Observed El Niño‐La Niña Asymmetry in a Linear Model

Martinez‐Villalobos, Cristian; Newman, Matthew; Vimont, Daniel J.; Penland, Cécile; Neelin, J. David

doi:10.1029/2019gl082922

Cited by 27 publications

(22 citation statements)

References 96 publications

(130 reference statements)

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“…All these changes translate into an enhanced air‐sea coupling, which, together with the strengthened nonlinear atmospheric convection, favors the increase of ENSO SST variability. Some studies found that ENSO‐related stochastic noise is getting stronger in recent decades (Kug et al, 2008), suggesting that multiplicative noise forcing may also play a role (Bianucci et al, 2018; Martinez‐Villalobos et al, 2019; Sura & Sardeshmukh, 2009). However, multiplicative noise forcing alone seems insufficient based on our simple model framework (Figures S8 and S9).…”

Section: Discussionmentioning

confidence: 99%

Two Types of ENSO Varying in Tandem Facilitated by Nonlinear Atmospheric Convection

Geng

Cai

2020

Geophysical Research Letters

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The El Niño‐Southern Oscillation (ENSO) has two distinct regimes, one in the eastern Pacific (EP) featuring strong El Niño and the other in the central Pacific (CP) characterized by strong La Niña. Establishment of atmospheric convection in the climatologically cold and dry EP region provides a major source of the nonlinearity, but its role in the relationship between CP‐ and EP‐ENSO variability and their long‐term change remain unclear. By constructing a nonlinear two‐box recharge‐oscillator model, here we show that nonlinear atmospheric convection operates to boost strong EP El Niño, strong CP La Niña, and evolution in tandem of variability and skewness of CP‐ and EP‐ENSO. Further, the observed coherent long‐term increase in frequency of strong EP El Niño, strong CP La Niña, and variability of the two‐type ENSO over the past decades is facilitated by a corresponding increase in the nonlinear atmospheric convection.

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Section: Discussionmentioning

confidence: 99%

Two Types of ENSO Varying in Tandem Facilitated by Nonlinear Atmospheric Convection

Geng

Cai

2020

Geophysical Research Letters

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“…This is partly due to the inability of the CS-LIM to capture asymmetric aspects of ENSO (e.g., Okumura and Deser 2010). Recently, Martinez-Villalobos et al (2019) attributed the asymmetry of El Niño (above normal) and La Niña (below normal) events to the correlated additive and multiplicative (CAM) noise forcing (Sardeshmukh and Penland 2015;Sardeshmukh et al 2015) of ENSO events, which is ignored in the LIMs developed here.…”

Section: ) Probabilistic Skillmentioning

confidence: 97%

Impact of Annual Cycle on ENSO Variability and Predictability

et al. 2021

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Low-order Linear Inverse Models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere-ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclo-stationary Linear Inverse Model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multi-Model Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST-SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result not only from explicitly accounting for the annual cycle of the background state, but also that of the stochastic forcing.

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“…Other studies have explored hindcast skill dependence on different weather regimes, often suggested to result from enhanced nonlinear predictability resulting from locally reduced forecast uncertainties (e.g., Ferranti et al, 2018;Vigaud et al, 2018). However, such state-dependent skill could also be consistent with the LIM framework (N2003), especially if the LIM could be constructed to include "correlated additive-multiplicative noise" (Martinez-Villalobos et al, 2019;, which allows for non-Gaussianity (Sura et al, 2005; while impacting only the unpredictable denominator of S in (2).…”

Section: Discussionmentioning

confidence: 99%

A Priori Identification of Skillful Extratropical Subseasonal Forecasts

Albers

Newman

2019

Geophysical Research Letters

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The current generation of subseasonal operational model forecasts has, on average, low skill for leads beyond 3 weeks. This is likely a fundamental property of the climate system, due to the relative high amplitude of unpredictable weather variability compared to potentially predictable, but generally weaker, climate signals. Thus, for subseasonal forecasts to be useful, their high versus low skill events should be identified at time of forecast. We show that a linear inverse model (LIM), an empirical‐dynamical model constructed from covariability statistics of wintertime (December–March) weekly averaged observational analyses, can be used to identify, a priori, the expected extratropical subseasonal surface and midtropospheric forecast skill. The LIM's predicted signal‐to‐noise ratio identifies the subset (10%–30%) of Weeks 3–6 forecasts—of the LIM and two operational models from the National Centers for Environmental Prediction and the European Centre for Medium‐Range Weather Forecasts—with relatively higher skill versus the much larger remainder of forecasts whose skill cannot be distinguished from random chance.

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Observed El Niño‐La Niña Asymmetry in a Linear Model

Cited by 27 publications

References 96 publications

Two Types of ENSO Varying in Tandem Facilitated by Nonlinear Atmospheric Convection

Two Types of ENSO Varying in Tandem Facilitated by Nonlinear Atmospheric Convection

Impact of Annual Cycle on ENSO Variability and Predictability

A Priori Identification of Skillful Extratropical Subseasonal Forecasts

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