Enhancing ENSO Prediction Skill by Combining Model-Analog and Linear Inverse Models (MA-LIM)

Abstract

To enhance El Nino-Southern Oscillation (ENSO) forecast skill, we devise a model analog (MA)-linear inverse model (LIM) by nudging sea surface temperature and sea surface height anomalies forecasted by the LIM into the MA. The performances of the LIM, MA, and MA-LIM are compared to general circulation model simulations and observations. At short (long) lead month tau, the LIM (MA) predicts the Nino 3.4 SST anomalies better than the MA (LIM). On the other hand, the MA-LIM shows the best performance at all tau. At tau = 6, the MA performs better than the LIM in the eastern equatorial Pacific and Indian Oceans but worse in other regions. The MA-LIM substantially remedies the undesirable aspects of the MA. The success of the MA-LIM appears to come from the use of more accurate initial conditions than the MA and an ad hoc implementation of seasonal cycle and nonlinearities into the LIM through nudging to the MA.