TY - JOUR
T1 - WWBs, ENSO predictability, the spring barrier and extreme events
AU - Lopez, Hosmay
AU - Kirtman, Ben P.
N1 - Funding Information:
The data supporting the results presented here can be requested from the corresponding author Hosmay Lopez via email to hlopez@rsmas. miami.edu. The research results reported here were supported by grants from the NSF AGS1137911, and NOAA (NA10OAR4310164, NA10AOR4310203, and NA12OAR4310089). The authors also acknowledge computational support from the University of Miami Center for Computational Science.
PY - 2014/9/16
Y1 - 2014/9/16
N2 - This study examines how semi-stochastic Westerly Wind Bursts (WWBs) affect El Niño Southern Oscillation (ENSO) predictability. An ensemble ENSO prediction experiment is presented in which the Community Climate System Model version 3 (CCSM3) and CCSM3 with a state-dependent WWB parameterization are used as both “truth” and as predictor systems. Inclusion of WWBs has little effect on ENSO predictability if the “truth” lacks WWBs. If the “truth” includes WWBs, the limit of ENSO predictability is larger for a forecast system that captures the correct statistics of WWBs. Predictability drops considerably if a forecast system that lacks WWB events is used to predict a “truth” that includes WWBs. At longer lead times, predictability is more dependent on the dynamical properties of the truth; that is, the importance of capturing the WWB statistics becomes less important and the statistics (e.g., signal-to-noise ratio) of the truth determine the limit of predictability. At short leads, ENSO predictability depends on the prediction system and the “truth.” ENSO prediction skill is model and phase dependent. Predictability of extreme warm events remains a challenge as the number of ensemble members required to capture these events is on the order of 100 members. Finally, we examine real ENSO predictions with and without the WWB parameterization. It is found that including WWBs in the prediction system significantly increases ENSO prediction skill compared with a prediction system that lacks WWBs. Also, it is found that the so-called forecast spring prediction barrier is, at least partially, caused by the lack of WWB representation in the forecast system.
AB - This study examines how semi-stochastic Westerly Wind Bursts (WWBs) affect El Niño Southern Oscillation (ENSO) predictability. An ensemble ENSO prediction experiment is presented in which the Community Climate System Model version 3 (CCSM3) and CCSM3 with a state-dependent WWB parameterization are used as both “truth” and as predictor systems. Inclusion of WWBs has little effect on ENSO predictability if the “truth” lacks WWBs. If the “truth” includes WWBs, the limit of ENSO predictability is larger for a forecast system that captures the correct statistics of WWBs. Predictability drops considerably if a forecast system that lacks WWB events is used to predict a “truth” that includes WWBs. At longer lead times, predictability is more dependent on the dynamical properties of the truth; that is, the importance of capturing the WWB statistics becomes less important and the statistics (e.g., signal-to-noise ratio) of the truth determine the limit of predictability. At short leads, ENSO predictability depends on the prediction system and the “truth.” ENSO prediction skill is model and phase dependent. Predictability of extreme warm events remains a challenge as the number of ensemble members required to capture these events is on the order of 100 members. Finally, we examine real ENSO predictions with and without the WWB parameterization. It is found that including WWBs in the prediction system significantly increases ENSO prediction skill compared with a prediction system that lacks WWBs. Also, it is found that the so-called forecast spring prediction barrier is, at least partially, caused by the lack of WWB representation in the forecast system.
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U2 - 10.1002/2014JD021908
DO - 10.1002/2014JD021908
M3 - Article
AN - SCOPUS:85018214944
VL - 119
SP - 10,114-10,138
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
SN - 2169-897X
IS - 17
ER -