竹杖芒鞋轻胜马分享 http://blog.sciencenet.cn/u/liqx 气候变化观测、物理学及其不确定性

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时隔十年,再次在《科学通报》发文,Mark一下

已有 1054 次阅读 2020-6-10 14:21 |系统分类:科研笔记

2010年在《科学通报》(中、英文)发表论文《Assessment of the uncertainties in temperature change in China during the last century》,目前SCI引用70次。

2020年,时隔十年又在《科学通报》、《Science Bulletin》各发文1篇。分别是:


1) 李庆祥,2020,用外部强迫因子对近百年陆地降水变化的统计建模试验,科学通报,doi:10.1360/TB-2020-0305

      这篇文章建立了一个基于气候要素变化和外部强迫因子(自然强迫和人为强迫)之间的回归关系,对观测气候变化进行统计归因。首先基于多元线性回归和差分自回归滑动平均组合模型(MLR+ARIMA),检测出外部强迫对于气候变化在总体上是否存在明显的影响信号,然后建立偏最小二乘回归(PLSR)模型,避免了多种外部强迫之间的多重共线性的干扰,分别检测了不同强迫因子贡献的相对大小和统计显著性。

     该模型的应用范围广泛,可以应用于多个学科领域。欢迎大家合作使用。

     (附注:PLSR模型我其实很早把它应用在气象领域,2008、2009年分别同北京大学原地球物理系主任黄嘉佑教授(那时候主要是黄老师带着我做)合作发表了两篇文章,但当时还真没有好好去推敲它的精髓。这次在带中山大学海洋学院本科生钱罡珍毕业论文时又通过反复试验和深入思考,对它的理解加深了许多。因此,后期优秀的钱同学的技术支撑非常重要。我们合写的另一篇做全球气温归因(成因)研究的论文(钱同学和我的博士生孙文彬同学牵头)已投了国外期刊。)


英文摘要:

Statistical Modeling Experiment of Land Precipitation Variations since the Start of 20th Century with External Forcing Factors

Qingxiang Li 1,2

1. School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou 510275, China;

2. Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai 519082, China

Corresponding author, E-mail: liqingx5@mail.sysu.edu.cn

    Fitting of the historic climate change with multiple linear regression (MLR) models often encounters the problem of residual autocorrelation and multiple correlation among the dependent factors, which leads the model not to be the Best Linear Unbiased Estimator (BLUE). To solve these problems, a set of sophisticated statistical modeling procedures are developed in this paper to detect the contributions of the external forcing to the global and regional land precipitation changes during the recent century. The radiative forcing (effective radiative forcing) data of multiple natural and anthropogenic factors are taken as independent variables and a combined model of MLR and Autoregressive Integrated Moving Average (ARIMA) is first used to separate the impacts of natural forcing and human activities (signals) and internal variability (noise) due to land precipitation changes since the start of 20th century. Based on the results, a partial least squares regression (PLSR) model is further adopted to quantify the respective contributions of the each components of anthropogenic forcing. Some preliminary conclusions were drawn:

1) The combined model of MLR and ARIMA has clearly separated the influence of anthropogenic forcing signals in the global and regional (middle and high latitude in Northern Hemisphere) land precipitation anomaly at 5% significance level, and the explained variance is relatively large;

2) The PLSR model overcomes the morbidity of ordinary linear regression equations to a certain extent, and better separated the respective impact (contribution) of various natural and anthropogenic forcing factors on global and regional (middle and high latitude in Northern Hemisphere) land precipitation anomaly; and the relative strength of each factor's contribution are determined. It has good application value for modeling and impact studies with multiple correlations among different independent variables;

3) The above combined model explains about 40% of the total variance of land precipitation anomalies change over global and middle latitude regions in Northern Hemisphere, and nearly 60% of the total variance in high latitude regions. Based on the fitting results, both the human activities and natural forcing are the deterministic impact factors of global land precipitation variations. But the natural forcing’s contribution is not significant, while human activities still explain a large part of the variance and thus have high significance for the regional land precipitation variations. In addition, the autocorrelation should also be considered as one of the important impact factors for global and regional land precipitation anomaly changes;

4) The anthropogenic aerosol and contrails have clear and significant positive contributions to the global and regional land precipitation anomalies, while the contributions of the remaining factors show obvious positive and negative differentiation, indicating that there is certain degree of uncertainty. Therefore, it is necessary to further optimize and improve the factors and models based on more accurate factor datasets for each region;

5) The modeling approach described above is primarily based on the idea to decompose and model the historical climate series. On one hand, it may put forward certain requirements for the "integrity" of the observational data; while on the other hand, it is not restricted by the study domains, climate variables and the independent variables. Moreover, it may even model /separate the anthropogenic /natural effects of climate changes in local scales. In addition, it can independently detect and analyze the relative contribution and significance of human activities or natural forcing to climate change in the historical observational series without using the climate system model. Therefore, it is proved to be a convenient approach for detecting the causes of climate change and a powerful supplement to model attribution.



2)Li Q., W. Sun, B. Huang, W. Dong, X. Wang, P. Zhai, P. Jones, Consistency of global

warming trends strengthened since 1880s, Science Bulletin (2020), doi: https://doi.org/10.1016/j.scib.2020.06.009

这篇文章基于我们最近研发的全球陆地气温(C-LSAT)和全球表面温度(CMST)数据集,对比了当前国际上5个同类数据集(分别是美国国家大气海洋局NOAA的NOAAGlobalT、美国国家宇航局NASA的GISS、Berlerly Lowrence实验室的BE、英国气象局Hadley中心与东安吉利尔大学气候研究中心合作的HafCRUT,还有就是我们中国的CMST)对全球1854-2019年的表面温度变化趋势,认为1880-2019年以来,各家数据集的研究结果空前一致,精度水平也相当;三套可追溯到1850s的数据集(HadCRUT、BE和CMST)在1850-1879年还存在一定的差异,主要是由于海温的不确定性所致(2019年Nature也发表了海温数据问题的论文)。

该基准数据集目前已经或即将在欧洲、美国的知名数据网站共享,欢迎大家使用和合作。


中文摘要:

题目:1880s以来全球温度变暖趋势一致性进一步加强

作者:李庆祥,孙文彬,Huang Boyin,董文杰, Wang Xiaolan,翟盘茂,Jones Phil

(单位:中山大学;美国NOAA/NCEI;加拿大ECCC;中国气科院;英国东安吉利尔大学CRU)

摘要:升级了新的1850年以来的全球陆地气温数据集(C-LSAT2.0),结合美国NOAA/NCEI研发的ERSSTv5,将全球表面温度(CMST)观测数据集延长至1854-2019年,为全球气候变化研究提供了一个新的基准数据。对比发现,基于CMST的全球温度变化序列在1880年以前略高于其他几个全球序列,差异主要来源于采用不同海温数据所致,各个序列之间存在结构性不确定性;1880年之后,五个全球表温度观测序列的一致性非常高,并具有显著的一致的变暖趋势,具有高可靠性。基于C-LSAT2.0和CMST,对1880-2019年全球变暖趋势进行了估计,结果表明:近140年,120年,60年和40年陆地平均气温增暖趋势分别为:0.103±0.016,0.115±0.020,0.252±0.035和0.293±0.055 ℃/10a;全球表面温度增暖趋势分别为:0.072±0.010,0.084±0.011,0.150±0.019和0.185±0.032℃/10a。进而对1900-2018年全球年均温度EOF分析表明,前两个特征向量明显地反映了全球温度变化的主要模态:即全球一致升温模态和与太平洋年代际振荡(IPO)密切相关的模态。说明近120年全球温度变化主要由外部强迫(人类活动)和自然变率(IPO)控制。


十年间,也跨越了《Chi Sci BUll》(影响因子1左右)到《SCI BULL》(影响因子6.277),值得纪念、记录一下。





http://blog.sciencenet.cn/blog-39247-1237268.html

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