Open Access
ENSO representation in climate models: from CMIP3 to CMIP5
TLDR
In this article, the ability of CMIP3 and CMIP5 coupled ocean-atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Nino-Southern Oscillation (ENSO) was analyzed.Abstract:
We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Nino-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.read more
Citations
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Journal ArticleDOI
The Pacific Decadal Oscillation, Revisited
Matthew Newman,Matthew Newman,Michael A. Alexander,Toby R. Ault,Kim M. Cobb,Clara Deser,Emanuele Di Lorenzo,Nathan J. Mantua,Arthur J. Miller,Shoshiro Minobe,Hisashi Nakamura,Niklas Schneider,Daniel J. Vimont,Adam S. Phillips,James D. Scott,James D. Scott,Catherine A. Smith,Catherine A. Smith +17 more
TL;DR: In this article, the Pacific decadal oscillation (PDO) is not a single phenomenon, but is instead the result of a combination of different physical processes, including remote tropical forcing and local North Pacific atmosphere-ocean interactions, which operate on different time scales to drive similar PDO-like SST anomaly patterns.
Journal ArticleDOI
Understanding ENSO Diversity
Antonietta Capotondi,Andrew T. Wittenberg,Matthew Newman,Emanuele Di Lorenzo,Jin-Yi Yu,Pascale Braconnot,Julia E. Cole,Boris Dewitte,Benjamin S. Giese,Eric Guilyardi,Fei-Fei Jin,Kristopher B. Karnauskas,Benjamin Kirtman,Tong Lee,Niklas Schneider,Yan Xue,Sang-Wook Yeh +16 more
TL;DR: The authors surveys the current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions, as well as identifying key gaps and promising future directions.
Journal ArticleDOI
ENSO and greenhouse warming
Wenju Cai,Wenju Cai,Agus Santoso,Guojian Wang,Sang-Wook Yeh,Soon Il An,Kim M. Cobb,Mat Collins,Eric Guilyardi,Eric Guilyardi,Fei-Fei Jin,Jong-Seong Kug,Matthieu Lengaigne,Michael J. McPhaden,Ken Takahashi,Axel Timmermann,Gabriel A. Vecchi,Masahiro Watanabe,Lixin Wu +18 more
TL;DR: A review of the state of knowledge on the El Nino/Southern Oscillation (ENSO), a natural climate phenomenon, can be found in this article, where the authors discuss recent advances and insights into how climate change will affect this natural climate varibility cycle.
Journal ArticleDOI
El Niño–Southern Oscillation complexity
Axel Timmermann,Axel Timmermann,Soon Il An,Jong-Seong Kug,Fei-Fei Jin,Wenju Cai,Wenju Cai,Wenju Cai,Antonietta Capotondi,Antonietta Capotondi,Kim M. Cobb,Matthieu Lengaigne,Michael J. McPhaden,Malte F. Stuecker,Malte F. Stuecker,Karl Stein,Andrew T. Wittenberg,Kyung-Sook Yun,Tobias Bayr,Han Ching Chen,Yoshimitsu Chikamoto,Boris Dewitte,Dietmar Dommenget,Pamela R. Grothe,Eric Guilyardi,Eric Guilyardi,Yoo-Geun Ham,Michiya Hayashi,Sarah Ineson,Daehyun Kang,Sunyong Kim,WonMoo Kim,June-Yi Lee,Tim Li,Jing-Jia Luo,Shayne McGregor,Yann Planton,Scott B. Power,Harun Rashid,Hong Li Ren,Agus Santoso,Ken Takahashi,Alexander Todd,Guomin Wang,Guojian Wang,Ruihuang Xie,Woo Hyun Yang,Sang-Wook Yeh,Jin-Ho Yoon,Elke Zeller,Xuebin Zhang +50 more
TL;DR: A synopsis of the current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system is provided and a unifying framework that identifies the key factors for this complexity is proposed.
Journal ArticleDOI
Deep learning for multi-year ENSO forecasts
TL;DR: It is shown that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years, overcoming a weakness of dynamical forecast models.
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