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CMIP6 skill at predicting interannual to multi-decadal summer monsoon precipitation variability

Monerie, P.-A., Robson, J. I., Ndiaye, C. D., Song, C. and Turner, A. G. (2023) CMIP6 skill at predicting interannual to multi-decadal summer monsoon precipitation variability. Environmental Research Letters. ISSN 1748-9326 (In Press)

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To link to this item DOI: 10.1088/1748-9326/acea96


Monsoons affect the economy, agriculture, and human health of two thirds of the world's population. Therefore, predicting variations in monsoon precipitation is societally important. We explore the ability of climate models from the 6th phase of the Climate Model Intercomparison Project (CMIP6) to predict summer monsoon precipitation variability by using hindcasts from the Decadal Climate Prediction Project (Component A). The multi-model ensemble-mean shows significant skill at predicting summer monsoon precipitation from one year to 6-9 years ahead. However, this skill is dependent on the model, monsoon domain, and lead-time. In general, the skill of the multi-model ensemble-mean prediction is low in year 1 but increases for longer-lead times and is largely consistent with externally forced changes. The best captured region is northern Africa for the 2-5- and 6-9-year forecast lead times. In contrast, there is no significant skill using the ensemble-mean over East and South Asia and, furthermore, there is significant spread in skill among models for these domains. By sub-sampling the ensemble we show that the difference in skill between models is tied to the simulation of the externally forced response over East and South Asia, with models with a more skilful forced response capable of better predictions. A further contribution is from skilful prediction of Pacific Ocean temperatures for the South Asian summer monsoon at longer lead-times. Therefore, these results indicate that predictions of the East and South Asian monsoons could be significantly improved.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:112717
Publisher:Institute of Physics


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