北极海冰融化影响东亚冬季天气和气候的研究进展以及学术争论焦点问题

北极历来是影响东亚冬季天气、气候的关键区域之一。北极表面增暖要比全球平均快2~3倍,即所谓北极的放大效应。随着全球增暖的持续以及北极海冰的持续融化,北极的生态环境正在发生显著的变化,进而可能对北半球中、低纬度的天气、气候产生影响。本文概述了有关北极海冰融化影响冬季东亚天气、气候的主要研究进展,特别是自2000年以来,北极海冰异常偏少影响东亚冬季气候变率以及极端严寒事件的可能途径、存在的科学问题,以及学术界的争论焦点。秋、冬季节是北极海冰快速形成时期,此时北极海冰对大气环流的影响要强于大气对海冰的影响。近二十年来的研究结果表明,北极海冰异常偏少,不仅影响北冰洋局地的气温和降水变化,而且通过复杂的相互作用和反馈过程,对北半球中、低纬度的天气、气候产生影响。北极海冰通过以下两个可能机制来影响东亚冬季的天气、气候:（1）北极海冰的负反馈机制;（2）由海冰异常偏少引起的平流层-对流层相互作用机制。秋、冬季节北极海冰持续异常偏少,特别是,巴伦支海-喀拉海海冰异常偏少,既可以加强冬季西伯利亚高压（东亚冬季风偏强）,也可以导致冬季风偏弱。导致海冰影响不确定性的部分原因是:（1）夏季北极大气环流状态影响北极海冰异常偏少对冬季大气环流的反馈效果;（2）冬季大气环流对北极海冰异常偏少响应的位置、强度不同造成的。秋、冬季节北极海冰持续异常偏少,在适宜的条件下（例如,前期夏季北极大气环流的热力和动力条件,有利于加强北极海冰偏少对冬季大气的反馈作用）,可以激发出有利于冬季亚洲大陆极端严寒过程的大气环流异常。目前学术界争论焦点主要集中在以下两个方面:（1）关于北极增暖、北极海冰融化对中纬度区域影响的争论;（2）关于1980年代后期以来,冬季欧亚大陆表面气温呈现降温趋势的原因。目前,有关北极海冰融化影响冬季欧亚大陆次季节变化以及极端天气、气候事件的过程和机制,我们认知非常有限,亟需开展深入细致的研究。     

Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions

Temperature inversions are a common feature of the Arctic wintertime boundary layer. They have important impacts on both radiative and turbulent heat fluxes and partly determine local climate-change feedbacks. Understanding the spread in inversion strength modelled by current global climate models is therefore an important step in better understanding Arctic climate and its present and future changes. Here, we show how the formation of Arctic air masses leads to the emergence of a cloudy and a clear state of the Arctic winter boundary layer. In the cloudy state, cloud liquid water is present, little to no surface radiative cooling occurs and inversions are elevated and relatively weak, whereas surface radiative cooling leads to strong surface-based temperature inversions in the clear state. Comparing model output to observations, we find that most climate models lack a realistic representation of the cloudy state. An idealised single-column model experiment of the formation of Arctic air reveals that this bias is linked to inadequate mixed-phase cloud microphysics, whereas turbulent and conductive heat fluxes control the strength of inversions within the clear state.     

Sensitivity of open-water ice growth and ice concentration evolution in a coupled atmosphere-ocean-sea ice model

A coupled atmosphere-ocean-sea ice model is applied to investigate to what degree the area-thickness distribution of new ice formed in open water affects the ice and ocean properties. Two sensitivity experiments are performed which modify the horizontal-to-vertical aspect ratio of open-water ice growth. The resulting changes in the Arctic sea-ice concentration strongly affect the surface albedo, the ocean heat release to the atmosphere, and the sea-ice production. The changes are further amplified through a positive feedback mechanism among the Arctic sea ice, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the Fram Strait sea ice import influences the freshwater budget in the North Atlantic Ocean. Anomalies in sea-ice transport lead to changes in sea surface properties of the North Atlantic and the strength of AMOC. For the Southern Ocean, the most pronounced change is a warming along the Antarctic Circumpolar Current (ACC), owing to the interhemispheric bipolar seasaw linked to AMOC weakening. Another insight of this study lies on the improvement of our climate model. The ocean component FESOM is a newly developed ocean-sea ice model with an unstructured mesh and multi-resolution. We find that the subpolar sea-ice boundary in the Northern Hemisphere can be improved by tuning the process of open-water ice growth, which strongly influences the sea ice concentration in the marginal ice zone, the North Atlantic circulation, salinity and Arctic sea ice volume. Since the distribution of new ice on open water relies on many uncertain parameters and the knowledge of the detailed processes is currently too crude, it is a challenge to implement the processes realistically into models. Based on our sensitivity experiments, we conclude a pronounced uncertainty related to open-water sea ice growth which could significantly affect the climate system sensitivity.     

FGOALS_gg1.1极地气候模拟

对中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室发展的气候系统模式FGOALS_g1.1的极地气候模拟现状进行了较为全面的评估.结果表明,FGOALS_g1.1对南北极海冰的主要分布特征、季节变化和年代际变化趋势具有一定的模拟能力.但也注意到,与观测相比,模式存在以下几方面的问题:(1)模拟的海冰总面积北极偏多,而南极偏少.北极,北大西洋海冰全年明显偏多;夏季,西伯利亚沿海海冰偏多,而波弗特海海冰偏少.南极,威德尔海和罗斯海冬季海冰偏少.南北极海冰边缘都存在异常的较大范围密集度很小的碎冰区,夏季尤为显著.(2)海冰流速在南北极海冰边缘和南极大陆沿岸附近较大.北极,模式没能模拟出波弗特涡流,并且由于模式网格中北极点的处理问题,造成其附近错误的海冰流场及厚度分布.这些海冰偏差与模式模拟的大气和海洋状况有着密切的联系.进一步分析表明,FGOALS_g1.1模拟的冰岛低压和南极绕极西风带明显偏弱,其通过大气环流和海表面风应力影响向极地的热量输送,在很大程度上导致上述的海冰偏差.此外,耦合模式中大气-海冰-海洋的相互作用可以放大子模式中的偏差.     

A link between Arctic sea ice and recent cooling trends over Eurasia

A band of cooling that extends across mid-latitude Eurasia is identified in the wintertime surface air temperatures of the latest ECMWF reanalysis. This cooling is related to extreme warming around the Kara Sea through changes in the meridional temperature gradient. Surface temperatures in the Arctic have risen faster than those at lower latitudes, and as the Arctic warming increases, this north–south temperature gradient is weakened. This change in the meridional temperature gradient causes a decrease in the westerly winds that help maintain the mild European climate by transporting heat from the Atlantic. Since decreasing sea ice concentrations have been shown to be a driving factor in Arctic amplification, a singular value decomposition analysis is used to confirm the co-variability of the Arctic sea ice, including the Kara Sea, and the temperatures over the mid-latitude Eurasia. These findings suggest that decreasing sea ice concentrations can change the meridional temperature gradient and hence the large-scale atmospheric flow of the Northern Hemisphere.     

Changes in the climate of the Alaskan North Slope and the ice concentration of the adjacent Beaufort Sea

A reliable data set of Arctic sea ice concentration based on satellite observations exists since 1972. Over this time period of 36 years western arctic temperatures have increased; the temperature rise varies significantly from one season to another and over multi-year time scales. In contrast to most of Alaska, however, on the North Slope the warming continued after 1976, when a circulation change occurred, as expressed in the PDO index. The mean temperature increase for Barrow over the 36-year period was 2.9°C, a very substantial change. Wind speeds increased by 18% over this time period, however, the increase were non-linear and showed a peak in the early 1990s. The sea ice extent of the Arctic Ocean has decreased strongly in recent years, and in September 2007 a new record in the amount of open water was recorded in the Western Arctic. We observed for the Southern Beaufort Sea a fairly steady increase in the mean annual amount of open water from 14% in 1972 to 39% in 2007, as deduced from the best linear fit. In late summer the decrease is much larger, and September has, on average, the least ice concentration (22%), followed by August (35%) and October (54%). The correlation coefficient between mean annual values of temperature and sea ice concentration was 0.84. On a monthly basis, the best correlation coefficient was found in October with 0.88. However, the relationship between winter temperatures and the sea ice break-up in summer was weak. While the temperature correlated well with the CO₂ concentration (r?=?0.86), the correlation coefficient between CO₂ and sea ice was lower (r?=??0.68). After comparing the ice concentration with 17 circulation indices, the best relation was found with the Pacific Circulation Index (r?=??0.59).     

Evaluation of pan-Arctic melt-freeze onset in CMIP5 climate models and reanalyses using surface observations

The seasonal melt-freeze transitions are fundamental features of the Arctic climate system. The representation of the pan-Arctic melt and freeze onset (north of 60°N) is assessed in two reanalyses and eleven CMIP5 global circulation models (GCMs). The seasonal melt-freeze transitions are retrieved from surface air temperature (SAT) across the land and sea-ice domains and evaluated against surface observations. While monthly averages of SAT are reasonably well represented in models, large model-observation and model–model disparities of timing of melt and freeze onset are evident. The evaluation against surface observations reveals that the ERA-Interim reanalysis performs the best, closely followed by some of the climate models. GCMs and reanalyses capture the seasonal melt-freeze transitions better in the central Arctic than in the marginal seas and across the land areas. The GCMs project that during the 21st century, the summer length—the period between melt and freeze onset—will increase over land by about 1 month at all latitudes, and over sea ice by 1 and 3 months at low and high latitudes, respectively. This larger summer-length increase over sea ice at progressively higher latitudes is related to a retreat of summer sea ice during the 21st century, since open water freezes roughly 40 days later than ice-covered ocean. As a consequence, by the year 2100, the freeze onset is projected to be initiated within roughly 10 days across the whole Arctic Ocean, whereas this transition varies by about 80 days today.     

秋季北极海冰对中国冬季气温的影响

利用海冰资料、中国地面气候资料、环流特征量资料及NCEP/NCAR再分析资料,研究了秋季北极海冰变化对中国冬季平均气温、日气温变率以及异常低温天气的影响。分析结果表明,秋季北极海冰异常偏多年中国冬季常为暖冬;异常偏少年中国冬季常为冷冬,且异常低温天气出现频率更高,常发生低温灾害事件。秋季北极海冰通过影响后期的北半球极涡、东亚冬季风和西伯利亚高压进而影响中国冬季的平均气温,且通过影响冬季异常强西伯利亚高压的出现频次,影响中国冬季异常低温天气的发生频次。合成分析结果表明,秋季北极海冰异常偏少年的冬季,中国以北亚欧大陆高纬度的偏北风较强,且中国及其以北的中高纬度地区空气异常偏冷,导致极地和高纬度的冷空气易向南爆发,造成中国冬季气温偏低,异常低温天气频发。     

The effects of aggressive mitigation on steric sea level rise and sea ice changes

With an increasing political focus on limiting global warming to less than 2 °C above pre-industrial levels it is vital to understand the consequences of these targets on key parts of the climate system. Here, we focus on changes in sea level and sea ice, comparing twenty-first century projections with increased greenhouse gas concentrations (using the mid-range IPCC A1B emissions scenario) with those under a mitigation scenario with large reductions in emissions (the E1 scenario). At the end of the twenty-first century, the global mean steric sea level rise is reduced by about a third in the mitigation scenario compared with the A1B scenario. Changes in surface air temperature are found to be poorly correlated with steric sea level changes. While the projected decreases in sea ice extent during the first half of the twenty-first century are independent of the season or scenario, especially in the Arctic, the seasonal cycle of sea ice extent is amplified. By the end of the century the Arctic becomes sea ice free in September in the A1B scenario in most models. In the mitigation scenario the ice does not disappear in the majority of models, but is reduced by 42 % of the present September extent. Results for Antarctic sea ice changes reveal large initial biases in the models and a significant correlation between projected changes and the initial extent. This latter result highlights the necessity for further refinements in Antarctic sea ice modelling for more reliable projections of future sea ice.     

近20年来中国极地大气科学研究进展

南极、北极和青藏高原是地球上的 3大气候敏感地区 ,是多个国际计划研究全球变化的关键地区。中国的南极和北极实地考察研究 ,分别始于 2 0世纪 80和 90年代 ,起步较晚 ,但近 2 0余年来有较大的进展。极地大气科学考察与研究是极地科学研究的重要组成部分。讫今为止 ,中国已组织了 2 0次南极考察和 3次北极考察 ,建立了中国南极长城站、中山站和北极黄河站等 3个常年科学考察站 ;进行了常规地面气象、Brewer大气臭氧、近地面物理、高层大气物理、冰雪和大气化学等观测 ,获得了较为系统的极地大气科学第一手资料 ;开展了有关极地与全球变化的研究 ,取得了新的进展。南极地区大气温度、臭氧和海冰的气候变化在时间和空间上都是多样的。南极地区的增暖主要发生在南极半岛地区 ,在南极大陆主体并不明显 ,近 10余年来还有降温趋势。中国南极长城站和中山站的观测资料也证实了这一点。此外 ,还揭示了南极半岛西侧和罗斯海外围的海冰变化具有“翘翘板”特征 ,由此定义的南极涛动指数可用来讨论南极海冰状况和海冰关键区的活动 ;用实地考察资料研究了极地不同下垫面的近地面物理和海 -冰 -气相互作用特征 ,给出了边界层特征参数 ;讨论了极地天气气候和大气环境特征及其对东亚大气环流和中国天气气候的影响 ;利用     

BCC-CSM2-MR模式对北极海冰和气候的模拟及预估

基于第六次耦合模式比较计划（CMIP6）,使用新一代全球模式BCC-CSM2-MR的历史试验和未来共享社会经济路径（SSPs）数据,依据Hadley中心的海表面温度和海冰密集度数据及NCEP/NCAR I再分析资料,评估了BCC-CSM2-MR模式对北极海冰及北极气候的模拟能力,并对未来变化进行了预估。结果表明：BCC-CSM2-MR模式可以较好再现北极海冰密集度、近地层大气平均温度和海表温度的多年平均空间分布特征。但模式对北极局地大气平均温度模拟存在一定偏差,可能在一定程度上导致相应地区海冰的模拟存在差异。21世纪,北极海冰范围持续减少,9月减少趋势显著,3月减少趋势相对较弱。3月北极大部地区表现为一致的增温,仅在北大西洋局部出现一定程度的降温,9月北极大气增温幅度弱于3月。与地表平均温度不同,3月和9月的北极大部地区海表温度均出现增加,且9月海表温度的增幅大于3月,仅拉布拉多海海温出现下降。     

Twenty-first century Arctic climate change in the CCSM3 IPCC scenario simulations

Arctic climate change in the Twenty-first century is simulated by the Community Climate System Model version 3.0 (CCSM3). The simulations from three emission scenarios (A2, A1B and B1) are analyzed using eight (A1B and B1) or five (A2) ensemble members. The model simulates a reasonable present-day climate and historical climate trend. The model projects a decline of sea-ice extent in the range of 1.4–3.9% per decade and 4.8–22.2% per decade in winter and summer, respectively, corresponding to the range of forcings that span the scenarios. At the end of the Twenty-first century, the winter and summer Arctic mean surface air temperature increases in a range of 4–14°C (B1 and A2) and 0.7–5°C (B1 and A2) relative to the end of the Twentieth century. The Arctic becomes ice-free during summer at the end of the Twenty-first century in the A2 scenario. Similar to the observations, the Arctic Oscillation (AO) is the dominant factor in explaining the variability of the atmosphere and sea ice in the 1870–1999 historical runs. The AO shifts to the positive phase in response to greenhouse gas forcings in the Twenty-first century. But the simulated trends in both Arctic mean sea-level pressure and the AO index are smaller than what has been observed. The Twenty-first century Arctic warming mainly results from the radiative forcing of greenhouse gases. The 1st empirical orthogonal function (explains 72.2–51.7% of the total variance) of the wintertime surface air temperature during 1870–2099 is characterized by a strong warming trend and a “polar amplification”-type of spatial pattern. The AO, which plays a secondary role, contributes to less than 10% of the total variance in both surface temperature and sea-ice concentration.     

Time-dependent response of a zonally averaged ocean–atmosphere–sea ice model to Milankovitch forcing

An ocean–atmosphere–sea ice model is developed to explore the time-dependent response of climate to Milankovitch forcing for the time interval 5–3 Myr BP. The ocean component is a zonally averaged model of the circulation in five basins (Arctic, Atlantic, Indian, Pacific, and Southern Oceans). The atmospheric component is a one-dimensional (latitudinal) energy balance model, and the sea-ice component is a thermodynamic model. Two numerical experiments are conducted. The first experiment does not include sea ice and the Arctic Ocean; the second experiment does. Results from the two experiments are used to investigate (1) the response of annual mean surface air and ocean temperatures to Milankovitch forcing, and (2) the role of sea ice in this response. In both experiments, the response of air temperature is dominated by obliquity cycles at most latitudes. On the other hand, the response of ocean temperature varies with latitude and depth. Deep water formed between 45°N and 65°N in the Atlantic Ocean mainly responds to precession. In contrast, deep water formed south of 60°S responds to obliquity when sea ice is not included. Sea ice acts as a time-integrator of summer insolation changes such that annual mean sea-ice conditions mainly respond to obliquity. Thus, in the presence of sea ice, air temperature changes over the sea ice are amplified, and temperature changes in deep water of southern origin are suppressed since water below sea ice is kept near the freezing point.     

Relationship Between Arctic Sea Ice Thickness Distribution and Climate of China

Based on the simulated ice thickness data from 1949 to 1999, monthly mean temperature data from 160 stations, and monthly mean 1°×1° precipitation data reconstructed from 749 stations in China from 1951 to 2000, the relationship between the Arctic sea ice thickness distribution and the climate of China is analyzed by using the singular value decomposition method. Climate patterns of temperature and precipitation are obtained through the rotated empirical orthogonal function analysis. The results are as follows. (1) Sea ice in Arctic Ocean has a decreasing trend as a whole, and varies with two major periods of 12-14 and 16-20 yr, respectively. (2) When sea ice is thicker in central Arctic Ocean and Beaufort-Chukchi Seas, thinner in Barents-Kara Seas and Baffin Bay-Labrador Sea, precipitation is less in southern China, Tibetan Plateau, and the north part of northeastern China than normal, and vice versa. (3) When sea ice is thinner in the whole Arctic seas, precipitation is less over the middle and lower reaches of Yellow River and north part of northeastern China, more in Tibetan Plateau and south part of northeastern China than normal, and the reverse is also true. (4) When sea ice is thinner in central Arctic Ocean, East Siberian Sea, Beaufort-Chukchi Seas, and Greenland Sea; and thicker in Baffin Bay-Labrador Sea, air temperature is higher in northeastern China, southern Tibetan Plateau, and Hainan Island than normal. (5) When sea ice is thicker in East Siberian Sea 5 months earlier, thinner in Baffin Bay-Labrador Sea 7-15 months earlier, air temperature is lower over the north of Tibetan Plateau and higher in the north part of northwestern China than normal, and a reverse correlation also exists.     

Ensemble of sea ice initial conditions for interannual climate predictions

Polar climate studies are severely hampered by the sparseness of the sea ice observations. We aim at filling this critical gap by producing two 5-member sea ice historical simulations strongly constrained by ocean and atmosphere observational data and covering the 1958–2006 and 1979–2012 periods. This is the first multi-member sea ice reconstruction covering more than 50 years. The obtained sea ice conditions are in reasonable agreement with the few available observations. These best estimates of sea ice conditions serve subsequently as initial sea ice conditions for a set of 28 3-year-long retrospective climate predictions. We compare it to a set in which the sea ice initial conditions are taken from a single-member sea ice historical simulation constrained by atmosphere observations only. We find an improved skill in predicting the Arctic sea ice area and Arctic near surface temperature but a slightly degraded skill in predicting the Antarctic sea ice area. We also obtain a larger spread between the members for the sea ice variables, thus more representative of the forecast error.     

冬季北极海冰与中国同期气温的关系

采用Hadley中心的海冰密集度资料和中国160站气温资料,对冬季北极海冰变化的主要模态进行了分析,定义了5个关键海区,重点讨论了冬季北极海冰异常与中国冬季气温的关系。结果表明,冬季北极海冰变化主要表现为第一模态,即太平洋、大西洋的海冰反位相分布。海冰变化的关键区域为区域Ⅰ巴伦支海、区域Ⅱ格陵兰海、区域Ⅲ戴维斯海峡、区域Ⅳ白令海以及区域Ⅴ鄂霍次克海。中国冬季平均气温、冬季最低气温、冬季最高气温均与北极关键海区的海冰异常有显著相关,但是与其对应的海区有所不同。     

冬季北极海冰与中国同期气温的关系

采用Hadley中心的海冰密集度资料和中国160站气温资料,对冬季北极海冰变化的主要模态进行了分析,定义了5个关键海区,重点讨论了冬季北极海冰异常与中国冬季气温的关系.结果表明,冬季北极海冰变化主要表现为第一模态,即太平洋、大西洋的海冰反位相分布.海冰变化的关键区域为区域Ⅰ巴伦支海、区域Ⅱ格陵兰海、区域Ⅲ戴维斯海峡、区...     

Arctic sea ice and Eurasian climate: A review

The Arctic plays a fundamental role in the climate system and has shown significant climate change in recent decades,including the Arctic warming and decline of Arctic sea-ice extent and thickness. In contrast to the Arctic warming and reduction of Arctic sea ice, Europe, East Asia and North America have experienced anomalously cold conditions, with record snowfall during recent years. In this paper, we review current understanding of the sea-ice impacts on the Eurasian climate.Paleo, observational and modelling studies are covered to summarize several major themes, including: the variability of Arctic sea ice and its controls; the likely causes and apparent impacts of the Arctic sea-ice decline during the satellite era,as well as past and projected future impacts and trends; the links and feedback mechanisms between the Arctic sea ice and the Arctic Oscillation/North Atlantic Oscillation, the recent Eurasian cooling, winter atmospheric circulation, summer precipitation in East Asia, spring snowfall over Eurasia, East Asian winter monsoon, and midlatitude extreme weather; and the remote climate response(e.g., atmospheric circulation, air temperature) to changes in Arctic sea ice. We conclude with a brief summary and suggestions for future research.     

Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900?2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model?data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°?90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.