  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.
