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The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land-sea distribution, and inter-annual and seasonalinternal forcing, strengthening the anomaly signal and increasing the correlation between variables.This method was applied to the reanalysisdata from the National Centers for Environmental Prediction/National Centerfor Atmospheric Research (NC EP/NCAR), to study the effects of Arctic factors (Arctic oscillation (AO) and Arctic polar vortex) on wintertime temperatures in the NorthernHemisphere and China.It was found that AO effectson zonal average temperature disturbance could persist for 1 month. In the AO negative phase in wintertime, the temperatures are lower in the mid-high latitudesthan in normal years, but higher in low latitudes.When the polar vortex area is bigger, the zonal average temperature is lower at50°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 antiphaseseesaw relationship. When theAO is in negative phase and the Arcticpolar vortex larger, the temperature is lowerin Siberia, but higher in Greenland and the Bering Strait. Influenced by westerlytroughs and ridges, the polar air dispersesmainly 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 influenceof the Siberia High and northeast cold vortex, the cold air flows southwards.
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Potential links between the Arctic sea-ice concentration anomalies and extreme precipitation in China are explored. Associations behind these links can be explained by physical interpretations aided by visualisations of temporarily lagged composites of variables such as atmospheric mean sea level pressure and sea surface temperature. This relatively simple approach is verified by collectively examining already known links between the Arctic sea ice and rainfall in China. For example, similarities in the extreme summer rainfall response to Arctic sea-ice concentration anomalies either in winter (DJF) or in spring (MAM) are highlighted. Furthermore, new links between the Arctic sea ice and the extreme weather in India and Eurasia are proposed. The methodology developed in this study can be further applied to identify other remote impacts of the Arctic sea ice variability.
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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 (piControl). 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 hPa circulation displays a positive Pacific North America (PNA) anomaly under abrupt4xCO2-piControl. 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.
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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.
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Using a regional atmospheric model for Arctic climate simulation, two groups of numerical experiments were carried out to study the influence of changes in the underlying surface (land surface, sea surface, and sea ice (LS/SS/SI)) from mild ice years to severe ice years on Arctic climate. In each experiment in the same group, the initial values and lateral boundary conditions were identical. The underlying surface conditions were updated every six hours. The model was integrated for 10 a and monthly mean results were saved for analysis. Variations in annual mean surface air temperature were closely correlated with changes in LS/SS/SI, with a maximum change of more than 15 K. The impact of changes in LS/SS/SI on low-level air temperature was also evident, with significant changes seen over the ocean. However, the maximum change was less than 2 K. For air temperature above 700 hPa, the impact of LS/SS/SI changes was not significant. The distribution of annual mean sea level pressure differences was coincident with the distribution of annual mean sea ice concentration. The difference centers were located in the Barents Sea, the Kara Sea, and the East Siberian Sea, with the maximum value exceeding 3 hPa. For geopotential height, some results passed and some failed a
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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 1960s, 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.
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The influence of spring Arctic sea ice variability on the Pacific Decadal Oscillation (PDO) like sea surface temperature (SST) variability is established and investigated using an Atmosphere Ocean General Circulation Model (AOGCM) of the Bergen Climate Model version 2 (BCM2). The spring Arctic sea ice variability affects the mid-latitudes and tropics through the propagation of the anomalous Eliassen-Palm (E-P) flux from the polar region to mid- and low-latitudes during boreal spring. The pathway includes anomalous upward wave activity, which propagates to the high troposphere from near the surface of the polar region, turns southward between 500 hPa and 200 hPa and extends downward between 50°N and 70°N, influencing the near surface atmospheric circulation. The alteration of the near surface atmospheric circulation then causes anomalous surface ocean circulation. These circulation changes consequently leads to the SST anomalies in the North Pacific which may persist until the following summer, named seasonal “foot printing” mechanism (SFPM).
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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.
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Remote sensing data from passive microwave and satellite-based altimeters, associated with the data measured underway, were used to characterize seasonal and spatial changes in sea ice conditions along the Arctic Northeast Passage (NEP) and the high-latitude sea route (HSR) north of the island groups in the eastern Arctic Ocean in 2007 and 2012. In both years, summer Arctic sea ice extent reached minima since satellite records began in 1979. However, there were large differences in spatial distribution of sea ice between the two years. Sea ice conditions in the eastern sections of the sea routes were relatively slight in the 2007 summer, because of the remarkable decline of sea ice in the Pacific sector. A belt of sea ice that blocked sections from the western Laptev Sea to the eastern Kara Sea resulted in both sea routes not completely opening through the 2007 summer. The combination of a great storm in early August causing sea ice to be sheared from the Arctic pack ice and the thick ice surviving the winter delayed the summer opening of the eastern parts of the sea routes in 2012. However, the average open period, defined by 50% ice concentration for the entire NEP and HSR, reached 82 d and 55 d, respectively. Thus, 2012 was the most accessible year since the satellite era began in 1979. The distinct decrease in sea ice in the western parts of the HSR in the 2012 summer can be attributed to the thinning preconditions of sea ice prior to the melt season. The HSR opening can benefit Arctic shipping of deeper-draft vessels.
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