摘要
基于CCMP卫星风场与全球耦合模式GFDL-CM3历史实验期的海表面风场、气压场、温度场等资料,使用经验正交函数和多元回归统计方式,获得高分辨率卫星风场与低分辨率气象要素场两者典型空间模态的长期统计关系,构建了我国海域近海面风速统计降尺度模型,进而结合GFDL-CM3气候模式RCPs情景未来预估数据,降尺度分析了2015-2050年我国海域近海面风速未来变化。结果表明:构建的降尺度模型具有良好模拟能力,模拟与观测风速空间分布相关性R值在0.98以上,风速变化趋势相关性R值在0.97以上,月平均风速模拟均方根误差(RMSE)全海域平均值约为1.32 m/s。我国海域近海面风速在未来35 a可能存在较明显的分时段变化特征,2015-2024年风速下降,2024-2035年风速增加,2035-2050年风速再次下降,同一时段内各海区风速变化趋势相同,但风速降幅或增幅存在差异,南海风速增降幅度较大,东海次之,黄渤海风速增降幅度相对较小。
The statistical method of empirical orthogonal function and multiple linear regression is applied to investigate the long-term statistical relationship of typical spatial modes derived from the CCMP wind data and the historical experiment data of the GFDL-CM3 model, including sea surface wind, sea level pressure and sea surface temperature. Based on the long-term statistical relationship of typical spatial modes, this study has constructed a statistical downscaling model of surface wind speed over China's sea areas and projected the prospective changes of sea surface wind speed from 2015 to 2050, by combining the RCPs output data of the GFDL-CM3 model. The results show that the constructed model is capable for downscaling simulation of sea surface wind speed. The correlation coefficient (R) of downscaling simulation and CCMP data is 0.98 for wind speed spatial pattern and 0.97 for wind speed variation trend, and the RMSE of monthly mean wind speed simulation is about 1.32 m/s. The downscaling estimations of sea surface wind speed indicate that there are three distinct phases of wind speed variation over China's sea areas during the period from 2015 to 2050. Sea surface wind speed will decrease in the phase from 2015 to 2024, increase from 2024 to 2035, and decrease again from 2035 to 2050. Though all sea zones have a similar trend of wind speed variation, their variation ranges are different. The variation range of wind speed is larger in the South China Sea, medium in the East China Sea, and relatively small in the Yellow Sea and Bohai Sea.
作者
李正泉
肖晶晶
马浩
冯涛
LI Zheng-quan XIAO Jing-jing MA Hao FENG Tao(Zhejiang Climate Center, Hangzhou 310017, Zhejiang province, China)
出处
《海洋技术学报》
2016年第6期10-16,共7页
Journal of Ocean Technology
基金
中国气象局气候变化专项资助项目(CCSF201427)
浙江省科技厅公益项目资助(2015C33055)