Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Abstract

This paper investigates potential factors that control the El Niño–Southern Oscillation (ENSO) Spring Persistence Barrier (SPB) strength in two different ENSO regimes and apply it to explain the ENSO SPB strength modulation after the 21st century. In a damped, noise‐driven model, the theoretical solution of SPB strength illustrates that a weaker ENSO growth rate strengthens SPB. In the self‐sustained regime, as in the Cane‐Zebiak model (chaotic system), the strengthened thermodynamic damping and weakened thermocline positive feedback lead to a more negative ENSO growth rate and, in turn, a stronger SPB. Therefore, in both ENSO regimes, a weaker ENSO growth rate intensifies the SPB. The application of the theory to the real world suggests that a more negative ENSO growth rate, corresponding to a more damped feedback system, is responsible for the stronger SPB in recent decades than in 1980–2000.

Sea Surface Salinity Strongly Weakens ENSO Spring Predictability Barrier

“…For example, during 1980–1999, the ACC is about 0.6 at 12 months lag (blue line in Figure S7a in Supporting Information ), which is significantly higher than that after 2000 (blue line in Figure S7b in Supporting Information ). It is consistent with the previous findings that ENSO predictability decreases after 2000 (Jin et al., 2020; McPhaden, 2012). However, by decoupling SSS, the forecast skill drops dramatically during 1980–1999 at 12 months lag time (about 0.6 in blue line in Figure S7a vs. about 0.2 in red line in Figure S7a in Supporting Information ), which suggests that SSS plays an important role in ENSO prediction.…”

The Role of Extratropical Pacific in Crossing ENSO Spring Predictability Barrier

“…One possible reason that leads to this SPB is the specific initial errors in the tropical Pacific (Mu, Duan, & Wang, 2007, 2007b). However, recent studies have identified numerically and analytically that the seasonally varying background of the tropical Pacific may be an important factor in causing the SPB (Y. Jin et al., 2019, 2020, 2021; Y. Jin and Liu, 2021a, b; A. F. Levine and McPhaden, 2015; Liu et al., 2019).…”

“…Thus, accurate simulation and prediction of ENSO one or more seasons in advance is of great importance. However, one significant obstacle in ENSO prediction is the spring predictability barrier (SPB), which consists of a dramatic drop in forecast skill when the prediction is made through spring (Z. Hou et al., 2018; Jin et al., 2008, 2019, 2020; Webster & Yang, 1992; Wu et al., 2009; Xue et al., 1994). One possible reason that leads to this SPB is the specific initial errors in the tropical Pacific (Mu, Duan, & Wang, 2007, 2007b).…”

The Role of Tropical Atlantic in ENSO Predictability Barrier

“…However, accurate ENSO forecasts several seasons in advance are still challenging (e.g., Tang et al., 2018). One significant obstacle in ENSO prediction is the so‐called “spring predictability barrier” (SPB), which consists of a dramatic drop in forecast skill when the numerical ENSO predictions are made through spring (e.g., Hou et al., 2019; Jin et al., 2008, 2019, 2020; Webster & Yang, 1992; Wu et al., 2009; Xue et al., 1994). The SPB is also known as the strong error growth in the boreal spring (Duan & Hu, 2016; Duan & Mu, 2018; Mu et al., 2007; Tao et al., 2019).…”