Source of low frequency modulation of ENSO amplitude in a CGCM

Moon, Byung-Kwon; Yeh, Sang‐Wook; Dewitte, Boris; Jhun, Jong-Ghap; Kang, In‐Sik

doi:10.1007/s00382-006-0219-4

Cited by 5 publications

(3 citation statements)

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“…8 Hence, it is to be expected that very low frequency changes in the mean state will affect the statistics and spatial structure of ENSO (e.g., Fedorov and Philander 2000). Moon et al (2007) argued that the low-frequency modulation of ENSO variance in the Center for Ocean-Land-Atmosphere (COLA) climate model was due to low-frequency changes in the stratification of the near-equatorial ocean that stemmed from wind stress forcing in the subtropical southern Pacific Ocean. Matei et al (2008) demonstrated that SST anomalies placed in the southern subtropical Pacific of the ECHAM5-Max Planck Institute Ocean Model (MPI-OM) coupled model eventually make their way to the equator and cause mean state changes that affect the variance in ENSO.…”

Section: Statementioning

confidence: 99%

100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability

Battisti

Vimont

Kirtman

2019

Meteorological Monographs

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In situ observation networks and reanalyses products of the state of the atmosphere and upper ocean show well-defined, large-scale patterns of coupled climate variability on time scales ranging from seasons to several decades. We summarize these phenomena and their physics, which have been revealed by analysis of observations, by experimentation with uncoupled and coupled atmosphere and ocean models with a hierarchy of complexity, and by theoretical developments. We start with a discussion of the seasonal cycle in the equatorial tropical Pacific and Atlantic Oceans, which are clearly affected by coupling between the atmosphere and the ocean. We then discuss the tropical phenomena that only exist because of the coupling between the atmosphere and the ocean: the Pacific and Atlantic meridional modes, the El Niño–Southern Oscillation (ENSO) in the Pacific, and a phenomenon analogous to ENSO in the Atlantic. For ENSO, we further discuss the sources of irregularity and asymmetry between warm and cold phases of ENSO, and the response of ENSO to forcing. Fundamental to variability on all time scales in the midlatitudes of the Northern Hemisphere are preferred patterns of uncoupled atmospheric variability that exist independent of any changes in the state of the ocean, land, or distribution of sea ice. These patterns include the North Atlantic Oscillation (NAO), the North Pacific Oscillation (NPO), and the Pacific–North American (PNA) pattern; they are most active in wintertime, with a temporal spectrum that is nearly white. Stochastic variability in the NPO, PNA, and NAO force the ocean on days to interannual times scales by way of turbulent heat exchange and Ekman transport, and on decadal and longer time scales by way of wind stress forcing. The PNA is partially responsible for the Pacific decadal oscillation; the NAO is responsible for an analogous phenomenon in the North Atlantic subpolar gyre. In models, stochastic forcing by the NAO also gives rise to variability in the strength of the Atlantic meridional overturning circulation (AMOC) that is partially responsible for multidecadal anomalies in the North Atlantic climate known as the Atlantic multidecadal oscillation (AMO); observations do not yet exist to adequately determine the physics of the AMO. We review the progress that has been made in the past 50 years in understanding each of these phenomena and the implications for short-term (seasonal-to-interannual) climate forecasts. We end with a brief discussion of advances of things that are on the horizon, under the rug, and over the rainbow.

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Section: Statementioning

confidence: 99%

100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability

Battisti

Vimont

Kirtman

2019

Meteorological Monographs

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“…ENSO modulation may be partly intrinsic, arising from chaotic and/or stochastically driven tropical Pacific dynamics (Kleeman and Power 1994;Blanke et al 1997;Timmermann et al 2003;Jochum and Murtugudde 2004;Fl€ ugel et al 2004;Yeh et al 2004;Power and Colman 2006;Vecchi et al 2006b;Gebbie et al 2007;Zavala-Garay et al 2008;Wittenberg 2009;Newman et al 2011a,b). ENSO may also be modulated by slow changes in background climate, arising from ENSO nonlinearities, non-ENSO climate modes, or natural and anthropogenic radiative forcings (Kirtman and Schopf 1998;Wang andAn 2001, 2002;Wittenberg 2002;Zhang and DeWitt 2006;Moon et al 2007;An et al 2008;Matei et al 2008;Anderson et al 2009;Imada and Kimoto 2009;DiNezio et al 2012;Ogata et al 2013). These mechanisms are compatible, and indeed existing observational records are not yet sufficient to rule out the possibility of a stochastically driven, chaotic ENSO perturbed by both non-ENSO climate modes and external forcings (Chang et al 1996;Penland et al 2000;Zhang et al 2003;Fedorov et al 2003;Fl€ ugel et al 2004;Kravtsov 2012).…”

Section: Introductionmentioning

confidence: 99%

ENSO Modulation: Is It Decadally Predictable?

et al. 2014

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Observations and climate simulations exhibit epochs of extreme El Niño–Southern Oscillation (ENSO) behavior that can persist for decades. Previous studies have revealed a wide range of ENSO responses to forcings from greenhouse gases, aerosols, and orbital variations, but they have also shown that interdecadal modulation of ENSO can arise even without such forcings. The present study examines the predictability of this intrinsically generated component of ENSO modulation, using a 4000-yr unforced control run from a global coupled GCM [GFDL Climate Model, version 2.1 (CM2.1)] with a fairly realistic representation of ENSO. Extreme ENSO epochs from the unforced simulation are reforecast using the same (“perfect”) model but slightly perturbed initial conditions. These 40-member reforecast ensembles display potential predictability of the ENSO trajectory, extending up to several years ahead. However, no decadal-scale predictability of ENSO behavior is found. This indicates that multidecadal epochs of extreme ENSO behavior can arise not only intrinsically but also delicately and entirely at random. Previous work had shown that CM2.1 generates strong, reasonably realistic, decadally predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO. However, those slow variations appear not to lend significant decadal predictability to this model’s ENSO behavior, at least in the absence of external forcings. While the potential implications of these results are sobering for decadal predictability, they also offer an expedited approach to model evaluation and development, in which large ensembles of short runs are executed in parallel, to quickly and robustly evaluate simulations of ENSO. Further implications are discussed for decadal prediction, attribution of past and future ENSO variations, and societal vulnerability.

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“…We attempt to address such limitations in this study and further improve the lead time of ENSO prediction. Moreover, ENSO shows strong physical linkages with the slowly evolving oceanic components in the tropical Pacific (Chen et al, 2004;Moon et al, 2007;Luo et al, 2008;Ramesh and Murtugudde, 2012;Ogata et al, 2013;Zhao et al, 2021), Indian Ocean (Behera et al, 2006;Izumo et al, 2010;Luo et al, 2010;Kug and Kang, 2006), Atlantic Ocean (Ham et al, 2013(Ham et al, , 2021aChikamoto et al, 2020;Richter and Tokinaga, 2020), western hemisphere warm pool (Park et al, 2018), and North Pacific regions (Larson and Kirtman, 2014;Pegion and Selman, 2017;Tseng et al, 2017) at longer lead times. They can work as potential sources of the long lead predictability of ENSO.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for skillful long-lead ENSO forecasts

Patil

Doi²,

Jayanthi³

et al. 2023

Front. Clim.

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El Niño-Southern Oscillation (ENSO) is one of the fundamental drivers of the Earth's climate variability. Thus, its skillful prediction at least a few months to years ahead is of utmost importance to society. Using both dynamical and statistical methods, several studies reported skillful ENSO predictions at various lead times. Predictions with long lead times, on the other hand, remain difficult. In this study, we propose a convolutional neural network (CNN)-based statistical ENSO prediction system with heterogeneous CNN parameters for each season with a modified loss function to predict ENSO at least 18–24 months ahead. The developed prediction system indicates that the CNN model is highly skillful in predicting ENSO at long lead times of 18–24 months with high skills in predicting extreme ENSO events compared with the Scale Interaction Experiment-Frontier ver. 2 (SINTEX-F2) dynamical system and several other statistical prediction systems. The analysis indicates that the CNN model can overcome the spring barrier, a major hindrance to dynamical prediction systems, in predicting ENSO at long lead times. The improvement in the prediction skill can partly be attributed to the heterogeneous parameters of seasonal CNN models used in this study and also to the use of a modified loss function in the CNN model. In this study, we also attempted to identify various precursors to ENSO events using CNN heatmap analysis.

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Source of low frequency modulation of ENSO amplitude in a CGCM

Cited by 5 publications

References 46 publications

100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability

100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability

ENSO Modulation: Is It Decadally Predictable?

Deep learning for skillful long-lead ENSO forecasts

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