首页 | 期刊介绍 | 编委会 | 道德声明 | 投稿指南 | 常用下载 | 过刊浏览 | In English
引用本文:
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  下载PDF阅读器  关闭
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 145次   下载 0  
分享到: 微信 更多
基于大数据分析下的气候模型
张宸豪,冯曦,冯卫兵,刘涛,丁坤
1.海岸灾害及防护教育部重点实验室;2.河海大学港口海岸与近海工程学院
摘要:
全球变暖的形势日趋严峻,局部地区却出现了“几十年一遇”的极度寒冷天气,使公众对全球变暖产生了怀疑。在加拿大按省份选择13个代表性测站10年的观测数据来探讨加拿大地区温度的时空变化趋势,采用经验正交函数(EOF)分析了海洋表面温度历史数据隐藏中变化规律。为预测未来25年气候变化,利用BP神经网络建立了年平均温度、日降水量与地球吸热、散热、海表面温度、当地纬度间的关系,还建立了“极寒天气”与气候变化的关系模型。研究及分析结果表明:高纬度地区温度、降水量普遍较低,同经度地区的温度差异较小且降水量变化不大;加拿大地区温度呈周期性变化,符合北半球的季节变化特征;北大西洋的东部与其它海洋的温度是反相关的,西太平洋南北回归线附近的海洋表面温度升高;“极寒天气”出现频率与气候变化有一定关系,局地极寒现象与全球变暖的大趋势并不矛盾。
关键词:  全球变暖  极寒天气  EOF法  BP神经网络  气候变化模型
DOI:10.11759/hykx20191122004
分类号:P717
基金项目:国家自然科学基金青年基金[No. 51709091];江苏省自然科学基金青年基金[No. BK20170874 ];中央高校基金[No. 2017B00514]
Climate model based on big data analysis
ZHANG Chen-hao1, FENG Xi1, FENG Wei-bing2, LIU Tao3, DING Kun3
1.Key Laboratory of Coastal Disaster and Protection of Ministry of Education Hohai University,Xi Kang Road #,Nanjing,Jiangsu Province,China, College of Harbor,Coastal and Offshore Engineering,Hohai University,Xi Kang Road #1,Nanjing,Jiangsu Province,China;2.College of Harbor, Coastal and Offshore Engineering, Hohai University, Xi Kang Road #1, Nanjing, Jiangsu Province, China;3.Key Laboratory of Coastal Disaster and Protection of Ministry of Education Hohai University,Xi Kang Road #1,Nanjing,Jiangsu Province,China, College of Harbor,Coastal and Offshore Engineering,Hohai University,Xi Kang Road #,Nanjing,Jiangsu Province,China
Abstract:
Global warming is becoming more severe, but the extreme cold in some areas "once in a few decades" has cast doubt on global warming. Ten years" observation data from 13 representative stations in Canada were selected according to the provinces to explore the spatio-temporal variation trend of the temperature in Canada, and the empirical orthogonal function (EOF) was used to analyze the variation rule of the sea surface temperature hidden in the historical data. In order to predict the climate change in the next 25 years, BP neural network was used to establish the relationship between the average annual temperature, daily precipitation and the heat absorption, heat dissipation, sea surface temperature and local latitude of our earth, as well as the relationship model between "extremely cold weather" and climate change. Here are the research and analysis results: the temperature and precipitation in the high latitudes are generally low, and the temperature difference between the high latitudes and the longitude areas is small and the precipitation changes little. The temperature in Canada changes periodically, which is consistent with the seasonal change of the northern hemisphere. The eastern part of the north Atlantic is inversely correlated with the temperature of other oceans. The occurrence frequency of "extreme cold weather" has a certain relationship with climate change, and the local extreme cold phenomenon does not contradict the general trend of global warming.
Key words:  Global warming  extreme cold weather  EOF method  BP neural network  climate change
版权所有 《海洋科学》编辑部 Copyright©2008 All Rights Reserved
主管单位:中国科学院 主办单位:中国科学院海洋研究所
地址:青岛市南海路七号  邮编:266071  电话:0532-82898755  E-mail:marinesciences@qdio.ac.cn
技术支持:北京勤云科技发展有限公司