A Theory for the Seasonal Predictability Barrier: Threshold, Timing, and Intensity

Liu, Zhengyu; Jin, Yishuai; Rong, Ximin

doi:10.1175/jcli-d-18-0383.1

Cited by 37 publications

(91 citation statements)

References 22 publications

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“…Here, − b ( t ) is the growth rate corresponding to Liu et al (2019). Therefore, a larger B (or more positive b 0 ) indicates a weaker growth rate and a more damping system.…”

Section: Methods Model and Datamentioning

confidence: 99%

“…The ACF can be described as a function of initial months t and lag months τ (Ren et al, 2016). According to Liu et al (2019), the ENSO SPB strength can be defined and derived from the ACF. First, for a calendar month t , we identify τ B ( t ) as the specific lag of maximum ACF decline, which is calculated as the lag gradient in the time step of 1 month as

S_{B} ((), t) = ({}, \frac{r [t, τ_{B} (t) - 1] - r [t, τ_{B} (t) + 1]}{2}) = \max_{τ} ({}, \frac{r (t, τ - 1) - r (t, τ + 1)}{2})

where S B ( t ) is the maximum gradient.…”

Section: Methods Model and Datamentioning

confidence: 99%

“…According to the definition of SPB strength (Equation ), in the Langevin model (Equations (2.24) and (2.25) for the growth rate case in Liu et al (2019)):

S_{B_{1}} = 1 + \frac{AB}{1 + B^{2}} \frac{\sqrt{4 A^{2} - 1}}{2 A} - \frac{B^{2}}{2 ((), 1 + B^{2})} = \frac{1}{2} + \frac{B \sqrt{4 A^{2} - 1} + 1}{2 ((), 1 + B^{2})}

…”

Section: Relationship Between Enso Growth Rate and Spb Strength In Twmentioning

confidence: 99%

“…

B_{1} = \frac{- 1 - \sqrt{1 + C^{2}}}{C}

(always smaller than 0);

B_{2} = \frac{- 1 + \sqrt{1 + C^{2}}}{C} = \frac{2 A - 1}{\sqrt{4 A^{2} - 1}} = \sqrt{\frac{2 A - 1}{2 A + 1}} \approx 1

when A is large. For example, according to Liu et al (2019), for a realistic ENSO case, A = 4 and B = 0.2. Therefore, the barrier strength has a positive correlation with B when B is smaller than 1 (in this case, based on Equation ,

s_{B_{1}}^{'}

is positive).…”

Section: Relationship Between Enso Growth Rate and Spb Strength In Twmentioning

confidence: 99%

“…Through the recharge oscillator model, Levine and McPhaden (2015) demonstrated that the seasonally varying growth rate was responsible for ENSO SPB. Liu et al (2019) further developed a theory that the growth rate could influence the SPB in the simplest stochastic climate model: the Langevin equation (Hasselmann, 1976; Jin, Liu, & Rong, 2019) with the incorporation of a seasonal cycle in the growth rate. This growth rate can be quantified in the dynamical framework of the recharge oscillator with a Bjerknes stability index (BJ index, a physical measure for ENSO growth rate of SST anomalies, Jin et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Jin

Liu

2020

Geophysical Research Letters

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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.

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“…Here, − b ( t ) is the growth rate corresponding to Liu et al (2019). Therefore, a larger B (or more positive b 0 ) indicates a weaker growth rate and a more damping system.…”

Section: Methods Model and Datamentioning

confidence: 99%

S_{B} ((), t) = ({}, \frac{r [t, τ_{B} (t) - 1] - r [t, τ_{B} (t) + 1]}{2}) = \max_{τ} ({}, \frac{r (t, τ - 1) - r (t, τ + 1)}{2})

where S B ( t ) is the maximum gradient.…”

Section: Methods Model and Datamentioning

confidence: 99%

“…According to the definition of SPB strength (Equation ), in the Langevin model (Equations (2.24) and (2.25) for the growth rate case in Liu et al (2019)):

S_{B_{1}} = 1 + \frac{AB}{1 + B^{2}} \frac{\sqrt{4 A^{2} - 1}}{2 A} - \frac{B^{2}}{2 ((), 1 + B^{2})} = \frac{1}{2} + \frac{B \sqrt{4 A^{2} - 1} + 1}{2 ((), 1 + B^{2})}

…”

Section: Relationship Between Enso Growth Rate and Spb Strength In Twmentioning

confidence: 99%

“…

B_{1} = \frac{- 1 - \sqrt{1 + C^{2}}}{C}

(always smaller than 0);

B_{2} = \frac{- 1 + \sqrt{1 + C^{2}}}{C} = \frac{2 A - 1}{\sqrt{4 A^{2} - 1}} = \sqrt{\frac{2 A - 1}{2 A + 1}} \approx 1

s_{B_{1}}^{'}

is positive).…”

Section: Relationship Between Enso Growth Rate and Spb Strength In Twmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Jin

Liu

2020

Geophysical Research Letters

Self Cite

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show abstract

ENSO Prediction

L’Heureux

Levine

Newman

et al. 2020

Geophysical Monograph Series

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Machine learning prediction of the Madden-Julian Oscillation

Silini

Barreiro

Masoller

2021

Preprint

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The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden-Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26-27 days (which competes with that obtained with most computationally demanding stateof-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated.

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A Theory for the Seasonal Predictability Barrier: Threshold, Timing, and Intensity

Cited by 37 publications

References 22 publications

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

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

ENSO Prediction

Machine learning prediction of the Madden-Julian Oscillation

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