陈丽凡^1,,
孙丞虎^1,,,
张冬斌¹,
曹丽娟¹,
李维京^2,3
1. 国家气象信息中心, 北京 100081
2. 国家气候中心, 北京 100081
3. 南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044

基金项目: 国家气象科技创新工程攻关任务"气象资料质量控制及多源数据融合与再分析"（CMAGGTD003-5）；国家气象信息中心青年科技基金（NMICQJ201801）资助


详细信息

作者简介: 陈丽凡, 女, 1989年生, 博士, 主要从事气象资料质量控制与处理分析.E-mail:chenlf@cma.gov.cn

通讯作者: 孙丞虎, 男, 1978年生, 研究员, 主要从事气候与气候变化研究.E-mail:sunch@cma.gov.cn

中图分类号: P467

收稿日期:2018-06-10
修回日期:2019-04-22
上线日期:2019-06-05

Bias correction and the dataset development of sea surface temperature over the Indian-Pacific Ocean from 1901 to 2016

CHEN LiFan^1,,
SUN ChengHu^1,,,
ZHANG DongBin¹,
CAO LiJuan¹,
LI WeiJing^2,3
1. National Meteorological Information Center, Beijing 100081, China
2. National Climate Center, Beijing 100081, China
3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China


More Information

Corresponding author: SUN ChengHu,E-mail:sunch@cma.gov.cn

MSC: P467

--> Received Date: 10 June 2018
Revised Date: 22 April 2019
Available Online: 05 June 2019


摘要
摘要:海表温度系统性观测偏差的订正是开展长历史序列网格化海表温度气候数据产品研制的关键.本文在引入美国SR02海表温度偏差订正方法的基础上，结合国家气象信息中心自主研发的全球海表观测定时值数据集，进行了相关参数的优化改进，从而研制了1901—2016年印度洋—太平洋核心海域月平均2°×2°分辨率的海表温度偏差订正数据集.对海温偏差订正量的时空分布特征分析表明，基于自主研制的基础数据和优化改进的方法求解的偏差订正量能有效反映海表温度观测手段的历史变迁，以及海表温度系统性偏差随季节变化的规律.同时，与ERSST订正量的对比表明，由于优化改进后的方法其阈值计算随空间样本而变，因而其局地变化特征的表现能力更强，且其订正量在观测手段转型期的变化更为明显.相较订正前的海表温度距平（SSTA）场，订正后的SSTA资料与ERSSTv5 SSTA间的偏差误差和均方根误差均有明显降低.其中，偏差误差的缩减比例在37.7%~87.9%之间，均方根误差可降低0.06℃.此外，与国际同类产品的对比表明，本文发展的SSTA订正数据集与国际同类SSTA产品序列的相关系数不低于0.97，且变化趋势类似.从差异对比上看，除中高纬东亚大陆近海区域外，本文的偏差订正数据集与国际上同类产品的SSTA差异基本在-0.2~0.2℃之间.
关键词: 海表温度/
偏差订正/
印太海域/
数据产品/
气候变化

Abstract:Bias correction of the systematic observation error is vital for the development of long-term gridded sea surface temperature (SST) dataset since 1900. In this study, based on the optimized SR02 bias correction method and the global hourly ocean surface observation dataset from National Meteorological Information Center, we have developed the monthly bias-corrected SSTA dataset over the Indian-Pacific Ocean from 1901 to 2016, with a spatial resolution of 2°×2°. The results show that the spatial-temporal distribution of the SST bias derived from our newly developed dataset is generally consistent with the history of SST observation techniques, and also indeed reflects the seasonal variation of SST systematic observation errors. As the threshold of the optimized method varies with the space sample sizes, the bias derived from it reflects more local characteristics, and changes more consistently with transformation of observation techniques as compared with the bias features of ERSST V4. Both the mean bias and root-mean-square error (RMSE) of the bias-corrected SSTA compared with ERSSTv5 are smaller than the original one, with varying of reduced mean bias from 37.7% to 87.9%, and decreasing RMSE around 0.06℃. In addition, the comparisons with international products (i.e., ERSST V5, HadSST3, HadISST1 and COBE2) demonstrate that our newly developed bias-corrected SSTA dataset shares high correlations over 0.97 with those, and comparable trend features. Except for the coastal region of the East Asia in the higher latitudes, the general differences between our newly developed bias-corrected SSTA dataset and the other international products are mainly between -0.2~0.2℃.
Key words:Sea surface temperature/
Bias correction/
Indian-Pacific Ocean/
Data product/
Climate change



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