# DADA: data assimilation for the detection and attribution of weather and climate-related events

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## Abstract

We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of *data assimilation* procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.

## Keywords

Event attribution Data assimilation Causality theory Modified Lorenz model## Notes

### Acknowledgements

It is a pleasure to thank Fredi Otto and Dáithí Stone, who provided careful and constructive reviews of the original paper. This work has been supported by grant DADA from the Agence Nationale de la Recherche (ANR, France: AH and all co-authors) and by the Multi-University Research Initiative (MURI) N00014-12-1-0911 from the the U.S. Office of Naval Research (MG).

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