Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes
- 1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, United States
- 2Center for Ocean–Land–Atmosphere Studies, George Mason University, Fairfax, Virginia, United States
- 3Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, United States
- 4Department of Astronomy, University of Maryland, College Park, Maryland, United States
- 5Geosciences Department and Laboratoire de Météorologie Dynamique, École Normale Supérieure, Paris, France
- 6Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, United States
Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode which determines the active and break phases of the monsoon, and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the rainfall portion corresponding to MISO.
Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO using a novel method. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics, and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve significant improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10–30 day lead times, an interval that is generally considered as a predictability gap. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point towards a future of combining dynamical and data-driven forecasts for Earth system prediction.
How to cite: Bach, E., Krishnamurthy, V., Shukla, J., Mote, S., Sharma, A. S., Kalnay, E., and Ghil, M.: Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10243, https://doi.org/10.5194/egusphere-egu23-10243, 2023.