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2. ENSO Dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 Climate Models

Author

Fei-Fei-Jin, Han-Ching Chen, Shaocheng Xie, Sen Zhao, and Andrew T. Wittenberg

Subjects
Atmospheric Science, El Niño Southern Oscillation, Climatology, Environmental science, Climate model
Abstract

This study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO’s key features, including: amplitude; timescale; spatial patterns; phase-locking; spring persistence barrier; and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models’ weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO’s sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models’ excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTA to extend farther west than observed. The models underestimate both ENSO’s positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed.

Published
2021

3. Simulations of ENSO Phase-Locking in CMIP5 and CMIP6

Author

Fei-Fei Jin and Han-Ching Chen

Subjects
Atmospheric Science, El Niño Southern Oscillation, Climatology, Environmental science, Phase locking
Abstract

The characteristics of El Niño–Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strength or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is codetermined by the seasonal modulations of both SST growth and phase transition rates. Further study of the joint effects of SST growth and phase transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: 1) correct phase of SST growth rate modulation peaking at the fall, 2) large-enough amplitude for the annual cycle in growth rate, 3) small amplitude of semiannual cycle in growth rate, and 4) small amplitude of seasonal modulation in SST phase transition rate.

Published
2021

4. Fundamental Behavior of ENSO Phase Locking

Author

Han-Ching Chen and Fei-Fei Jin

Subjects
Physics, Atmospheric Science, El Niño Southern Oscillation, 010504 meteorology & atmospheric sciences, Oscillation, Climatology, 010502 geochemistry & geophysics, 01 natural sciences, Physics::Atmospheric and Oceanic Physics, Phase locking, Physics::Geophysics, 0105 earth and related environmental sciences
Abstract

El Niño–Southern Oscillation (ENSO) events tend to peak at the end of the calendar year, a phenomenon called ENSO phase locking. This phase locking is a fundamental ENSO property that is determined by its basic dynamics. The conceptual ENSO recharge oscillator (RO) model is adopted to examine the ENSO phase-locking behavior in terms of its peak time, strength of phase locking, and asymmetry between El Niño and La Niña events. The RO model reproduces the main phase-locking characteristics found in observations, and the results show that the phase locking of ENSO is mainly dominated by the seasonal modulation of ENSO growth/decay rate. In addition, the linear/nonlinear mechanism of ENSO phase preference/phase locking is investigated using RO model. The difference between the nonlinear phase-locking mechanism and linear phase-preference mechanism is largely smoothed out in the presence of noise forcing. Further, the impact on ENSO phase locking from annual cycle modulation of the growth/decay rate, stochastic forcing, nonlinearity, and linear frequency are examined in the RO model. The preferred month of ENSO peak time depends critically on the phase and strength of the seasonal modulation of the ENSO growth/decay rate. Furthermore, the strength of phase locking is mainly controlled by the linear growth/decay rate, the amplitude of seasonal modulation of growth/decay rate, the amplitude of noise, the SST-dependent factor of multiplicative noise, and the linear frequency. The asymmetry of the sharpness of ENSO phase locking is induced by the asymmetric effect of state-dependent noise forcing in El Niño and La Niña events.

Published
2020

8. Combined Role of High- and Low-Frequency Processes of Equatorial Zonal Transport in Terminating an ENSO Event

Author

Han-Ching Chen, Bohua Huang, Chung-Hsiung Sui, and Yu-Heng Tseng

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Event (relativity), Phase (waves), Low frequency, 010502 geochemistry & geophysics, 01 natural sciences, Ocean dynamics, symbols.namesake, El Niño Southern Oscillation, Climatology, symbols, Thermocline, Kelvin wave, Geology, 0105 earth and related environmental sciences
Abstract

This study investigates the sudden reversal of anomalous zonal equatorial transport above thermocline at the peak phase of ENSO. The oceanic processes associated with zonal transport are separated into low-frequency ENSO cycle and high-frequency oceanic wave processes. Both processes can generate a reversal of equatorial zonal current at the ENSO peak phase, which is a trigger for the rapid termination of ENSO events. For the low-frequency process, zonal transport exhibits slower and basinwide evolution. During the developing phase of El Niño (La Niña), eastward (westward) transport prevails in the central-eastern Pacific, which enhances ENSO. At the peak of ENSO, a basinwide reversal of the zonal transport resulting from the recharge–discharge process occurs and weakens the existing SST anomalies. High-frequency zonal transport presents clear eastward propagation related to Kelvin wave propagation at the equator, reflection at the eastern boundary, and the westward propagating Rossby waves. The major westerly wind bursts (easterly wind surges) occur in late boreal summer and fall with coincident downwelling (upwelling) Kelvin waves for El Niño (La Niña) events. After the peak of El Niño (La Niña), Kelvin waves reach the eastern boundary in boreal winter and reflect as off-equatorial Rossby waves; then, the zonal transport switches from eastward (westward) to westward (eastward). The high-frequency zonal transport can be represented by equatorial wave dynamics captured by the first three EOFs based on the high-pass-filtered equatorial thermocline. The transport anomaly during the decaying phase is dominated by the low-frequency process in El Niño. However, the transport anomaly is caused by both low- and high-frequency processes during La Niña.

Published
2018

9. Enhancing the ENSO Predictability beyond the Spring Barrier

Author

Ruiqiang Ding, Zeng-Zhen Hu, Yu-Heng Tseng, and Han-Ching Chen

Subjects
Tropical pacific, Multidisciplinary, 010504 meteorology & atmospheric sciences, lcsh:R, Forecast skill, lcsh:Medicine, 010502 geochemistry & geophysics, 01 natural sciences, Article, Atmosphere, El Niño Southern Oscillation, Ocean sciences, 13. Climate action, Climatology, Extratropical cyclone, Environmental science, Atmospheric science, Climate change, lcsh:Q, 14. Life underwater, Predictability, lcsh:Science, 0105 earth and related environmental sciences
Abstract

El Niño-Southern Oscillation (ENSO) is the dominant interseasonal–interannual variability in the tropical Pacific and substantial efforts have been dedicated to predicting its occurrence and variability because of its extensive global impacts. However, ENSO predictability has been reduced in the 21st century, and the impact of extratropical atmosphere on the tropics has intensified during the past 2 decades, making the ENSO more complicated and harder to predict. Here, by combining tropical preconditions/ocean–atmosphere interaction with extratropical precursors, we provide a novel approach to noticeably increase the ENSO prediction skill beyond the spring predictability barrier. The success of increasing the prediction skill results mainly from the longer lead-time of the extratropical–tropical ocean-to-atmosphere interaction process, especially for the first 2 decades of the 21st century.

Published
2019

10. Author Correction: El Niño–Southern Oscillation complexity

Author

Wenju Cai, Karl Stein, Pamela R. Grothe, Sang-Wook Yeh, Jin-Ho Yoon, Tim Li, WonMoo Kim, Yann Planton, Sunyong Kim, Xuebin Zhang, Hong Li Ren, Agus Santoso, Ken Takahashi, Yoshimitsu Chikamoto, Scott B. Power, Sarah Ineson, Fei-Fei Jin, Matthieu Lengaigne, Kyung-Sook Yun, Daehyun Kang, Han Ching Chen, Woo Hyun Yang, Alexander Todd, Boris Dewitte, Andrew T. Wittenberg, June-Yi Lee, Eric Guilyardi, Shayne McGregor, Kim M. Cobb, Michiya Hayashi, Dietmar Dommenget, Tobias Bayr, Jong-Seong Kug, Axel Timmermann, Malte F. Stuecker, Ruihuang Xie, Soon Il An, Antonietta Capotondi, Michael J. McPhaden, Elke Zeller, Harun Rashid, Guomin Wang, Guojian Wang, Jing-Jia Luo, Yoo-Geun Ham, Institute of Basic Science (IBS), International Pacific Research Center (IPRC), School of Ocean and Earth Science and Technology (SOEST), University of Hawai‘i [Mānoa] (UHM)-University of Hawai‘i [Mānoa] (UHM), Department of Atmospheric Sciences [Seoul], Yonsei University, Pohang University of Science and Technology (POSTECH), Unité de Mathématiques Pures et Appliquées (UMPA-ENSL), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon), CSIRO Marine and Atmospheric Research [Aspendale], Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA), School of Earth and Atmospheric Sciences [Atlanta], Georgia Institute of Technology [Atlanta], Océan et variabilité du climat (VARCLIM), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), National Taiwan University [Taiwan] (NTU), Department of Plants, Soils and Climate, Utah State University (USU), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), School of Earth, Atmosphere and Environment, Monash University, Monash University [Clayton], University of Hawai‘i [Mānoa] (UHM), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Department of Civil and Environmental Engineering, ATLSS Center, Lehigh University, Centre for Australian Weather and Climate Research (CAWCR), University of Sussex, University of South Wales (USW), University of Occupational and Environmental Health [Kitakyushu] (UEOH), College of Engineering, Mathematics and Physical Sciences [Exeter] (EMPS), University of Exeter, Center for Ocean-Land-Atmosphere Studies (COLA), Gwangju Institute of Science and Technology (GIST), CSIRO Marine and Atmosphere Research [Hobart], École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), and School of Earth, Atmosphere and Environment

Subjects
Multidisciplinary, El Niño Southern Oscillation, Climatology, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], CobB, Mathematics
Abstract

In this Review, the middle initial of author Kim M. Cobb was omitted. The original Review has been corrected online.

Published
2019

11. The Role of Reversed Equatorial Zonal Transport in Terminating an ENSO Event

Author

Zeng-Zhen Hu, Han-Ching Chen, Bohua Huang, and Chung-Hsiung Sui

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Advection, Ocean current, Equator, Zonal and meridional, 010502 geochemistry & geophysics, Atmospheric sciences, 01 natural sciences, Physics::Geophysics, Ocean dynamics, El Niño Southern Oscillation, Climatology, Physics::Space Physics, Thermocline, Physics::Atmospheric and Oceanic Physics, Geology, 0105 earth and related environmental sciences
Abstract

This study shows the sudden basinwide reversal of anomalous equatorial zonal transport above the thermocline at the peaking phase of ENSO triggers rapid termination of ENSO events. The anomalous equatorial zonal transport is controlled by the concavity of anomalous thermocline meridional structure across the equator. During the developing phase of ENSO, opposite zonal transport anomalies form in the western-central and central-eastern equatorial Pacific, respectively. Both are driven by the equatorial thermocline anomalies in response to zonal wind anomalies over the western-central equatorial ocean. At this stage, the anomalous zonal transport in the east enhances ENSO growth through zonal SST advection. In the mature phase of ENSO, off-equatorial thermocline depth anomalies become more dominant in the eastern Pacific because of the reflection of equatorial signals at the eastern boundary. As a result, the meridional concavity of the thermocline anomalies is reversed in the east. This change reverses zonal transport rapidly in the central-to-eastern equatorial Pacific, joining with the existing reversed zonal transport anomalies farther to the west, and forms a basinwide transport reversal throughout the equatorial Pacific. This basinwide transport reversal weakens the ENSO SST anomalies by reversed advection. More importantly, the reversed zonal transport reduces the existing zonal tilting of the equatorial thermocline and weakens its feedback to wind anomalies effectively. This basinwide reversal is built in at the peak phase of ENSO as an oceanic control on the evolution of both El Niño and La Niña events. The reversed zonal transport anomaly after the mature phase weakens El Niño in the eastern Pacific more efficiently than it weakens La Niña.

Published
2016

12. An ENSO prediction approach based on ocean conditions and ocean–atmosphere coupling

Author

Han-Ching Chen, Ruiqiang Ding, Yu-Heng Tseng, and Zeng-Zhen Hu

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Anomaly (natural sciences), Multivariate ENSO index, 010502 geochemistry & geophysics, Atmospheric sciences, 01 natural sciences, La Niña, Sea surface temperature, symbols.namesake, Climatology, Extratropical cyclone, symbols, Environmental science, Hindcast, Thermocline, Kelvin wave, 0105 earth and related environmental sciences
Abstract

A simple statistical model for the El Nino–Southern Oscillation (ENSO) prediction is derived based on the evolution of the ocean heat condition and the oceanic Kelvin wave propagation associated with westerly wind events (WWEs) and easterly wind surges (EWSs) in the tropical Pacific. The multivariate linear regression model solely relies on the pentad thermocline depth anomaly evolution in 25 days along with the zonal surface wind modulation. It successfully hindcasts all ENSOs except for the 2000/01 La Nina, using the pentad (or monthly) mean tropical atmosphere ocean array data since 1994 with an averaged skill (measured by anomaly correlation) of 0.62 (or 0.67) with a 6-month lead. The exception is mainly due to the long-lasting cold sea surface temperature anomalies in the subtropics resulting from the strong 1998/99 La Nina, even though the tropical warm water volume (WWV) had rebounded and turned phases after 2000. We also note that the hindcast skill is comparable using pentad or monthly mean NCEP global ocean data assimilation system data for the same time period. The hindcast skill of the proposed statistical model is better than that based on the WWV index in terms of the monthly correlation, normalized RMSEs and ENSO occurrences, which suggest that including the evolution of the subsurface ocean temperature anomaly and the WWEs/EWSs in the central tropical Pacific can enhance the ability to predict ENSO. The hindcast skill is also comparable to the predictions using other dynamical and statistical models, indicating that these processes are the keys to ENSO development. The dynamics behind the statistical model are consistent with the physical processes of ENSO development as follows: the tropical WWV resulting from the interannually-varying meridional subtropical cell transport provides a sufficient heat source. When the seasonal phase lock of ocean–atmosphere coupling triggers the positive (negative) zonal wind anomaly in boreal summer and fall, an El Nino (a La Nina) will develop as evidenced by the Kelvin wave propagation. The triggering dynamic may be suppressed or enhanced by the influence of extratropical Pacific sea surface temperature.

Published
2016

13. El Niño–Southern Oscillation complexity

Author

Ken Takahashi, Matthieu Lengaigne, Guomin Wang, Tim Li, Antonietta Capotondi, Tobias Bayr, Michael J. McPhaden, Axel Timmermann, WonMoo Kim, Wenju Cai, Malte F. Stuecker, Sunyong Kim, Ruihuang Xie, Harun Rashid, Fei-Fei Jin, Alexander Todd, Jong-Seong Kug, Boris Dewitte, Sang-Wook Yeh, Yoo-Geun Ham, Kim M. Cobb, Elke Zeller, Agus Santoso, Scott B. Power, Dietmar Dommenget, Daehyun Kang, Michiya Hayashi, Soon Il An, Kyung-Sook Yun, Pamela R. Grothe, Guojian Wang, June-Yi Lee, Karl Stein, Jing-Jia Luo, Shayne McGregor, Eric Guilyardi, Yoshimitsu Chikamoto, Xuebin Zhang, Sarah Ineson, Andrew T. Wittenberg, Woo Hyun Yang, Han Ching Chen, Jin-Ho Yoon, Yann Planton, Hong Li Ren, Institute of Basic Science (IBS), Pusan National University, Department of Atmospheric Sciences, Yonsei University, Pohang University of Science and Technology (POSTECH), School of Ocean and Earth Science and Technology (SOEST), University of Hawai‘i [Mānoa] (UHM), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Qingdao National Laboratory for Marine Science and Technology, NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado [Boulder]-National Oceanic and Atmospheric Administration (NOAA), School of Earth and Atmospheric Sciences [Atlanta], Georgia Institute of Technology [Atlanta], Océan et variabilité du climat (VARCLIM), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), National Taiwan University [Taiwan] (NTU), Department of Plants, Soils and Climate, Utah State University (USU), Centro de Estudios Avanzados en Zonas Aridas (CEAZA), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), School of Earth, Atmosphere and Environment, Monash University, Monash University [Clayton], NCAS-Climate [Reading], Department of Meteorology [Reading], University of Reading (UOR)-University of Reading (UOR), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Centre for Australian Weather and Climate Research (CAWCR), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), ARC Centre of Excellence for Climate System Science, University of New South Wales [Sydney] (UNSW)-Australian Research Council [Canberra] (ARC), Instituto Geofísico del Perú (IGP), Exeter Climate Systems, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, United Kingdom, Korean Ocean Research and Development Institute (KORDI), Gwangju Institute of Science and Technology (GIST), CSIRO, Oceans and Atmosphere, Hobart, TAS, Australia, Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), School of Earth, Atmosphere and Environment, Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), and University of Exeter

Subjects
El Nino-Southern Oscillation, Tropical Climate, Multidisciplinary, 010504 meteorology & atmospheric sciences, Climate Change, Equator, Climate change, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 15. Life on land, 010502 geochemistry & geophysics, 01 natural sciences, Earth system science, Sea surface temperature, La Niña, El Niño, 13. Climate action, Climatology, Tropical climate, Water Movements, Environmental science, Terrestrial ecosystem, 14. Life underwater, 0105 earth and related environmental sciences
Abstract

El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.

Published
2018

14. An Analysis of the Linkage of Pacific Subtropical Cells with the Recharge–Discharge Processes in ENSO Evolution

Author

Chung-Hsiung Sui, Han-Ching Chen, Yu-Heng Tseng, and Bohua Huang

Subjects
Atmospheric Science, Sea surface temperature, El Niño Southern Oscillation, Simple Ocean Data Assimilation, Climatology, Tropical climate, Environmental science, Groundwater recharge, Stage (hydrology), Subtropics, Structural basin
Abstract

The Simple Ocean Data Assimilation, version 2.2.4 (SODA 2.2.4), analysis for the period of 1960–2010 is used to study the variability of Pacific subtropical cells (STCs) and its causal relation with tropical climate variability. Results show that the interior STC transport into the equatorial basin through 9°S and 9°N is well connected with equatorial sea surface temperature (SST) (9°S–9°N, 180°–90°W). The highest correlation at interannual time scales is contributed by the western interior STC transport within 160°E and 130°W. It is known that the ENSO recharge–discharge cycle experiences five stages: the recharging stage, recharged stage, warmest SST stage, discharging stage, and discharged stage. A correlation analysis of interior STC transport convergence, equatorial warm water volume (WWV), wind stress curl, and SST identifies the time intervals between the five stages, which are 8, 10, 2, and 8 months, respectively. A composite analysis for El Niño–developing and La Niña–developing events is also performed. The composited ENSO evolutions are in accordance with the recharge–discharge theory and the corresponding time lags between the above denoted five stages are 4–12, 6, 2, and 4 months, respectively. For stronger El Niño events, the discharge due to interior STC transport at 9°N terminates earlier than that at 9°S because of the southward migration of westerly winds following the El Niño peak phase. This study clarifies subsurface transport processes and their time intervals, which are useful for refinement of theoretical models and for evaluating coupled ocean–atmosphere general circulation model results.

Published
2015

15. An Assessment of the Impact of ATMS and CrIS Data Assimilation on Precipitation Prediction over the Tibetan Plateau

Author

Tong Xue, Jianjun Xu, Zhaoyong Guan, Long S. Chiu, Han-Ching Chen, and Min Shao

Abstract

Using the National Oceanic and Atmospheric Administration’s Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research’s Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and shows an orographic bias described as an overestimation in the upwind of the mountains and an underestimation in the south of the rainbelt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24-hourand last 24-hour accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy rainfall periods of 3–6, 13–16, and 22–25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3–6 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy rainfall process than the control. Overall, the satellite data assimilation can enhance the WRF-ARW model’s ability to predict the spatial and temporal pattern of precipitation in July 2015 although the model capability exists a significant limitation in the complex terrain area.

Published
2017

16. Multiple-Decision Procedures for Testing the Homogeneity of Mean for k Exponential Distributions

Author

Nae-Sheng Wang, Her Pei Shan, and Han-Ching Chen

Subjects
Article Subject, Homogeneity (statistics), lcsh:Mathematics, Decision rule, Critical value, lcsh:QA1-939, Upper and lower bounds, Infimum and supremum, Exponential function, Sample size determination, Modeling and Simulation, Statistics, Null hypothesis, Mathematics
Abstract

In multiple-decision procedures, a crucial objective is to determine the association between the probability of a correct decision (CD) and the sample size. A review of some methods is provided, including a subset selection formulation proposed by Huang and Panchapakesan, a multidecision procedure for testing the homogeneity of means by Huang and Lin, and a similar procedure for testing the homogeneity of variances by Lin and Huang. In this paper, we focus on the use of the Lin and Huang method for testing the null hypothesisH0of homogeneity of means forkexponential distributions. We discuss the decision ruleR, evaluation of the critical valueC, and the infimum ofP(CD∣R)forkindependent random samples fromkexponential distributions. In addition, we also observed that a lower bound for the probability of CD relative to the number of the common sample size is determined based on the desired probability of CD when the largest mean is sufficiently larger than the other means. We explain the results by using two examples.

Published
2014
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