Most ENSO models can describe the main/classic ENSO mechanism, including the whole physical processes involved in the Bjerknes positive feedback, and the ocean dynamics that provides the delayed negative feedback. The models therefore can simulate and forecast the ENSO characteristics and ENSO events well with skillful data assimilation. Still, some ENSO-related processes are missing or less considered in these models.
To mimic the presence of missing processes and high-frequency stochastic noises, Dr. ZHENG Fei and Dr. ZHU Jiang from Institute of Atmospheric Physics proposed a new stochastic perturbation technique, which can improve the ENSO prediction skills by using an intermediate coupled model. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations.
Anomaly Correlations (above) and RMS error (below) of the Niño3.4 SST anomalies for the deterministic forecast, ensemble-mean forecast and persistence, as functions of the lead time. The results are obtained for all of the predictions that were made during the period 1993-2013 regardless of their starting month. (Figure plotted by IAP)
The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting. Improvement happens mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.
Their findings were recently published in Climate Dynamics.
Zheng, F., and J. Zhu, 2016: Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model. Clim. Dyn., 47, 3901–3915, doi: 10.1007/s00382-016-3048-0. http://link.springer.com/article/10.1007/s00382-016-3048-0
Contact: Dr. ZHENG Fei,