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Title: Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

Abstract

Hindcasts and real-time predictions of the east-central tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982–2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002–2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledgingmore » a hindcast versus real-time inconsistency in the 2002–2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Columbia Univ., New York, NY (United States). Earth Institute
  2. Columbia Univ., New York, NY (United States); King Abdulaziz Univ., Jeddah (Saudi Arabia)
  3. Swathmore College, PA (United States)
  4. National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). Climate Prediction Center
Publication Date:
Research Org.:
Columbia Univ., New York, NY (United States)
Sponsoring Org.:
USDOE; National Oceanic and Atmospheric Administration (NOAA); US Department of the Navy, Office of Naval Research (ONR)
OSTI Identifier:
1623604
Grant/Contract Number:  
NA12OAR4310082; NA14OAR431018; N00014-12-1-0911; N00014-16-1-2073
Resource Type:
Accepted Manuscript
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Volume: 53; Journal Issue: 12; Journal ID: ISSN 0930-7575
Publisher:
Springer-Verlag
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; meteorology & atmospheric sciences; ENSO predictability; El Nino Southern Oscillation (ENSO); anomaly correlation; hindcast; plume prediction

Citation Formats

Barnston, Anthony G., Tippett, Michael K., Ranganathan, Meghana, and L’Heureux, Michelle L. Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. United States: N. p., 2017. Web. doi:10.1007/s00382-017-3603-3.
Barnston, Anthony G., Tippett, Michael K., Ranganathan, Meghana, & L’Heureux, Michelle L. Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. United States. https://doi.org/10.1007/s00382-017-3603-3
Barnston, Anthony G., Tippett, Michael K., Ranganathan, Meghana, and L’Heureux, Michelle L. Mon . "Deterministic skill of ENSO predictions from the North American Multimodel Ensemble". United States. https://doi.org/10.1007/s00382-017-3603-3. https://www.osti.gov/servlets/purl/1623604.
@article{osti_1623604,
title = {Deterministic skill of ENSO predictions from the North American Multimodel Ensemble},
author = {Barnston, Anthony G. and Tippett, Michael K. and Ranganathan, Meghana and L’Heureux, Michelle L.},
abstractNote = {Hindcasts and real-time predictions of the east-central tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982–2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002–2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconsistency in the 2002–2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.},
doi = {10.1007/s00382-017-3603-3},
journal = {Climate Dynamics},
number = 12,
volume = 53,
place = {United States},
year = {Mon Mar 13 00:00:00 EDT 2017},
month = {Mon Mar 13 00:00:00 EDT 2017}
}

Journal Article:
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Figures / Tables:

Table 1 Table 1: Basic information for the 8 models of the NMME used in the study

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