Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles |
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| Authors: | Fei Zheng Jiang Zhu Hui Wang Rong-Hua Zhang |
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| Institution: | International Center for Climate and Environment Science (ICCES),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry ( LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;National Meteorological Center, Beijing 100081;Earth System Science Interdisciplinary Center ( ESSIC), University of Maryland, College Park, Maryland, USA |
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| Abstract: | Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS)
has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating
the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded
into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts
over the 120 year period 1886--2005 using the EPS with 100 ensemble members and with initial conditions
obtained by only assimilating historic SST anomaly observations.
By examining the retrospective ensemble forecasts and available observations, the verification results
show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast
using the same ICM, with a distinct improvement of both the correlation and root mean square (RMS) error
between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The
RMS error of the ensemble mean is almost 0.2oC smaller than that of the deterministic forecast at a lead
time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following
the SST observations well, and the areas under the relative operating characteristic (ROC) curves for three
different ENSO states (warm events, cold events, and neutral events) are all above 0.55 out to 12 months
lead time.
However, both deterministic and probabilistic prediction skills of the EPS show
an interdecadal variation. For the deterministic skill, there is high skill in
the late 19th century and in the middle-late 20th century (which includes some
artificial skill due to the model training period), and low skill during the
period from 1906 to 1961. For probabilistic skill, for the three different ENSO
states, there is still a similar interdecadal variation of ENSO probabilistic
predictability during the period 1886--2005. There is high skill in the late
19th century from 1886 to 1905, and a decline to a minimum of skill around
1910--50s, beyond which skill rebounds and increases with time until the
2000s. |
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| Keywords: | ENSO ensemble prediction system interdecadal predictability hindcast |
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