DASH

Goal:

Advance the detection and attribution (D&A) of climate change by incorporating advanced machine learning, statistical learning and data science methods.

Impact:

Advance the state-of-the-art on D&A techniques using data science, to finally guide climate model development based on the agreement or mismatch of models and observations, and improve the confidence in the detection and attribution statements.

Proposed approach:

Incorporate new statistical techniques for D&A using machine learning and causal inference to achieve robust predictions. We developed a novel detection and attribution methodology based on supervised statistical learning that uses explicitly the information contained in a target of interest to identify “fingerprints” of climate change (see Figure attached for a temperature fingerprint) that are representative of the target, such as anthropogenic or volcanic fingerprints. The methodology relies on a hypothesis testing framework and uses a statistical learning techniques inspired from causal inference that incorporates robustness constraints. These constraints help to increase the confidence in the detection and attribution statements. This methodology was used for both attribution of greenhouse gases and aerosols, and for detection to make it robust even under potentially large internal climate variability, a remaining long standing issue in climate science. In parallel work, we showed that a climate change signal can be detected from daily patterns of temperature and humidity at the global scale for every day since 2012. On a different line of work, we refined a traditional climate science method (“dynamical adjustment”) using data science, and we show that this allows to uncover the forced climate response as a residual from circulation-induced variability.