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CN 34-1054/N

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Optimal weighted model for ensemble forecast of the surface air temperature in mainland China and its regional applications

  • 1.

    School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026

  • 2.

    National Climate Center, China Meteorological Administration, Beijing 100081

  • 3.

    Wuhan Regional Climate Center, Wuhan 430074

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.03.004
  • Received Date: 10 April 2017
  • Rev Recd Date: 02 June 2017
  • Publish Date: 31 March 2018
    Abstract

  • Abstract

    Based on Coupled Model Intercomparison Project,Phase5(CMIP5) muti-models, an optimal weighted model for ensemble forecast (Op-SE) of the surface air temperature in mainland China was presented. In order to assess the capability of Op-SE, it was compared with equally-weighted ensemble (EE) and superensemble (SE), and anomaly correlation coefficient (ACC) and root-mean-square-error (RMSE) were chosen to evaluate their forecasting skills. As shown from the results, ACC between Op-SE and observation is optimal in most of China, especially eastern China, which indicates that ACC has passed the significance test at 0.5 level. However EE has poor performance in ACC. As for RMSE, EE is also relatively weak. And Op-SE is better than SE in eastern China, while SE is better in a few other areas like Sichuan Basin. The comprehensive assessments of ACC and RMSE show that the forecast skill of Op-SE is best in northeast, north, eastern, southwest and northwest of China, but is not good enough in Sichuan Basin and southern Gansu. In conclusion, Op-SE provides a new method on selecting outstanding models into ensemble climate forecast, which can improve the forecast skill in regions to some extent.

    Abstract

    Based on Coupled Model Intercomparison Project,Phase5(CMIP5) muti-models, an optimal weighted model for ensemble forecast (Op-SE) of the surface air temperature in mainland China was presented. In order to assess the capability of Op-SE, it was compared with equally-weighted ensemble (EE) and superensemble (SE), and anomaly correlation coefficient (ACC) and root-mean-square-error (RMSE) were chosen to evaluate their forecasting skills. As shown from the results, ACC between Op-SE and observation is optimal in most of China, especially eastern China, which indicates that ACC has passed the significance test at 0.5 level. However EE has poor performance in ACC. As for RMSE, EE is also relatively weak. And Op-SE is better than SE in eastern China, while SE is better in a few other areas like Sichuan Basin. The comprehensive assessments of ACC and RMSE show that the forecast skill of Op-SE is best in northeast, north, eastern, southwest and northwest of China, but is not good enough in Sichuan Basin and southern Gansu. In conclusion, Op-SE provides a new method on selecting outstanding models into ensemble climate forecast, which can improve the forecast skill in regions to some extent.
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    • References(26)
  • [1]
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    FENG Jinming, FU Congbin. Inter-comparison of long-term simulations of temperature and precipitation over China by different regional climate models[J]. Chinese Journal of Atmospheric Sciences, 2007，31(5): 805-814.
    [2]
    叶笃正, 季劲钧. 迎接大气科学发展即将到来的新飞跃[J]. 地球科学进展, 2005， 20(10): 1047-1052.
    YE Duzheng, JI Jingjun. Prospect the overflying development of atmospheric science[J]. Advances in Earth Science, 2005, 20(10): 1047-1052.
    [3]
    刘敏, 江志红. 13 个 IPCC AR4 模式对中国区域近 40a 气候模拟能力的评估[J]. 南京气象学院学报, 2009, 32(2): 256-268.
    LIU Min, JIANG Zhihong. Simulation ability evaluation of surface temperature and precipitation by thirteen IPCC AR4 coupled climate models in China during 1961-2000[J]. Journal of Nanjing Institute of Meteorology, 2009, 32(2): 256-268.
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    XU Ying, XU Chonghai. Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models[J]. Atmospheric and Oceanic Science Letters, 2012, 5(6): 489-494.
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    张艳武, 张莉, 徐影. CMIP5模式对中国地区气温模拟能力评估与预估[J]. 气候变化研究进展, 2016, 12(1):10-19.
    ZHANG Yanwu, ZHANG Li, XU Ying. Simulations and projections of the surface air temperature in China by CMIP5 models[J]. Advances in Climate Change Research, 2016, 12(1):10-19.
    [9]
    姜大膀, 王会军, 郎咸梅. 全球变暖背景下东亚气候变化的最新情景预测[J]. 地球物理学报, 2004, 47(4): 590-596.
    JIANG Dabang, WANG Huijun, LANG Xianmei. East Asian climate change trend under global warming background[J]. Chinese Journal of Geophysics, 2004, 47(4): 590-596.
    [10]
    ZHOU T, YU R. Twentieth-century surface air temperature over China and the globe simulated by coupled climate models[J]. Journal of Climate, 2006, 19(22): 5843-5858.
    [11]
    李振朝, 韦志刚, 吕世华, 等. CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J]. 高原气象, 2013, 32(4):921-928.
    LI Zhenchao, WEI Zhigang, LV Shihua, et al. Verifications of surface air temperature and precipitation from CMIP5 model in Northern Hemisphere and Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2013, 32(4):921-928.
    [12]
    张冰, 巩远发, 徐影, 等. CMIP5 全球气候模式对中国地区干旱变化模拟能力评估[J]. 干旱气象, 2014, 32(5): 694-700.
    ZHANG Bing, GONG Yuanfa, XU Ying, et al. Evaluation on the simulation of the drought change in China based on global climate models from CMIP5[J]. Journal of Arid Meteorology , 2014, 32(5): 694-700.
    [13]
    SONG F, ZHOU T. Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean–western Pacific anticyclone teleconnection[J]. Journal of Climate, 2014, 27(4): 1679-1697.
    [14]
    陈晓晨, 徐影, 许崇海, 等. CMIP5 全球气候模式对中国地区降水模拟能力的评估[J]. 气候变化研究进展, 2014, 10(3): 217-225.
    CHEN Xiaochen, XU Ying, XU Chonghai, et al. Assessment of precipitation simulations in China by CMIP5 Multi-models[J]. Advances in Climate Change Research, 2014, 10(3): 217-225.
    [15]
    丁一汇.季节气候预测的进展和前景[J]. 气象科技进展, 2011, 1(3): 14-27.
    DING Yihui. Progress and prospects of seasonal climate prediction[J]. Advances in Meteorological Science and Technology, 2011, 1(3): 14-27.
    [16]
    KRISHNAMURTI T N, KISHTAWAL C M, LAROW T E, et al. Improved weather and seasonal climate forecasts from multimodel superensemble[J]. Science, 1999, 285(5433): 1548-1550.
    [17]
    VIJAYA KUMAR T S V, KRISHNAMURTI T N, FIORINO M, et al. Multimodel superensemble forecasting of tropical cyclones in the Pacific[J]. Monthly Weather Review, 2003, 131(3): 574-583.
    [18]
    ROSS R S, KRISHNAMURTI T N. Reduction of forecast error for global numerical weather prediction by the Florida State University (FSU) Superensemble[J]. Meteorology and Atmospheric Physics, 2005, 88(3/4): 215-235.
    [19]
    KRISHNAMURTI T N, MISHRA A K, CHAKRABORTY A, et al. Improving global model precipitation forecasts over India using downscaling and the FSU superensemble. Part I: 1-5-day forecasts[J]. Monthly Weather Review, 2009, 137(9): 2713-2735.
    [20]
    刘长征, 杜良敏, 柯宗建, 等. 国家气候中心多模式解释应用集成预测[J]. 应用气象学报, 2013, 24(6): 677-685.
    LIU Changzheng, DU Liangmin, KE Zongjian, et al. Multi-model downscaling ensemble prediction in National Climate Center[J]. Journal of Applied Meteorological Science, 2013, 24(6): 677-685.
    [21]
    智协飞, 林春泽, 白永清, 等. 北半球中纬度地区地面气温的超级集合预报[J]. 气象科学, 2009, 29(5): 569-574.
    ZHI Xiefei, LIN Chunze, BAI Yongqing, et al. Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes[J]. Journal of the Meteorological Sciences, 2009, 29(5): 569-574.
    [22]
    周天军, 邹立维. IPCC第五次评估报告全球和区域气候预估图集评述[J]. 气候变化研究进展, 2014, 10(2):149-152.
    ZHOU Tianjun, ZOU Liwei. Atlas of global and regional climate projections[J]. Advances in Climate Change Research, 2014, 10(2):149-152.
    [23]
    胡芩, 姜大膀, 范广洲. CMIP5全球气候模式对青藏高原地区气候模拟能力评估[J]. 大气科学, 2014, 38(5):924-938.
    HU Qin, JIANG Dabang, FAN Guangzhou. Evaluation of CMIP5 Models over the Qinghai-Tibetan Plateau[J]. Chinese Journal of Atmospheric Sciences, 2014, 38(5):924-938.
    [24]
    《气候变化国家评估报告》编写委员会. 第二次气候变化国家评估报告[M]. 北京市:科学出版社,2011.
    [25]
    MOSS R H, EDMONDS J A, HIBBARD K A, et al. The next generation of scenarios for climate change research and assessment[J]. Nature, 2010, 463(7282): 747-756
    [26]
    陈晓晨. CMIP5全球气候模式对中国降水模拟能力的评估[D]. 北京：中国气象科学研究院, 2014.
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    [1]
    冯锦明, 符淙斌. 不同区域气候模式对中国地区温度和降水的长期模拟比较[J]. 大气科学, 2007，31(5): 805-814.
    FENG Jinming, FU Congbin. Inter-comparison of long-term simulations of temperature and precipitation over China by different regional climate models[J]. Chinese Journal of Atmospheric Sciences, 2007，31(5): 805-814.
    [2]
    叶笃正, 季劲钧. 迎接大气科学发展即将到来的新飞跃[J]. 地球科学进展, 2005， 20(10): 1047-1052.
    YE Duzheng, JI Jingjun. Prospect the overflying development of atmospheric science[J]. Advances in Earth Science, 2005, 20(10): 1047-1052.
    [3]
    刘敏, 江志红. 13 个 IPCC AR4 模式对中国区域近 40a 气候模拟能力的评估[J]. 南京气象学院学报, 2009, 32(2): 256-268.
    LIU Min, JIANG Zhihong. Simulation ability evaluation of surface temperature and precipitation by thirteen IPCC AR4 coupled climate models in China during 1961-2000[J]. Journal of Nanjing Institute of Meteorology, 2009, 32(2): 256-268.
    [4]
    ZENG N, DING Y, PAN J, et al. Climate change:The Chinese challenge[J]. Science, 2008, 319(5864): 730-731.
    [5]
    TAYLOR K E, STOUFFER B J, MEEHL G A. An overview of CMIP5 and the experiment design [J]. Bulletin of the American Meteorological Society, 2012, 93 (4): 485-498.
    [6]
    GUO Y, DONG W J, REN F M, et al. Surface air temperature simulations over China with CMIP5 and CMIP3 [J]. Advances in Climate Change Research, 2013, 4 (3): 145–152.
    [7]
    XU Ying, XU Chonghai. Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models[J]. Atmospheric and Oceanic Science Letters, 2012, 5(6): 489-494.
    [8]
    张艳武, 张莉, 徐影. CMIP5模式对中国地区气温模拟能力评估与预估[J]. 气候变化研究进展, 2016, 12(1):10-19.
    ZHANG Yanwu, ZHANG Li, XU Ying. Simulations and projections of the surface air temperature in China by CMIP5 models[J]. Advances in Climate Change Research, 2016, 12(1):10-19.
    [9]
    姜大膀, 王会军, 郎咸梅. 全球变暖背景下东亚气候变化的最新情景预测[J]. 地球物理学报, 2004, 47(4): 590-596.
    JIANG Dabang, WANG Huijun, LANG Xianmei. East Asian climate change trend under global warming background[J]. Chinese Journal of Geophysics, 2004, 47(4): 590-596.
    [10]
    ZHOU T, YU R. Twentieth-century surface air temperature over China and the globe simulated by coupled climate models[J]. Journal of Climate, 2006, 19(22): 5843-5858.
    [11]
    李振朝, 韦志刚, 吕世华, 等. CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J]. 高原气象, 2013, 32(4):921-928.
    LI Zhenchao, WEI Zhigang, LV Shihua, et al. Verifications of surface air temperature and precipitation from CMIP5 model in Northern Hemisphere and Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2013, 32(4):921-928.
    [12]
    张冰, 巩远发, 徐影, 等. CMIP5 全球气候模式对中国地区干旱变化模拟能力评估[J]. 干旱气象, 2014, 32(5): 694-700.
    ZHANG Bing, GONG Yuanfa, XU Ying, et al. Evaluation on the simulation of the drought change in China based on global climate models from CMIP5[J]. Journal of Arid Meteorology , 2014, 32(5): 694-700.
    [13]
    SONG F, ZHOU T. Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean–western Pacific anticyclone teleconnection[J]. Journal of Climate, 2014, 27(4): 1679-1697.
    [14]
    陈晓晨, 徐影, 许崇海, 等. CMIP5 全球气候模式对中国地区降水模拟能力的评估[J]. 气候变化研究进展, 2014, 10(3): 217-225.
    CHEN Xiaochen, XU Ying, XU Chonghai, et al. Assessment of precipitation simulations in China by CMIP5 Multi-models[J]. Advances in Climate Change Research, 2014, 10(3): 217-225.
    [15]
    丁一汇.季节气候预测的进展和前景[J]. 气象科技进展, 2011, 1(3): 14-27.
    DING Yihui. Progress and prospects of seasonal climate prediction[J]. Advances in Meteorological Science and Technology, 2011, 1(3): 14-27.
    [16]
    KRISHNAMURTI T N, KISHTAWAL C M, LAROW T E, et al. Improved weather and seasonal climate forecasts from multimodel superensemble[J]. Science, 1999, 285(5433): 1548-1550.
    [17]
    VIJAYA KUMAR T S V, KRISHNAMURTI T N, FIORINO M, et al. Multimodel superensemble forecasting of tropical cyclones in the Pacific[J]. Monthly Weather Review, 2003, 131(3): 574-583.
    [18]
    ROSS R S, KRISHNAMURTI T N. Reduction of forecast error for global numerical weather prediction by the Florida State University (FSU) Superensemble[J]. Meteorology and Atmospheric Physics, 2005, 88(3/4): 215-235.
    [19]
    KRISHNAMURTI T N, MISHRA A K, CHAKRABORTY A, et al. Improving global model precipitation forecasts over India using downscaling and the FSU superensemble. Part I: 1-5-day forecasts[J]. Monthly Weather Review, 2009, 137(9): 2713-2735.
    [20]
    刘长征, 杜良敏, 柯宗建, 等. 国家气候中心多模式解释应用集成预测[J]. 应用气象学报, 2013, 24(6): 677-685.
    LIU Changzheng, DU Liangmin, KE Zongjian, et al. Multi-model downscaling ensemble prediction in National Climate Center[J]. Journal of Applied Meteorological Science, 2013, 24(6): 677-685.
    [21]
    智协飞, 林春泽, 白永清, 等. 北半球中纬度地区地面气温的超级集合预报[J]. 气象科学, 2009, 29(5): 569-574.
    ZHI Xiefei, LIN Chunze, BAI Yongqing, et al. Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes[J]. Journal of the Meteorological Sciences, 2009, 29(5): 569-574.
    [22]
    周天军, 邹立维. IPCC第五次评估报告全球和区域气候预估图集评述[J]. 气候变化研究进展, 2014, 10(2):149-152.
    ZHOU Tianjun, ZOU Liwei. Atlas of global and regional climate projections[J]. Advances in Climate Change Research, 2014, 10(2):149-152.
    [23]
    胡芩, 姜大膀, 范广洲. CMIP5全球气候模式对青藏高原地区气候模拟能力评估[J]. 大气科学, 2014, 38(5):924-938.
    HU Qin, JIANG Dabang, FAN Guangzhou. Evaluation of CMIP5 Models over the Qinghai-Tibetan Plateau[J]. Chinese Journal of Atmospheric Sciences, 2014, 38(5):924-938.
    [24]
    《气候变化国家评估报告》编写委员会. 第二次气候变化国家评估报告[M]. 北京市:科学出版社,2011.
    [25]
    MOSS R H, EDMONDS J A, HIBBARD K A, et al. The next generation of scenarios for climate change research and assessment[J]. Nature, 2010, 463(7282): 747-756
    [26]
    陈晓晨. CMIP5全球气候模式对中国降水模拟能力的评估[D]. 北京：中国气象科学研究院, 2014.
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