DADA: Data Assimilation for the Detection and Attribution of Weather- and Climate-related Events

TL;DR

This paper introduces DADA, a novel method that leverages existing data assimilation processes in weather forecasting to enable near-real-time causal attribution of weather and climate events, integrating detection and attribution into operational centers.

Contribution

The paper presents a new approach that uses routine data assimilation procedures for causal attribution, bridging weather forecasting and event attribution in a practical, operational framework.

Findings

Demonstrated the approach using the Lorenz model with additional forcing.

Showed that causal attribution can be derived as a by-product of data assimilation.

Outlined research questions for operational implementation.

Abstract

We describe a new approach allowing for systematic causal attribution of weather and climate-related events, in near-real time. The method is purposely designed to facilitate its implementation at meteorological centers by relying on data treatments that are routinely performed when numerically forecasting the weather. Namely, we show that causal attribution can be obtained as a by-product of so-called data assimilation procedures that are run on a daily basis to update the meteorological model 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. Several theoretical and practical research questions that need to be addressed to make the proposal readily operational within weather forecasting centers are finally laid out.