Evaluating the Joint Effect of Tropical and Extratropical Pacific Initial Errors on Two Types of El Niño Prediction Using Particle Filter Approach

Author:

Hou Meiyi12ORCID,Duan Wansuo34,Zhi Xiefei5

Affiliation:

1. Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210024, China

2. College of Oceanography, Hohai University, Nanjing 210024, China

3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

4. University of Chinese Academy of Sciences, Beijing 100049, China

5. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disasters, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

The accuracy of different types of El Niño-Southern Oscillation (ENSO) predictions is sensitive to initial errors in different key areas of the Pacific Ocean. To improve the accuracy of the forecast, assimilation techniques can be utilized to eliminate these initial errors. However, limited studies have measured the extent to which assimilating ocean temperature data from different key regions in the Pacific Ocean can enhance two types of ENSO predictions. In previous research, three critical regions were identified as having initial errors in ocean temperature most interfering with two types of El Niño predictions, namely the North Pacific for Victoria Mode-like initial errors, the South Pacific for South Pacific Meridional Mode-like initial errors, and the subsurface layer of the western equatorial Pacific. Based on these initial error patterns, we quantified the effect of assimilating ocean temperature observation datasets in these three key regions using the particle filter method. The result indicates that ocean temperature initial accuracy in the tropical western area near the thermocline region is important for improving the prediction skill of CP-El Niño compared with the other two sensitive areas. However, three key areas are all important for EP-El Niño predictions. The most critical area varies among different models. Assimilating observations from the north and south Pacific proves to be the most effective for improving both types of El Niño predictions compared to the other two areas’ choices. This suggests that the initial accuracy of ocean temperature in these two regions is less dependent on each other for enhancing El Niño predictions. Additionally, assimilating observations from all three sensitive areas has the best results. In conclusion, to enhance the accuracy of two types of El Niño predictions, we need to ensure the initial accuracy of ocean temperature in both tropical and extratropical regions simultaneously.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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