ENSO stability in coupled climate models and its association with mean state

Kim, Seon Tae; Cai, Wenju; Jin, Fei‐Fei; Yu, Jin‐Yi

doi:10.1007/s00382-013-1833-6

Cited by 131 publications

(137 citation statements)

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“…1). This excessive cold tongue error has prevailed in several generations of CGCMs, limiting their skill in simulating and predicting ENSO as well as its global teleconnections (Latif et al 2001;Guilyardi 2006;AchutaRao and Sperber 2006;Wittenberg et al 2006;Ham and Kug 2012;Kim et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An Intermodel Approach to Identify the Source of Excessive Equatorial Pacific Cold Tongue in CMIP5 Models and Uncertainty in Observational Datasets

et al. 2015

Journal of Climate

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An excessive cold tongue error in the equatorial Pacific has prevailed in several generations of climate models. However, the causes of this problem remain a mystery, partly owing to uncertainty and/or a lack of observational datasets. Based on the multimodel ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5), this study introduces a novel intermodel approach to identify the bias source by going beyond comparison with observational datasets. Intermodel statistics show that the excessive cold tongue bias could be traced back to a too strong oceanic dynamic cooling linked to a too shallow thermocline along the equatorial Pacific. A heat budget analysis suggests that the excessive oceanic dynamic cooling is balanced by the surface latent heat flux (LHF) adjustment. This is consistent with a variety of oceanic and atmospheric observations but at odds with the popular objectively analyzed air-sea heat fluxes (OAFlux) products. Further analyses suggest an alarming overestimation of OAFlux net surface heat flux (Q net ) into the tropical Pacific, mainly ascribed to observational uncertainly in air specific humidity. Implications for intermodel statistics in assessing model processes, validating observational data, and regulating future climate projections are discussed.

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Section: Introductionmentioning

confidence: 99%

An Intermodel Approach to Identify the Source of Excessive Equatorial Pacific Cold Tongue in CMIP5 Models and Uncertainty in Observational Datasets

et al. 2015

Journal of Climate

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“…the response of surface wind stress to sea surface temperature anomalies, is important. This coupling has been found to be generally weak in models and varies in strength between them (Guilyardi 2006;Lloyd et al 2009;Kim and Jin 2010a;Kim et al 2013). The strength of this coupling is slightly improved in atmosphere-only models and CMIP5 relative to CMIP3 (Lloyd et al 2010;Bellenger et al 2013;Kim et al 2013) though the reasons behind this improvement are unclear.…”

Section: Introductionmentioning

confidence: 92%

“…This feedback is usually too weak in models and shows large variations in both CMIP3 and CMIP5 (Lloyd et al 2009;Kim and Jin 2010a;Kim et al 2013). This is often primarily related to an underestimated shortwave damping response caused by weak atmospheric ascent in response to east Pacific sea surface temperature anomalies during El Niños as well as a weak response of clouds (Lloyd et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…ENSO amplitude and period are often diverse in multi-model ensembles. Here the focus is on the dominant positive and negative ENSO feedbacks, which are found in a number of studies assessing ENSO feedbacks in models to be areas for improvement (Guilyardi 2006;Lin 2007;Lloyd et al 2009Lloyd et al , 2012Kim and Jin 2010a;Bellenger et al 2013;Kim et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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ENSO feedbacks and their relationships with the mean state in a flux adjusted ensemble

Ferrett

Collins

2016

Clim Dyn

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“…The horizontal resolution of the sea ice component is exactly the same as the ocean component [49,50], while that of the land surface component is the same as the atmospheric component. In the tropical Pacific, the model captures well the El Niño-Southern Oscillation (ENSO) [51,52] variability and cloud radiative forcing [53]. However, there are severe warm biases over the ACT region, as most of the CCMs have (detailed in Section 2.3) [4].…”

Section: Introductionmentioning

confidence: 99%

Origin of Warm SST Bias over the Atlantic Cold Tongue in the Coupled Climate Model FGOALS-g2

et al. 2018

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Abstract:Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10 • S-3 • N, 20 • W-10 • E) during June-August (JJA) and September-November (SON). In this study, the origins of the ACT SST bias and their relative contributions to the bias are explored by conducting a set of sensitivity experiments, which are based on an ocean-ice model, and by ignoring the nonlinear effects of each origin. The origins for the warm bias over the ACT in the coupled climate model during JJA are estimated as follows: westerly wind bias along the equator (5 • S-5 • N) during March-May (MAM; contributes approximately 32.6% of the warm bias), northerly bias over the southern tropical Atlantic (25 • S-3 • N, 40 • W-20 • E) during MAM and JJA (21.4%), bias in the surface specific humidity and surface air temperature (11.9%), and downward shortwave radiation bias (6.5%). The origins of the ACT bias during SON are as follows: northerly bias over the southern tropical Atlantic during SON (31.2%), bias in the surface specific humidity and surface air temperature (27.9%), downward shortwave radiation bias (17.4%), and zonal wind bias (13.4%). Note that these contribution ratios of these origins may be model-dependent. In addition, the local and non-local effects of the zonal wind bias are explored explicitly, while those of all the other biases are examined implicitly. Therefore, a better-performing atmospheric component is crucial when simulating zonal winds during MAM along the equator (5 • S-5 • N) and meridional winds during MAM, JJA, and SON over the southern tropical Atlantic, which will alleviate the warm bias over the ACT region in the coupled climate model.

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ENSO stability in coupled climate models and its association with mean state

Cited by 131 publications

References 50 publications

An Intermodel Approach to Identify the Source of Excessive Equatorial Pacific Cold Tongue in CMIP5 Models and Uncertainty in Observational Datasets

An Intermodel Approach to Identify the Source of Excessive Equatorial Pacific Cold Tongue in CMIP5 Models and Uncertainty in Observational Datasets

ENSO feedbacks and their relationships with the mean state in a flux adjusted ensemble

Origin of Warm SST Bias over the Atlantic Cold Tongue in the Coupled Climate Model FGOALS-g2

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