2020/2021年冬季中国气候冷暖转折成因分析

2020/2021年冬季,我国气候"前冬冷干、后冬暖湿"特征明显,冷、暖两个阶段气温振幅极大,多地观测气温分别打破了建站以来的最低、最高纪录。前冬(2020年12月1日至2021年1月10日),全国大部地区气温偏低、降水偏少,而后冬(2021年1月13日至2月28日),全国大部地区转入明显偏暖期,且2月上旬开始我国北方地区降水增多,暖湿特征明显。分析发现,乌拉尔山阻塞高压、西伯利亚高压和东亚冬季风强度、极地冷空气主体位置以及西北太平洋副热带高压的强度和位置等均发生了转折性变化,这是导致我国冬季气候由冷干转为暖湿的直接原因。进一步分析表明:La Nina事件配合北极冰偏少和北大西洋中纬度暖流,符合启动前冬"暖北极、冷欧亚"效应的条件,导致前冬欧亚中高纬经向环流偏强、乌拉尔山阻塞高压发展、及西伯利亚高压和东亚冬季风偏强,致使我国出现干冷型气候;而后冬,北极平流层发生爆发性增温事件,导致北极涛动持续负位相,极涡主体偏向西半球,但同时乌拉尔山阻塞高压崩溃,东亚冬季风转弱,我国大范围回暖增温。     

柴达木盆地气候由暖干向暖湿转型的变化特征分析

利用1961-2010年柴达木盆地格尔木等10个气象站的观测资料以及水文、植被等资料,研究了柴达木盆地气候由暖干向暖湿转型的变化特征.结果表明,20世纪80年代中后期柴达木盆地出现了以气温上升、降水量和径流量增加、沙尘暴日数减少、潜在蒸散量下降、湖泊水位显著上升和植被覆盖率增加为主要特征的气候由暖干向暖湿转型的事实;全球气候变暖、水循环加快、高空水汽不断增加和黑碳气溶胶浓度增加是柴达木盆地气候由暖干向暖湿转型的主要气候原因.     

气候变化对植物气候生产力的影响

分析了我国大体分布均匀的３５０个站点近４０年植物气候生产力的时空变化及５０、６０、７０、８０年代距平场变化，并讨论了“暖干型“、“暖湿型“、“冷湿型“、“冷干型“气候对植物气候生产力的影响。     

北极海冰变化的时间和空间型

利用 4 4a(195 1～ 1994年 )北极海冰密度逐月资料 ,分析提出了一种与北极冰自然季节变化相吻合的分季法 ,并根据这种分季法 ,使用EOF分解 ,揭示了北极各季海冰面积异常的特征空间型及其对应的时间变化尺度。结果表明 :(1)北极冰面积异常变化的关键区 ,冬季 (2～ 4月 )主要位于北大西洋一侧的格陵兰海、巴伦支海和戴维斯海峡以及北太平洋一侧的鄂霍次克海和白令海 ,夏季 (8～ 10月 )则主要限于从喀拉海、东西伯利亚海、楚科奇海到波佛特海的纬向带状区域内 ,格陵兰海和巴伦支海是北极海冰面积异常变化的最重要区域 ;(2 )春 (5～ 7月 )、秋 (11月～次年 1月 )季各主要海区海冰面积异常基本呈同相变化 ,夏季东西伯利亚海、楚科奇海、波佛特海一带海冰面积异常和喀拉海呈反相变化 ,而冬季巴伦支海、格陵兰海海冰面积异常和戴维斯海峡、拉布拉多海、白令海、鄂霍次克海的海冰变化呈反相变化 ;(3)北极冰总面积过去 4 4a来确实经历了一种趋势性的减少 ,并且叠加在这种趋势变化之上的是年代尺度变化 ,其中春季 (5～ 7月 )海冰面积异常变化对年平均北极冰总面积异常变化作出了主要贡献 ;(4)位于北太平洋一侧极冰面积异常型基本具有半年的持续性 ,而位于北大西洋一侧极冰面积异常型具有半年至一年的持续性     

北极夏季无海冰状态时的东亚气候变化数值模拟研究

利用CMIP3模式在IPCC SRES A1B情景下对未来气候的预测结果,得到北极夏季无海冰的一种情况,即"free Arctic".利用相应的海温场和CO2含量驱动全球大气环流模式,模拟北极夏季无海冰时的东亚气候.试验结果表明,夏季北极无海冰时,全球表面气温有不同程度的明显升高,高纬地区升温幅度大干低纬地区,同纬度地...     

BCC_CSM北极海冰模拟性能的改进对东亚冬季气候模拟的影响

国家气候中心气候系统模式(BCC_CSM)将美国Los Alamos国家实验室发展的海冰模式CICE5.0替代原有的海冰模式SIS,形成一个新版本耦合模式,很好地提高了模式对北极海冰和北极气候的模拟能力.在此基础上,本文评估新耦合模式对1985-2014年东亚冬季气候的模拟性能,检验北极海冰模拟性能的改进对东亚冬季气候...     

北极III区海冰面积低频变化对北半球冬季大气环流异常的作用

北极III区海冰面积的低频变化所引起的热力强迫作用，可以激发出EA和类似于WP的大气遥相关型。冰气系统之间存在着3～4年的不规则振荡，在振荡过程中，它们是相互作用的。对比表明，北极III区的重冰年和轻冰年，北半球冬季中纬度地区大气环流以及我国的天气气候特征有十分明显的差异。     

Comparing the Arctic climate in Chinese and other CMIP6 models

本研究采用来自耦合模式相互比较项目第六阶段(CMIP6)模式,评估和对比了10个中国模式和27个其他国际模式对北极冬季气候的历史模拟性能.本文的主要目的是展现中国模式对北极气候的模拟能力,并了解其在国际上的模拟水平.结果表明,对于气候态的模拟,中国模式在模拟北极温度场和大气场这些气候学方面与其他国际模式相当.而在趋势方面,中国模式同样和其他国际模式都能很好地模拟出北极变暖的特征.此外,与观测到的环流相比,CMIP6多模式集合平均值(MME)并没有显著的正趋势,这可能是因为外部强迫的作用.     

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。     

高被引论文选编

正"北极涛动/ENSO"主题来源数据库:SCI-E和CAJD,检索时段:2014—2015年目前的北极放大效应和中纬度极端天气——Recent Arctic amplification and extreme mid-latitude weather.Nature Geoscience,2014,Vol.7,No.9.北极放大效应,是指近几十年北极的变暖趋势比全球平均变暖趋势的两倍还要多。快速的北极变暖已经导致北极海冰和春季雪覆盖的大量消融,这种消融幅度比气候模式的模     

Effective sea ice area based on a thickness threshold

As Arctic sea ice declines in response to climate change, a shift from thick multiyear ice to a thinner ice cover is occurring. With this transition, ice thicknesses approach a threshold below which ice no longer insulates the atmosphere from oceanic surface fluxes. While this is well known, there are no estimates of the magnitude of this threshold, nor of the proportion of sea ice area that is below this threshold as ice thins. We determine this threshold by simulating the atmospheric response to varying thicknesses, ranging from 0.0 to 2.0 m and determine that threshold to be 0.40–0.50 m. The resulting “effective” ice area is 4–14% lower than reported total ice area, as 0.39–0.97 × 10⁶ km² of the total ice area falls below the threshold throughout the twentieth century, including during notable ice minima. The atmosphere above large non-insulating ice-covered regions is susceptible to more than 2 °C of warming despite ice presence. Observed mean Arctic Ocean ice thickness is projected to fall below this threshold as early as the mid-2020s. Studies on ocean–atmosphere interactions in relation to sea ice area should focus on this insulating sea ice area, where ice is at least 0.40–0.50 m thick, and treat ice regions below 0.40–0.50 m thickness with caution.

     

Atmospheric Precursors of and Response to Anomalous Arctic Sea Ice in CMIP5 Models

This study examines pre-industrial control simulations from CMIP5 climate models in an effort to better understand the complex relationships between Arctic sea ice and the stratosphere, and between Arctic sea ice and cold winter temperatures over Eurasia. We present normalized regressions of Arctic sea-ice area against several atmospheric variables at extended lead and lag times. Statistically significant regressions are found at leads and lags, suggesting both atmospheric precursors of, and responses to, low sea ice; but generally, the regressions are stronger when the atmosphere leads sea ice, including a weaker polar stratospheric vortex indicated by positive polar cap height anomalies. Significant positive midlatitude eddy heat flux anomalies are also found to precede low sea ice. We argue that low sea ice and raised polar cap height are both a response to this enhanced midlatitude eddy heat flux. The so-called "warm Arctic, cold continents" anomaly pattern is present one to two months before low sea ice, but is absent in the months following low sea ice, suggesting that the Eurasian cooling and low sea ice are driven by similar processes. Lastly, our results suggest a dependence on the geographic region of low sea ice, with low Barents–Kara Sea ice correlated with a weakened polar stratospheric vortex, whilst low Sea of Okhotsk ice is correlated with a strengthened polar vortex. Overall, the results support a notion that the sea ice, polar stratospheric vortex and Eurasian surface temperatures collectively respond to large-scale changes in tropospheric circulation.     

Evaluating observed and projected future climate changes for the Arctic using the K?ppen-Trewartha climate classification

The ecosystems in the Arctic region are known to be very sensitive to climate changes. The accelerated warming for the past several decades has profoundly influenced the lives of the native populations and ecosystems in the Arctic. Given that the K?ppen-Trewartha (K-T) climate classification is based on reliable variations of land-surface types (especially vegetation), this study used the K-T scheme to evaluate climate changes and their impact on vegetation for the Arctic (north of 50°N) by analyzing observations as well as model simulations for the period 1900–2099. The models include 16 fully coupled global climate models from the Intergovernmental Panel on Climate Change Fourth Assessment. By the end of this century, the annual-mean surface temperature averaged over Arctic land regions is projected to increase by 3.1, 4.6 and 5.3°C under the Special Report on Emissions Scenario (SRES) B1, A1b, and A2 emission scenarios, respectively. Increasing temperature favors a northward expansion of temperate climate (i.e., Dc and Do in the K-T classification) and boreal oceanic climate (i.e., Eo) types into areas previously covered by boreal continental climate (i.e., Ec) and tundra; and tundra into areas occupied by permanent ice. The tundra region is projected to shrink by ?1.86?×?10⁶?km² (?33.0%) in B1, ?2.4?×?10⁶?km² (?42.6%) in A1b, and ?2.5?×?10⁶?km² (?44.2%) in A2 scenarios by the end of this century. The Ec climate type retreats at least 5° poleward of its present location, resulting in ?18.9, ?30.2, and ?37.1% declines in areal coverage under the B1, A1b and A2 scenarios, respectively. The temperate climate types (Dc and Do) advance and take over the area previously covered by Ec. The area covered by Dc climate expands by 4.61?×?10⁶?km² (84.6%) in B1, 6.88?×?10⁶?km² (126.4%) in A1b, and 8.16?×?10⁶?km² (149.6%) in A2 scenarios. The projected redistributions of K-T climate types also differ regionally. In northern Europe and Alaska, the warming may cause more rapid expansion of temperate climate types. Overall, the climate types in 25, 39.1, and 45% of the entire Arctic region are projected to change by the end of this century under the B1, A1b, and A2 scenarios, respectively. Because the K-T climate classification was constructed on the basis of vegetation types, and each K-T climate type is closely associated with certain prevalent vegetation species, the projected large shift in climate types suggests extensive broad-scale redistribution of prevalent ecoregions in the Arctic.     

The impact of Arctic sea ice on the Arctic energy budget and on the climate of the Northern mid-latitudes

The atmospheric general circulation model EC-EARTH-IFS has been applied to investigate the influence of both a reduced and a removed Arctic sea ice cover on the Arctic energy budget and on the climate of the Northern mid-latitudes. Three 40-year simulations driven by original and modified ERA-40 sea surface temperatures and sea ice concentrations have been performed at T255L62 resolution, corresponding to 79?km horizontal resolution. Simulated changes between sensitivity and reference experiments are most pronounced over the Arctic itself where the reduced or removed sea ice leads to strongly increased upward heat and longwave radiation fluxes and precipitation in winter. In summer, the most pronounced change is the stronger absorption of shortwave radiation which is enhanced by optically thinner clouds. Averaged over the year and over the area north of 70° N, the negative energy imbalance at the top of the atmosphere decreases by about 10?W/m² in both sensitivity experiments. The energy transport across 70° N is reduced. Changes are not restricted to the Arctic. Less extreme cold events and less precipitation are simulated in sub-Arctic and Northern mid-latitude regions in winter.     

秋季北极海冰对EP型ENSO的非线性响应

利用1961—2015年Hadley中心逐月海表温度资料、海冰密集度资料以及NCEP/NCAR再分析资料,探讨了秋季北极海冰对于EP型ENSO事件的异常响应,并进一步研究了这种异常响应的可能原因。结果表明,秋季北极海冰对EP型ENSO的响应具有非线性,特别是喀拉海海域(60°～90°E,70°～80°N)海冰无论在EP型El Ni?o或是La Ni?a位相,均表现为显著的负异常。进一步研究发现,不同ENSO位相造成该区域海冰异常偏少的机制有明显不同。EP型El Ni?o年秋季菲律宾附近海域对流活动被抑制,所激发的经向波列在高纬地区形成异常反气旋环流,其南风分量向喀拉海输送暖平流,造成海冰异常偏少。而EP型La Ni?a年喀拉海海域则主要受到来自大西洋开放性海域西风异常的影响,合成结果和个例年均显示EP型La Ni?a年秋季北大西洋上空存在一个显著的西风急流中心,有利于北大西洋开放性海域较暖海水向下游输送,进而影响喀拉海海冰。这些结果表明,热带外地区大气环流场对EP型ENSO的非线性响应导致了喀拉海海冰对EP型ENSO事件的响应也表现出明显的非线性。     

An Unprecedented Record Low Antarctic Sea-ice Extent during Austral Summer 2022

Seasonal minimum Antarctic sea ice extent (SIE) in 2022 hit a new record low since recordkeeping began in 1978 of 1.9 million km² on 25 February, 0.17 million km² lower than the previous record low set in 2017. Significant negative anomalies in the Bellingshausen/Amundsen Seas, the Weddell Sea, and the western Indian Ocean sector led to the new record minimum. The sea ice budget analysis presented here shows that thermodynamic processes dominate sea ice loss in summer through enhanced poleward heat transport and albedo–temperature feedback. In spring, both dynamic and thermodynamic processes contribute to negative sea ice anomalies. Specifically, dynamic ice loss dominates in the Amundsen Sea as evidenced by sea ice thickness (SIT) change, while positive surface heat fluxes contribute most to sea ice melt in the Weddell Sea.     

Mean climatic characteristics in high northern latitudes in an ocean-sea ice-atmosphere coupled model

Emphasizing the model‘s ability in mean climate reproduction in high northern latitudes, resultsfrom an ocean-sea ice-atmosphere coupled model are analyzed. It is shown that the coupled model cansimulate the main characteristics of annual mean global sea surface temperature and sea level pressurewell, but the extent of ice coverage produced in the Southern Hemisphere is not large enough. The maindistribution characteristics of simulated sea level pressure and temperature at 850 hPa in high northernlatitudes agree well with their counterparts in the NCEP reanalysis dataset, and the model can reproducethe Arctic Oscillation (AO) mode successfully. The simulated seasonal variation of sea ice in the NorthernHemisphere is rational and its main distribution features in winter agree well with those from observations.But the ice concentration in the sea ice edge area close to the Eurasian continent in the inner Arctic Oceanis much larger than the observation. There are significant interannual variation signals in the simulated seaice concentration in winter in high northern latitudes and the most significant area lies in the GreenlandSea, followed by the Barents Sea. All of these features agree well with the results from observations.     

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.     

夏、秋季北极海冰异常与秋、冬季青藏高原极端低温日数的关系及影响机理

利用1979~2012年青藏高原125个基本、基准站观测日最高及最低气温数据、Hadley中心月平均海冰覆盖率资料、ERA-Interim的风场、高度场等再分析资料,根据相关统计分析、合成分析等方法系统地分析了青藏高原地区秋、冬季冷昼和冷夜日数(低温日数)与关键影响海区海冰的关系及影响机理。结果表明,夏、秋季关键海区海冰偏少时,秋、冬季极地和青藏高原地区500 h Pa位势高度减小,中高纬西伯利亚地区位势高度增强,北极至青藏高原有明显由北向南波动通量,高压反气旋系统在西伯利亚地区形成与壮大,青藏高原以北风场呈现明显偏北风,Rossby波在青藏高原及其以北地区呈现由北向南波动形式,青藏高原以北的西风带地区Rossby波东传减缓,导致经向活动加强,北部冷空气易于通过气流向高原侵袭,秋、冬季青藏高原低温日数将偏多。     

Unprecedented low twentieth century winter sea ice extent in the Western Nordic Seas since A.D. 1200

We reconstructed decadal to centennial variability of maximum sea ice extent in the Western Nordic Seas for A.D. 1200–1997 using a combination of a regional tree-ring chronology from the timberline area in Fennoscandia and δ¹⁸O from the Lomonosovfonna ice core in Svalbard. The reconstruction successfully explained 59% of the variance in sea ice extent based on the calibration period 1864–1997. The significance of the reconstruction statistics (reduction of error, coefficient of efficiency) is computed for the first time against a realistic noise background. The twentieth century sustained the lowest sea ice extent values since A.D. 1200: low sea ice extent also occurred before (mid-seventeenth and mid-eighteenth centuries, early fifteenth and late thirteenth centuries), but these periods were in no case as persistent as in the twentieth century. Largest sea ice extent values occurred from the seventeenth to the nineteenth centuries, during the Little Ice Age (LIA), with relatively smaller sea ice-covered area during the sixteenth century. Moderate sea ice extent occurred during thirteenth–fifteenth centuries. Reconstructed sea ice extent variability is dominated by decadal oscillations, frequently associated with decadal components of the North Atlantic Oscillation/Arctic Oscillation (NAO/AO), and multi-decadal lower frequency oscillations operating at ~50–120 year. Sea ice extent and NAO showed a non-stationary relationship during the observational period. The present low sea ice extent is unique over the last 800 years, and results from a decline started in late-nineteenth century after the LIA.