Advanced Search
Article Contents

The Investigation of Atmospheric Angular Momentum as a Contributor to Polar Wobble and Length of Day Change with AMIP II GCM Data


doi: 10.1007/s00376-002-0023-y

  • The atmospheric angular momentum (AAM) functions in terms of contribution to polar wobble and length of day change, are calculated from the output data of GSM9603 global circulation model (GCM) of Japan Meteorological Agency (JMA), from the reanalysis data of the National Centers for the Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR), and from the operational objective analysis data of JMA, respectively. The comparison shows that during the period from 1985 to 1995, the values of the pressure terms in the equatorial components of AAM functions calculated from three data sets agree with each other better along 90°E longitude than along Greenwich meridian direction. The axial component of relative AAM function estimated from GSM 9603 agrees well with those from the other two data sets in terms of seasonal variations with the moderate amplitudes, but not so well with the composite axial component of relative AAM functions estimated from 23 GCM models anticipating in the first phase of AMIP. In addition, its interannual variation from 1979 to 1996 shows the main characteristics of ENSO evolution, just as does the axial component of relative AAM function estimated from NCEP reanalysis data except for the period of anomalous ENSO from 1991 to 1993.
  • [1] LI Guoqing, ZONG Haifeng, ZHANG Qingyun, 2011: 27.3-day and Average 13.6-day Periodic Oscillations in the Earth's Rotation Rate and Atmospheric Pressure Fields Due to Celestial Gravitation Forcing, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 45-58.  doi: 10.1007/s00376-010-0011-6
    [2] LI Jun, WU Guoxiong, 2010: Atmospheric Angular Momentum Transport and Balance in the AGCM-SAMIL, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1183-1192.  doi: 10.1007/s00376-009-9157-5
    [3] Fang Juan, Wu Rongsheng, 1998: Influences of Vorticity Source and Momentum Source on Atmospheric Circulation, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 41-46.  doi: 10.1007/s00376-998-0016-6
    [4] Chengjun XIE, Tongwen WU, Jie ZHANG, Kalli FURTADO, Yumeng ZHOU, Yanwu ZHANG, Fanghua WU, Weihua JIE, He ZHAO, Mengzhe ZHENG, 2023: Spatial Inhomogeneity of Atmospheric CO2 Concentration and Its Uncertainty in CMIP6 Earth System Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2108-2126.  doi: 10.1007/s00376-023-2294-4
    [5] Yaping Zhou, Ken C. Rutledge, Thomas P. Charlock, Norman G. Loeb, Seiji Kato, 2001: Atmospheric Corrections Using MODTRAN for TOA and Surface BRDF Characteristics from High Resolution Spectroradiometric/Angular Measurements from a Helicopter Platform, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 984-1004.  doi: 10.1007/BF03403518
    [6] Dohyeong KIM, Myoung-Hwan AHN, Minjin CHOI, 2015: Inter-comparison of the Infrared Channels of the Meteorological Imager Onboard COMS and Hyperspectral IASI Data, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 0-.  doi: 10.1007/s00376-014-4124-1
    [7] FENG Jinming, FU Congbin, 2006: Inter-Comparison of 10-year Precipitation Simulated by Several RCMs for Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 531-542.  doi: 10.1007/s00376-006-0531-2
    [8] Bian HE, Qing BAO*, Xiaocong WANG, Linjiong ZHOU, Xiaofei WU, Yimin LIU, Guoxiong WU, Kangjun CHEN, Sicheng HE, Wenting HU, Jiandong LI, Jinxiao LI, Guokui NIAN, Lei WANG, Jing YANG, Minghua ZHANG, Xiaoqi ZHANG, 2019: CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, , 771-778.  doi: 10.1007/s00376-019-9027-8
    [9] ZHOU Zaixing, ZHENG Xunhua, XIE Baohua, HAN Shenghui, LIU Chunyan, 2010: A process-based model of N2O emission from a rice-winter wheat rotation agroecosystem: structure, validation and sensitivity, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 137-150.  doi: 10.1007/s00376-009-8191-7
    [10] KANG Xianbiao, HUANG Ronghui, WANG Zhanggui, ZHANG Rong-Hua, 2014: Sensitivity of ENSO Variability to Pacific Freshwater Flux Adjustment in the Community Earth System Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1009-1021.  doi: 10.1007/s00376-014-3232-2
    [11] CAO Jian, Bin WANG, Baoqiang XIANG, Juan LI, WU Tianjie, Xiouhua FU, WU Liguang, MIN Jinzhong, 2015: Major Modes of Short-Term Climate Variability in the Newly Developed NUIST Earth System Model (NESM), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 585-600.  doi: 10.1007/s00376-014-4200-6
    [12] Ruichao LI, Jinbo XIE, Zhenghui XIE, Binghao JIA, Junqiang GAO, Peihua QIN, Longhuan WANG, Si CHEN, 2023: Coupling of the Calculated Freezing and Thawing Front Parameterization in the Earth System Model CAS-ESM, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1671-1688.  doi: 10.1007/s00376-023-2203-x
    [13] Ni Yunqi, S. E. Zebiak, M. A. Cane, D. M. Straus, 1996: Comparison of Surface Wind Stress Anomalies over the Tropical Pacific Simulated by an AGCM and by a Simple Atmospheric Model, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 229-243.  doi: 10.1007/BF02656865
    [14] WANG Jun, BAO Qing, Ning ZENG, LIU Yimin, WU Guoxiong, JI Duoying, 2013: Earth System Model FGOALS-s2: Coupling a Dynamic Global Vegetation and Terrestrial Carbon Model with the Physical Climate System Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1549-1559.  doi: 10.1007/s00376-013-2169-1
    [15] ZHI Hai, ZHANG Rong-Hua, LIN Pengfei, WANG Lanning, 2015: Simulation of Salinity Variability and the Related Freshwater Flux Forcing in the Tropical Pacific: An Evaluation Using the Beijing Normal University Earth System Model (BNU-ESM), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1551-1564.  doi: 10.1007/s00376-015-4240-6
    [16] Wang Weiguo, Xie Yingqi, Qiu Jinhuan, Liu Qing, 1998: The Regional Dynamical Model of the Atmospheric Ozonosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 74-82.  doi: 10.1007/s00376-998-0019-3
    [17] Bao Chenglan, Ruan Junshi, Zhu Yaojian, 1986: A STUDY ON THE RELATIONSHIP BETWEEN THE ROTATION OF BINARY TYPHOONS AND STEERING CURRENT, ADVANCES IN ATMOSPHERIC SCIENCES, 3, 115-124.  doi: 10.1007/BF02680050
    [18] Li Guoqing, Robin Kung, Richard L. Pfeffer, 1993: Some Effects of Rotation Rate on Planetary-Scale Wave Flows, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 296-306.  doi: 10.1007/BF02658135
    [19] Tan Zhemin, Wang Yuan, 2002: Wind Structure in an Intermediate Boundary Layer Model Based on Ekman Momentum Approximation, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 266-278.  doi: 10.1007/s00376-002-0021-0
    [20] ZHANG Kai, WAN Hui, WANG Bin, ZHANG Meigen, 2008: Consistency Problem with Tracer Advection in the Atmospheric Model GAMIL, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 306-318.  doi: 10.1007/s00376-008-0306-z

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 March 2002
Manuscript revised: 10 March 2002
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

The Investigation of Atmospheric Angular Momentum as a Contributor to Polar Wobble and Length of Day Change with AMIP II GCM Data

  • 1. Institute of Geodesy and Geophysics, Chinese Academy of Sciences Wuhan 430077;LASG, Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing 100029,Institute of Geodesy and Geophysics, Chinese Academy of Sciences Wuhan 430077,Institute of Geodesy and Geophysics, Chinese Academy of Sciences Wuhan 430077;LASG, Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing 100029

Abstract: The atmospheric angular momentum (AAM) functions in terms of contribution to polar wobble and length of day change, are calculated from the output data of GSM9603 global circulation model (GCM) of Japan Meteorological Agency (JMA), from the reanalysis data of the National Centers for the Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR), and from the operational objective analysis data of JMA, respectively. The comparison shows that during the period from 1985 to 1995, the values of the pressure terms in the equatorial components of AAM functions calculated from three data sets agree with each other better along 90°E longitude than along Greenwich meridian direction. The axial component of relative AAM function estimated from GSM 9603 agrees well with those from the other two data sets in terms of seasonal variations with the moderate amplitudes, but not so well with the composite axial component of relative AAM functions estimated from 23 GCM models anticipating in the first phase of AMIP. In addition, its interannual variation from 1979 to 1996 shows the main characteristics of ENSO evolution, just as does the axial component of relative AAM function estimated from NCEP reanalysis data except for the period of anomalous ENSO from 1991 to 1993.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return