Pub Date : 2021-03-24DOI: 10.1002/9781119815617.CH4
S. Zou, G. Perry, J. Foster
{"title":"Recent Advances in Polar Cap Density Structure Research","authors":"S. Zou, G. Perry, J. Foster","doi":"10.1002/9781119815617.CH4","DOIUrl":"https://doi.org/10.1002/9781119815617.CH4","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"1 1","pages":"67-82"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88317954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.1002/9781119815617.CH16
L. Goncharenko, V. Harvey, Huixin Liu, N. Pedatella
{"title":"Sudden Stratospheric Warming Impacts on the Ionosphere–Thermosphere System","authors":"L. Goncharenko, V. Harvey, Huixin Liu, N. Pedatella","doi":"10.1002/9781119815617.CH16","DOIUrl":"https://doi.org/10.1002/9781119815617.CH16","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"81 1","pages":"369-400"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90153816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.1002/9781119815617.CH6
J. Foster, S. Zou, R. Heelis, P. Erickson
{"title":"Ionospheric Storm‐Enhanced Density Plumes","authors":"J. Foster, S. Zou, R. Heelis, P. Erickson","doi":"10.1002/9781119815617.CH6","DOIUrl":"https://doi.org/10.1002/9781119815617.CH6","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"108 1","pages":"115-126"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89420471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.1002/9781119815617.CH8
E. Mishin, A. Streltsov
{"title":"Mesoscale and Small‐Scale Structure of the Subauroral Geospace","authors":"E. Mishin, A. Streltsov","doi":"10.1002/9781119815617.CH8","DOIUrl":"https://doi.org/10.1002/9781119815617.CH8","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"150 1","pages":"139-158"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75256729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1002/9781119548164.ch9
E. Guilyardi, A. Capotondi, M. Lengaigne, S. Thual, A. Wittenberg
Climate models are essential tools for understanding ENSO mechanisms and exploring the future, either via seasonal‐to‐decadal forecasting or climate projections. Because so few events are well observed, models are also needed to help reconstruct past variability, explore ENSO diversity, and understand the roles of the background mean state and external forcings in mediating ENSO behavior. In this chapter we review the history of ENSO mod eling, showing the gradual improvement of models since the pioneering studies of the 1980s and 1990s and describing the existing hierarchy of model complexity. The rest of the chapter is devoted to coupled general circulation models (GCMs) and how these models perform, related model development and improvements, associ ated systematic biases and the strategies developed to address them, and methods of model evaluation in a multi model context with reference to observations. We also review how successive generations of multimodel intercomparisons help bridge the gap between our theoretical understanding of ENSO and the representation of ENSO in coupled GCMs. Much of the improved understanding of ENSO in recent decades, addressed in other chapters of this monograph, was obtained from simulation strategies in which part of the coupled ocean‐atmosphere system was either simplified or omitted, such as atmosphere‐only, ocean‐only, partially coupled, or nudged simula tions. We here review these strategies and the associated best practices, including their advantages and limitations. The ability of coupled GCMs to simulate ENSO continues to improve, offering exciting opportunities for research, forecasting, understanding past variations, and projecting the future behavior of ENSO and its global impacts. We list the challenges the community is facing, as well as opportunities for further improving ENSO simulations. 1 LOCEAN-IPSL, CNRS/Sorbonne University/IRD/MNHN, Paris, France; and NCAS-Climate, University of Reading, Reading, UK 2 University of Colorado, CIRES, Boulder, CO, USA; and NOAA Physical Sciences Laboratory, Boulder, CO, USA 3 LOCEAN-IPSL, Sorbonne Universités/UPMC-CNRS-IRDMNHN, Paris, France; and MARBEC, University of Montpellier, CNRS, IFREMER, IRD, Sète, France 4 Institute of Atmospheric Sciences/Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China 5 NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA 202 EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE Studies of coupled models began to reveal biases that had been concealed in the ocean‐only or atmosphere‐only simulations used up to then. A community of scientists, working at the interface between the ocean and the atmosphere, steadily grew and now forms the core of ENSO expertise in the tropics. A series of coupled model intercomparison projects (CMIPs) have shown steady progress in simulating ENSO and related global variability using state‐of‐the‐art cou pled GCMs (AchutaRao & Sperber, 2006; van Oldenborgh et
气候模式是理解ENSO机制和探索未来的重要工具，无论是通过季节到十年的预测还是气候预测。由于观测到的事件很少，因此还需要模型来帮助重建过去的变率，探索ENSO多样性，并了解背景平均状态和外部强迫在介导ENSO行为中的作用。在本章中，我们回顾了ENSO建模的历史，展示了自20世纪80年代和90年代的开创性研究以来模型的逐步改进，并描述了现有的模型复杂性层次结构。本章的其余部分致力于耦合环流模型(GCMs)以及这些模型的性能，相关模型的开发和改进，相关的系统偏差和为解决它们而开发的策略，以及参考观测在多模型背景下的模型评估方法。我们还回顾了连续几代的多模式相互比较如何帮助弥合我们对ENSO的理论理解与耦合gcm中ENSO的表示之间的差距。近几十年来，我们对ENSO的理解有了很大的提高，在本专著的其他章节中也有提到，这些改进都是通过简化或省略部分海洋-大气耦合系统的模拟策略获得的，例如仅大气、仅海洋、部分耦合或微耦合模拟。我们在这里回顾这些策略和相关的最佳实践，包括它们的优点和局限性。耦合gcm模拟ENSO的能力不断提高，为研究、预测、理解过去的变化、预测ENSO的未来行为及其全球影响提供了令人兴奋的机会。我们列出了社区面临的挑战，以及进一步改进ENSO模拟的机会。1法国巴黎CNRS/索邦大学/IRD/MNHN LOCEAN-IPSL;2美国科罗拉多州博尔德市科罗拉多大学气候研究中心;3法国巴黎索邦大学(Sorbonne university) /UPMC-CNRS-IRDMNHN LOCEAN-IPSL;和法国蒙彼利埃大学MARBEC, CNRS, IFREMER, IRD, s<e:1> 4中国上海复旦大学大气科学研究所/大气与海洋科学系5美国普林斯顿NOAA地球物理流体动力学实验室202 EL NIÑO气候变化中的南方涛动耦合模式的研究开始揭示迄今为止仅用于海洋或大气模拟中隐藏的偏差。在海洋和大气交界处工作的一个科学家团体稳步发展，现在形成了热带ENSO专业知识的核心。一系列耦合模式比对项目(CMIPs)在利用最先进的耦合gcm模拟ENSO和相关全球变率方面取得了稳步进展(AchutaRao & Sperber, 2006;van Oldenborgh等人，2005;Guilyardi 2006;Capotondi et al.， 2006;Wittenberg et al.， 2006;Bellenger et al.， 2014;C. Chen等人，2017)。模型公式和分辨率的改进使ENSO的许多关键特征得到了更好的表现;见政府间气候变化专门委员会第四次和第五次评估报告(IPCC AR4和AR5)以及《气候变化中的海洋和冰冻圈特别报告》。与20世纪90年代相比，过去二十年的进展是渐进的。然而，许多研究已经指出了在耦合的GCM中真实模拟ENSO的关键因素，特别是在大气分量中适当地表示深层对流和云(这在很大程度上取决于大气水平网格分辨率)，以及在海洋分量中适当地表示赤道波动力学、上升流和垂直混合(海洋网格分辨率的一个强大功能)。特别是在赤道附近的子午线和垂直方向)。CMIP5模型相对于CMIP3模型表现出进步，因为所有CMIP5模型都表现出某种类似ENSO的行为。然而，最佳CMIP5模型仅略好于最佳CMIP3模型。CMIP5还包括非线性行为增加的模式，这些模式主要源于分辨率更高的大气过程，如对流阈值或模拟季节内变率的能力，如麦登-朱利安涛动(MJO)和西风爆发(WWBs，也称为西风事件或wes)。然而，正如第9.4节所详述的那样，在首次发现错误几十年后，系统错误仍然存在。在21世纪初，一旦模型能够模拟更接近观测到的ENSO特性(例如振幅和频率)，模型评估就开始包括基于过程的指标，以确保正确的特性是出于正确的原因而不是通过误差补偿来模拟的(Guilyardi等)。 气候模式是理解ENSO机制和探索未来的重要工具，无论是通过季节到十年的预测还是气候预测。由于观测到的事件很少，因此还需要模型来帮助重建过去的变率，探索ENSO多样性，并了解背景平均状态和外部强迫在介导ENSO行为中的作用。在本章中，我们回顾了ENSO建模的历史，展示了自20世纪80年代和90年代的开创性研究以来模型的逐步改进，并描述了现有的模型复杂性层次结构。本章的其余部分致力于耦合环流模型(GCMs)以及这些模型的性能，相关模型的开发和改进，相关的系统偏差和为解决它们而开发的策略，以及参考观测在多模型背景下的模型评估方法。我们还回顾了连续几代的多模式相互比较如何帮助弥合我们对ENSO的理论理解与耦合gcm中ENSO的表示之间的差距。近几十年来，我们对ENSO的理解有了很大的提高，在本专著的其他章节中也有提到，这些改进都是通过简化或省略部分海洋-大气耦合系统的模拟策略获得的，例如仅大气、仅海洋、部分耦合或微耦合模拟。我们在这里回顾这些策略和相关的最佳实践，包括它们的优点和局限性。耦合gcm模拟ENSO的能力不断提高，为研究、预测、理解过去的变化、预测ENSO的未来行为及其全球影响提供了令人兴奋的机会。我们列出了社区面临的挑战，以及进一步改进ENSO模拟的机会。1法国巴黎CNRS/索邦大学/IRD/MNHN LOCEAN-IPSL;2美国科罗拉多州博尔德市科罗拉多大学气候研究中心;3法国巴黎索邦大学(Sorbonne university) /UPMC-CNRS-IRDMNHN LOCEAN-IPSL;和法国蒙彼利埃大学MARBEC, CNRS, IFREMER, IRD, s<e:1> 4中国上海复旦大学大气科学研究所/大气与海洋科学系5美国普林斯顿NOAA地球物理流体动力学实验室202 EL NIÑO气候变化中的南方涛动耦合模式的研究开始揭示迄今为止仅用于海洋或大气模拟中隐藏的偏差。在海洋和大气交界处工作的一个科学家团体稳步发展，现在形成了热带ENSO专业知识的核心。一系列耦合模式比对项目(CMIPs)在利用最先进的耦合gcm模拟ENSO和相关全球变率方面取得了稳步进展(AchutaRao & Sperber, 2006;van Oldenborgh等人，2005;Guilyardi 2006;Capotondi et al.， 2006;Wittenberg et al.， 2006;Bellenger et al.， 2014;C. Chen等人，2017)。模型公式和分辨率的改进使ENSO的许多关键特征得到了更好的表现;见政府间气候变化专门委员会第四次和第五次评估报告(IPCC AR4和AR5)以及《气候变化中的海洋和冰冻圈特别报告》。与20世纪90年代相比，过去二十年的进展是渐进的。然而，许多研究已经指出了在耦合的GCM中真实模拟ENSO的关键因素，特别是在大气分量中适当地表示深层对流和云(这在很大程度上取决于大气水平网格分辨率)，以及在海洋分量中适当地表示赤道波动力学、上升流和垂直混合(海洋网格分辨率的一个强大功能)。特别是在赤道附近的子午线和垂直方向)。CMIP5模型相对于CMIP3模型表现出进步，因为所有CMIP5模型都表现出某种类似ENSO的行为。然而，最佳CMIP5模
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Pub Date : 2020-10-23DOI: 10.1002/9781119548164.ch15
J. Sprintall, S. Cravatte, B. Dewitte, Yan Du, Alexander Sen Gupta
{"title":"ENSO Oceanic Teleconnections","authors":"J. Sprintall, S. Cravatte, B. Dewitte, Yan Du, Alexander Sen Gupta","doi":"10.1002/9781119548164.ch15","DOIUrl":"https://doi.org/10.1002/9781119548164.ch15","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"18 1","pages":"337-359"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85553352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-23DOI: 10.1002/9781119548164.ch18
N. Holbrook, D. Claar, A. Hobday, K. McInnes, E. Oliver, Alexander Sen Gupta, M. Widlansky, Xuebin Zhang
{"title":"ENSO‐Driven Ocean Extremes and Their Ecosystem Impacts","authors":"N. Holbrook, D. Claar, A. Hobday, K. McInnes, E. Oliver, Alexander Sen Gupta, M. Widlansky, Xuebin Zhang","doi":"10.1002/9781119548164.ch18","DOIUrl":"https://doi.org/10.1002/9781119548164.ch18","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"176 1","pages":"409-428"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79858991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-23DOI: 10.1002/9781119548164.ch11
J. Kug, J. Vialard, Y. Ham, Jin‐Yi Yu, M. Lengaigne
{"title":"ENSO Remote Forcing","authors":"J. Kug, J. Vialard, Y. Ham, Jin‐Yi Yu, M. Lengaigne","doi":"10.1002/9781119548164.ch11","DOIUrl":"https://doi.org/10.1002/9781119548164.ch11","url":null,"abstract":"","PeriodicalId":12539,"journal":{"name":"Geophysical monograph","volume":"8 1","pages":"247-265"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81966860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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