Sub-seasonal to seasonal

Predictability of sea surface temperature anomalies at the eastern pole of the Indian Ocean Dipole—using a convolutional neural network model

Ming Feng, Fabio Boschetti, Fenghua Ling, Xuebin Zhang, Jason R. Hartog, Akhtar Mahmood, Li Shi, Brint Gardner, Jing‐Jia Luo, Alistair James Hobday

Frontiers in Climate · 25 August 2022 · Nanjing University of Information Science & Technology (NUIST), Bureau of Meteorology (Australia)

PaperPDF

In one sentence. A CNN trained on CMIP5/6 models forecasts eastern Indian Ocean Dipole SST anomalies up to 6 months ahead, matching the ACCESS-S2 dynamical model.

Abstract

In this study, we train a convolutional neural network (CNN) model using a selection of Coupled Model Intercomparison Project (CMIP) phase 5 and 6 models to investigate the predictability of the sea surface temperature (SST) variability off the Sumatra-Java coast in the tropical southeast Indian Ocean, the eastern pole of the Indian Ocean Dipole (IOD). Results show that the CNN model can beat the persistence of the interannual SST variability, such that the eastern IOD (EIOD) SST variability can be forecast up to 6 months in advance. Visualizing the CNN model using a gradient weighted class activation map shows that the strong positive IOD events (cold EIOD SST anomalies) can stem from different processes: internal Indian Ocean dynamics were associated with the 1994 positive IOD, teleconnection from the equatorial Pacific was important in 1997, and cooling off the Australian coast in the southeast Indian Ocean contributed to the 2019 positive IOD. The CNN model overcomes the winter prediction barrier of the IOD, to a large extent due to the frequent transition from a warm state of the Indian Ocean to a negative IOD condition (warm EIOD SST anomalies) over the boreal winter to the following spring period. The forecasting skills of the CNN model are on par with predictions from a coupled seasonal forecasting model (ACCESS-S2), even outperforming this dynamic model in seasons leading to the IOD peaks. The ability of the CNN model to identify key dynamic drivers of the EIOD SST variability suggests that the CMIP models can capture the internal Indian Ocean variability and its teleconnection with the Pacific climate variability.

Labels

Topic

Sub-seasonal to seasonal, Climate analysis & attribution

Type

New model or method

Method

CNN / U-Net

Time scale

Seasonal to annual

Domain

Regional

Data

CMIP / climate model output

Status

Research

Identifiers

DOI:10.3389/fclim.2022.925068 OpenAlex:W4293036644

Added

2026-10-22

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