Systematic biases over the equatorial Indian Ocean and their influence on seasonal forecasts of the IOD

Accurate seasonal prediction of the Indian Ocean Dipole (IOD) is crucial given its socioeconomic impacts on countries surrounding the Indian Ocean. Using hindcasts from the Met Office Global Seasonal Forecasting System (GloSea6), coupled mean-state biases in the western and eastern equatorial Indian Ocean (WEIO and EEIO) and their impacts on IOD prediction are examined. Results show that GloSea6 exhibits a pronounced cold bias in the EEIO that rapidly develops after the monsoon onset in boreal summer (JJA, July-August) and persists into autumn (SON, September-November). This cold bias is linked to erroneous easterlies and a shallow thermocline, likely associated with the monsoon circulation. The seasonal evolution and relative timing of the precipitation biases, such that they develop through JJA in the EEIO but follow in the WEIO in SON, suggests that the EEIO plays the leading role in the development of coupled feedbacks that lead to the large dipole pattern of coupled biases. Analysis of skill metrics for the IOD shows that GloSea6 achieves a high anomaly correlation coefficient at short lead times, though it tends to overestimate IOD amplitude. This overestimation is larger in the eastern IOD pole than in the western pole and is likely linked to the poor representation of the evolution of the sea surface temperature anomalies in the EEIO during IOD events in SON. This study highlights the crucial role of regional biases, particularly in the EEIO, in shaping IOD variability and demonstrates that addressing such biases in GloSea6 could improve IOD prediction.

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Publication Details

Journal
Climate Dynamics
Published
2025-08-01
DOI
https://doi.org/10.1007/s00382-025-07794-6
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Systematic biases over the equatorial Indian Ocean and their influence on seasonal forecasts of the IOD

Caroline M. Wainwright, Marimel Gler, Andrew G. Turner, Linda Hirons et al.
1 links
Climate Dynamics
Climate variability and models
article

Systematic biases over the equatorial Indian Ocean and their influence on seasonal forecasts of the IOD

Caroline M. Wainwright, Marimel Gler, Andrew G. Turner, Linda Hirons, Charline Marzin
article en

Abstract

Accurate seasonal prediction of the Indian Ocean Dipole (IOD) is crucial given its socioeconomic impacts on countries surrounding the Indian Ocean. Using hindcasts from the Met Office Global Seasonal Forecasting System (GloSea6), coupled mean-state biases in the western and eastern equatorial Indian Ocean (WEIO and EEIO) and their impacts on IOD prediction are examined. Results show that GloSea6 exhibits a pronounced cold bias in the EEIO that rapidly develops after the monsoon onset in boreal summer (JJA, July-August) and persists into autumn (SON, September-November). This cold bias is linked to erroneous easterlies and a shallow thermocline, likely associated with the monsoon circulation. The seasonal evolution and relative timing of the precipitation biases, such that they develop through JJA in the EEIO but follow in the WEIO in SON, suggests that the EEIO plays the leading role in the development of coupled feedbacks that lead to the large dipole pattern of coupled biases. Analysis of skill metrics for the IOD shows that GloSea6 achieves a high anomaly correlation coefficient at short lead times, though it tends to overestimate IOD amplitude. This overestimation is larger in the eastern IOD pole than in the western pole and is likely linked to the poor representation of the evolution of the sea surface temperature anomalies in the EEIO during IOD events in SON. This study highlights the crucial role of regional biases, particularly in the EEIO, in shaping IOD variability and demonstrates that addressing such biases in GloSea6 could improve IOD prediction.

Climate DynamicsVol. 63(8)
Met Office (GB), University of Leeds (GB), National Centre for Atmospheric Science (GB), University of Reading (GB), Cardiff University (GB)
Met Office, Natural Environment Research Council
Openalex Percentile: Top 80%
Climate variability and models
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