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1.
Estimates of near surface layer parameters over 78°N drifting ice in ice camp over the Arctic ocean are made using bulk transfer methods with the data from the experiments operated by the Chinese Arctic Scientific Expedition in August 22-September 3,2003.The results show that the net radiation received by the snow surface is only 3.6 W/m2,among which the main part transported into atmosphere in term of sensible heat and latent heat,which account for 52% and 31% respectively,and less part being transported to deep ice in the conductive process.The bulk transfer coefficient of momentum is about 1.16×10-3 in the near neutral layer,which is a little smaller than that obtained over 75°N drifting ice.However,to compare with the results observed over 75°N drifting ice over the Arctic Ocean in 1999,it can be found that the thermodynamic and momentum of interactions between sea and air are significant different with latitudes,concentration and the scale of sea ice.It is very important on considering the effect of sea-air-ice interaction over the Arctic Ocean when studying climate modeling.  相似文献   

2.
The negative freeboard of sea ice(i.e., the height of ice surface below sea level) with subsequent flooding is widespread in the Southern Ocean, as opposed to the Arctic, due to the relatively thicker ice and thinner snow. In this study, we used the observations of snow and ice thickness from 103 ice mass balance buoys(IMBs) and NASA Operation IceBridge Aircraft Missions to investigate the spatial distribution of negative freeboard of Arctic sea ice. The Result showed that seven IMBs recorded negative freeboards, which were sporadically located in the seas around Northeast Greenland, the Central Arctic Ocean, and the marginal areas of the Chukchi–Beaufort Sea. The observed maximum values of negative freeboard could reach-0.12 m in the seas around Northeast Greenland. The observations from IceBridge campaigns also revealed negative freeboard comparable to those of IMBs in the seas around North Greenland and the Beaufort Sea. We further investigated the large-scale distribution of negative freeboard using NASA CryoSat-2 radar altimeter data, and the result indicates that except for the negative freeboard areas observed by IMBs and IceBridge, there are negative freeboards in other marginal seas of the Arctic Ocean. However, the comparison of the satellite data with the IMB data and IceBridge data shows that the Cryosat-2 data generally overestimate the extent and magnitude of the negative freeboard in the Arctic.  相似文献   

3.
This study investigates recent climate change over the Arctic and its link to the mid-latitudes using the ERA-Interim global atmospheric reanalysis data from the European Center for Medium-Range Weather Forecast （ECMWF）. Since 1979, sub- stantial surface warming, associated with the increase in anthropogenic greenhouse gases, has occurred over the Arctic. The great- est warming in winter has taken place offshore in the Kara-Barents Sea, and is associated with the increase in turbulent heat fluxes from the marginal ice zone. In contrast to the marked warming over the Arctic Ocean in winter, substantial cooling appears over Siberia and eastern Asia, linked to the reduction of Arctic sea ice during the freezing season （September-March）. However, in summer, very little change is observed in surface air temperature over the Arctic because increased radiative heat melts the sea ice and the amount of turbulent heat gain from the ocean is relatively small. The heat stored in the upper ocean mixed layer in summer with the opening of the Arctic Ocean is released back to the atmosphere as turbulent heat fluxes during the autumn and through to the following spring. This warming of the Arctic and the reduced sea ice amplifies surface cooling over Siberia and eastern Asia in winter.  相似文献   

4.
The global climate is intimately connected to changes in the polar oceans. The variability of sea ice coverage affects deep-water formations and large-scale thermohaline circulation patterns. The polar radiative budget is sensitive to sea-ice loss and consequent surface albedo changes. Aerosols and polar cloud microphysics are crucial players in the radioactive energy balance of the Arctic Ocean. The main biogenic source of sulfate aerosols to the atmosphere above remote seas is dimethylsulfide (DMS). Recent research suggests the flux of DMS to the Arctic atmosphere may change markedly under global warming. This paper describes climate data and DMS production (based on the five years from 1998 to 2002) in the region of the Barents Sea (30–35°E and 70–80°N). A DMS model is introduced together with an updated calibration method. A genetic algorithm is used to calibrate the chlorophyll-a (CHL) measurements (based on satellite SeaWiFS data) and DMS content (determined from cruise data collected in the Arctic). Significant interannual variation of the CHL amount leads to significant interannual variability in the observed and modeled production of DMS in the study region. Strong DMS production in 1998 could have been caused by a large amount of ice algae being released in the southern region. Forcings from a general circulation model (CSIRO Mk3) were applied to the calibrated DMS model to predict the zonal mean sea-to-air flux of DMS for contemporary and enhanced greenhouse conditions at 70–80°N. It was found that significantly decreasing ice coverage, increasing sea surface temperature and decreasing mixed-layer depth could lead to annual DMS flux increases of more than 100% by the time of equivalent CO2 tripling (the year 2080). This significant perturbation in the aerosol climate could have a large impact on the regional Arctic heat budget and consequences for global warming.  相似文献   

5.
The sea ice cover in the Arctic Ocean has been reducing and hit the low record in the summer of 2007. The anomaly was extremely large in the Pacific sector. The sea level height in the Bering Sea vs. the Greenland Sea has been analyzed and compared with the current meter data through the Bering Strait. A recent peak existed as a consequence of atmospheric circulation and is considered to contribute to inflow of the Pacific Water into the Arctic Basin. The timing of the Pacific Water inflow matched with the sea ice reduction in the Pacific sector and suggests a significant increase in heat flux. This component should be included in the model prediction for answering the question when the Arctic sea ice becomes a seasonal ice cover.  相似文献   

6.
Oceanic heat flux(Fw) is the vertical heat flux that is transmitted to the base of sea ice. It is the main source of sea ice bottom melting. The residual method was adopted to study oceanic heat flux under sea ice. The data acquired by 28 ice mass balance buoys(IMBs) deployed over the period of 2004 to 2013 in the Arctic Ocean were used. Fw values presented striking seasonal and spatial variations. The average summer Fw values for the Canada Basin, Transpolar Drift, and Multiyear Ice area were 16.8, 7.7, and 5.9 W m^-2, respectively. The mean summer F-w for the whole Arctic was 10.1 W m^-2, which was equivalent to a bottom melt of 0.4 m. Fw showed an autumn peak in November in the presence of the near-surface temperature maximum(NSTM). The average Fw for October to December was 3.7 W m^-2. And the average Fw for January to March was 1.0 W m^-2, which was approximately one third of the average Fw in the presence of NSTM. The summer Fw was almost wholly attributed to the incident solar radiation that enters the upper ocean through leads and the open water. Fw calculated through the residual method using IMB data was compared with that calculated through the parameterization method using Autonomous Ocean Flux Buoy data. The results revealed that the Fw provided by the two methods were consistent when the sea ice concentration exceeded 70% and mixing layer temperature departure from freezing point was less than 0.15℃. Otherwise, the Fw yielded by the residual method was approximately one third smaller than that provided by the parameterization method.  相似文献   

7.
Foreword     
正Rapid changes of Arctic sea ice cover have been in the focus of the international climate research community in recent years.Quite a few of nations have completed a large number of related surveys and research projects in the Arctic Ocean.Up to now,China has performed six research cruises to the Arctic Ocean resulting in a significant volume of research output.Improved knowledge on the atmospheree-sea ice-ocean interactions in the Arctic is a  相似文献   

8.
The sea ice community plays an important role in the Arctic marine ecosystem. Because of the predicted environmental changes in the Arctic environment and specifically related to sea ice, the Arctic pack ice biota has received more attention in recent years using modem ice-breaking research vessels. Studies show that the Arctic pack ice contains a diverse biota and besides ice algae, the bacterial and protozoan biomasses can be high. Surprisingly high primary production values were observed in the pack ice of the central Arctic Ocean. Occasionally biomass maximum were discovered in the interior of the ice floes, a habitat that had been ignored in most Arctic studies. Many scientific questions, which deserve special attention, remained unsolved due to logistic limitations and the sea ice characteristics. Little is know about the pack ice community in the central Arctic Ocean. Almost no data exists from the pack ice zone for the winter season. Concerning the abundance of bacteria and protozoa, more studies are needed to understand the microbial network within the ice and its role in material and energy flows. The response of the sea ice biota to global change will impact the entire Arctic marine ecosystem and a long-term monitoring program is needed. The techniques, that are applied to study the sea ice biota and the sea ice ecology, should be improved.  相似文献   

9.
Wang  Yunhe  Bi  Haibo  Huang  Haijun  Liu  Yanxia  Liu  Yilin  Liang  Xi  Fu  Min  Zhang  Zehua 《中国海洋湖沼学报》2019,37(1):18-37
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).  相似文献   

10.
Three ship-based observational campaigns were conducted to survey sea ice and snow in Prydz Bay and the surrounding waters(64.40°S–69.40°S, 76.11°E–81.29°E) from 28 November 2012 to 3 February 2013. In this paper, we present the sea ice extent and its variation, and the ice and snow thickness distributions and their variations with time in the observed zone. In the pack ice zone, the southern edge of the pack ice changed little, whereas the northern edge retreated significantly during the two earlier observation periods. Compared with the pack ice, the fast ice exhibited a significantly slower variation in extent with its northernmost edge retreating southwards by 6.7 km at a rate of 0.37 km?d-1. Generally, ice showed an increment in thickness with increasing latitude from the end of November to the middle of December. Ice and snow thickness followed an approximate normal distribution during the two earlier observations(79.7±28.9 cm, 79.1±19.1 cm for ice thickness, and 11.6±6.1 cm, 9.6±3.4 cm for snow thickness, respectively), and the distribution tended to be more concentrated in mid-December than in late November. The expected value of ice thickness decreased by 0.6 cm, whereas that of snow thickness decreased by 2 cm from 28 November to 18 December 2012. Ice thickness distribution showed no obvious regularity between 31 January and 3 February, 2013.  相似文献   

11.
During years 1980/1981–2012/2013, inter-annual variations in sea ice and snow thickness in Kemi, in the northern coast of the Gulf of Bothnia, Baltic Sea, depended on the air temperature, snow fall, and rain. Inter-annual variations in the November—April mean air temperature, accumulated total precipitation, snow fall, and rain, as well as ice and snow thickness in Kemi and ice concentration in the Gulf of Bothnia correlated with inter-annual variations of the Pacific Decadal Oscillation(PDO), Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Scandinavian Pattern(SCA), and Polar / Eurasian Pattern(PEU). The strong role of PDO is a new finding. In general, the relationships with PDO were approximately equally strong as those with AO, but rain and sea ice concentration were better correlated with PDO. The correlations with PDO were, however, not persistent; for a study period since 1950 the correlations were much lower. During 1980/1981—2012/2013, also the Pacific / North American Pattern(PNA) and El Nino–Southern Oscillation(ENSO) had statistical connections with the conditions in the Gulf of Bothnia, revealed by analyzing their effects combined with those of PDO and AO. A reduced autumn sea ice area in the Arctic was related to increased rain and total precipitation in the following winter in Kemi. This correlation was significant for the Pan-Arctic sea ice area in September, October, and November, and for the November sea ice area in the Barents / Kara seas.  相似文献   

12.
Dominant statistical patterns of winter Arctic surface wind(WASW) variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function(CVEOF) method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns(WASWP1 and WASWP2). Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWP1 and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland—Barents—Kara seas from autumn to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns(WASWP3 and WASWP4). Their time evolutions are significantly correlated with the North Atlantic Oscillation(NAO) index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure(SLP) north of 70°N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations: Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation(or NAO), effectively affect the spatial distribution of Arctic sea ice anomalies.  相似文献   

13.
One way to identify the mechanisms that are crucial to Arctic climate change is to use existing data that exhibit interannual-to-decadal variability in the sea ice and ocean interior due to atmospheric forcing. Since around 1960 s, valuable geochemical data of the ocean interior, together with atmospheric and sea ice data, have been analyzed and examined in a coupled ice–ocean model with an idealized configuration of the Arctic Basin. This is fundamentally driven by negative salt flux, in addition to atmospheric circulation and cooling. This strategy has a clear advantage over more sophisticated models with higher resolution that require extensive data collections for verification. Around 1990, the dominant atmospheric mode shifted from the Northern Annular Mode(NAM) to the Arctic Dipole Mode(ADM). The variability of sea ice cover was explained by these two modes sequentially and reproduced in the model. In particular, the geochemical fields indicated a movement of the Transpolar Drift Stream due to the NAM and an oscillation of the Pacific water between the Atlantic and Pacific sides due to the ADM. Both these features were reproduced reasonably well by the oceanic tracers in the model, including the time lags of about one third of the oscillation periods. Thus, this strategy can suggest methods and locations for monitoring oceanographic responses to Arctic climate change.  相似文献   

14.
Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic 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.  相似文献   

15.
An overview of the seasonal variation of sea-ice cover in Baffin Bay and the Labrador Sea is given. A coupled ice-ocean model, CECOM, has been developed to study the seasonal variation and associated ice-ocean processes. The sea-ice component of the model is a multi-category ice model in which mean concentration and thickness are expressed in terms of a thickness distribution function. Ten categories of ice thickness are specified in the model. Sea ice is coupled dynamically and thermodynamically to the Princeton Ocean Model. Selected results from the model including the seasonal variation of sea ice in Baffin Bay, the North Water polynya and ice growth and melt over the Labrador Shelf are presented.  相似文献   

16.
One of sea ice core samples was taken from Arctic by the First Chinese National Arctic Research Expedition Team in 1999. 20 vertical and 2 horizontal ice sections were cut out of the ice core sample 2.22 m in length, which covered the ice sheet from surface to bottom except losses for during sampling and section cutting. From the observation and analysis of the fabrics and crystals along the depth of the ice core sample, followings were found. Whole ice sheet consists of columnar, refrozen clastic pieces, granular, columnar, refrozen clastic pieces, granular, columnar and refrozen clastic pieces. This indicates that the ice core sample was 3-year old, and the ice sheet surface thawed and the melt water flowed into ice sheet during summer. Hence, the annual energy balance in Arctic can be determined by the ice sheet surface thawing in summer, and bottom growth in winter. The thickness of the ice sheet is kept constantly at a certain position based on the corresponding climate and ocean conditions; A new  相似文献   

17.
An annual cycle of atmospheric variations for 1989 in the Arctic has been simulated with the Weather Research and Forecasting （WRF） model. A severe cold bias was found around a cold center in surface air temperature over the Arctic Ocean, compared with results from ERA-Interim reanalysis. Four successive numerical experiments have been carried out to find out the reasons for this. The results show that the sea ice albedo scheme has the biggest influence in summer, and the effect of the cloud microphysics scheme is significant in both summer and winter. The effect of phase transition between ice and water has the biggest influence over the region near the sea ice edge in summer, and contributes little to improvement of the severe cold bias. The origi- nal crude albedo parameterization in the surface process scheme is the main reason for the large simulated cold bias of the cold center in summer. With a different land surface scheme than in the control run, cold biases of simulated surface air temperature over the Arctic Ocean are greatly reduced, by as much as 10 K, implying that the land surface scheme is critical for polar climate simulation.  相似文献   

18.
As an important component of the cryosphere,sea ice is very sensitive to the climate change.The study of the sea ice physics needs accurate sea ice thickness.This paper presents an electromagnetic-induction(EM) technique which can be used to measure the sea ice thickness distribution efficiently,and the successful application in Bothnian Bay.Based on the electromagnetic field theory and the electrical properties of sea ice and seawater,EM technique can detect the distance between the instrument and the ice/water interface accurately,than the sea ice thickness is obtained.Contrastive analysis of the apparent conductivity data obtained by EM and the value of drill-hole at same positions allows a construction of a transformable formula of the apparent conductivity to sea ice thickness.The verification of the sea ice thickness calculated by this formula indicates that EM technique is able to get reliable sea ice thickness with average relative error of only 12%.The statistic of all ice thickness profiles shows that the level ice distribution in Bothnian Bay was 0.4-0.6 m.  相似文献   

19.
We analyze sea ice changes from eight different earth system models that have conducted experiment abrupt4xCO2 of the Coupled Model Intercomparison Project Phase 5(CMIP5). In response to abrupt quadrupling of CO2 from preindustrial levels, Arctic temperatures dramatically rise by about 10°C—16°C in winter and the seasonal sea ice cycle and sea ice concentration are significantly changed compared with the pre-industrial control simulations(pi Control). Changes of Arctic sea ice concentration are spatially correlated with temperature patterns in all seasons and highest in autumn. Changes in sea ice are associated with changes in atmospheric circulation patterns at heights up to the jet stream. While the pattern of sea level pressure changes is generally similar to the surface air temperature change pattern, the wintertime 500 h Pa circulation displays a positive Pacific North America(PNA) anomaly under abrupt4xCO2-pi Control. This large scale teleconnection may contribute to, or feedback on, the simulated sea ice cover change and is associated with an intensification of the jet stream over East Asia and the north Pacific in winter.  相似文献   

20.
As an important part of global climate system, the Polar sea ice is effccting on global climate changes through ocean surface radiation balance, mass balance, energy balance as well as the circulating of sea water temperature and salinity. Sea ice research has a centuries - old history. The many correlative sea ice projects were established through the extensive international cooperation during the period from the primary research of intensity and the boaring capacity of sea ice to the development of sea/ice/air coupled model. Based on these reseamhes, the sea ice variety was combined with the global climate change. All research about sea ice includes： the physical properties and processes of sea ice and its snow cover, the ecosystem of sea ice regions, sea ice and upper snow albedo, mass balance of sea ice regions, sea ice and climate coupled model. The simulation suggests that the both of the area and volume of polar sea ice would be reduced in next century. With the developing of the sea ice research, more scientific issues are mentioned. Such as the interaction between sea ice and the other factors of global climate system, the seasonal and regional distribution of polar sea ice thickness, polar sea ice boundary and area variety trends, the growth and melt as well as their influencing factors, the role of the polynya and the sea/air interactions. We should give the best solutions to all of the issues in future sea ice studying.  相似文献   

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