Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific

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

doi:10.1029/2019GL086765

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Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific

2020
By Ding, Hui ; Newman, Matthew ; Alexander, Michael A. ; ...
Source: Geophysical Research Letters 47(5): e2019GL086765, 2020

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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) ; GFDL (Geophysical Fluid Dynamics Laboratory) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:

We examine links between tropical Pacific mean state biases and El Niño/Southern Oscillation forecast skill, using model‐analog hindcasts of sea surface temperature (SST; 1961–2015) and precipitation (1979–2015) at leads of 0–12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model‐analog forecast skill has been demonstrated to match or even exceed traditional assimilation‐initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10‐m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6‐month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model‐analog hindcast skill—that is, how well a model can capture the observed evolution of tropical Pacific anomalies—is an informative El Niño/Southern Oscillation metric for climate simulations.
Keywords:

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Numerical weather forecasting El Niño/Southern Oscillation Sea Surface Temperatures
Source:

Geophysical Research Letters 47(5): e2019GL086765, 2020
DOI:

https://doi.org/10.1029/2019GL086765
Document Type:

Journal Article
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Pacific Ocean
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urn:sha256:0da7ef598b18dc3d1160d8a149d1ca789aa131fd0606628e015fd231cb627ffd
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Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific

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