Investigating decadal variations of the seasonal predictability limit of sea surface temperature in the tropical Pacific

Zhaolu Hou,Jie Feng,Jianping Li,Ruiqiang Ding

doi:10.1007/s00382-022-06179-3

Abstract

El Niño and the Southern Oscillation (ENSO) have a worldwide impact on seasonal to yearly climate. However, there are decadal variations in the seasonal prediction skill of ENSO in dynamical and statistical models; in particular, ENSO prediction skill has declined since 2000. The shortcomings of models mean that it is very important to study ENSO seasonal predictability and its decadal variation using observational/reanalysis data. Here we quantitatively estimate the seasonal predictability limit (PL) of ENSO from 1900 to 2015 using Nonlinear local Lyapunov exponent (NLLE) theory with an observational/reanalysis dataset and explore its decadal variations. The mean PL of sea surface temperature (SST) is high in the central/eastern tropical Pacific and low in the western tropical Pacific, reaching 12–15 and 7–8 months, respectively. The PL in the tropical Pacific varies on a decadal timescale, with an interdecadal standard deviation of up to 2 months in the central tropical Pacific that has similar spatial structure to the mean PL. Taking the PL of SST in the Niño 3.4 region as representative of the PL in the central/eastern tropical Pacific, there are clearly higher values in the 1900s, mid-1930s, mid-1960s, and mid-1990s, and lower values in the 1920s, mid-1940s, and mid-2010s. Meanwhile, the PL of SST in the Niño 6 region—whose average value is 7 months—is in good agreement with the PL of most regions in the western tropical Pacific, with higher values in the 1910s, 1940s, and 1980s and lower values in the 1930s, 1950s, and mid-1990s. In the framework of NLLE theory, the PL is determined by the error growth rate (representing the dissipation rate of the predictable signal) and the saturation value of relative error (representing predictable signal intensity). We reveal that the spatial structure of the mean PL in the tropical Pacific is determined mainly by the error growth rate. The decadal variability of PL is affected more by the variation of the saturation value of relative error in the equatorial Pacific, whereas the error growth rate cannot be ignored in the PL of some regions. As an important source of predictability in ENSO dynamics, the relationship between warm water volume and SST in the Niño 3.4 region has a critical role in the decadal variability of PL in the tropical Pacific through the error growth rate and saturation value of relative error. This strong relationship reduces the error growth rate in the initial period and increases the saturated relative error, contributing to the high PL.

Highlights

Using the ocean temperature and depth of the 20 °C isotherm (D20) from both Simple Ocean Data Assimilation dataset (SODA) and GODAS, a warm water volume (WWV) index is defined as the average of D20 over the region 5° S–5° N, 120° E–80° W (Meinen and McPhaden 2000)
We have investigated the mean predictability limit (PL) of sea surface temperature (SST) and its decadal variation in the tropical Pacific from 1900 to 2015 using observational SST data
There are decadal variations in the PL in the tropical Pacific that can be as large as 2 months as measured by the standard deviation of the PL

Summary

Introduction

As the strongest seasonal-to-interannual variability in the tropical Pacific, El Niño and the Southern Oscillation (ENSO) has major impacts on weather and climate in many parts of the world via the atmospheric bridge effect (Cai et al 2020; Chen et al 2004; Duan and Mu 2018; Li et al 2019; Timmermann et al 2018; Yeh et al 2018; Zhang et al 2021). Decadal variations in ENSO forecasting skill have been identified in several analyses of numerical model predictions (Balmaseda et al 1995; Chen et al 2004; Kumar et al 2017; Tang et al 2008). The variation may be associated with the deficiencies of the ENSO prediction models, but it depends strongly on ENSO predictability itself (Chen and Cane 2008; Duan and Mu 2018; Kirtman and Schopf 1998; Tang et al 2008)

Methods

Results

Conclusion