Evaluation of Seasonal Teleconnections to Remote Drivers of Australian Rainfall in CMIP5 and CMIP6 Models

Evaluation of Seasonal Teleconnections to Remote Drivers of Australian Rainfall in CMIP5 and CMIP6 Models

September 12, 2023

This study describes how coupled climate models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulate the primary climate drivers that affect Australian climate, and their seasonal relationship to Australian rainfall, namely the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Southern Annular Mode (SAM). As results from the earlier generation of models (CMIP5) are still in use, the CMIP6 multi-model mean teleconnections between climate drivers and seasonal Australian rainfall are compared to CMIP5. Collectively, an improvement is found in CMIP6 relative to CMIP5 in the representation of the relationship between ENSO and IOD events and Australia’s springtime rainfall. Overall, CMIP6 models are also able to reproduce the asymmetric relationship between ENSO and eastern Australian rainfall, which exhibits a more robust signal during La Niña than during El Niño years. Both CMIP5 and CMIP6 models are also generally able to capture the stronger relationship between Central Pacific La Niñas, compared to Eastern Pacific La Niñas. However, the large spread in model-to-model behaviour, and among ensemble members, remains a source of uncertainty. Although CMIP6 models have improved in their representation of SAM variability, the simulated relationship between SAM and Australian rainfall has not materially improved. Additionally, this study is accompanied by an extensive Appendix in which each model’s ENSO, IOD and SAM seasonal teleconnection patterns to rainfall are presented and ranked.

Read more: Chung C., Boschat G., Taschetto A., Narsey S., McGregor S., Santoso A., & Delage F. (2023) Evaluation of seasonal teleconnections to remote drivers of Australian rainfall in CMIP5 and CMIP6 models. Journal of Southern Hemisphere Earth Systems Science, 73, 219-261. doi: https://doi.org/10.1071/ES23002

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