Conditional Multi-Step Attribution for Climate Forcings

Music City Center 

Attribution of climate impacts to a source forcing is critical to understanding, communicating, and addressing the effects of human influence on the climate. Traditional attribution methods are "single-step", directly relating a source forcing and final impact. These methods are unable to utilize additional climate information to improve attribution certainty. To address this shortcoming, we developed a novel multi-step attribution approach which is capable of analyzing multiple climate variables conditionally. That is, a connected series of climate effects are treated as dependent. Significant relationships found in intermediary steps of a causal pathway are leveraged to better characterize the forcing impact. This enables attribution of the forcing level responsible for the observed impacts, while equivalent single-step approaches fail.

This multi-step approach utilizes a carefully designed scalar feature describing the forcing impact, simple forcing response models, and a conditional Bayesian formulation to identify the correct source forcing magnitude. We demonstrate this method for the 1991 eruption of Mt. Pinatubo, which is an example of a short-term, high-variance forcing. Results indicate that including stratospheric and surface temperature and radiative flux measurements increase attribution certainty compared to analyses derived solely from temperature measurements. This conditional framework has broad potential impacts on improving climate attribution assessments in which standard attribution methods may fail.

Climate Change

Detection & Attribution