A coupled regional Arctic sea ice-ocean model: configuration and application

A regional sea ice-ocean coupled model for the Arctic Ocean was developed, based on the MITgcm ocean circulation model and classical Hibler79 type two category thermodynamics-dynamics sea ice model. The sea ice dynamics and thermodynamics were considered based on Viscous-Plastic (VP) and Winton three-layer models, respectively. A detailed configuration of coupled model has been introduced. Special attention has been paid to the model grid setup, subgrid paramerization, ice-ocean coupling and open boundary treatment. The coupled model was then applied and two test run examples were presented. The first model run was a climatology simulation with 10 years(1992—2002) averaged NCAR/NCEP reanalysis data as atmospheric forcing. The second model run was a seasonal simulation for the period of 1992—2007. The atmospheric forcing was daily NCAR/NCEP reanalysis. The climatology simulation captured the general pattern of the sea ice thickness distribution of the Arctic, i.e., the thickest sea ice is situated around the Canada Archipelago and the north coast of the Greenland. For the second model run, the modeled September Sea ice extent anomaly from 1992—2007 was highly correlated with the observations, with a linear correlation coefficient of 0.88. The minimum of the Arctic sea ice area in the September of 2007 was unprecedented. The modeled sea ice area and extent for this minimum was overestimated relative to the observations. However, it captured the general pattern of the sea ice retreat

Using the physical decomposition method to study the effects of Arctic factors on wintertime temperatures in the Northern Hemisphere and China

The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land–sea distribution, and inter-annual and seasonal internal forcing, strengthening the anomaly signal and increasing the correlation between variables. This method was applied to the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), to study the effects of Arctic factors (Arctic oscillation (AO) and Arctic polar vortex) on wintertime temperatures in the Northern Hemisphere and China. It was found that AO effects on zonal average temperature disturbance could persist for 1 month. In the AO negative phase in wintertime, the temperatures are lower in the mid–high latitudes than in normal years, but higher in low latitudes. When the polar vortex area is bigger, the zonal average temperature is lower at 50°N. Influenced mainly by meridional circulation enhancement, cold air flows from high to low latitudes; thus, the temperatures in Continental Europe and the North American continent exhibit an antiphase seesaw relationship. When the AO is in negative phase and the Arctic polar vortex larger, the temperature is lower in Siberia, but higher in Greenland and the Bering Strait. Influenced by westerly troughs and ridges, the polar air disperses mainly along the tracks of atmospheric activity centers. The AO index can be considered a predictor of wintertime temperature in China. When the AO is in negative phase or the Asian polar vortex is intensified, temperatures in Northeast China and Inner Mongolia are lower, because under the influence of the Siberia High and northeast cold vortex, the cold air flows southwards

Status of the Recent Declining of Arctic Sea Ice Studies

In the past 30 years, a large-scale change occurred in the Arctic climatic system, which had never been observed before 1980s. At the same time, the Arctic sea ice experienced a special evolution with more and more rapidly dramatic declining. In this circumstance, the Arctic sea ice became a new focus of the Arctic research. The recent advancements about abrupt change of the Arctic sea ice are reviewed in this paper. The previous analyses have demonstrated the accelerated declining trend of Arctic sea ice extent in the past 30 years, based on in-situ and satellite-based observations of atmosphere, as well as the results of global and regional climate simulations. Especially in summer, the rate of decrease for the ice extents was above 10% per decade. In present paper, the evolution characteristics of the arctic sea ice and its possible cause are discussed in three aspects, i.e. the sea ice physical properties, the interaction process of sea ice, ocean and atmosphere and its response and feedback mechanism to global and arctic climate system

Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a combined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone

The rise of sea ice research collaboration between China and Finland

Collaboration between China and Finland in marine sciences was commenced in winter 1988. The main topic was then short-term sea ice forecasting in the seasonal sea ice zone (SSIZ), particularly in the Bohai Sea in China and the Baltic Sea in Finland. The sea ice in SSIZ is thin and highly dynamic so that ice conditions may change rapidly. While the length scales of the Baltic Sea and the Bohai Sea are similar, the main difference between them is that the former is brackish and non-tidal while the latter is oceanic for the salinity and possesses a large tidal amplitude. The Bohai Sea is located at latitudes 37°N–41°N, and the Baltic Sea is located at latitudes 55°N–66°N. However, the same sea ice model is applicable for both. The main application field of sea ice forecasting was winter shipping in Finland and oil drilling in China. The collaboration was successful and in late 1990s the research was expanded to polar seas, lakes, and to climate change applications

Some discussions on Arctic vortex

The Arctic vortex is a persistent large-scale cyclonic circulation in the middle and upper troposphere and the stratosphere. Its activity and variation control the semi-permanent active centers of Pan-Arctic and the short-time cyclone activity in the subarctic areas. Its strength variation, which directly relates to the atmosphere, ocean, sea ice and ecosystem of the Arctic, can affect the lower atmospheric circulation, the weather of subarctic area and even the weather of middle latitude areas. The 2003 Chinese Second Arctic Research Station experienced the transition of the stratospheric circulation from a warm anticyclone to a cold cyclone during the ending period of Arctic summertime, a typical establishing process of the polar vortex circulation. The impact of the polar vortex variation on the low-level circulation has been investigated by some scientists through studying the coupling mechanisms of the stratosphere and troposphere. The impact of the Stratospheric Sudden Warming (SFW) events on the polar vortex variation was drawing people's great attention in the fifties of the last century. The Arctic Oscillation (AO), relating to the variation of the Arctic vortex, has been used to study the impact of the Arctic vortex on climate change. The recent Arctic vortex studies are simply reviewed and some discussions on the Arctic vertex are given in the paper. Some different views and questions are also discussed

Snow and sea ice thermodynamics in the Arctic: Model validation and sensitivity study against SHEBA data

Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamics snow/ice model (HIGHTSI). Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance

An inter-comparison of six latent and sensible heat flux products over the Southern Ocean

The latent heat fluxes (LHF) and sensible heat fluxes (SHF) over the Southern Ocean from six different data sets are inter-compared for the period 1988- 2000. The six data sets include three satellite-based products, namely, the second version of the Goddard Satellite-Based Surface Turbulent Fluxes data set (GSSTF-2), the third version of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS-3) and the Japanese Ocean Fluxes Data Sets with Use of Remote Sensing Observations (J-OFURO); two global reanalysis products, namely, the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 data set (NCEP-2) and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis data set (ERA-40); and the Objectively Analyzed Air-Sea Fluxes for the Global Oceans data set (OAFlux). All these products reveal a similar pattern in the averaged flux fields. The zonal mean LHF fields all exhibit a continuous increase equatorward. With an exception of HOAPS-3, the zonal mean SHF fields display a minimum value near 50°S, increasing both pole- and equatorward. The differences in the standard deviation for LHF are larger among the six data products than the differences for SHF. Over the regions where the surface fluxes are significantly influenced by the Antarctic Oscillation and the Pacific-South American teleconnection, the values and distributions of both LHF and SHF are consistent among the six products. It was found that the spatial patterns of the standard deviations and trends of LHF and SHF can be explained primarily by sea-air specific humidity and temperature differences; wind speed plays a minor role.Keywords: Latent heat flux; sensible heat flux; Southern Ocean(Published: 17 November 2011)Citation: Polar Research 2011, 30, 10167, DOI: 10.3402/polar.v30i0.1016

The intraseasonal variability of winter semester surface air temperature in Antarctica

This study investigates systematically the intraseasonal variability of surface air temperature over Antarctica by applying empirical orthogonal function (EOF) analysis to the National Centers for Environmental Prediction, US Department of Energy, Reanalysis 2 data set for the period of 1979 through 2007. The results reveal the existence of two major intraseasonal oscillations of surface temperature with periods of 26 - 30 days and 14 days during the Antarctic winter season in the region south of 60°S. The first EOF mode shows a nearly uniform spatial pattern in Antarctica and the Southern Ocean associated with the Antarctic Oscillation. The mode-1 intraseasonal variability of the surface temperature leads that of upper atmosphere by one day with the largest correlation at 300-hPa level geopotential heights. The intraseasonal variability of the mode-1 EOF is closely related to the variations of surface net longwave radiation the total cloud cover over Antarctica. The other major EOF modes reveal the existence of eastward propagating phases over the Southern Ocean and marginal region in Antarctica. The leading two propagating modes respond to Pacific-South American modes. Meridional winds induced by the wave train from the tropics have a direct influence on the surface air temperature over the Southern Ocean and the marginal region of the Antarctic continent. Key words: Antarctic climate, surface air temperature, intraseasonal variability, Antarctic Oscillation (Published: 22 February 2011) Citation: Polar Research 2011, 30, 6039, DOI: 10.3402/polar.v30i0.603