Predicting patterns of near-surface air temperature using empirical data

被引:9
|
作者
Anisimov, OA [1 ]
机构
[1] State Hydrol Inst, Dept Climatol, St Petersburg 199053, Russia
基金
美国国家科学基金会;
关键词
D O I
10.1023/A:1010658014439
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The signal of recent global warming has been detected in meteorological records, borehole temperatures and by several indirect climate indicators. Anthropogenic warming continues to evolve, and various methods are used to study and predict the changes of the global and regional climate. Results derived from GCMs, palaeoclimate reconstructions, and regional climate models differ in detail. An empirical model could be used to predict the spatial pattern of the near-surface air temperature and to narrow the range of regional uncertainties. The idea behind this approach is to study the correlations between regional and global temperature using century-scale meteorological records, and to evaluate the regional pattern of the future climate using regression analysis and the global-mean air temperature as a predictor. This empirical model, however, is only applicable to those parts of the world where regional near-surface air temperature reacts linearly to changes of the global thermal regime. This method and data from a set of approximately 2000 weather stations with continuous century-scale records of the monthly air temperature was applied to develop the empirical map of the regional climate sensitivity. Data analysis indicated that an empirical model could be applied to several large regions of the World, where correlations between local and global air temperature are statistically significant. These regions are the western United States, southern Canada, Alaska, Siberia, south-eastern Asia, southern Africa and Australia, where the correlation coefficient is typically above 0.9. The map of regional climate sensitivity has been constructed using calculated coefficients of linear regression between the global-mean and regional annual air temperature. As long as the correlations between the local and global air temperature are close to those in the last several decades, this map provides an effective tool to scale down the projection of the global air temperature to regional level. According to the results of this study, maximum warming at the beginning of the 21st century will take place in the continental parts of North America and Eurasia. The empirical regional climate sensitivity defined here as the response of the mean-annual regional temperature to 1 degreesC global warming was found to be 5-6 degreesC in southern Alaska, central Canada, and over the continental Siberia, 3-4 degreesC on the North Slope of Alaska and western coast of the U.S.A., and 1-2 degreesC in most of the central and eastern U.S.A. and eastern Canada. Regions with negative sensitivity are located in the southeastern U.S.A., north-western Europe and Scandinavia. The local tendency towards cooling, although statistically confirmed by modern data, could, however, change in the near future.
引用
收藏
页码:297 / 315
页数:19
相关论文
共 50 条
  • [1] Predicting Patterns of Near-Surface Air Temperature Using Empirical Data
    Oleg A. Anisimov
    Climatic Change, 2001, 50 : 297 - 315
  • [2] Estimation of Daily Average Near-Surface Air Temperature Using MODIS and AIRS Data
    Zhang, Hongmei
    2017 2ND INTERNATIONAL CONFERENCE ON FRONTIERS OF SENSORS TECHNOLOGIES (ICFST), 2017, : 377 - 381
  • [3] Checking and archiving of the global near-surface air temperature data at the Rosgidrometcenter
    Bagrov, A.N.
    Gordin, V.A.
    Loktionova, E.A.
    Ochan, N.Yu.
    Meteorologiya i Gidrologiya, 1993, (02): : 18 - 26
  • [4] Estimation of daytime near-surface air temperature from satellite data
    Labbi, Abdelkader
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2020, 18
  • [5] Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features
    Lin, Xiaohui
    Zhang, Wen
    Huang, Yao
    Sun, Wenjuan
    Han, Pengfei
    Yu, Lingfei
    Sun, Feifei
    REMOTE SENSING, 2016, 8 (08)
  • [6] Predicting monthly near-surface soil temperature from air temperature and the leaf area index
    Amato, Matthew T.
    Gimenez, Daniel
    AGRICULTURAL AND FOREST METEOROLOGY, 2024, 345
  • [7] Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China
    Yang, Yuan Z.
    Cai, Wen H.
    Yang, Jian
    REMOTE SENSING, 2017, 9 (05)
  • [8] An Evaluation of HIRS Near-Surface Air Temperature Product in the Arctic with SHEBA Data
    Peng, Ge
    Shi, Lei
    Stegall, Steve T.
    Matthews, Jessica L.
    Fairall, Christopher W.
    JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2016, 33 (03) : 453 - 460
  • [9] A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data
    Henn, Brian
    Raleigh, Mark S.
    Fisher, Alex
    Lundquist, Jessica D.
    JOURNAL OF HYDROMETEOROLOGY, 2013, 14 (03) : 929 - 945