In this collaborative work, we examine climate change detection and attribution through a data-driven lens and using sets of large ensembles of climate model simulations. In addition to considering methods from deep learning and explainable AI, we also approach climate attribution through counterfactual simulations and storyline approaches to robustly examine extreme events while considering the important influences of internal variability from daily to multidecadal timescales. Further, we are also interested in examining how seasonal-to-decadal climate predictions can be improved and used to inform stakeholders about the probability of extreme and/or persistent temperature and precipitation events occurring on regional scales.
Summaries of selected climate attribution and extreme event studies…
Detection of future climate scenarios
A key question for regional climate services is related to which climate change scenario is most likely to evolve over the 21st century, especially when comparing climate model projections to real-world observations. Although this can already be tracked in near real-time through indices like global mean surface temperature or greenhouse gas concentrations, it remains unclear how to attribute regional patterns of climate change to different climate scenarios or to policy-relevant thresholds like 1.5°C/2.0°C of global warming.
