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Long-lead statistical forecasts of the Indian Summer Monsoon rainfall based on causal precursors

Urheber*innen
/persons/resource/dicapua

Di Capua,  Giorgia
Potsdam Institute for Climate Impact Research;

/persons/resource/kretschmer

Kretschmer,  Marlene
Potsdam Institute for Climate Impact Research;

Runge,  J.
External Organizations;

Alessandri,  A.
External Organizations;

/persons/resource/Reik.Donner

Donner,  Reik V.
Potsdam Institute for Climate Impact Research;

van den Hurk,  B.
External Organizations;

Vellore,  R.
External Organizations;

Krishnan,  R.
External Organizations;

/persons/resource/coumou

Coumou,  Dim
Potsdam Institute for Climate Impact Research;

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Zitation

Di Capua, G., Kretschmer, M., Runge, J., Alessandri, A., Donner, R. V., van den Hurk, B., Vellore, R., Krishnan, R., Coumou, D. (2019): Long-lead statistical forecasts of the Indian Summer Monsoon rainfall based on causal precursors. - Weather and Forecasting, 34, 5, 1377-1394.
https://doi.org/10.1175/WAF-D-19-0002.1


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_23273
Zusammenfassung
Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency, remote processes that provide sources of predictability for the ISMR. The algorithm identifies causal precursors without any a priori assumptions, apart from the selected variables and lead times. Using these causal precursors, a statistical hindcast model is formulated to predict seasonal ISMR that yields valuable skill with correlation coefficient (CC) ~0.8 at a 4-month lead time. The causal precursors identified are generally in agreement with statistical predictors conventionally used by the India Meteorological Department (IMD); however, our methodology provides precursors that are automatically updated, providing emerging new patterns. Analyzing ENSO-positive and ENSO-negative years separately helps to identify the different mechanisms at play during different years and may help to understand the strong nonstationarity of ISMR precursors over time. We construct operational forecasts for both shorter (2-month) and longer (4-month) lead times and show significant skill over the 1981–2004 period (CC ~0.4) for both lead times, comparable with that of IMD predictions (CC ~0.3). Our method is objective and automatized and can be trained for specific regions and time scales that are of interest to stakeholders, providing the potential to improve seasonal ISMR forecasts.