纳入厄尔尼诺/南方涛动相关数量的滞后-WALS方法用于南海测高年际SLA预报

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Pengfei Yang, Hok Sum Fok
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引用次数: 0

摘要

提出了一种利用滞后赤道太平洋厄尔尼诺-南方涛动(ENSO)相关量的滞后加权平均最小二乘法(Lag-WALS)预报南海海平面年际异常(SLA)的新方法。通过经验正交函数(EOF)和小波相干方法,我们首先研究了赤道太平洋海面温度(SST)和SLA(立体海平面(SSL)和非立体海平面(NSSL))之间的关系,然后探讨了它们与SCS SLA年际的交叉相关性。结果发现,SLA/SSL 的 EOF 第一时空模式(即 EOF1 和第一主成分(PC1))与赤道太平洋的 SST 之间具有很强的一致性,两者在 EOF1 中都表现出典型的厄尔尼诺/南方涛动马蹄形空间模式。SCS SLA 与 SST/SLA/SSL PC1 具有良好的一致性,在大多数网格位置,SCS SLA 比 SST、SLA 和 SSL 滞后几个月。相比之下,NSSL 与 SST PC1 或 SCS SLA 的年际差异很大。滞后-WALS模式在SCS边界的表现要好于在中心区域的表现,内部(外部)精度的平均STD/MAE/Bias(RMSE/MAE/Bias)分别为1.01/0.80/-0.002厘米(1.39/1.13/-0.08厘米)。测高观测的 SLA 季节模式与 Lag-WALS 模式预测的 SLA 一致。区域平均 SLA 时间序列也有类似情况。这些结果表明,Lag-WALS 模式有能力在年际尺度上准确预报南中国海的海平面上升斜率,这对南中国海海平面异常变化的早期预警至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lag-WALS approach incorporating ENSO-related quantities for altimetric interannual SLA forecasts in the South China Sea
A novel approach using lag weighted-average least squares (Lag-WALS) is proposed to forecast the interannual sea level anomaly (SLA) in the South China Sea (SCS) using lagged equatorial Pacific El Niño–Southern Oscillation (ENSO)-related quantities. Through empirical orthogonal function (EOF) and wavelet coherence method, we first investigated the relationships between sea surface temperature (SST) and SLA (both steric sea level (SSL) and non-steric sea level (NSSL)) in the equatorial Pacific, and then explored their cross-correlations with the interannual SCS SLA. A robust alignment was found between the first spatiotemporal mode of EOF (i.e. EOF1 and first principal component (PC1)) from SLA/SSL and SST across the equatorial Pacific, both of which exhibited a typical ENSO horseshoe spatial pattern in EOF1. Good consistency between the SCS SLA and the SST/SLA/SSL PC1 was revealed, with the SCS SLA lagging behind the SST, SLA, and SSL by several months at most grid locations. In contrast, the NSSL exhibited large disparities with the SST PC1 or the interannual SCS SLA. The lag-WALS model performed better at the SCS boundaries than in the central region, with an average STD/MAE/Bias (RMSE/MAE/Bias) for internal (external) accuracies of 1.01/0.80/–0.002 cm (1.39/1.13/–0.08 cm), respectively. The altimetric-observed SLA seasonal patterns agreed with the Lag-WALS model-forecasted SLA. A similar situation applies to regionally-averaged SLA time series. These results underscore the ability of the Lag-WALS model to accurately forecast the SCS SLA at the interannual scale, which is crucial for early warning of abnormal sea level changes in the SCS.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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