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

Abstract

Abstract To enhance El Niño–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 , the LIM (MA) predicts the Niño 3.4 SST anomalies better than the MA (LIM). On the other hand, the MA‐LIM shows the best performance at all . At , 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.