a(2011–2015) sea ice thickness record retrieved from Cryo Sat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux(WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center(NSIDC) and the Institut Francais de Recherche pour d'Exploitation de la Mer(IFREMER), amounts to 1 029 km~3(NSIDC) and1 463 km~3(IFREMER), respectively. For this period, a mean monthly volume flux(area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is –62 km~3 per month(–18×10~6 km~2 per month).Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen(UB), and the two products agree relatively well with a mean monthly bias of(5.7±45.9) km~3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990 s. Compared with P1(1990/1991–1993/1994) and P2(2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km~3 in P3(2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.     

Some parameters in arctic sea ice dynamics from an expedition in the summer of 2003

1Introduction Seaiceoccupiesthemainpartofthesurfaceof theArcticOcean.ThefocusoftheSecondChineseNa- tionalArcticResearchExpedition(CHINAE-2003) wastounderstandthevariationsofarcticmarineenvi- ronmentsandtheseaiceeffectsontheclimatechanges ofglobalextent,inmiddleandlowerlatitudesareas, especiallyinChina.Therefore,thejointsea-ice-airob- servationforseaicestudieswasoneofthekeypro- jectsinCHINARE-2003.Theinvestigatedareacov- ered3000kmfromsouthtonorthand900kmfrom westtoeast.Seventemporali…     

Distribution of chlorophyll a and diatom in the sea ice of the Weddell Sea, Antarctica

Diatoms are major primary producers of microbial biomass in the Antarctica. They are found in the water and sea ice. The distribution, abundance of the ice diatoms and their relation to the environmental factors inside and outside the ice have been studied for its special role in the Antarctic Ocean ecology. In this paper we describe the abundance, distribution and composition of diatom assemblages in     

An improved optical flow method to estimate Arctic sea ice velocity (winter 2014?2016)

1 Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology/Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China; 3 Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China     

Arctic sea ice volume export through the Fram Strait from combined satellite and model data: 1979–2012

By combing satellite-derived ice motion and concentration with ice thickness fields from a popular model PIOMAS we obtain the estimates of ice volume flux passing the Fram Strait over the 1979–2012 period. Since current satellite and field observations for sea ice thickness are limited in time and space, the use of PIOMAS is expected to fill the gap by providing temporally continued ice thickness fields. Calculated monthly volume flux exhibits a prominent annual cycle with the peak record in March(roughly 145 km3/month) and the trough in August(10 km~3/month). Annual ice volume flux(1 132 km~3) is primarily attributable to winter(October through May) outflow(approximately 92%). Uncertainty in annual ice volume export is estimated to be 55 km~3(or 5.7%). Our results also verified the extremely large volume flux appearing between late 1980 s and mid-1990 s. Nevertheless, no clear trend was found in our volume flux results. Ice motion is the primary factor in the determination of behavior of volume flux. Ice thickness presented a general decline trend may partly enhance or weaken the volume flux trend. Ice concentration exerted the least influences on modulating trends and variability in volume flux. Moreover, the linkage between winter ice volume flux and three established Arctic atmospheric schemes were examined. Compared to NAO, the DA and EOF3 mechanism explains a larger part of variations of ice volume flux across the strait.     

Summer Arctic sea fog

I~IOXSea fog is a kind of dangerous weather. Chinese sea fog experts, Wang Binhua (1983),Hu Ruijin and Zhou Faxiu (1998) and Hu Jifu et al. (1996) studied sea fog rather Systematically. FOreign Experts also Paid great attention to sea fog. Ernlnons and Montgomery(1974), chipper (1994) and Rayrnond et al. (1989) have studied sea fog thorOUghly.HOwever, studies on Arctic sea ice have rarely been carried Out becauSe of the sever environment and less htnnan activity in the region. There …     

Crucial physical characteristics of sea ice in the Arctic section of 143°–180°W during August and early September 2008

Sea-ice physical characteristics were investigated in the Arctic section of 143°-180°W during August and early September 2008. Ship-based observations show that both the sea-ice thickness and concentration recorded during southward navigation from 30 August to 6 September were remarkably less than those recorded during northward navigation from 3 to 30 August, especially at low latitudes. Accordingly, the marginal ice zone moved from about 74.0°N to about 79.5°N from mid-August to early September. Melt-pond coverage increased with increasing latitude, peaking at 84.4°N, where about 27% of ice was covered by melt ponds. Above this latitude, melt-pond coverage decreased evidently as the ice at high latitudes experienced a relatively short melt season and commenced its growth stage by the end of August. Regional mean ice thickness increased from 0.8 (±0.5) m at 75.0°N to 1.5 (±0.4) m at 85.0°N along the northward navigation while it decreased rapidly to 0.6 (±0.3) m at 78.0°N along the southward navigation. Because of relatively low ice concentration and thin ice in the investigated Arctic sector, both the short-term ice stations and ice camp could only be set up over multiyear sea ice. Observations of ice properties based on ice cores collected at the short-term ice stations and the ice camp show that all investigated floes were essentially isothermal with high temperature and porosity, and low density and salinity. Most ices had salinity below 2 and mean density of 800-860 kg/m~3 . Significant ice loss in the investigated Arctic sector during the last 15 a can be identified by comparison with the previous observations.     

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations(SIC)derived from passive microwave(PM) products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team(NT), Bootstrap(BT) and Climate Data Record(CDR) algorithms based on the SSMIS sensor, as well as the BT,enhanced NASA-Team(NT2) and ARTIST Sea Ice(ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients(CC), biases and root mean square deviations(RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2) to 0.95(SSMIS NT), biases range from-3.96%(SSMIS NT) to 12.05%(AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81%(SSMIS NT) to 20.15%(AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best(worst) agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.