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].     

Ocean circulation and sea ice distribution in a finite element global sea ice–ocean model

A newly developed global Finite Element Sea Ice–Ocean Model (FESOM) is presented. The ocean component is based on the Finite Element model of the North Atlantic (FENA) but has been substantially updated and extended. In addition to a faster realization of the numerical code, state-of-the-art parameterizations of subgrid-scale processes have been implemented. A Redi/GM scheme is employed to parameterize the effects of mesoscale eddies on lateral tracer distribution. Vertical mixing and convection are parameterized as a function of the Richardson number and the Monin–Obukhov length. A finite element dynamic-thermodynamic sea ice–model has been developed and coupled to the ocean component. Sea ice thermodynamics have been derived from the standard AWI sea ice model featuring a prognostic snow layer but neglecting internal heat storage. The dynamic part offers the viscous-plastic and elastic-viscous-plastic rheologies. All model components are discretized on a triangular/tetrahedral grid with a continuous, conforming representation of model variables. The coupled model is run in a global configuration and forced with NCEP daily atmospheric reanalysis data for 1948–2007. Results are analysed with a slight focus on the Southern Hemisphere. Many aspects of sea ice distribution and hydrography are found to be in good agreement with observations. As in most coarse-scale models, Gulf Stream transport is underestimated, but transports of the Kuroshio and the Antarctic Circumpolar Current appear realistic. The seasonal cycles of Arctic and Antarctic sea ice extents and Antarctic sea ice thickness are well captured; long- and short-term variability of ice coverage is found to be reproduced realistically in both hemispheres. The coupled model is now ready to be used in a wide range of applications.     

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     

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…     

A parameterization of ice shelf–ocean interaction for climate models

Model results from a regional model (BRIOS) of the Southern Ocean that includes ice shelf cavities and the interaction between ocean and ice shelves are used to derive a simple parameterization for ice shelf melting and the corresponding fresh water flux in large-scale ocean climate models. The parameterization assumes that the heat loss and fresh water gain due to the ice shelves are proportional to the difference in freezing temperature at the ice shelf edge base and the oceanic temperature on the shelf/slope area of the adjacent ocean as well as an effective area of interaction. This area is proportional to the along-shelf width of ice shelf and an effective cross-shelf distance, which turns out to be rather uniform (5–15 km) for a variety of different ice shelves. The proposed parameterization is easy to implement and valid for a wide range of circumstances. An application of the proposed scheme in a global ice ocean model (CLIO) supports our hypothesis that it can be used successfully and improves both the ocean and sea ice component of the model. This parameterization should also be used in models of the climate system that include a coupling between an ice sheet and an oceanic component.     

The variation features of the Antarctic sea ice (Ⅱ)

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Reproduction of the large-scale state of water and sea ice in the arctic ocean in 1948–2002: Part I. Numerical model

Calculations were performed using a model of the combined circulation of the Atlantic Ocean (from 20° S), the Arctic Ocean, and the Bering Sea with a resolution of 0.25° by latitude and longitude for 1958–2006. The results are compared with observational data and results obtained by other models. Model estimates were obtained for the evolution of the Atlantic water inflow into the Arctic basin through the Fram Strait and the Barents Sea. Increased transports of Atlantic water inflow into the Arctic basin were found for the first half of the 1990s and 2004–2006. The relation between Atlantic water transports into the Arctic basin and variations in the North Atlantic oscillation is shown. A positive trend of Atlantic water inflow into the Arctic basin through the Fram Strait (0.061 Sv per year) was revealed. The evolution of the freshwater-layer thickness in the Beaufort Circulation (BC) is considered. There are three periods of its increased values combined with the increased anticyclonic vorticity of BC currents: the 1960s, the 1980s, and from 1999 until now. The model estimate for a statistical mean timescale of the cycle of freshwater concentration and sink from the BC is 16 years, which is close to currently existing estimates. The evolution of anticyclonic vorticity of currents leads the variations in the freshwater-layer thickness of the BC by 1.75 years. Since the mid-1970s, there have been long positive trends of both the freshwater-layer thickness and anticyclonic vorticity of currents in the BC. In the same time period, there has been a satellite-registered negative trend in the ice area in the Arctic, which was reproduced by the model.     

The sources of Antarctic bottom water in a global ice–ocean model

Two mechanisms contribute to the formation of Antarctic bottom water (AABW). The first, and probably the most important, is initiated by the brine released on the Antarctic continental shelf during ice formation which is responsible for an increase in salinity. After mixing with ambient water at the shelf break, this salty and dense water sinks along the shelf slope and invades the deepest part of the global ocean. For the second one, the increase of surface water density is due to strong cooling at the ocean–atmosphere interface, together with a contribution from brine release. This induces deep convection and the renewal of deep waters. The relative importance of these two mechanisms is investigated in a global coupled ice–ocean model. Chlorofluorocarbon (CFC) concentrations simulated by the model compare favourably with observations, suggesting a reasonable deep water ventilation in the Southern Ocean, except close to Antarctica where concentrations are too high. Two artificial passive tracers released at surface on the Antarctic continental shelf and in the open-ocean allow to show clearly that the two mechanisms contribute significantly to the renewal of AABW in the model. This indicates that open-ocean convection is overestimated in our simulation. Additional experiments show that the amount of AABW production due to the export of dense shelf waters is quite sensitive to the parameterisation of the effect of downsloping and meso-scale eddies. Nevertheless, shelf waters always contribute significantly to deep water renewal. Besides, increasing the P.R. Gent, J.C. McWilliams [Journal of Physical Oceanography 20 (1990) 150–155] thickness diffusion can nearly suppress the AABW formation by open-ocean convection.     

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.     

A coupled ice–ocean model for the Greenland,Iceland and Norwegian Seas

Simulations from a coupled ice–ocean model that highlight the importance of synoptic forcing on sea-ice dynamics are described. The ocean model is a non-hydrostatic primitive equation model coupled to a dynamic thermodynamic sea ice model. The ice modelling sensitivity study presented here is part of an ongoing research programme to define the role played by sea ice in the energy balance of the Greenland Sea. The different categories of sea ice found in the subpolar regions are simulated through the use of equations for thin ice, thick ice and the Marginal Ice Zone. A basin scale numerical model of the Greenland, Iceland and Norwegian Seas has a horizontal resolution of 20 km and a vertical grid spacing of 50 m. This resolution is adequate for resolving the mesoscale topographic structures known to control the circulation in this region. The spin-up reproduces the main features of the circulation, including the cyclonic gyres in the Norwegian and Greenland Basins and Iceland Plateau. Topographic steering of the flow is evident. The baroclinic Rossby radius of deformation is between 5 and 10 km so that the model is not eddy-resolving. The coupled ice–ocean model was run for a period of two weeks. The influence of horizontal resolution of the atmospheric model was tested by comparing simulations using six hourly wind fields from the ECMWF with those generated using six hourly fields from a HIRLAM, with horizontal resolutions of 1° and 0.18° respectively. The simulations show reasonable agreement with satellite ice compactness data and data of ice transports across sections at 79°N, 75°N and Denmark Strait.     

Impact of ice cover in the Arctic on ocean–atmosphere turbulent heat fluxes

The impact of spatiotemporal variability of the ice-covered area in the Arctic on the value and interannual dynamics of turbulent heat fluxes on the ocean–atmosphere border is considered. An expected inverse dependence of the heat fluxes integrated over the Arctic area and the area of ice is not detected. The largest interannual oscillations of heat fluxes from the ocean to the atmosphere are timed to the varying position of the ice edge and, to a lesser extent, are connected with total area of ice. The role of the marginal ice zone in oceanic heat transfer is analyzed. In particular, it is shown that while moving along the marginal zone from the ice-free surface to the surface with an ice concentration of 0.8, latent and sensible heat fluxes are reduced by a factor of 2.5–3.     

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.     

Realistic representation of the surface freshwater flux in an ice–ocean general circulation model

Ocean general circulation models usually use an equivalent salt-flux in order to represent the freshwater surface inflow/outflow. This unphysical approach has numerous shortcomings, especially for climate studies. A more physical representation has been originally proposed by R.X. Huang [Journal of Physical Oceanography 23 (1993) 2428–2446] for ocean models. It consists in taking into account the vertical velocity at the sea surface. Here this formulation is introduced in a coupled ice–ocean general circulation model designed for climate studies. The treatment of the ice–ocean exchanges needs special care in order to conserve salt and freshwater masses, and to correctly represent the physics involved. This formulation allows to simulate the Goldsbrough–Stommel circulation and the meridional pathway of the freshwater at the ocean surface. Furthermore, the meridional freshwater transport diagnosed using such an approach is more directly comparable to the atmospheric water-vapor transport. Nevertheless, it produces only small changes in the ocean general circulation.     

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…     

Can an atmospherically forced ocean model accurately simulate sea surface temperature during ENSO events?

The performance of an atmospherically forced ocean general circulation model (OGCM) in simulating daily and monthly sea surface temperature (SST) is examined during the historical El Niño Southern Oscillation (ENSO) events during the time period 1993–2003. For this purpose, we use the HYbrid Coordinate Ocean Model (HYCOM) configured for the North Pacific north of 20°S at a resolution of ≈9 km. There is no assimilation of (or relaxation to) SST data and no date-specific assimilation of any data type. The ability of the model in simulating temporal variations of SST anomalies is discussed by comparing model results with two satellite-based SST products. The HYCOM simulation gives a basin-averaged monthly mean bias of 0.3 °C and rms difference of 0.6 °C over the North Pacific Ocean during 1993–2003. While the model is able to simulate SST anomalies with mean biases  <0.5 °C  in comparison to observations during most of the ENSO events, limitations in the accuracy of atmospheric forcing (specifically, net short-wave radiation) have some influence on the accuracy of simulations. This is specifically demonstrated during the 1998 transition period from El Niño to La Niña, when a record large SST drop of  ≈7 °C  occurred in the eastern equatorial Pacific Ocean.