INTERANNUAL VARIABILITY OF REGIONAL CLIMATE AND ITS CHANGE DUE TO THE GREENHOUSE-EFFECT

被引:13
|
作者
LIANG, XZ
WANG, WC
DUDEK, MP
机构
[1] Atmospheric Sciences Research Center, State University of New York, Albany, New York
基金
美国国家科学基金会;
关键词
D O I
10.1016/0921-8181(94)00027-B
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Interannual variability of regional climate was investigated on a seasonal basis. Observations and two global climate model (GCM) simulations were intercompared to identify model biases and climate change signals due to the enhanced greenhouse effect. Observed record length varies from 40 to 100 years, while the model output comes from two 100-year equilibrium climate simulations corresponding to atmospheric greenhouse gas concentrations at observed 1990 and projected 2050 levels. The GCM includes an atmosphere based on the NCAR CCM1 with the addition of the radiative effects of CH4, N2O and CFCs, a bulk layer land surface and a mixed-layer ocean with thermodynamic sea-ice and fixed meridional oceanic heat transport. Because comparisons of interannual variability are sensitive to the time period chosen, a climate ensemble technique has been developed. This technique provides comparisons between variance ratios of two time series for all possible contiguous sub-periods of a fixed length. The time autocorrelation is thus preserved within each sub-period. The optimal sub-period length was found to be 30 years, based on which robust statistics of the ensemble were obtained to identify substantial differences in interannual variability that are both physically important and statistically significant. Several aspects of observed interannual variability were reproduced by the GCM. These include: global surface air temperature; Arctic sea-ice extent; and regional variability of surface air temperature, sea level pressure and 500 mb height over about one quarter of the observed data domains. Substantial biases, however, exist over broad regions, where strong seasonality and systematic links between variables were identified. For instance, during summer substantially greater model variability was found for both surface air temperature and sea-level pressure over land areas between 20-50 degrees N, while this tendency was confined to 20-30 degrees N in other seasons. When greenhouse gas concentrations increase, atmospheric moisture variability is substantially larger over areas that experience the greatest surface warming. This corresponds to an intensified hydrologic cycle and, hence, regional increases in precipitation variability. Surface air temperature variability increases where hydrologic processes vary greatly or where mean soil moisture is much reduced. In contrast, temperature variability decreases substantially where sea-ice melts completely. These results indicate that regional changes in interannual variability due to the enhanced greenhouse effect are associated with mechanisms that depend on the variable and season.
引用
收藏
页码:217 / 238
页数:22
相关论文
共 50 条
  • [31] Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071–2100)
    Filippo Giorgi
    Xunqiang Bi
    Jeremy Pal
    Climate Dynamics, 2004, 23 : 839 - 858
  • [32] Modelling regional variability of irrigation requirements due to climate change in Northern Germany
    Riediger, Jan
    Breckling, Broder
    Svoboda, Nikolai
    Schroeder, Winfried
    SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 541 : 329 - 340
  • [33] Interdecadal variability of regional climate change: implications for the development of regional climate change scenarios
    Giorgi, F
    METEOROLOGY AND ATMOSPHERIC PHYSICS, 2005, 89 (1-4) : 1 - 15
  • [34] Interdecadal variability of regional climate change: implications for the development of regional climate change scenarios
    F. Giorgi
    Meteorology and Atmospheric Physics, 2005, 89 : 1 - 15
  • [35] Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071-2100)
    Giorgi, F
    Bi, XQ
    Pal, J
    CLIMATE DYNAMICS, 2004, 23 (7-8) : 839 - 858
  • [36] Nonlinear responses in interannual variability of lake ice to climate change
    Richardson, David C.
    Filazzola, Alessandro
    Woolway, R. Iestyn
    Imrit, M. Arshad
    Bouffard, Damien
    Weyhenmeyer, Gesa A.
    Magnuson, John
    Sharma, Sapna
    LIMNOLOGY AND OCEANOGRAPHY, 2024, 69 (04) : 789 - 801
  • [37] Interannual Variability of the Normalized Difference Vegetation Index on the Tibetan Plateau and Its Relationship with Climate Change
    周定文
    范广洲
    黄荣辉
    方之芳
    刘雅勤
    李洪权
    AdvancesinAtmosphericSciences, 2007, (03) : 474 - 484
  • [38] Influences of Climate Change and Its Interannual Variability on Surface Energy Fluxes from 1948 to 2000
    Sheng Li
    Liu Shuhua
    Liu, Heping
    ADVANCES IN ATMOSPHERIC SCIENCES, 2010, 27 (06) : 1438 - 1452
  • [39] Climate change enhances interannual variability of the Nile river flow
    Siam, Mohamed S.
    Eltahir, Elfatih A. B.
    NATURE CLIMATE CHANGE, 2017, 7 (05) : 350 - +
  • [40] Will interannual variability in sand grassland communities increase with climate change?
    S. Bartha
    G. Campetella
    E. Ruprecht
    A. Kun
    J. Házi
    A. Horváth
    K. Virágh
    Zs. Molnár
    Community Ecology, 2008, 9 : 13 - 21