中国近海海平面气压数据集构建及其变化特征

Construction of the sea level pressure dataset in China′s offshore areas and its variation characteristics

中文关键词:  海平面气压  适用性分析  数据集构建  变化特征  机制分析

英文关键词:sea level pressure  applicability analysis  dataset construction  variation characteristics  mechanism analysis

中文摘要:

      海平面气压 （SLP） 是对流层低层大气的关键要素之一，对全球气候变化和气候预测具有重要意义。本文利用 1980－2022 年中国沿海 38 个气象观测站的 SLP 数据对 JRA-55 再分析数据进行逐步订正，并通过分析新 SLP 数据集，得到中国近海 SLP 的分布和变化特征，及影响其变化的物理机制。分析结果表明：（1） JRA-55再分析资料对中国沿海 SLP 的重现效果优于 ERA5 和 NCEP，其年平均值和月平均值与观测值的相关系数分别为 0.92 和 1.00，均方根误差分别为 0.14 hPa 和 0.07 hPa；（2） 中国近海 SLP 的年平均值随纬度升高而增大，变化范围为 1 010.5~1 017.75 hPa，近年来受 SST 和气温持续变暖的影响，SLP 以-0.15 hPa/10 a 的平均速率波动下降，下降最显著的区域位于长江口外海域，同时，SLP 及其变化趋势的分布呈显著季节变化特征；（3） 中国近海 SLP 前 3 个 EOF 模态的解释方差总计为 79.00%，第一模态的主振荡周期为 2~4 年，主要受 ENSO影响，第二模态主要受 NAO 和 ENSO 的调节，主振荡周期为 4 年，第三模态的周期为 2~4年，与 AO密切相关。

英文摘要:

      The sea level pressure (SLP) is a crucial component of the lower troposphere, and plays a significant role in global climate change and climate prediction. Utilizing SLP data during 1980－2022 from 38 meteorological observation stations along the coast of China, this study constructs a new SLP dataset on the basis of the JRA-55 re-analysis data through a successive correction method. In addition, the variation characteristics of SLP in the China′s offshore areas are analyzed. The results indicate that: (1) the JRA-55 re-analysis dataset performs better than ERA5 and NCEP in reproducing the SLP along China′s coast, with correlation coefficients of 0.92 and 1.00 and root-mean-square errors of 0.14 hPa and 0.07 hPa for annual and monthly averages, respectively; (2) the annual mean of SLP in China′s offshore areas, ranging from 1 010.5 to 1 017.75 hPa, increases with latitude, and influenced by the continuous warming of sea surface temperature (SST) and air temperature, SLP has shown a declining trend at a rate of -0.15 hPa/10 a in recent years, with the most significant decrease occuring in areas adjacent to the Yangtze River Estuary. Meanwhile, the distributions and trends of SLP are featured with distinct seasonal variations; (3) the total explained variance of the first three EOF modes of SLP is 79.00%. The primary oscillation period of the first mode is 2~4 years, and is largely influenced by ENSO; the second mode is mainly modulated by the NAO and ENSO, with a main oscillation period of 4 years; the third mode, with an oscillation period of 2~4 years, is closely related with the AO.