| 基于贝叶斯模型的器测、古气候重建与气候模拟数据的融合试验 |
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| 引用本文： | 余君,李庆祥,张同文,徐文慧,张雷,崔妍.基于贝叶斯模型的器测、古气候重建与气候模拟数据的融合试验[J].气象学报,2018,76(2):304-314. |
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| 作者姓名： | 余君 李庆祥 张同文 徐文慧 张雷 崔妍 |
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| 作者单位： | 1.重庆市气象信息与技术保障中心, 重庆, 401147 |
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| 基金项目： | 中国气象局气候变化专项项目（CCSF201338）、国家公益性行业专项项目（GYHY201406016）。 |
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| 摘 要： | 气象部门馆藏的西部最早的器测气象资料始于20世纪30年代，不能满足建立20世纪以来中国气候变化序列的需求，而古气候重建或气候模拟资料则可以延伸到器测时代以前。为了探讨长序列多源气候资料序列融合方法，采用贝叶斯方法对中国北疆地区8条树轮气温重建资料、器测资料与国际耦合模式比较计划第5阶段（CMIP5）模式模拟资料进行了融合试验。首先利用器测资料对气温代用资料进行校验与网格重建，并以此作为贝叶斯模型的先验分布，然后，用泰勒图选出了该区域气候模拟效果最佳的几个模式；最后将网格重建和气候模拟序列用贝叶斯模型进行了融合试验。结果表明，贝叶斯融合模型能有效提取各种数据来源的有用信息进行融合，融合结果的长期变化（线性）趋势更接近器测气候序列，并在一定程度上提高了序列的精度，减小了结果的不确定性；并且，融合结果能够纠正先验分布及气候模拟数据的明显偏差，为长年代气候序列重建提供了一个可行的思路。
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| 关 键 词： | 贝叶斯方法 融合 气温序列 CMIP5 古气候重建资料 |
| 收稿时间： | 2017/4/14 0:00:00 |
| 修稿时间： | 2017/8/2 0:00:00 |
The merging test using measurements, paleoclimate reconstruction and climate model data based on Bayesian model |
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| YU Jun,LI Qingxiang,ZHANG Tongwen,XU Wenhui,ZHANG Lei and CUI Yan.The merging test using measurements, paleoclimate reconstruction and climate model data based on Bayesian model[J].Acta Meteorologica Sinica,2018,76(2):304-314. |
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| Authors: | YU Jun LI Qingxiang ZHANG Tongwen XU Wenhui ZHANG Lei and CUI Yan |
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| Institution: | Chongqing Meteorological Information and Technical Support Centre, Chongqing 401147, China,School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China,Institute of Desert Meteorology, China Meteorological Administration, Key Laboratory of Tree-ring Physical and Chemical Research of China Meteorological Administration, Key Laboratory of Tree-ring Ecology of Uigur Autonomous Region, Xinjiang, Urumqi 830002, China,National Meteorological Information Center, Beijing 100081, China,National Meteorological Information Center, Beijing 100081, China and Shenyang Regional Climate Center, Shenyang 110016, China |
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| Abstract: | The earliest observed meteorological data in western China archived in China Meteorological Administration began in the 1930s, which cannot meet the users'' needs for the establishment of century-scale climate change series over China. Meanwhile, the reconstruction of paleoclimate proxies or climate model simulations can be extended to the time before the instrumental era. In order to investigate the merging approach of long-term, multi-source climate data, the Bayesian approach was used to merge the tree-ring reconstruction, observations and the CMIP5 model surface air temperature data. The temperature proxy data were calibrated and reconstructed first based on observations, and were used as a prior distribution of the Bayesian model. Several models that have the best modeling effect were then selected based on Taylor diagrams. Finally, the Bayesian model was used to merge the paleoclimate reconstruction dataset and climate modeling series. The results show that the Bayesian model can effectively extract useful information from various data sources for merging. The long-term change (linear) trend of the merged series tends to be closer to the observed climate series, and the accuracy of the series was improved to a certain extent, and the uncertainty of the results was reduced. Moreover, the merged results can in a certain degree correct obvious deviations of the prior distribution and the climate model data, respectively. Results of the present study provide a feasible idea for the reconstruction of long climate time series. |
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| Keywords: | Bayesian method Merging Temperature series CMIP5 Paleoclimate reconstruction data |
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