Diagnosing Secular Variations in Retrospective ENSO Seasonal Forecast Skill Using CMIP5 Model-Analogs

Ding, Hui; Newman, Matthew; Alexander, Michael A.; Wittenberg, Andrew T.

doi:10.1029/2018GL080598

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Diagnosing Secular Variations in Retrospective ENSO Seasonal Forecast Skill Using CMIP5 Model-Analogs

2019
By Ding, Hui ; Newman, Matthew ; Alexander, Michael A. ; ...
Source: Geophysical Research Letters 46(3): 1721-1730, 2019

[PDF-3.76 MB]

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Journal Title:

Geophysical Research Letters
Personal Author:

Ding, Hui ; Newman, Matthew ; Alexander, Michael A. ; Wittenberg, Andrew T.

Ding, Hui ; Newman, Matthew ; Alexander, Michael A. ; Wittenberg, Andrew T. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory) ; GFDL (Geophysical Fluid Dynamics Laboratory) ; CPO (Climate Program Office) ; ... More +

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory) ; GFDL (Geophysical Fluid Dynamics Laboratory) ; CPO (Climate Program Office) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:

Retrospective tropical Indo‐Pacific forecasts for 1961–2015 are made using 28 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) plus four models from the North American Multi‐Model Ensemble (NMME), using a model‐analog technique. Forecast ensembles are extracted from preexisting model simulations, by finding those states that initially best match an observed anomaly and tracking their subsequent evolution, requiring no additional model integrations. Model‐analog forecasts from the 10 “best” CMIP5 models have skill for sea surface temperature and precipitation comparable to that of both the NMME model‐analog forecast ensemble and (since 1982) traditional assimilation‐initialized NMME hindcasts. The El Niño–Southern Oscillation (ENSO) forecast skill has no trend over the 55‐year period, and its decadal variations appear largely random, although the skill does improve during epochs of increased ENSO activity. Including the CMIP5‐projected effects of external radiative forcings improves the tropical sea surface temperature skill of the model‐analog forecasts but not within the ENSO region.
Keywords:

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Climatology El Niño Current Mathematical Models
Source:

Geophysical Research Letters 46(3): 1721-1730, 2019
DOI:

https://doi.org/10.1029/2018GL080598
Document Type:

Journal Article
Place as Subject:

Pacific Ocean
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urn:sha256:aea5b842aab5d07dd67f17db28c0ea67cfd2ea9a441502c81a716d2e5d8f2563
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Diagnosing Secular Variations in Retrospective ENSO Seasonal Forecast Skill Using CMIP5 Model-Analogs

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