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Simulations of Eurasian Winter Temperature Trends in Coupled and Uncoupled CFSv2


doi: 10.1007/s00376-017-6294-0

  • Conflicting results have been presented regarding the link between Arctic sea-ice loss and midlatitude cooling, particularly over Eurasia. This study analyzes uncoupled (atmosphere-only) and coupled (ocean-atmosphere) simulations by the Climate Forecast System, version 2 (CFSv2), to examine this linkage during the Northern Hemisphere winter, focusing on the simulation of the observed surface cooling trend over Eurasia during the last three decades. The uncoupled simulations are Atmospheric Model Intercomparison Project (AMIP) runs forced with mean seasonal cycles of sea surface temperature (SST) and sea ice, using combinations of SST and sea ice from different time periods to assess the role that each plays individually, and to assess the role of atmospheric internal variability. Coupled runs are used to further investigate the role of internal variability via the analysis of initialized predictions and the evolution of the forecast with lead time. The AMIP simulations show a mean warming response over Eurasia due to SST changes, but little response to changes in sea ice. Individual runs simulate cooler periods over Eurasia, and this is shown to be concurrent with a stronger Siberian high and warming over Greenland. No substantial differences in the variability of Eurasian surface temperatures are found between the different model configurations. In the coupled runs, the region of significant warming over Eurasia is small at short leads, but increases at longer leads. It is concluded that, although the models have some capability in highlighting the temperature variability over Eurasia, the observed cooling may still be a consequence of internal variability.
    摘要: 针对北极海冰减少和中纬度地区特别是欧亚地区的变冷之间关系的研究, 存在一些争议或者相冲突的结果. 本文利用CFSv2系统的非耦合大气模式(AMIP)和海洋-大气耦合模式模拟, 来检验北半球冬季的这种关联, 重点是模拟过去30年观测到的欧亚地区的地表变冷趋势. 首先利用非耦合的大气模式并以季节循环的海温(SST)和海冰密集度(SIC)作为强迫场驱动, 选取不同时期的平均SST和SIC, 并通过SST和SIC之间的多种组合强迫, 分析强迫场及大气内部变化的影响. 进一步利用海洋-大气耦合模式, 通过初始化预测及提前预报的演变, 分析大气内部变化的作用. AMIP模拟结果显示, 由于海温变化, 欧亚地区平均增暖, 但欧亚气温对海冰变化几乎没有响应. 个别试验模拟出了较低的欧亚气温, 同时出现的是较强的西伯利亚高压和格陵兰岛的增暖. 不同的模式配置(即不同的海温海冰强迫组合)并不会造成欧亚表面温度变化的明显差异. 耦合试验结果显示, 短期预测试验模拟的欧亚显著增暖区域很小, 但随着预报时段的增长, 更多的欧亚区域变暖. 因此, 尽管模式具有突出欧亚大陆温度变化的能力, 但观测到的地表变冷仍可能是大气内部变率的结果.
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  • Alexand er, M. A., U. S. Bhatt, J. E. Walsh, M. S. Timlin, J. S. Miller, J. D. Scott, 2004: The atmospheric response to realistic Arctic sea ice anomalies in an AGCM during winter. J. Climate, 17, 890-905, .https://doi.org/10.1175/1520-0442(2004)017<0890:TARTRA>2.0,CO;210.1175/1520-0442(2004)017<0890:TARTRA>2.0.CO;2fc0ae199b9c2388844c40377f6b33268http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JCli...17..890A%26amp%3Bdb_key%3DPHY%26amp%3Blink_type%3DABSTRACT%26amp%3Bhigh%3D06304http://journals.ametsoc.org/doi/abs/10.1175/1520-0442%282004%29017%3C0890%3ATARTRA%3E2.0.CO%3B2
    Ayarzagüena, B., J. A. Screen, 2016: Future Arctic sea ice loss reduces severity of cold air outbreaks in midlatitudes.Geophys. Res. Lett.,43,2801-2809, https://doi.org/10.1002/2016GL068092.10.1002/2016GL068092438b3f1c3b6ec03db8a2cb18261ab806http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2016GL068092%2Ffullhttp://doi.wiley.com/10.1002/2016GL068092The effects of Arctic sea-ice loss on cold air outbreaks (CAOs) in midlatitudes remains unclear. Previous studies have defined CAOs relative to present-day climate, but changes in CAOs, defined in such a way, may reflect changes in mean climate and not in weather variability, and society is more sensitive to the latter. Here we revisit this topic but applying changing temperature thresholds relating to climate conditions of the time. CAOs do not change in frequency or duration in response to projected sea-ice loss. However, they become less severe, mainly due to advection of warmed polar air, since the dynamics associated with the occurrence of CAOs are largely not affected. CAOs weaken even in midlatitude regions where the winter-mean temperature decreases in response to Arctic sea-ice loss. These results are robustly simulated by two atmospheric models prescribed with differing future sea ice states and in transient runs where external forcings are included.
    Barnes E. A., 2013: Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes.Geophys. Res. Lett.,40,4734-4739, https://doi.org/10.1002/grl.50880.10.1002/grl.508804eac873e26e3495cfdaa4b0a5b97ec51http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50880%2Ffullhttp://doi.wiley.com/10.1002/grl.50880Previous studies have suggested that Arctic amplification has caused planetary-scale waves to elongate meridionally and slow down, resulting in more frequent blocking patterns and extreme weather. Here trends in the meridional extent of atmospheric waves over North America and the North Atlantic are investigated in three reanalyses, and it is demonstrated that previously reported positive trends are likely an artifact of the methodology. No significant decrease in planetary-scale wave phase speeds are found except in October-November-December, but this trend is sensitive to the analysis parameters. Moreover, the frequency of blocking occurrence exhibits no significant increase in any season in any of the three reanalyses, further supporting the lack of trends in wave speed and meridional extent. This work highlights that observed trends in midlatitude weather patterns are complex and likely not simply understood in terms of Arctic amplification alone.
    Barnes E. A., E. Dunn-Sigouin G. Masato, and T. Woollings, 2014: Exploring recent trends in Northern Hemisphere blocking.Geophys. Res. Lett.,41,638-644, https://doi.org/10.1002/2013GL058745.10.1002/2013GL058745ced64c628dd9b61b16dc6f2091d45ed0http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2013GL058745%2Fpdfhttp://doi.wiley.com/10.1002/2013GL058745Observed blocking trends are diagnosed to test the hypothesis that recent Arctic warming and sea ice loss has increased the likelihood of blocking over the Northern Hemisphere. To ensure robust results, we diagnose blocking using three unique blocking identification methods from the literature, each applied to four different reanalyses. No clear hemispheric increase in blocking is found for any blocking index, and while seasonal increases and decreases are found for specific isolated regions and time periods, there is no instance where all three methods agree on a robust trend. Blocking is shown to exhibit large interannual and decadal variability, highlighting the difficulty in separating any potentially forced response from natural variability.
    Chen M.Y., W. Q. Wang, and A. Kumar, 2013: Lagged ensembles,forecast configuration,and seasonal predictions. Mon. Wea. Rev.,141, 3477-3497,.https://doi.org/10.1175/MWR-D-12-00184.1
    Cohen J. L., J. C. Furtado, M. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Asymmetric seasonal temperature trends,Geophys. Res. Lett.,39,L04705, https://doi.org/10.1029/2011GL050582.
    Cohen, J., Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather.Nature Geoscience,7,627-637, https://doi.org/10.1038/NGEO2234.10.1038/ngeo22344cd471caba2fba3502432bd1eab5ae32http%3A%2F%2Fwww.nature.com%2Fabstractpagefinder%2F10.1038%2Fngeo2234http://www.nature.com/doifinder/10.1038/ngeo2234The Arctic region has warmed more than twice as fast as the global average a phenomenon known as Arctic amplification. The rapid Arctic warming has contributed to dramatic melting of Arctic sea ice and spring snow cover, at a pace greater than that simulated by climate models. These profound changes to the Arctic system have coincided with a period of ostensibly more frequent extreme weather events across the Northern Hemisphere mid-latitudes, including severe winters. The possibility of a link between Arctic change and mid-latitude weather has spurred research activities that reveal three potential dynamical pathways linking Arctic amplification to mid-latitude weather: changes in storm tracks, the jet stream, and planetary waves and their associated energy propagation. Through changes in these key atmospheric features, it is possible, in principle, for sea ice and snow cover to jointly influence mid-latitude weather. However, because of incomplete knowledge of how high-latitude climate change influences these phenomena, combined with sparse and short data records, and imperfect models, large uncertainties regarding the magnitude of such an influence remain. We conclude that improved process understanding, sustained and additional Arctic observations, and better coordinated modelling studies will be needed to advance our understanding of the influences on mid-latitude weather and extreme events.
    Comiso J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in the arctic sea ice cover,Geophys. Res. Lett.,35,L01703, https://doi.org/10.1029/2007GL031972.10.1029/2007GL031972343feb606e7415f45d25b40e917085b6http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL031972%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2007GL031972/pdfSatellite data reveal unusually low Arctic sea ice coverage during the summer of 2007, caused in part by anomalously high temperatures and southerly winds. The extent and area of the ice cover reached minima on 14 September 2007 at 4.1 10kmand 3.6 10km, respectively. These are 24% and 27% lower than the previous record lows, both reached on 21 September 2005, and 37% and 38% less than the climatological averages. Acceleration in the decline is evident as the extent and area trends of the entire ice cover (seasonal and perennial ice) have shifted from about -2.2 and -3.0% per decade in 1979-1996 to about -10.1 and -10.7% per decade in the last 10 years. The latter trends are now comparable to the high negative trends of -10.2 and -11.4% per decade for the perennial ice extent and area, 1979-2007.
    Dee, D. P., Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system.Quart. J. Roy. Meteor. Soc.,137,553-597, https://doi.org/10.1002/qj.828.10.1002/qj.8285b3115ec8b338ee97111270a1831c4b2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.828%2Fpdfhttp://doi.wiley.com/10.1002/qj.v137.656ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright 2011 Royal Meteorological Society
    Deser C., G. Magnusdottir, R. Saravanan, and A. Phillips, 2004: The effects of North Atlantic SST and sea ice anomalies on the winter circulation in CCM3. Part II: Direct and indirect components of the response. J. Climate, 17, 877-889, https://doi.org/10.1175/1520-0442(2004)017 <0877:TEONAS>2.0,CO;2.10.1175/1520-0442(2004)0172.0.CO;2cb7f9af6c5c735dc8ecff23c70090363http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004JCli...17..877Dhttp://journals.ametsoc.org/doi/abs/10.1175/1520-0442%282004%29017%3C0877%3ATEONAS%3E2.0.CO%3B2Observed multidecadal trends in extratropical atmospheric flow, such as the positive trend in the North Atlantic Oscillation (NAO) index, may be attributable to a number of causes. This study addresses the question of whether the atmospheric trends may be caused by observed trends in oceanic boundary forcing. Experiments were carried out using the NCAR atmospheric general circulation model with specified sea surface temperature (SST) and sea ice anomalies confined to the North Atlantic sector. The spatial pattern of the anomalous forcing was chosen to be realistic in that it corresponds to the recent 40-yr trend in SST and sea ice, but the anomaly amplitude was exaggerated in order to make the response statistically more robust. The wintertime response to both types of forcing resembles the NAO to first order. Even for an exaggerated amplitude, the atmospheric response to the SST anomaly is quite weak compared to the observed positive trend in the NAO, but has the same sign, indicative of a weak positive feedback. The anomalies in sea ice extent are more efficient than SST anomalies at exciting an atmospheric response comparable in amplitude to the observed NAO trend. However, this atmospheric response has the opposite sign to the observed trend, indicative of a significant negative feedback associated with the sea ice forcing. Additional experiments using SST anomalies with opposite sign to the observed trend indicate that there are significant nonlinearities associated with the atmospheric response. The transient eddy response to the observed SST trend is consistent with the positive NAO response, with the North Atlantic storm track amplifying downstream and developing a more pronounced meridional tilt. In contrast, the storm track response to the observed sea ice trend corresponds to a weaker, southward-shifted, more zonal storm track, which is consistent with the negative NAO response. 1.
    Ding, Q. H., Coauthors, 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice.Nat. Clim. Change,7,289-295, https://doi.org/10.1038/nclimate3241.10.1038/nclimate32411da74620564d58c7ba8872dcc685e144http%3A%2F%2Fwww.nature.com%2Fnclimate%2Fjournal%2Fv7%2Fn4%2Fnclimate3241%2Fmetricshttp://www.nature.com/doifinder/10.1038/nclimate3241The Arctic is warming and sea ice is declining, but how the two link is unclear. This study shows changes in summertime atmospheric circulation and internal variability may have caused up to 60% of September sea-ice decline since 1979.
    Francis J. A., S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid-latitudes,Geophys. Res. Lett.,39,L06801, https://doi.org/10.1029/2012GL051000.10.1029/2012GL051000c6c56c6a021edd43d82f7e1fc8bad5d9http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012GL051000%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2012GL051000/fullIn this presentation, we will build on analysis presented in Francis and Vavrus (GRL, 2012) in which mechanisms were proposed and demonstrated that link enhanced warming in the Arctic during recent decades with changes in the trajectory of the upper-level flow in mid-latitudes. Evidence was presented that suggests Arctic Amplification may have contributed to an increase in large-scale wave amplitude and slower zonal winds, both of which favor more persistent weather patterns in mid-latitudes. Prolonged weather conditions are often associated with extreme weather -- such as droughts, cold spells, heat waves, and some flooding events, -- which have become more frequent in recent years. New analysis of fields from reanalyses will be presented that illustrates the response of mid-latitude upper-level flow characteristics to Arctic Amplification, in particular the regional and seasonal variability in large-scale wave propagation speed and wave amplitude that may favor an increase in extreme weather events.
    Francis J. A., S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming,Environmental Research Letters,10,014005, https://doi.org/10.1088/1748-9326/10/1/014005.10.1088/1748-9326/10/1/014005642468448ecf3fa179cb674a410f211ehttp%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fiop%2Ferl%2F2015%2F00000010%2F00000001%2Fart014005http://stacks.iop.org/1748-9326/10/i=1/a=014005?key=crossref.74581076f734b2377ec8042d3aebe25dNew metrics and evidence are presented that support a linkage between rapid Arctic warming, relative to Northern hemisphere mid-latitudes, and more frequent high-amplitude (wavy) jet-stream configurations that favor persistent weather patterns. We find robust relationships among seasonal and regional patterns of weaker poleward thickness gradients, weaker zonal upper-level winds, and a more meridional flow direction. These results suggest that as the Arctic continues to warm faster than elsewhere in response to rising greenhouse-gas concentrations, the frequency of extreme weather events caused by persistent jet-stream patterns will increase.
    Furtado J. C., J. L. Cohen, A. H. Butler, E. E. Riddle and A. Kumar, 2015: Eurasian snow cover variability and links to winter climate in the CMIP5 models.Climate Dyn.,45,2591-2605, https://doi.org/10.1007/s00382-015-2494-4.10.1007/s00382-015-2494-4e4c5b6e8b26fd8f6a5b570abb999193fhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs00382-015-2494-4http://link.springer.com/10.1007/s00382-015-2494-4Observational studies and modeling experiments illustrate that variability in October Eurasian snow cover extent impacts boreal wintertime conditions over the Northern Hemisphere (NH) through a dynamical pathway involving the stratosphere and changes in the surface-based Arctic Oscillation (AO). In this paper, we conduct a comprehensive study of the Eurasian snow-AO relationship in twenty coupled climate models run under pre-industrial conditions from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our analyses indicate that the coupled climate models, individually and collectively, do not capture well the observed snow-AO relationship. The models lack a robust lagged response between October Eurasian snow cover and several NH wintertime variables (e.g., vertically propagating waves and geopotential heights). Additionally, the CMIP5 models do not simulate the observed spatial distribution and statistics of boreal fall snow cover across the NH including Eurasia. However, when analyzing individual 40-year time slices of the models, there are periods of time in select models when the observed snow-AO relationship emerges. This finding suggests that internal variability may play a significant role in the observed relationship. Further analysis demonstrates that the models poorly capture the downward propagation of stratospheric anomalies into the troposphere, a key facet of NH wintertime climate variability irrespective of the influence of Eurasian snow cover. A weak downward propagation signal may be related to several factors including too few stratospheric vortex disruptions and weaker-than-observed tropospheric wave driving. The analyses presented can be used as a roadmap for model evaluations in future studies involving NH wintertime climate variability, including those considering future climate change.
    Gao, Y. Q., Coauthors, 2015: Arctic sea ice and Eurasian climate: A review.Adv. Atmos. Sci.,32,92-114, https://doi.org/10.1007/s00376-014-0009-6.10.1007/s00376-014-0009-6d871921aaa624189bf66d49c90050b24http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00376-014-0009-6http://link.springer.com/10.1007/s00376-014-0009-6北极在气候系统起一个基本作用,包括温暖的北极和北极海冰程度和厚度的衰落并且在最近的十年显示出重要气候变化。与温暖的北极和北极海冰的减小相对照,欧洲,东亚和北美洲经历了反常地冷的条件,与在最近的年期间的记录降雪。在这篇论文,我们在欧亚的气候上考察海冰影响的当前的理解。Paleo,观察并且建模研究被盖住总结几个主要主题,包括:北极海冰和它的控制的可变性;可能的原因和北极海冰的明显的影响在卫星时代,以及过去和投射未来影响和趋势期间衰退;在北极海冰和北极摆动 / 北方大西洋摆动之间的连接和反馈机制,最近的欧亚的冷却,大气的循环,在东亚的夏天降水,在欧亚大陆上的春天降雪,东方亚洲冬季季风,和 midlatitude 极端捱过的冬季;并且遥远的气候反应(例如,大气的循环,空气温度) 到在北极海冰的变化。我们为未来研究与一篇简短和建议得出结论。
    Griffies S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2003: Technical guide to MOM4. GFDL Ocean Group Technical Report No. 5 NOAA/Geophysical Fluid Dynamics Laboratory, 337 pp. [Available online at .]https://www.gfdl.noaa.gov/\simfms94ea74fdf82b7f26c2f9c7d8a96f1d86http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F245105251_A_Technical_Guide_to_MOM4http://www.researchgate.net/publication/245105251_A_Technical_Guide_to_MOM4Available online at www.gfdl.noaa.gov Information about how to download and run MOM4 can be found at the GFDL Flexible Modeling System (FMS) web site accessible from www.gfdl.noaa.gov. This document was prepared using L ATEX as described by Lamport (1994) and Goosens et al. (1994).CONTENTS I Basics of MOM4 11
    Hand orf, D., R. Jaiser, K. Dethloff, A. Rinke, J. Cohen, 2015: Impacts of Arctic sea ice and continental snow cover changes on atmospheric winter teleconnections.Geophys. Res. Lett.,42,2367-2377, https://doi.org/10.1002/2015GL063203.10.1002/2015GL0632039f31473f829920b4fd2ac9efde054976http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2015GL063203%2Fpdfhttp://doi.wiley.com/10.1002/2015GL063203Abstract Extreme winters in Northern Hemisphere midlatitudes in recent years have been connected to declining Arctic sea ice and continental snow cover changes in autumn following modified planetary waves in the coupled troposphere-stratosphere system. Through analyses of reanalysis data and model simulations with a state-of-the-art atmospheric general circulation model, we investigate the mechanisms between Arctic Ocean sea ice and Northern Hemisphere land snow cover changes in autumn and atmospheric teleconnections in the following winter. The observed negative Arctic Oscillation in response to sea ice cover changes is too weakly reproduced by the model. The planetary wave train structures over the Pacific and North America regions are well simulated. The strengthening and westward shift of the Siberian high-pressure system in response to sea ice and snow cover changes is underestimated compared to ERA-Interim data due to deficits in the simulated changes in planetary wave propagation characteristics.
    Hartmann, D. L., Coauthors, 2013: Observations: Atmosphere and Surface. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, Cambridge, United Kingdom, New York, NY, USA, 159- 254.
    Honda M., J. Inoue, and S. Yamane, 2009: Influence of low arctic sea-ice minima on anomalously cold Eurasian winters,Geophys. Res. Lett.,36,L08707, https://doi.org/10.1029/2008GL037079.10.1029/2008GL03707911ff7459b32da24cee92554351efd9cbhttp%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103055291.htmlhttp://www.cabdirect.org/abstracts/20103055291.htmlInfluence of low Arctic sea-ice minima in early autumn on the wintertime climate over Eurasia is investigated. Observational evidence shows that significant cold anomalies over the Far East in early winter and zonally elongated cold anomalies from Europe to Far East in late winter are associated with the decrease of the Arctic sea-ice cover in the preceding summer-to-autumn seasons. Results fro...
    Hurrell J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the community atmosphere model.J. Climate,21,5145-5153, https://doi.org/10.1175/2008JCLI2292.1.10.1175/2008JCLI2292.10ff603cbc3bfb3d5101e80534d7908a6http%3A%2F%2Ficesjms.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F2008JCLI2292.1%26amp%3Blink_type%3DDOIhttp://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2292.1
    Inoue J., M. E. Hori, and K. Takaya, 2012: The role of Barents Sea ice in the wintertime cyclone track and emergence of a warm-arctic cold-Siberian anomaly.J. Climate,25,2561-2568, https://doi.org/10.1175/JCLI-D-11-00449.1.10.1175/JCLI-D-11-00449.188b1d18f3d346383e29f96c85749d9dfhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012JCli...25.2561Ihttp://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00449.1Abstract Sea ice variability over the Barents Sea with its resultant atmospheric response has been considered one of the triggers of unexpected downstream climate change. For example, East Asia has experienced several major cold events while the underlying temperature over the Arctic has risen steadily. To understand the influence of sea ice in the Barents Sea on atmospheric circulation during winter from a synoptic perspective, this study evaluated the downstream response in cyclone activities with respect to the underlying sea ice variability. The composite analysis, including all cyclone events over the Nordic seas, revealed that an anticyclonic anomaly prevailed along the Siberian coast during light ice years over the Barents Sea. This likely caused anomalous warm advection over the Barents Sea and cold advection over eastern Siberia. The difference in cyclone paths between heavy and light ice years was expressed as a warm-Arctic cold-Siberian (WACS) anomaly. The lower baroclinicity over the Barents Sea during the light ice years, which resulted from a weak gradient in sea surface temperature, prevented cyclones from traveling eastward. This could lead to fewer cyclones and hence to an anticyclonic anomaly over the Siberian coast.
    Johansson A., 2007: Prediction skill of the NAO and PNA from daily to seasonal time scales.J. Climate,20,1957-1975, https://doi.org/10.1175/JCLI4072.1.10.1175/JCLI4072.18aa9ec741768986e267b247dd92d1a20http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006AGUSM.A42A..06Jhttp://journals.ametsoc.org/doi/abs/10.1175/JCLI4072.1The skill of state-of-the-art operational dynamical models in predicting the two most important modes of variability in the Northern Hemisphere extratropical atmosphere, the North Atlantic Oscillation (NAO) and Pacific09“North American (PNA) teleconnection patterns, is investigated at time scales ranging from daily to seasonal. Two uncoupled atmospheric models used for deterministic forecasting in the short to medium range as well as eight fully coupled atmosphere09“land09“ocean forecast models used for monthly and seasonal forecasting are examined and compared. For the short to medium range, the level of forecast skill for the two indices is higher than that for the entire Northern Hemisphere extratropical flow. The forecast skill of the PNA is higher than that of the NAO. The forecast skill increases with the magnitude of the NAO and PNA indices, but the relationship is not pronounced. The probability density function (PDF) of the NAO and PNA indices is negatively skewed, in agreement with the distribution of skewness of the geopotential field. The models maintain approximately the observed PDF, including the negative skewness, for the first week. Extreme negative NAO/PNA events have larger absolute values than positive extremes in agreement with the negative skewness of the two indices. Recent large extreme events are generally well forecasted by the models. On the intraseasonal time scale it is found that both NAO and PNA have lingering forecast skill, in contrast to the Northern Hemisphere extratropical flow as a whole. This fact offers some hope for extended range forecasting, even though the skill is quite low. No conclusive positive benefit is seen from using higher horizontal resolution or coupling to the oceans. On the monthly and seasonal time scales, the level of forecast skill for the two indices is generally quite low, with the exception of winter predictions at short lead times.
    Kumar, A., Coauthors, 2010: Contribution of sea ice loss to Arctic amplification,Geophys. Res. Lett.,37,L21701, https://doi.org/10.1029/2010GL045022.10.1029/2010GL045022839e22a6dd0175cb443a8e41b6517af4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2010GL045022%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2010GL045022/pdfAtmospheric climate models are subjected to the observed sea ice conditions during 2007 to estimate the regionality, seasonality, and vertical pattern of temperature responses to recent Arctic sea ice loss. It is shown that anomalous sea ice conditions accounted for virtually all of the estimated Arctic amplification in surface-based warming over the Arctic Ocean, and furthermore they accounted for a large fraction of Arctic amplification occurring over the high-latitude land between 60ºN and the Arctic Ocean. Sea ice loss did not appreciably contribute to observed 2007 land temperature warmth equatorward of 60ºN. Likewise, the observed warming of the free atmosphere attributable to sea ice loss is confined to Arctic latitudes, and is vertically confined to the lowest 1000 m. The results further highlight a strong seasonality of the temperature response to the 2007 sea ice loss. A weak signal of Arctic amplification in surface based warming is found during boreal summer, whereas a dramatically stronger signal is shown to develop during early autumn that persisted through December even as sea ice coverage approached its climatological values in response to the polar night.
    Liu J. P., J. A. Curry, H. J. Wang, M. R. Song, and R. M. Horton, 2012: Impact of declining Arctic sea ice on winter snowfall.Proceedings of the National Academy of Sciences of the United States of America,109(11),4074-4079, https://doi.org/10.1073/pnas.1114910109.10.1073/pnas.111491010922371563e869b196cae446b30762b978476557d4http%3A%2F%2Fwww.jstor.org%2Fstable%2F41507098http://www.pnas.org/cgi/doi/10.1073/pnas.1114910109While the Arctic region has been warming strongly in recent decades, anomalously large snowfall in recent winters has affected large parts of North America, Europe, and east Asia. Here we demonstrate that the decrease in autumn Arctic sea ice area is linked to changes in the winter Northern Hemisphere atmospheric circulation that have some resemblance to the negative phase of the winter Arctic oscillation. However, the atmospheric circulation change linked to the reduction of sea ice shows much broader meridional meanders in midlatitudes and clearly different interannual variability than the classical Arctic oscillation. This circulation change results in more frequent episodes of blocking patterns that lead to increased cold surges over large parts of northern continents. Moreover, the increase in atmospheric water vapor content in the Arctic region during late autumn and winter driven locally by the reduction of sea ice provides enhanced moisture sources, supporting increased heavy snowfall in Europe during early winter and the northeastern and midwestern United States during winter. We conclude that the recent decline of Arctic sea ice has played a critical role in recent cold and snowy winters.
    McCusker K. E., J. C. Fyfe, and M. Sigmond, 2016: Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss.Nature Geoscience,9,838-842, https://doi.org/10.1038/NGEO2820.10.1038/ngeo282086ebb2cb4509993b8b6992484cd04e67http%3A%2F%2Fwww.nature.com%2Fngeo%2Fjournal%2Fv9%2Fn11%2Fngeo2820%2Fmetricshttp://www.nature.com/doifinder/10.1038/ngeo2820Winter cooling over Eurasia has been suggested to be linked to Arctic sea-ice loss. Climate model simulations reveal no evidence for such a link and instead suggest that a persistent atmospheric circulation pattern is responsible.
    Moorthi S., H.-L. Pan, and P. Caplan, 2001: Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system,NOAA Technical Bulletin No. 484,National Centers for Environmental Prediction,Washington, DC, 14 pp.08872ea833cfe1bce97dbde8442a7f39http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F284698001_Changes_to_the_2001_NCEP_operational_MRFAVN_global_analysisforecast_systemhttp://www.researchgate.net/publication/284698001_Changes_to_the_2001_NCEP_operational_MRFAVN_global_analysisforecast_systemCiteSeerX - Scientific documents that cite the following paper: 2001: Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast systemhttp://www.researchgate.net/publication/284698001_Changes_to_the_2001_NCEP_operational_MRFAVN_global_analysisforecast_system
    Mori M., M. Wananabe, H. Shiogama, J. Inoue, and M. Kimoto, 2014: Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades.Nature Geoscience,7,869-873, https://doi.org/10.1038/NGEO2277.10.1038/ngeo2277cdef8a86f56c39f6052fde6e5d1dd7bbhttp%3A%2F%2Fwww.nature.com%2Fngeo%2Fjournal%2Fv7%2Fn12%2Fabs%2Fngeo2277.htmlhttp://www.nature.com/doifinder/10.1038/ngeo2277Over the past decade, severe winters occurred frequently in mid-latitude Eurasia, despite increasing global- and annual-mean surface air temperatures. Observations suggest that these cold Eurasian winters could have been instigated by Arctic sea-ice decline, through excitation of circulation anomalies similar to the Arctic Oscillation. In climate simulations, however, a robust atmospheric response to sea-ice decline has not been found, perhaps owing to energetic internal fluctuations in the atmospheric circulation. Here we use a 100-member ensemble of simulations with an atmospheric general circulation model driven by observation-based sea-ice concentration anomalies to show that as a result of sea-ice reduction in the Barents-Kara Sea, the probability of severe winters has more than doubled in central Eurasia. In our simulations, the atmospheric response to sea-ice decline is approximately independent of the Arctic Oscillation. Both reanalysis data and our simulations suggest that sea-ice decline leads to more frequent Eurasian blocking situations, which in turn favour cold-air advection to Eurasia and hence severe winters. Based on a further analysis of simulations from 22 climate models we conclude that the sea-ice-driven cold winters are unlikely to dominate in a warming future climate, although uncertainty remains, due in part to an insufficient ensemble size.
    Nakamura T., K. Yamazaki, K. Iwamoto, M. Honda, Y. Miyoshi, Y. Ogawa, and J. Ukita, 2015: A negative phase shift of the winter AO/NAO due to the recent Arctic sea-ice reduction in late autumn.J. Geophys. Res.,120,3209-3227, https://doi.org/10.1002/2014JD022848.10.1002/2014JD022848d1e2e6ebc5048d2580b8cab05197bd59http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JD022848%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/2014JD022848/pdfAbstract This paper examines the possible linkage between the recent reduction in Arctic sea-ice extent and the wintertime Arctic Oscillation (AO)/North Atlantic Oscillation (NAO). Observational analyses using the ERA interim reanalysis and merged Hadley/Optimum Interpolation Sea Surface Temperature data reveal that a reduced (increased) sea-ice area in November leads to more negative (positive) phases of the AO and NAO in early and late winter, respectively. We simulate the atmospheric response to observed sea-ice anomalies using a high-top atmospheric general circulation model (AGCM for Earth Simulator, AFES version 4.1). The results from the simulation reveal that the recent Arctic sea-ice reduction results in cold winters in mid-latitude continental regions, which are linked to an anomalous circulation pattern similar to the negative phase of AO/NAO with an increased frequency of large negative AO events by a factor of over two. Associated with this negative AO/NAO phase, cold air advection from the Arctic to the mid-latitudes increases. We found that the stationary Rossby wave response to the sea-ice reduction in the Barents Sea region induces this anomalous circulation. We also found a positive feedback mechanism resulting from the anomalous meridional circulation that cools the mid-latitudes and warms the Arctic, which adds an extra heating to the Arctic air column equivalent to about 60% of the direct surface heat release from the sea-ice reduction. The results from this high-top model experiment also suggested a critical role of the stratosphere in deepening the tropospheric annular mode and modulation of the NAO in mid to late winter through stratosphere-troposphere coupling.
    Overland, J. E., J. A. Francis, R. Hall, E. Hanna, S.-J. Kim, T. Vihma, 2015: The melting Arctic and midlatitude weather patterns: are they connected? J.Climate,28,7917-7932, https://doi.org/10.1175/JCLI-D-14-00822.1.10.1175/JCLI-D-14-00822.10eff56c315ae22f8ed4dcb680ca380fchttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JCli...28.7917Ohttp://journals.ametsoc.org/doi/10.1175/JCLI-D-14-00822.1The potential of recent Arctic changes to influence hemispheric weather is a complex and controversial topic with considerable uncertainty, as time series of potential linkages are short (<10 yr) and understanding involves the relative contribution of direct forcing by Arctic changes on a chaotic climatic system. A way forward is through further investigation of atmospheric dynamic mechanisms. During several exceptionally warm Arctic winters since 2007, sea ice loss in the Barents and Kara Seas initiated eastward-propagating wave trains of high and low pressure. Anomalous high pressure east of the Ural Mountains advected Arctic air over central and eastern Asia, resulting in persistent cold spells. Blocking near Greenland related to low-level temperature anomalies led to northerly flow into eastern North America, inducing persistent cold periods. Potential Arctic connections in Europe are less clear. Variability in the North Pacific can reinforce downstream Arctic changes, and Arctic amplification can accentuate the impact of Pacific variability. The authors emphasize multiple linkage mechanisms that are regional, episodic, and based on amplification of existing jet stream wave patterns, which are the result of a combination of internal variability, lower-tropospheric temperature anomalies, and midlatitude teleconnections. The quantitative impact of Arctic change on midlatitude weather may not be resolved within the foreseeable future, yet new studies of the changing Arctic and subarctic low-frequency dynamics, together with additional Arctic observations, can contribute to improved skill in extended-range forecasts, as planned by the WMO Polar Prediction Project (PPP). 2015 American Meteorological Society.
    Overland, J. E., 2016: A difficult Arctic science issue: Midlatitude weather linkages.Polar Science,10,210-216, 2016. 04. 011.https://doi.org/10.1016/j.polar.10.1016/j.polar.2016.04.011d47184414b704542138e770ad96c7174http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS187396521630024Xhttp://linkinghub.elsevier.com/retrieve/pii/S187396521630024XThere is at present unresolved uncertainty whether Arctic amplification (increased air temperatures and loss of sea ice) impacts the location and intensities of recent major weather events in midlatitudes. There are three major impediments. The first is the null hypothesis where the shortness of time series since major amplification (15 years) is dominated by the variance of the physical process in the attribution calculation. This makes it impossible to robustly distinguish the influence of Arctic forcing of regional circulation from random events. The second is the large chaotic jet stream variability at midlatitudes producing a small Arctic forcing signal-to-noise ratio. Third, there are other potential external forcings of hemispheric circulation, such as teleconnections driven by tropical and midlatitude sea surface temperature anomalies. It is, however, important to note and understand recent emerging case studies. There is evidence for a causal connection of Barents-Kara sea ice loss, a stronger Siberian High, and cold air outbreaks into eastern Asia. Recent cold air penetrating into the southeastern United States was related to a shift in the long-wave atmospheric wind pattern and reinforced by warmer temperatures west of Greenland. Arctic Linkages is a major research challenge that benefits from an international focus on the topic.
    Perlwitz J., M. Hoerling, and R. Dole, 2015: Arctic tropospheric warming: causes and linkages to lower latitudes.J. Climate,28,2154-2167, https://doi.org/10.1175/JCLI-D-14-00095.1.10.1175/JCLI-D-14-00095.1e1f15b352e80b6968f6ca4e5cee932b6http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JCli...28.2154Phttp://journals.ametsoc.org/doi/10.1175/JCLI-D-14-00095.1Arctic temperatures have risen dramatically relative to those of lower latitudes in recent decades, with a common supposition being that sea ice declines are primarily responsible for amplified Arctic tropospheric warming. This conjecture is central to a hypothesis in which Arctic sea ice loss forms the beginning link of a causal chain that includes weaker westerlies in midlatitudes, more persistent and amplified midlatitude waves, and more extreme weather. Through model experimentation, the first step in this chain is examined by quantifying contributions of various physical factors to October揇ecember (OND) mean Arctic tropospheric warming since 1979. The results indicate that the main factors responsible for Arctic tropospheric warming are recent decadal fluctuations and long-term changes in sea surface temperatures (SSTs), both located outside the Arctic. Arctic sea ice decline is the largest contributor to near-surface Arctic temperature increases, but it accounts for only about 20% of the magnitude of 1000500-hPa warming. These findings thus disconfirm the hypothesis that deep tropospheric warming in the Arctic during OND has resulted substantially from sea ice loss. Contributions of the same factors to recent midlatitude climate trends are then examined. It is found that pronounced circulation changes over the North Atlantic and North Pacific result mainly from recent decadal ocean fluctuations and internal atmospheric variability, while the effects of sea ice declines are very small. Therefore, a hypothesized causal chain of hemisphere-wide connections originating from Arctic sea ice loss is not supported.
    Petoukhov V., V. A. Semenov, 2010: A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents,J. Geophys. Res.,115,D21111, https://doi.org/10.1029/2009JD013568.10.1029/2009JD013568dc1ac9e62c94b87f316ae99122829c96http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2009JD013568%2Fpdfhttp://doi.wiley.com/10.1029/2009JD013568The recent overall Northern Hemisphere warming was accompanied by several severe northern continental winters, as for example, extremely cold winter 2005-2006 in Europe and northern Asia. Here we show that anomalous decrease of wintertime sea ice concentration in the Barents-Kara (B-K) seas could bring about extreme cold events like winter 2005-2006. Our simulations with the ECHAM5 general circulation model demonstrate that lower-troposphere heating over the B-K seas in the Eastern Arctic caused by the sea ice reduction may result in strong anticyclonic anomaly over the Polar Ocean and anomalous easterly advection over northern continents. This causes a continental-scale winter cooling reaching -1.5ºC, with more than 3 times increased probability of cold winter extremes over large areas including Europe. Our results imply that several recent severe winters do not conflict the global warming picture but rather supplement it, being in qualitative agreement with the simulated large-scale atmospheric circulation realignment. Furthermore, our results suggest that high-latitude atmospheric circulation response to the B-K sea ice decrease is highly nonlinear and characterized by transition from anomalous cyclonic circulation to anticyclonic one and then back again to cyclonic type of circulation as the B-K sea ice concentration gradually reduces from 100% to ice free conditions. We present a conceptual model that may explain the nonlinear local atmospheric response in the B-K seas region by counter play between convection over the surface heat source and baroclinic effect due to modified temperature gradients in the vicinity of the heating area.
    Riddle E. E., A. H. Butler, J. C. Furtado, J. L. Cohen, and A. Kumar, 2013: CFSv2 ensemble prediction of the wintertime Arctic Oscillation.Climate Dyn.,41,1099-1116, https://doi.org/10.1007/s00382-013-1850-5.10.1007/s00382-013-1850-53fc1c67d31d4bbf2b4383b206e63fd56http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1007%2Fs00382-013-1850-5http://link.springer.com/10.1007/s00382-013-1850-5Lagged ensembles from the operational Climate Forecast System version 2 (CFSv2) seasonal hindcast dataset are used to assess skill in forecasting interannual variability of the December-February Arctic Oscillation (AO). We find that a small but statistically significant portion of the interannual variance (>20 %) of the wintertime AO can be predicted at leads up to 2 months using lagged ensemble averages. As far as we are aware, this is the first study to demonstrate that an operational model has discernible skill in predicting AO variability on seasonal timescales. We find that the CFS forecast skill is slightly higher when a weighted ensemble is used that rewards forecast runs with the most accurate representations of October Eurasian snow cover extent (SCE), hinting that a stratospheric pathway linking October Eurasian SCE with the AO may be responsible for the model skill. However, further analysis reveals that the CFS is unable to capture many important aspects of this stratospheric mechanism. Model deficiencies identified include: (1) the CFS significantly underestimates the observed variance in October Eurasian SCE, (2) the CFS fails to translate surface pressure anomalies associated with SCE anomalies into vertically propagating waves, and (3) stratospheric AO patterns in the CFS fail to propagate downward through the tropopause to the surface. Thus, alternate boundary forcings are likely contributing to model skill. Improving model deficiencies identified in this study may lead to even more skillful predictions of wintertime AO variability in future versions of the CFS.
    Saha, S., Coauthors, 2010: The NCEP climate forecast system reanalysis.Bull. Amer. Meteor. Soc.,91,1015-1057, https://doi.org/10.1175/2010BAMS3001.1.10.1175/2010BAMS3001.1http://journals.ametsoc.org/doi/10.1175/2010BAMS3001.1
    Saha, S., Coauthors, 2014: The NCEP climate forecast system version 2.J. Climate,27,2185-2208, https://doi.org/10.1175/JCLI-D-12-00823.1.10.1175/JCLI-D-12-00823.139ad083f324a6b82519f0f80e2a8a0a5http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014JCli...27.2185Shttp://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00823.1The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979-2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982-2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.
    Sato K., J. Inoue, and M. Watanabe, 2014: Influence of the Gulf Stream on the Barents Sea ice retreat and Eurasian coldness during early winter,Environmental Research Letters,9,084009, https://doi.org/10.1088/1748-9326/9/8/084009.10.1088/1748-9326/9/8/0840099c5d56ad9ccb94480ed2f47502d57e80http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014AGUFM.A33D3218Shttp://stacks.iop.org/1748-9326/9/i=8/a=084009?key=crossref.6f28bcabaff7bcbdba0494c16983ca82Abnormal sea-ice retreat over the Barents Sea during early winter has been considered a leading driver of recent midlatitude severe winters over Eurasia. However, causal relationships between such retreat and the atmospheric circulation anomalies remains uncertain. Using a reanalysis dataset, we found that poleward shift of a sea surface temperature front over the Gulf Stream likely induces warm southerly advection and consequent sea-ice decline over the Barents Sea sector, and a cold anomaly over Eurasia via planetary waves triggered over the Gulf Stream region. The above mechanism is supported by the steady atmospheric response to the diabatic heating anomalies over the Gulf Stream region obtained with a linear baroclinic model. The remote atmospheric response from the Gulf Stream would be amplified over the Barents Sea region via interacting with sea-ice anomaly, promoting the warm Arctic and cold Eurasian pattern. (letter)
    Screen J. A., C. Deser, and I. Simmonds, 2012: Local and remote controls on observed Arctic warming,Geophys. Res. Lett.,39,L10709, https://doi.org/10.1029/2012GL051598.10.1029/2012GL0515980d2bcba75b9feef142830ce668ae7653http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012GL051598%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2012GL051598/pdfThe Arctic is warming two to four times faster than the global average. Debate continues on the relative roles of local factors, such as sea ice reductions, versus remote factors in driving, or amplifying, Arctic warming. This study examines the vertical profile and seasonality of observed tropospheric warming, and addresses its causes using atmospheric general circulation model simulations. The simulations enable the isolation and quantification of the role of three controlling factors of Arctic warming: 1) observed Arctic sea ice concentration (SIC) and sea surface temperature (SST) changes; 2) observed remote SST changes; and 3) direct radiative forcing (DRF) due to observed changes in greenhouse gases, ozone, aerosols, and solar output. Local SIC and SST changes explain a large portion of the observed Arctic near-surface warming, whereas remote SST changes explain the majority of observed warming aloft. DRF has primarily contributed to Arctic tropospheric warming in summer.
    Screen J. A., C. Deser, I. Simmonds, and R. Tomas, 2014: Atmospheric impacts of Arctic sea-ice loss,1979-2009: Separating forced change from atmospheric internal variability. Climate Dyn.,43,333-344,.https://doi.org/10.1007/s00382-013-1830.910.1007/s00382-013-1830-978a94d276b2c35549fb7c59cae5959d7http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1007%2Fs00382-013-1830-9http://link.springer.com/10.1007/s00382-013-1830-9The ongoing loss of Arctic sea-ice cover has implications for the wider climate system. The detection and importance of the atmospheric impacts of sea-ice loss depends, in part, on the relative magnitudes of the sea-ice forced change compared to natural atmospheric internal variability (AIV). This study analyses large ensembles of two independent atmospheric general circulation models in order to separate the forced response to historical Arctic sea-ice loss (19792009) from AIV, and to quantify signal-to-noise ratios. We also present results from a simulation with the sea-ice forcing roughly doubled in magnitude. In proximity to regions of sea-ice loss, we identify statistically significant near-surface atmospheric warming and precipitation increases, in autumn and winter in both models. In winter, both models exhibit a significant lowering of sea level pressure and geopotential height over the Arctic. All of these responses are broadly similar, but strengthened and/or more geographically extensive, when the sea-ice forcing is doubled in magnitude. Signal-to-noise ratios differ considerably between variables and locations. The temperature and precipitation responses are significantly easier to detect (higher signal-to-noise ratio) than the sea level pressure or geopotential height responses. Equally, the local response (i.e., in the vicinity of sea-ice loss) is easier to detect than the mid-latitude or upper-level responses. Based on our estimates of signal-to-noise, we conjecture that the local near-surface temperature and precipitation responses to past Arctic sea-ice loss exceed AIV and are detectable in observed records, but that the potential atmospheric circulation, upper-level and remote responses may be partially or wholly masked by AIV.
    Screen J. A., I. Simmonds, 2013: Exploring links between Arctic amplification and mid-latitude weather.Geophys. Res. Lett.,40,959-964, https://doi.org/10.1002/GRL.50174.10.1002/grl.501740cd6e8a4c2dcc0572533baa4173acb28http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50174%2Ffullhttp://doi.wiley.com/10.1002/grl.50174This study examines observed changes (19792011) in atmospheric planetary-wave amplitude over northern mid-latitudes, which have been proposed as a possible mechanism linking Arctic amplification and mid-latitude weather extremes. We use two distinct but equally-valid definitions of planetary-wave amplitude, termed meridional amplitude, a measure of north-south meandering, and zonal amplitude, a measure of the intensity of atmospheric ridges and troughs at 45 degrees N. Statistically significant changes in either metric are limited to few seasons, wavelengths, and longitudinal sectors. However in summer, we identify significant increases in meridional amplitude over Europe, but significant decreases in zonal amplitude hemispherically, and also individually over Europe and Asia. Therefore, we argue that possible connections between Arctic amplification and planetary waves, and implications of these, are sensitive to how waves are conceptualized. The contrasting meridional and zonal amplitude trends have different and complex possible implications for midlatitude weather, and we encourage further work to better understand these.
    Screen J. A., 2014: Arctic amplification decreases temperature variance in northern mid- to high-latitudes.Nat. Clim. Change,4,577-582, 1038/NCLIMATE 2268.https://doi.org/10.10.1038/nclimate22680981f852a5d3ddb77b2a2925d26f94b0http%3A%2F%2Fwww.nature.com%2Fnclimate%2Fjournal%2Fv4%2Fn7%2Ffig_tab%2Fnclimate2268_f3.htmlhttp://www.nature.com/doifinder/10.1038/nclimate2268Changes in climate variability are arguably more important for society and ecosystems than changes in mean climate, especially if they translate into altered extremes [1, 2, 3]. There is a common perception and growing concern that human-induced climate change will lead to more volatile and extreme weather [4]. Certain types of extreme weather have increased in frequency and/or severity [5, 6, 7], in part because of a shift in mean climate but also because of changing variability [1, 2, 3, 8, 9, 10]. In spite of mean climate warming, an ostensibly large number of high-impact cold extremes have occurred in the Northern Hemisphere mid-latitudes over the past decade [11]. One explanation is that Arctic amplificationhe greater warming of the Arctic compared with lower latitudes [12] associated with diminishing sea ice and snow covers altering the polar jet stream and increasing temperature variability [13, 14, 15, 16]. This study shows, however, that subseasonal cold-season temperature variability has significantly decreased over the mid- to high-latitude Northern Hemisphere in recent decades. This is partly because northerly winds and associated cold days are warming more rapidly than southerly winds and warm days, and so Arctic amplification acts to reduce subseasonal temperature variance. Previous hypotheses linking Arctic amplification to increased weather extremes invoke dynamical changes in atmospheric circulation [11, 13, 14, 15, 16], which are hard to detect in present observations [17, 18] and highly uncertain in the future [19, 20]. In contrast, decreases in subseasonal cold-season temperature variability, in accordance with the mechanism proposed here, are detectable in the observational record and are highly robust in twenty-first-century climate model simulations.
    Screen J. A., C. Deser, and L. T. Sun, 2015: Reduced risk of North American cold extremes due to continued Arctic sea ice loss.Bull. Amer. Meteor. Soc.,96,1489-1503, https://doi.org/10.1175/BAMS-D-14-00185.1.10.1175/BAMS-D-14-00185.11abe8e08ba8f26d31c3662db25ad62aahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015BAMS...96.1489Shttp://journals.ametsoc.org/doi/10.1175/BAMS-D-14-00185.1Contrary to recent claims, North American cold extremes are expected to become less frequent as a result of continuing Arctic sea ice loss.
    Serreze M. C., M. M. Holland , and J. Stroeve, 2007: Perspectives on the Arctic's shrinking sea-ice cover.Science,315,1533-1536, https://doi.org/10.1126/science.1139426.10.1126/science.1139426173636647c4c5ff088ffbe067cf10e6490eb6cdehttp%3A%2F%2Feuropepmc.org%2Fabstract%2FMED%2F17363664http://www.sciencemag.org/cgi/doi/10.1126/science.1139426Linear trends in arctic sea-ice extent over the period 1979 to 2006 are negative in every month. This ice loss is best viewed as a combination of strong natural variability in the coupled ice-ocean-atmosphere system and a growing radiative forcing associated with rising concentrations of atmospheric greenhouse gases, the latter supported by evidence of qualitative consistency between observed trends and those simulated by climate models over the same period. Although the large scatter between individual model simulations leads to much uncertainty as to when a seasonally ice-free Arctic Ocean might be realized, this transition to a new arctic state may be rapid once the ice thins to a more vulnerable state. Loss of the ice cover is expected to affect the Arctic's freshwater system and surface energy budget and could be manifested in middle latitudes as altered patterns of atmospheric circulation and precipitation.
    Serreze M. C., R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis.Global and Planetary Change,77,85-96, 2011. 03. 004.https://doi.org/10.1016/j.gloplacha.10.1016/j.gloplacha.2011.03.004e13513b501cd20f1448d7b3a5223b0d8http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0921818111000397http://linkinghub.elsevier.com/retrieve/pii/S092181811100039778 Temperature changes in the Arctic tend to exceed those for the globe as whole. 78 This phenomenon is termed Arctic amplification. 78 Arctic amplification has many causes operating on different time and space scales. 78 Recent Arctic amplification is strongly linked to declining sea ice extent. 78 Arctic amplification is expected to strengthen in coming decades. 78 Impacts of Arctic amplification will extend well beyond the Arctic region.
    Sillmann J., M. G. Donat, J. C. Fyfe, and F. W, Zwiers, 2014: Observed and simulated temperature extremes during the recent warming hiatus,Environmental Research Letters,9,064023, https://doi.org/10.1088/1748-9326/9/6/064023.10.1088/1748-9326/9/6/06402396c7a7def7fa2ea0d431155b458e6dd0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014ERL.....9f4023Shttp://stacks.iop.org/1748-9326/9/i=6/a=064023?key=crossref.d1187cdc6b9128d413b4f529b6d3fe1fThe discrepancy between recent observed and simulated trends in global mean surface temperature has provoked a debate about possible causes and implications for future climate change projections. However, little has been said in this discussion about observed and simulated trends in global temperature extremes. Here we assess trend patterns in temperature extremes and evaluate the consistency between observed and simulated temperature extremes over the past four decades (1971-2010) in comparison to the recent 15 years (1996-2010). We consider the coldest night and warmest day in a year in the observational dataset HadEX2 and in the current generation of global climate models (CMIP5). In general, the observed trends fall within the simulated range of trends, with better consistency for the longer period. Spatial trend patterns differ for the warm and cold extremes, with the warm extremes showing continuous positive trends across the globe and the cold extremes exhibiting a coherent cooling pattern across the Northern Hemisphere mid-latitudes that has emerged in the recent 15 years and is not reproduced by the models. This regional inconsistency between models and observations might be a key to understanding the recent hiatus in global mean temperature warming.
    Sun L. T., J. Perlwitz, and M. Hoerling, 2016: What caused the recent "Warm Arctic,Cold Continents" trend pattern in winter temperatures? Geophys. Res. Lett.,43,5345-5352,https://doi.org/10.1002/2016GL069024.
    Tang Q. H., X. J. Zhang, X. H. Yang, and J. A. Francis, 2013: Cold winter extremes in northern continents linked to arctic sea ice loss,Environmental Research Letters,8,014036, https://doi.org/10.1088/1748-9326/8/1/014036.10.1088/1748-9326/8/1/0140364a2689c416c3793196f6b65170ec673dhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013ERL.....8a4036Thttp://stacks.iop.org/1748-9326/8/i=1/a=014036?key=crossref.3b07e12952c5b61acb8cedc9b207e051中国科学院机构知识库(中国科学院机构知识库网格(CAS IR GRID))以发展机构知识能力和知识管理能力为目标,快速实现对本机构知识资产的收集、长期保存、合理传播利用,积极建设对知识内容进行捕获、转化、传播、利用和审计的能力,逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力,开展综合知识管理。
    Wallace J. M., I. M. Held, D. W. J. Thompson, K. E. Trenberth, and J. E. Walsh, 2014: Global warming and winter weather.Science,343,729-730, .http://doi.org/10.1126/science/343/6172.72910.1126/science.343.6172.7292453195382fe21d5fa572c5ffb9cfd325d2ac105http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F24531953http://www.sciencemag.org/cgi/doi/10.1126/science.343.6172.729Authors: John M. Wallace, Isaac M. Held, David W. J. Thompson, Kevin E. Trenberth, John E. Walsh
    Zhang X. D., C. H. Lu, and Z. Y. Guan, 2012: Weakened cyclones,intensified anticyclones and recent extreme cold winter weather events in Eurasia.Environmental Research Letters,7,044044, https://doi.org/10.1088/1748-9326/7/4/044044.10.1088/1748-9326/7/4/044044434a3ad75227708aaa996fd09e7521b2http%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fiop%2Ferl%2F2012%2F00000007%2F00000004%2Fart044044http://stacks.iop.org/1748-9326/7/i=4/a=044044?key=crossref.98b6b9a08772130a16c83249896492d1Extreme cold winter weather events over Eurasia have occurred more frequently in recent years in spite of a warming global climate. To gain further insight into this regional mismatch with the global mean warming trend, we analyzed winter cyclone and anticyclone activities, and their interplay with the regional atmospheric circulation pattern characterized by the semi-permanent Siberian high. We found a persistent weakening of both cyclones and anticyclones between the 1990s and early 2000s, and a pronounced intensification of anticyclone activity afterwards. It is suggested that this intensified anticyclone activity drives the substantially strengthening and northwestward shifting/expanding Siberian high, and explains the decreased midlatitude Eurasian surface air temperature and the increased frequency of cold weather events. The weakened tropospheric midlatitude westerlies in the context of the intensified anticyclones would reduce the eastward propagation speed of Rossby waves, favoring persistence and further intensification of surface anticyclone systems. (letter)
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Manuscript received: 30 November 2016
Manuscript revised: 18 May 2017
Manuscript accepted: 16 June 2017
通讯作者: 陈斌, bchen63@163.com
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Simulations of Eurasian Winter Temperature Trends in Coupled and Uncoupled CFSv2

  • 1. INNOVIM, LLC, Greenbelt, MD 20770, USA
  • 2. NOAA/NCEP Climate Prediction Center, College Park, MD 20740, USA

Abstract: Conflicting results have been presented regarding the link between Arctic sea-ice loss and midlatitude cooling, particularly over Eurasia. This study analyzes uncoupled (atmosphere-only) and coupled (ocean-atmosphere) simulations by the Climate Forecast System, version 2 (CFSv2), to examine this linkage during the Northern Hemisphere winter, focusing on the simulation of the observed surface cooling trend over Eurasia during the last three decades. The uncoupled simulations are Atmospheric Model Intercomparison Project (AMIP) runs forced with mean seasonal cycles of sea surface temperature (SST) and sea ice, using combinations of SST and sea ice from different time periods to assess the role that each plays individually, and to assess the role of atmospheric internal variability. Coupled runs are used to further investigate the role of internal variability via the analysis of initialized predictions and the evolution of the forecast with lead time. The AMIP simulations show a mean warming response over Eurasia due to SST changes, but little response to changes in sea ice. Individual runs simulate cooler periods over Eurasia, and this is shown to be concurrent with a stronger Siberian high and warming over Greenland. No substantial differences in the variability of Eurasian surface temperatures are found between the different model configurations. In the coupled runs, the region of significant warming over Eurasia is small at short leads, but increases at longer leads. It is concluded that, although the models have some capability in highlighting the temperature variability over Eurasia, the observed cooling may still be a consequence of internal variability.

摘要: 针对北极海冰减少和中纬度地区特别是欧亚地区的变冷之间关系的研究, 存在一些争议或者相冲突的结果. 本文利用CFSv2系统的非耦合大气模式(AMIP)和海洋-大气耦合模式模拟, 来检验北半球冬季的这种关联, 重点是模拟过去30年观测到的欧亚地区的地表变冷趋势. 首先利用非耦合的大气模式并以季节循环的海温(SST)和海冰密集度(SIC)作为强迫场驱动, 选取不同时期的平均SST和SIC, 并通过SST和SIC之间的多种组合强迫, 分析强迫场及大气内部变化的影响. 进一步利用海洋-大气耦合模式, 通过初始化预测及提前预报的演变, 分析大气内部变化的作用. AMIP模拟结果显示, 由于海温变化, 欧亚地区平均增暖, 但欧亚气温对海冰变化几乎没有响应. 个别试验模拟出了较低的欧亚气温, 同时出现的是较强的西伯利亚高压和格陵兰岛的增暖. 不同的模式配置(即不同的海温海冰强迫组合)并不会造成欧亚表面温度变化的明显差异. 耦合试验结果显示, 短期预测试验模拟的欧亚显著增暖区域很小, 但随着预报时段的增长, 更多的欧亚区域变暖. 因此, 尽管模式具有突出欧亚大陆温度变化的能力, 但观测到的地表变冷仍可能是大气内部变率的结果.

1. Introduction
  • Over the last 30 years, global sea surface temperatures (SSTs) have substantially increased (Hartmann et al., 2013) and Arctic sea ice has declined (Serreze et al., 2007; Comiso et al., 2008). Decreases in Arctic sea-ice cover further increase near-surface temperatures in the Arctic due to the lower albedo of the open ocean compared to sea ice, allowing greater absorption of solar radiation. As a result, temperatures have warmed more in the Arctic than in the lower latitudes ——a phenomenon known as Arctic amplification (Kumar et al., 2010; Serreze and Barry, 2011). Numerous studies have attempted to link Arctic amplification with changes in the observed midlatitude synoptic variability, such as the frequency of extreme events or winter temperature decreases over Eurasia and North America, which have been extensively reviewed in (Cohen et al., 2014), (Gao et al., 2015), and (Overland et al., 2015). Because of the large uncertainty in the literature, questions remain regarding whether the recent observed changes in extratropical and high-latitude variability are a result of changes to the external forcing (Arctic sea-ice decline, low-frequency changes in SSTs, changes in greenhouse gas concentrations, volcanic forcing, and solar variations), or are a manifestation of natural variability (defined as changes in various aspects of the extratropical and high-latitude circulation on different timescales, which are unrelated to changes in external forcing). These natural circulation changes can also have a notable impact on Arctic sea-ice loss (Ding et al., 2017). However, for the purposes of this study, the long-term changes in SSTs and sea ice are represented as external forcing.

    The hypothesized pathway for Arctic amplification to impact midlatitude climate, as suggested by (Francis and Vavrus, 2012), is that the increased Arctic warming creates a reduced latitudinal temperature gradient between the equator and North Pole, reducing the zonal upper-level winds through the hypsometric relationship. This in turn allows for a slower progression of upper-level atmospheric waves and a wavier jet stream (Francis and Vavrus, 2015), leading to more persistent weather patterns, thus increasing the probability for the occurrence of extreme events. These impacts on upper-level patterns are argued to have a variety of atmospheric consequences, both local and non-local. Some examples include a decrease in temperature variance in the mid and high latitudes (Screen, 2014), reduction in the strength of cold-air outbreaks in the midlatitudes (Ayarzagüena and Screen, 2016), and increased winter Eurasian snowfall due to increased moisture availability from melted sea ice (Liu et al., 2012).

    Other studies have illustrated that Arctic sea ice can force a negative phase of the Arctic Oscillation (AO), which causes cooler temperatures over the midlatitudes (Nakamura et al., 2015). However, (Mori et al., 2014) showed that the cooling over Eurasia is independent of the AO and more related to autumn sea-ice loss in the Barents and Kara seas. (Petoukhov and Semenov, 2010) also proposed a pathway linking sea-ice loss in the Barents and Kara seas to cooling over Eurasia, but showed the relationship is highly nonlinear in terms of the temperature response to different magnitudes of sea-ice loss, while stressing the importance of the possible role of atmospheric teleconnection patterns. (Sato et al., 2014) deduced that a northward shift in the Gulf Stream, which results from a weaker Atlantic Meridional Overturning Circulation, reduces the sea ice in the Barents Sea, which then yields altered planetary wave patterns that promote Eurasian cooling. Several studies (Honda et al., 2009; Inoue et al., 2012; Zhang et al., 2012; Tang et al., 2013) have claimed that sea-ice decline ultimately leads to a stronger Siberian high, which enhances wintertime cooling over Eurasia. Other studies have explored the issue of conditional dependence (Overland, 2016), suggesting that for Arctic amplification to impact midlatitude weather, there must be some preexisting condition.

    Linkages between Arctic warming and midlatitude cooling have also been contested. For example, (Barnes, 2013) and (Screen and Simmonds, 2013) highlighted that calculated differences in atmospheric wave patterns are sensitive to the parameters used to define them, and that under different definitions the linkages would not be as conclusive. (Screen et al., 2014) and (Perlwitz et al., 2015) demonstrated that, although sea ice plays a large role in the increase of near-surface Arctic temperatures, the signal is lost at higher altitudes, which would not support any feedbacks to the midlatitudes. In addition, (Barnes et al., 2014) argued that there has been no increase in the frequency of blocking patterns in the last three decades, and a link to Arctic sea-ice loss is nonexistent. (Screen et al., 2015) stated that the probability of colder winters over the midlatitudes will decrease due to sea-ice loss, while (McCusker et al., 2016) and (Sun et al., 2016) concluded that the cooler Eurasian winters are most likely due to atmospheric internal variability.

    The present study attempts to determine the ability of an uncoupled (atmosphere-only) model and a coupled (ocean-atmosphere) model in producing the cooling over Eurasia, to provide further insight into the subject. The main questions that we address are: (1) Do uncoupled runs forced with either SST or sea-ice boundary conditions from 2005-14 simulate the observed December-February atmospheric changes relative to simulations forced with 1981-90 boundary conditions? (2) Are changes prevalent in the modeled variability of wintertime temperatures over Eurasia, and are there systematic atmospheric patterns associated with warm and cold winters seen within the different model configurations for the external forcing? (3) Can initialized coupled-model runs simulate the observed characteristics and, if so, is there a dependence on the forecast lead time? Answers to these questions will provide insights into the assessment of whether the observed changes are due to sea-ice changes, SST changes, or more related to atmospheric internal variability.

2. Methods
  • The Climate Forecast System, version 2 (CFSv2), is a fully coupled atmosphere-ocean-ice-land model developed at the National Centers for Environmental Prediction (Saha et al., 2014). For this study, the atmospheric component of CFSv2 is used for uncoupled Atmospheric Model Intercomparison Project (AMIP) simulations, starting at 0000 UTC 1 January 1985, with seasonal sea-ice and SST boundary conditions repeating annually, which are applied globally. Because only the atmospheric component of the model is used, the prescribed SSTs and sea ice will not be changed through direct model integration. The atmospheric component of CFSv2 is the Global Forecast System Model (Moorthi et al., 2001), which uses a T126 horizontal grid (100 km grid spacing) and finite differencing in the vertical grid with 64 sigma-pressure hybrid layers. Combinations of boundary conditions are used based on sea-ice concentration (SIC) and SST monthly mean 10-year average values for 1981-90 and 2005-14 from the Merged Hadley-NOAA/OI dataset (Hurrell et al., 2008).

    The seasonal differences (2005-14 minus 1981-90) of SST and SIC from the Merged Hadley-NOAA/OI data are shown in Figs. 1 and 2, respectively. SSTs show a mostly uniform warming throughout the Northern Hemisphere, except for the west coast of the United States. Mean seasonal differences between the two periods for all points north of 30°N are 0.29 K for March-April-May (MAM; Fig. 1a), 0.58 K for June-July-August (JJA; Fig. 1b), 0.56 K for September-October-November (SON; Fig. 1c), and 0.34 K for December-January-February (DJF; Fig. 1d). For SIC (Fig. 2) the changes are most pronounced in JJA (Fig. 2b) and SON (Fig. 2c), with large decreases seen over the Chukchi Sea extending along the Siberian coast and the Arctic Ocean. Modest decreases are still seen in MAM (Fig. 2a) and DJF (Fig. 2d) as well.

    Figure 1.  SST change (K; top 10 m of ocean) between 1981-90 and 2005-14 from the Merged Hadley-NOAA/OI product: (a) MAM; (b) JJA; (c) SON; (d) DJF.

    The average conditions of SST and sea ice for 1981-90 (2005-14) are denoted as SST1 and ICE1 (SST2 and ICE2), respectively. Configurations used for the runs are SST1ICE1, SST2ICE1, SST1ICE2, and SST2ICE2. SST1ICE1 utilizes both SST and SIC monthly means from 1981-90. SST2ICE1 uses SST from 2005-14 and SIC from 1981-90, with the SST changes only having an impact outside the sea-ice domain. Because the model is uncoupled, there is no interplay between the SSTs and sea ice; atmospheric impacts are controlled by fluxes from either the sea ice or ocean surface. Therefore, altering the SSTs will not have an impact on surface-to-atmosphere fluxes in ice-covered regions. SST1ICE2 uses SST from 1981-90 and SIC from 2005-14. In places where sea ice is removed, SST remains at the freezing point of sea water, as in the study by (Liu et al., 2012), with the advantage that the response to sea-ice loss is fully isolated. Other studies have perturbed SSTs to include the SST response that would occur due to sea-ice loss (i.e., Screen, 2014), but either method is considered valid, with each having advantages. Finally, SST2ICE2 incorporates SIC and SST from 2005-14, representing the combined impact of sea-ice loss and SST increase. In SST2ICE2, the SST changes will have an impact on a larger domain than in SST2ICE1, given there is less sea-ice coverage. For this reason, the results are not directly additive (adding SST1ICE2 to SST2ICE1 will not equal SST2ICE2).

    Figure 2.  As in Fig. 1, but for SIC (%).

    For each of the configurations, the uncoupled model is integrated for 101 years from the atmospheric initial state at 0000 UTC 1 January 1985 from the Climate Forecast System Reanalysis (CFSR; Saha et al., 2010). The first 11 months are excluded in the analysis. Paired differences of these runs are used to analyze the influence of changing SST, SIC, and both parameters. For example, differences between SST2ICE1 and SST1ICE1 are considered to be to the result of SST changes between the two 10-year periods, as that is the only parameter changed between the two simulations; while the differences between SST2ICE2 and SST1ICE1 are the result of impacts from both SST and SIC changes.

    Starting with the first December of the 101-year runs, all DJF periods are averaged, creating a total of 100 seasonal means. DJF is chosen as the season of interest due to the large area of observed cooling seen over the Asian continent (Figs. 3a and d), and the goal is to see to what extent this can be duplicated in model simulations. For each configuration, the 100 DJF means are further divided into 10 sets of 10 individual DJF seasons and averaged together for comparisons with the 10-year reanalysis periods (1981-90 and 2005-14). All results from the simulations are based on differences with respect to the SST1ICE1 simulation. A dataset of differences is created using all possible combinations from the 10 sets of each configuration and the 10 sets of SST1ICE1, which equate to 100 possible combinations. The ensemble mean differences between two AMIP simulations highlight the responses from SST and/or SIC forcing, while the differences among individual runs within each simulation represent the contribution from atmospheric internal variability.

  • Another way to quantify the contribution of atmospheric internal variability is based on the analysis initialized predictions using hindcasts from the coupled CFSv2 model. If the observed anomalies have a contribution from atmospheric internal variability, then the capability of initialized predictions to retain these anomalies would diminish with lead time, with the impact of the changed boundary conditions becoming more apparent at longer leads. Therefore, using this approach, we assess the influence of internal variability in a coupled forecast setting, and on the time scale this influence is retained.

    To provide insights into the impacts of initial conditions, the coupled hindcasts with CFSv2 are examined over the two periods that closely match those used in deriving the initial SST and SIC conditions in the AMIP simulations, with the caveat that the CFSv2 hindcasts are not available before 1982. Therefore, the periods compared are DJF 1982-90 and 2005-13. For these runs, the atmospheric model is coupled to the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model, version 4 (MOM4) (Griffies et al., 2003), and the GFDL sea-ice simulator. MOM4 contains 40 vertical layers with a resolution of 10 m at the surface that decreases to approximately 500 m at the bottom layer. The sea-ice model contains two equal layers of sea ice and one layer of snow, along with five sea-ice thickness categories (Saha et al., 2010). The model is integrated four times a day (0000 UTC, 0600 UTC, 1200 UTC, and 1800 UTC), every five days.

    Mean DJF 2-m temperatures and 200-hPa heights are reconstructed from the hindcast simulations by averaging hindcasts with equal lead days and the four initial times from those days. For example, for the shortest lead time, which is approximately 5 days, the DJF mean for December, January and February would be substantially influenced by the initial conditions on 27 November, 27 December, and 26 January, respectively, and would also average the four initial times on those days for a total of 36 forecasts for each lead within each 9-year period. Leads out to 120 days are analyzed to determine if the observed temperatures and heights can be simulated in the model, and if there is a lead-time dependency. This approach was documented in (Chen et al., 2013). A plus/ minus five-day lead smoothing is also applied; so, as an example, a lead of 10 days would also incorporate the data for 5- and 15-day leads. This is done to reduce the large variations seen among consecutive lead times and produce more coherent results.

3. Results
  • Figure 3 shows the mean DJF 2-m temperature and 200-hPa height differences (2005-14 minus 1981-90) for ERA-Interim (Dee et al., 2011) (Fig. 3a) and CFSR (Fig. 3b). The green outlined region in Fig. 3, and subsequent maps, signify the domain of interest for this study and encompass the region of the greatest cooling in ERA-Interim (40°-60°N, 30°-150°E). Geographically, this includes much of Eastern Europe and Central Asia. Both ERA-Interim and CFSR show pronounced cooling over this domain, not seen in other seasons (not shown). Both reanalyses show substantial warming in the Arctic.

    The Eurasian cooling in the reanalyses is not simulated when comparing the means of the AMIP simulations, which all show either warming or no change (Figs. 4a-c) in 2-m DJF temperatures over Eurasia. The area-weighted mean 2-m DJF temperature change between the two periods over this domain in ERA-Interim is -0.27 K (Fig. 3a). For the uncoupled AMIP simulations (Figs. 4a-c), the mean 2-m temperature change is 0.72 K, 0.54 K and 0.32 K for SST2ICE2, SST2ICE1 and SST1ICE2, respectively —— all relative to SST1ICE1. In general, significant warming is confined to the Arctic for SST1ICE2, while the warming is more uniform throughout the Northern Hemisphere in SST2ICE1. The SST2ICE2 difference shows both the strong Arctic warming, and weaker but uniform warming at lower latitudes. Changes in 200-hPa heights are prevalent in ERA-Interim (Fig. 3a) and CFSR (Fig. 3b) and, albeit to a lesser degree, are still present in SST2ICE2 (Fig. 4a) and SST2ICE1 (Fig. 4b), particularly over the Arctic and extending south to Alaska. However, changes in the 200-hPa heights are more sporadic in the SST1ICE2 (Fig. 4c) runs.

    Figure 3.  DJF 2-m temperature change (K; shaded) and 200-hPa geopotential height change (dm; contoured at intervals of 2 dm) between 1981-90 and 2005-14 from (a) ERA-Interim and (b) CFSR. The region outlined in green denotes the domain covering Eurasia used in this study.

    Figures 4d and e show the zonal-mean 2-m temperature and 200-hPa height differences, respectively. For the reanalysis and the SST2ICE2 and SST2ICE1 simulations, the largest increases in 2-m temperature are in the Arctic, with smaller and more equal changes at lower latitudes. For 200-hPa heights, ERA-Interim shows the largest increase over the Arctic (consistent with Fig. 2a), with SST2ICE2 the next highest, followed by SST2ICE1, and finally SST1ICE2. Changes are also seen over lower latitudes for SST2ICE2 and SST2ICE1, but not for SST1ICE2.

    Figure 4.  Mean DJF 2-m temperature (K; shaded) and 200-hPa geopotential height (dm; contoured at intervals of 2 dm) differences determined using all combinations of differences with respect to SST1ICE1 and using 10-year groupings of (a) SST2ICE2, (b) SST2ICE1, (c) and SST1ICE2. All plotted temperature differences are significant at the 95% confidence level. The region outlined in green denotes the Eurasian domain of interest. Zonal-mean 2-m temperature and 200-hPa geopotential height differences based on (a-c) (and Fig. 3a for ERA-Interim) are shown in panels (d, e), respectively.

    Figure 5.  Mean DJF zonally averaged temperature (K; shaded) and geopotential height (dm; contours) differences: (a) ERA-Interim (2005-14 minus 1981-90); (b) SST2ICE2 minus SST1ICE1; (c) SST2ICE1 minus SST1ICE1; (d) SST1ICE2 minus SST1ICE1.

    Figure 5 illustrates the zonal-mean vertical profiles of DJF temperatures and geopotential heights for ERA-Interim (Fig. 5a) and each of the AMIP configurations (Figs. 5b-d). A comparison of the two periods in ERA-Interim reveals warming throughout the Arctic troposphere, and this is also shown to some degree —— albeit with a weaker magnitude —— in the simulations that impose the SST forcing from 2005-14. Because the warming is vertically more extensive in ERA-Interim than in the AMIP runs, the 200-hPa geopotential height fields are under-simulated in AMIP relative to ERA-Interim, as seen in Fig. 4. The SST1ICE2 simulation only shows warming mainly in the lower Arctic troposphere, which is consistent with previous results (e.g., Kumar et al., 2010; Perlwitz et al., 2015). For Figs. 4 and 5, the results are not exactly additive, meaning that adding the impact of just the SST change (SST2ICE1) to the impact of just the sea-ice change (SST1ICE2) does not equal the result of the simulation where both SSTs and sea ice are changed (SST2ICE2). However, the combined result resembles the SST2ICE2 result (not shown), and small differences are anticipated due to the interplay between SSTs and sea ice, as well as because of issues related to sampling. However, the differences are generally smaller than those seen in the individual runs, and therefore nonlinearity is not expected to significantly impact the results.

    To quantify the role of atmospheric internal variability, distributions of differences in the mean 2-m temperature relative to SST1ICE1 are shown in Fig. 6. For Fig. 6a, which takes the differences of SST1ICE1 with itself, a normal distribution is found, as expected, with a mean of 0 K. The standard deviation is 0.46 K. For SST2ICE2, SST2ICE1 and SST1ICE2, the standard deviation is 0.50 K, 0.49 K and 0.50 K, respectively, indicating that the amount of variability is similar in all configurations. The means shift towards warmer temperatures for all configurations (Figs. 6b-d), in accordance with the results shown in Fig. 2, but cooling is still seen in a small percentage of runs. The percentage of negative temperature differences, representing cooling, over Eurasia relative to SST1ICE1, is 45%, 11%, 17% and 24% for SST1ICE1, SST2ICE2, SST2ICE1 and SST1ICE2, respectively.

    Figures 7 and 8 consider 2-m temperature (Fig. 7) and 200-hPa height (Fig. 8) patterns associated with the 10 warmest and coolest differences out of the 100 total differences over the domain of interest, with the goal to determine local and remote changes associated with warm and cold winters over Asia and, further, to assess if there is consistency among the different configurations.

    For 2-m temperatures, the warmest Eurasian winters in the uncoupled simulations all show increased temperatures throughout the Eurasian domain (Figs. 7a-d). Arctic temperatures are increased in the configurations where sea ice from 2005-14 is used (Figs. 7b and d). The coldest DJF periods all show lower 2-m temperatures in the Eurasian domain (Figs. 7e-h), and have a slightly warmer Arctic than seen in the warm Eurasia differences for each configuration. However, significant warming is only noted near Greenland for all configurations when the warm differences are subtracted from the cold differences (Figs. 7i-l).

    Analysis of the 200-hPa height patterns associated with the warmest and coldest DJF periods plotted in Fig. 7 reveals that the coldest DJF periods are associated with increased heights in the Arctic (Figs. 8e-h); specifically, significant increases are seen along the northern coast of Siberia for SST1ICE1 (Fig. 8i) and SST1ICE2 (Fig. 8l), and more expansively throughout the Arctic Ocean when SSTs from 2005-14 are used (SST2ICE2 and SST2ICE1, plotted in Figs. 8j and k, respectively). Due to the presence of these synoptic features in all configurations along with similar variability, it is plausible that the observed cooling over Eurasia is more related to internal atmospheric variability, rather than a change in SST or SIC forcing.

    Figure 6.  Distribution of the area-weighted mean temperature change (K) over the Eurasian domain using the 100 combinations of differences relative to the control AMIP simulation: (a) SST1ICE1; (b) SSTICE2; (c) SST2ICE1; (d) SST1ICE2. The shorter vertical bars denote the mean and the horizontal black bars span 1 standard deviation from the mean.

    Figure 7.  (a-d) DJF 2-m temperature differences (K) with respect to the control run using the 10 warmest DJF periods over the Eurasian domain based on the distribution in Fig. 5: (a) SST1ICE1; (b) SST2ICE2; (c) SST2ICE1; (d) SST1ICE2. (e-h) Mean of the 10 coldest DJF periods over the Eurasian domain: (e) SST1ICE1; (f) SST2ICE2; (g) SST2ICE1; (h) SST1ICE2. (i-1) Differences at the 95% confidence level between the two panels directly above (coldest minus warmest) for each configuration: (i) SST1ICE1; (j) SST2ICE2; (k) SST2ICE1; (l) SST1ICE2.

    Figure 8.  As in Fig. 7, but for 200-hPa geopotential height differences (dm).

  • To further assess the contribution of atmospheric internal variability and coupled ocean-atmosphere interactions, the initialized predictions based on the coupled model are analyzed next. The background warming of SSTs and decline of sea ice between the two periods is present, with internal year-to-year variability represented within the forecast initial conditions. The coupled predictions show that the Arctic warming seen in ERA-Interim and CFSR is still replicated, but not the Eurasian cooling. Over Eurasia, 2-m temperature changes are small, but are generally positive at short leads and increase at longer leads. Figure 9 illustrates the DJF 2-m temperature difference based on reconstructed seasonal means at varying lead times. For all leads plotted, significant warming is seen at higher latitudes and, although Eurasian cooling is not seen, significant warming is more sporadic. For a 10-day lead, the area of significant warming is limited across the Eurasian domain (Figs. 9a and h), but becomes more prevalent at longer leads.

    Figure 9g shows the evolution of the mean 2-m temperature difference over Eurasia as a function of lead time. The trend, based on least-squares fitting, is small (0.04 K per 30 days oflead), with the mean change for a 10-30-day lead being 0.88 K, and increasing to 1.04 K for a 90-110-day lead. However, the fraction of significant warming within the Eurasian domain increases with lead —— specifically, at a rate of 11.1% per 30 days of lead. The mean percentage of the domain that undergoes significant warming for a 10-day lead is 43%, but this increases to 94% at a 110-day lead. There is also a slight decline in spatial correlation with ERA-Interim differences, but consistent Arctic warming allows spatial correlations to remain modest, with a trend of -0.02 per 30 days of lead (Fig. 9i). Similar results are found when the CFSR data are used instead of ERA-Interim (not shown).

    The 200-hPa geopotential height differences are more lead-time-dependent than the 2-m temperature differences. The greatest magnitude of significant Arctic height increases is seen at short leads (out to 30 days; Figs. 10a and b), which is similar to the differences between the two periods seen in the ERA-Interim and CFSR reanalysis products. For longer leads, the height increases become smaller (Figs. 10c-f). There is no discernible pattern noted across Eurasia. For the first 10-30-day lead period, the mean height increase over the Arctic (60°N and polewards, represented by the circular domain in Figs. 10a-f), is 4.47 dm, but only 2.68 dm for the 90-110-day lead period, indicating a sharp downward trend of -0.6 dm per 30 days, which is significant at the 95% confidence level (Fig. 10g). Spatial correlation trends follow a similar pattern, with higher correlations within the first 10-30-day lead (0.49) to no relationship for the 90-110-day lead period (-0.09), resulting in a linear trend in the spatial correlation of -0.19 per 30 days. The downward trend and decreasing spatial correlation with lead time indicates a weakening of the impact of atmospheric initial conditions, implying that cooling, even if initialized, cannot be sustained. As with 2-m temperature, the results are similar for CFSR (not shown).

    Figure 9.  DJF reconstructed mean 2-m temperature differences (K) using the CFSv2 coupled hindcasts (2005-13 minus 1982-90) at lead times of (a) 10 days, (b) 30 days, (c) 50 days, (d) 70 days, (e) 90 days, and (f)110 days. All differences in (a-f) are significant at the 95% confidence level and are smoothed with the last and next lead. (g) Area-weighted mean DJF 2-m temperature difference over the Eurasian domain as a function of lag. (h) Percentage of points within the Eurasian domain with a significant increase in temperature at the 95% confidence level. (i) Spatial correlation with ERA-Interim DJF 2-m temperature difference over the same period using all points polewards of 30°N.

    Figure 10.  DJF reconstructed mean 200-hPa geopotential height differences (dm) using the CFSv2 coupled hindcasts (2005-13 minus 1982-90): (a) 10-day lag; 30-day lag; (c) 50-day lag; (d) 70-day lag; (e) 90-day lag; (f) 110-day lag. All differences in (a-f) are significant at the 95% confidence level and are smoothed with the last and next lead. (g) Area-weighted mean DJF 200-hPa geopotential height difference over the Arctic (points 60°N and polewards) as a function of lag. (h) Spatial correlation with ERA-Interim DJF 200-hPa geopotential height differences over the same period using all points polewards of 30°N.

4. Discussion and conclusion
  • Results from the uncoupled and coupled simulations show that the Eurasian cooling in the reanalysis products is not well simulated as a response to changes in SST and sea-ice conditions. When sea-ice forcing is changed from 1981-90 mean conditions to the 2005-14 mean, the significant 2-m temperature changes are confined to the Arctic, with minimal geopotential height changes at 200 hPa and insignificant non-local changes —— a similar result to that of (Perlwitz et al., 2015). These changes in the Arctic do not propagate into the midlatitudes as suggested by other studies (i.e., Liu et al., 2012). For the SST forcing change, there is a significant increase in 2-m temperature throughout much of the Northern Hemisphere, along with a more robust change in the 200-hPa height field than when only the sea-ice forcing is changed. These results are in line with (Screen et al., 2012), showing that local sea-ice loss is the dominant cause of surface Arctic amplification but contributes much less to geopotential height increases at 200 hPa, which are driven by remote SST changes. Based on the results of Figs. 7 and 8, warm and cold Eurasian winters arise due to roughly the same large-scale atmospheric circulation conditions in all model configurations, implying internal variability as the dominant cause rather than changes in ocean surface forcing.

    The hypothesized reasons for the inadequacy of models to simulate the Eurasian cooling fall into two main categories: (i) model results suggest that the cooling is a product of internal variability (McCusker et al., 2016) and a mean warming trend is expected to re-emerge (Sillmann et al., 2014; Sun et al., 2016), or (ii) models suffer from biases and deficiencies (Furtado et al., 2015; Handorf et al., 2015) that need to be addressed; for example, those related to stratospheric coupling, which will allow for better representation of surface patterns. Although the conclusion of (Mori et al., 2014) supported a relationship between sea-ice loss over the Barents and Kara seas with Eurasian temperatures, simulations using Coupled Model Intercomparison Project Phase 5 (CMIP5) models have revealed that the frequency of cool winters decreases despite continuing sea-ice decline. This was also supported by (Sun et al., 2016), who suggested that a warm Arctic and cold continents is only a temporary, transient phenomenon, and will become more unlikely under a warming climate.

    In our simulations, each configuration of the model can produce cold winters on an individual sample basis, which are shown to be linked to higher Arctic heights resembling the AO pattern, as also shown in (Deser et al., 2004) and (Alexander et al., 2004). It is worth noting that the DJF AO averages more negatively over the 2005-14 period (-0.25) than over the 1981-90 period (0.00), with a greater frequency of stronger negative AO winters (e.g., 2009) in the later period but more strongly positive AO winters (1988 being the largest) in the earlier period. Because the variability of the Eurasian DJF temperatures in each model configuration is nearly the same, it cannot be concluded that the cooling is a response to SST or sea ice. Even in a warming climate, harsh winters will still occur-just less frequently, as highlighted by (Wallace et al., 2014). While the sea-ice forcing from 2005-14 does produce a larger probability of cooler simulations over Eurasia than the changed SST forcing, it is still warmer than the control simulation.

    At short lead times, the coupled simulations produce warming and height increases in the Arctic and weaker to sometimes little warming at lower latitudes. At longer leads there is a gradual decrease in the ability of the coupled model simulations to match the reanalysis, especially in terms of 200-hPa heights, which show little correlation beyond a 30-day lead. Meanwhile, 2-m temperatures maintain some resemblance to the reanalysis products throughout the entire period, due to the stable, warmer Arctic and cooler midlatitudes pattern. Interannual variability patterns, such as the AO, Pacific-North America pattern, and the North Atlantic Oscillation, have shown a certain predictive ability at short lead times (Johansson, 2007; Riddle et al., 2013). As noted by (Cohen et al., 2012), due to a model's inability to predict the AO at longer leads, the lack of skill in predicting the 200-hPa height patterns beyond 30 days suggests that the changes at shorter leads are more related to the negative phase of the AO or the internal variability manifested within the initial conditions, rather than sea-ice or SST forcing, or air-sea interactions that exist in the coupled simulations.

    The results of this study are valid on a seasonal scale only and do not take into account the frequency or magnitude of cold events within the DJF periods, which may or may not be influenced by the change in SST or sea-ice forcing. Another factor is that this study uses climatological-mean sea ice, as opposed to a single extremely low sea-ice year (e.g., 2012), which could have exerted more robust impacts on the atmosphere. However, because not every year will equate to 2012 levels, the climatological forcing used is more representative of our current climate. With the results of the uncoupled simulations showing that a subset of cooler Eurasian winters is simulated across all model configurations despite a mean warming, and the sharp drop-off in skill in predicting 200-hPa height changes in the coupled simulations after 30 days, the final message is that the observed changes in Eurasian winter 2-m temperatures may be more related to internal variability rather than any specific forcing, but with the caveats mentioned above taken into consideration.

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