MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics

Wang, Jiabao; Kim, Hyemi; Kim, Daehyun; Henderson, Stephanie A.; Stan, Cristiana; Maloney, Eric D.

doi:10.1175/JCLI-D-19-0253.1

Title: MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics

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Abstract

Abstract We propose a set of MJO teleconnection diagnostics that enables an objective evaluation of model simulations, a fair model-to-model comparison, and a consistent tracking of model improvement. Various skill metrics are derived from teleconnection diagnostics including five performance-based metrics that characterize the pattern, amplitude, east–west position, persistence, and consistency of MJO teleconnections and additional two process-oriented metrics that are designed to characterize the location and intensity of the anomalous Rossby wave source (RWS). The proposed teleconnection skill metrics are used to compare the characteristics of boreal winter MJO teleconnections (500-hPa geopotential height anomaly) over the Pacific–North America (PNA) region in 29 global climate models (GCMs). The results show that current GCMs generally produce MJO teleconnections that are stronger, more persistent, and extend too far to the east when compared to those observed in reanalysis. In general, models simulate more realistic teleconnection patterns when the MJO is in phases 2–3 or phases 7–8, which are characterized by a dipole convection pattern over the Indian Ocean and western to central Pacific. The higher model skill for phases 2, 7, and 8 may be due to these phases producing more consistent teleconnection patterns between individual MJO events than other phases, although themore »« less

Authors:

Wang, Jiabao ^[1];

^[1]; Kim, Daehyun ^[2]; Henderson, Stephanie A. ^[3]; Stan, Cristiana ^[4]; Maloney, Eric D. ^[5]

School of Marine and Atmospheric Sciences, Stony Brook University, State University of New York, Stony Brook, New York
Department of Atmospheric Sciences, University of Washington, Seattle, Washington
Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin
Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Publication Date:: Tue Jan 07 00:00:00 EST 2020

Research Org.:: Univ. of Washington, Seattle, WA (United States)

Sponsoring Org.:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:: 1581477

Alternate Identifier(s):: OSTI ID: 1659502

Grant/Contract Number:: SC0016223; AGS-1652289; NA16OAR4310070; KMI2018-03110; NA18OAR4310300; 80NSSC17K0227; AGS-1441916; AGS-1841754; NA16OAR4310064; NA18OAR4310299

Resource Type:: Published Article

Journal Name:: Journal of Climate

Additional Journal Information:: Journal Name: Journal of Climate Journal Volume: 33 Journal Issue: 3; Journal ID: ISSN 0894-8755

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES

Citation Formats


                    Wang, Jiabao, Kim, Hyemi, Kim, Daehyun, Henderson, Stephanie A., Stan, Cristiana, and Maloney, Eric D. MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics.  United States: N. p., 2020. 
Web.  doi:10.1175/JCLI-D-19-0253.1.

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                    Wang, Jiabao, Kim, Hyemi, Kim, Daehyun, Henderson, Stephanie A., Stan, Cristiana, & Maloney, Eric D. MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics.  United States.  https://doi.org/10.1175/JCLI-D-19-0253.1

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                    Wang, Jiabao, Kim, Hyemi, Kim, Daehyun, Henderson, Stephanie A., Stan, Cristiana, and Maloney, Eric D. Tue .  
"MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics".  United States.  https://doi.org/10.1175/JCLI-D-19-0253.1.

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@article{osti_1581477,

  title        = {MJO Teleconnections over the PNA Region in Climate Models. Part I: Performance- and Process-Based Skill Metrics},

  author       = {Wang, Jiabao and Kim, Hyemi and Kim, Daehyun and Henderson, Stephanie A. and Stan, Cristiana and Maloney, Eric D.},

  abstractNote = {Abstract We propose a set of MJO teleconnection diagnostics that enables an objective evaluation of model simulations, a fair model-to-model comparison, and a consistent tracking of model improvement. Various skill metrics are derived from teleconnection diagnostics including five performance-based metrics that characterize the pattern, amplitude, east–west position, persistence, and consistency of MJO teleconnections and additional two process-oriented metrics that are designed to characterize the location and intensity of the anomalous Rossby wave source (RWS). The proposed teleconnection skill metrics are used to compare the characteristics of boreal winter MJO teleconnections (500-hPa geopotential height anomaly) over the Pacific–North America (PNA) region in 29 global climate models (GCMs). The results show that current GCMs generally produce MJO teleconnections that are stronger, more persistent, and extend too far to the east when compared to those observed in reanalysis. In general, models simulate more realistic teleconnection patterns when the MJO is in phases 2–3 or phases 7–8, which are characterized by a dipole convection pattern over the Indian Ocean and western to central Pacific. The higher model skill for phases 2, 7, and 8 may be due to these phases producing more consistent teleconnection patterns between individual MJO events than other phases, although the consistency is lower in most models than observed. Models that simulate realistic RWS patterns better reproduce MJO teleconnection patterns.},

  doi          = {10.1175/JCLI-D-19-0253.1},

  journal      = {Journal of Climate},

  number       = 3,

  volume       = 33,

  place        = {United States},

  year         = {Tue Jan 07 00:00:00 EST 2020},

  month        = {Tue Jan 07 00:00:00 EST 2020}

}

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