A Piecewise Modeling Approach for Climate Sensitivity Studies: Tests with a Shallow-Water Model

+ Author Affiliations + Find other works by these authors
  • Corresponding author: SHAO Aimei, sam@lzu.edu.cn
  • Funds:

    Supported by the National Natural Science Foundation of China (41330527 and 41275102), Fundamental Research Funds for the Central Universities (lzujbky-2013-k16), and Program for New Century Excellent Talents in Universities (NCET-11-0213).

  • doi: 10.1007/s13351-015-5026-6

PDF

  • In model-based climate sensitivity studies, model errors may grow during continuous long-term integrations in both the reference and perturbed states and hence the climate sensitivity (defined as the difference between the two states). To reduce the errors, we propose a piecewise modeling approach that splits the continuous long-term simulation into subintervals of sequential short-term simulations, and updates the modeled states through re-initialization at the end of each subinterval. In the re-initialization processes, this approach updates the reference state with analysis data and updates the perturbed states with the sum of analysis data and the difference between the perturbed and the reference states, thereby improving the credibility of the modeled climate sensitivity. We conducted a series of experiments with a shallow-water model to evaluate the advantages of the piecewise approach over the conventional continuous modeling approach. We then investigated the impacts of analysis data error and subinterval length used in the piecewise approach on the simulations of the reference and perturbed states as well as the resulting climate sensitivity. The experiments show that the piecewise approach reduces the errors produced by the conventional continuous modeling approach, more effectively when the analysis data error becomes smaller and the subinterval length is shorter. In addition, we employed a nudging assimilation technique to solve possible spin-up problems caused by re-initializations by using analysis data that contain inconsistent errors between mass and velocity. The nudging technique can effectively diminish the spin-up problem, resulting in a higher modeling skill.
  • 加载中
  • [1]

    Charney, J., M. Halem, and R. Jastrow, 1969: Use of incomplete historical data to infer the present state of the atmosphere. J. Atmos. Sci., 26, 1160-1163.
    [2]

    Danforth, C. M., E. Kalnay, and T. Miyoshi, 2007: Estimating and correcting global weather model error. Mon. Wea. Rev., 135, 281-299, doi: 10.1175/MWR3289.1.
    [3]

    Dong, L., T. J. Zhou, and B. Wu, 2014: Indian ocean warming during 1958-2004 simulated by a climate system model and its mechanism. Climate Dyn., 42, 203-217, doi: 10.1007/s00382-013-1722-z. Douglass, D. H., J. R. Christy, B. D. Pearson, et al., 2008:.
    [4]

    A comparison of tropical temperature trends with model predictions. Int. J. Climatol., 28, 1693-1701, doi:  10.1002/joc.1651.
    [5]

    Harkey, M., and T. Holloway, 2013: Constrained dy-namical downscaling for assessment of climate im-pacts. J. Geophys. Res., 118, 2136-2148, doi: 10.1002/jgrd.50223.
    [6]

    Hoke, J. E., and R. A. Anthes, 1976: The initialization of numerical models by a dynamic-initialization tech-nique. Mon. Wea. Rev., 104, 1551-1556.
    [7]

    Huang Yanyan, Qian Yongfu, and Wan Qilin, 2006: Sim-ulation and analysis about the effects of geopotential height anomaly in tropical and subtropical region on droughts or floods in the Yangtze River valley and North China. Acta Meteor. Sinica, 20, 426-436.
    [8]

    Kim, J. -E., and S. -Y. Hong, 2007: Impact of soil mois-ture anomalies on summer rainfall over East Asia: A regional climate model study. J. Climate, 20, 5732-5743, doi:  10.1175/2006JCLI1358.1.
    [9]

    Klocke, D., P. Robert, and Q. Johannes, 2011: On constraining estimates of climate sensitivity with present-day observations through model weighting. J. Climate, 24, 6092-6099.
    [10]

    Li Qiaoping, Ding Yihui, and Dong Weijie, 2007: A nu-merical simulation study of impacts of historical land-use changes on the regional climate in China since 1700. Acta Meteor. Sinica, 21, 9-23.
    [11]

    Lian Lishu, Shu Jiong, and Li Chaoyi, 2009: The impacts of grassland degradation on regional climate over the origin area of three rivers in Qinghai-Tibetan Plateau, China. Acta Meteor. Sinica, 67, 580-590. (in Chinese).
    [12]

    Lo, J. C. -F., Z. L. Yang, and R. A. Pielke Sr., 2008: Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. J. Geophys. Res., 113, D09112, doi:  10.1029/2007JD009216.
    [13]

    Meehl, G. A., W. M. Washington, D. J. Erickson III, et al., 1996: Climate change from increased CO2 and the direct and indirect effects of sulfate aerosols. Geophys. Res. Lett., 23, 3755-3758.
    [14]

    Meehl, G. A., W. M. Washington, J. M. Arblaster, et al., 2000: Anthropogenic forcing and decadal climate variability in sensitivity experiments of twentieth-and twenty-first-century climate. J. Climate, 13,3728-3744.
    [15]

    Mohino, E., B. Rodrguez-Fonseca, C. R. Mechoso, et al., 2011: Impacts of the tropical Pacific/Indian oceans on the seasonal cycle of the West African monsoon. J. Climate, 24, 3878-3891, doi:  10.1175/02011JCLI3988.1.
    [16]

    Notaro, M., K. Holman, A. Zarrin, et al., 2013: Influence of the Laurentian Great Lakes on regional climate. J. Climate, 26, 789-804, doi:  10.1175/JCLI-D-12-00140.1.
    [17]

    Peings, Y., D. Saint-Martin, and H. Douville, 2012: A nu-merical sensitivity study of the influence of Siberian snow on the northern annular mode. J. Climate, 25, 592-607, doi:  10.1175/JCLI-D-11-00038.1.
    [18]

    Peings, Y., and G. Magnusdottir, 2014: Role of sea sur-face temperature, Arctic sea ice and Siberian snow in forcing the atmospheric circulation in winter of 2012-2013. Climate Dyn., doi:  10.1007/s00382-014-2368-1.
    [19]

    Qian, J. H., A. Seth, and S. Zebiak, 2003: Reinitialized versus continuous simulations for regional climate downscaling. Mon. Wea. Rev., 131, 2857-2874.
    [20]

    Salameh, T., P. Drobinski, and T. Dubos, 2010: The effect of indiscriminate nudging time on large and small scales in regional climate modelling: Appli-cation to the Mediterranean basin. Quart. J. Roy. Meteor. Soc., 136, 170-182, doi:  10.1002/qj.518.
    [21]

    Sanderson, B. M., 2011: A multimodel study of paramet-ric uncertainty in predictions of climate response to rising greenhouse gas concentrations. J. Climate, 24, 1362-1377, doi:  10.1175/2010JCLI3498.1.
    [22]

    Santer, B. D., K. E. Taylor, T. M. L. Wigley, et al., 1996: A search for human influences on the thermal structure of the atmosphere. Nature, 382, 39-46, doi:  10.1038/382039a0.
    [23]

    Santer, B. D., M. F. Wehner, T. M. L. Wigley, et al., 2003: Contributions of anthropogenic and natural forcing to recent tropopause height changes. Sci-ence, 301, 479-483.
    [24]

    Seol, K. -H., and S. -Y. Hong, 2009: Relationship be-tween the Tibetan snow in spring and the East Asian summer monsoon in 2003: A global and re-gional modeling study. J. Climate, 22, 2095-2110.
    [25]

    Stainforth, D. A., T. Aina, C. Christensen, et al., 2005: Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433, 403-406, doi:  10.1038/nature03301.
    [26]

    Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250-1277.
    [27]

    Vavrus, S., M. Notaro, and A. Zarrin, 2013: The role of ice cover in heavy lake-effect snowstorms over the Great Lakes basin as simulated by RegCM4. Mon. Wea. Rev., 141, 148-165, doi:  10.1175/MWR-D-12-00107.1.
    [28]

    Von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscal-ing purposes. Mon. Wea. Rev., 128, 3664-3673.
    [29]

    Wang Die, Miao Junfeng, and Zhang Da-Lin, 2015: Nu-merical simulations of local circulation and its re-sponse to land cover changes over the Yellow Moun-tains of China. J. Meteor. Res., 29, 667-681, doi:  10.1007/s13351-015-4070-6.
    [30]

    Wei, T., S. L. Yang, J. C. Moore, et al., 2012: Developed and developing world responsibilities for historical climate change and CO2 mitigation. Proc. Natl. Acad. Sci. USA., 109, 12911-12915.
    [31]

    Žagar, N., M. Žagar, J. Cedilnik, et al., 2006: Validation of mesoscale low-level winds obtained by dynamical downscaling of ERA40 over complex terrain. Tellus A, 58, 445-455, doi:  10.1111/j.1600-0870.2006.00186.x.
    [32]

    Zhang Zhifu, Qiu Chongjian, and Wang Chenghai, 2008: A piecewise-integration method for simulating the influence of external forcing on climate. Prog. Nat. Sci., 18, 1239-1247.
    [33]

    Zhou, T. J., and R. C. Yu, 2006: Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J. Climate,19, 5843-5858, doi:  10.1175/JCLI3952.1.
  • 加载中
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

A Piecewise Modeling Approach for Climate Sensitivity Studies: Tests with a Shallow-Water Model

    Corresponding author: SHAO Aimei, sam@lzu.edu.cn
  • 1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China;
  • 2. Department of Hydrology and Water Resources,University of Arizona,Tucson,AZ 85721,USA;
  • 3. Biosphere 2,University of Arizona,Tucson,AZ 85738,USA
Funds: Supported by the National Natural Science Foundation of China (41330527 and 41275102), Fundamental Research Funds for the Central Universities (lzujbky-2013-k16), and Program for New Century Excellent Talents in Universities (NCET-11-0213).

Abstract: In model-based climate sensitivity studies, model errors may grow during continuous long-term integrations in both the reference and perturbed states and hence the climate sensitivity (defined as the difference between the two states). To reduce the errors, we propose a piecewise modeling approach that splits the continuous long-term simulation into subintervals of sequential short-term simulations, and updates the modeled states through re-initialization at the end of each subinterval. In the re-initialization processes, this approach updates the reference state with analysis data and updates the perturbed states with the sum of analysis data and the difference between the perturbed and the reference states, thereby improving the credibility of the modeled climate sensitivity. We conducted a series of experiments with a shallow-water model to evaluate the advantages of the piecewise approach over the conventional continuous modeling approach. We then investigated the impacts of analysis data error and subinterval length used in the piecewise approach on the simulations of the reference and perturbed states as well as the resulting climate sensitivity. The experiments show that the piecewise approach reduces the errors produced by the conventional continuous modeling approach, more effectively when the analysis data error becomes smaller and the subinterval length is shorter. In addition, we employed a nudging assimilation technique to solve possible spin-up problems caused by re-initializations by using analysis data that contain inconsistent errors between mass and velocity. The nudging technique can effectively diminish the spin-up problem, resulting in a higher modeling skill.

Reference (33)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return