Attribution research in the context of climate change investigates the extent to which human influence, via different factors, contributes to changes and events in the climate system and their impacts on natural, managed, and human systems. Disentangling external forcing and climate variability as well as isolating climate change impacts from other drivers is a challenging task engaging various approaches.
The field of Detection and Attribution (D&A) identifies historical changes over long timescales, typically multi-decadal, of weather and climate as well as their impacts. D&A specifically quantifies the contributions of various external forcings as their signal emerges from internal climate variability. Driven by complex mechanisms, internal variability can itself change under external forcing, complicating D&A analyses and the projection of future changes. Moreover, event attribution (EA) assesses how human-induced climate change is modifying the frequency and/or intensity of extreme weather events (e.g. a heatwave), their impacts (e.g., economic loss or loss of life associated with flooding), or events from an impact perspective (e.g., a crop failure). These and other analyses focusing on attributing impacts combine observations with model-based evidence or process understanding. The attribution of climate change impacts is particularly complex due to the influence of additional non-climatic human influences.
This session highlights recent studies from the broad spectrum of attribution research that address some or all steps of the climate-impact chain from emissions to climate variables, to impacts in natural, managed, and human systems and aims to explore the diversity of methods employed across disciplines and schools of thought. It also covers a broad range of applications, case studies, current challenges of the field, and avenues for expanding the attribution research community. It specifically also includes studies that focus on the influence of specific externally forced changes as well as separating, quantifying, and understanding internal variability as both constitute a key uncertainty in climate attribution.
Presentations will cover common and new methodologies (improved statistical methods, statistical causality, Artificial Intelligence) using single climate realisations, large ensembles, or other methods to derive counterfactuals, on single climate variable or compound/cascading events, on impacts on natural, managed, or human systems.
The field of Detection and Attribution (D&A) identifies historical changes over long timescales, typically multi-decadal, of weather and climate as well as their impacts. D&A specifically quantifies the contributions of various external forcings as their signal emerges from internal climate variability. Driven by complex mechanisms, internal variability can itself change under external forcing, complicating D&A analyses and the projection of future changes. Moreover, event attribution (EA) assesses how human-induced climate change is modifying the frequency and/or intensity of extreme weather events (e.g. a heatwave), their impacts (e.g., economic loss or loss of life associated with flooding), or events from an impact perspective (e.g., a crop failure). These and other analyses focusing on attributing impacts combine observations with model-based evidence or process understanding. The attribution of climate change impacts is particularly complex due to the influence of additional non-climatic human influences.
This session highlights recent studies from the broad spectrum of attribution research that address some or all steps of the climate-impact chain from emissions to climate variables, to impacts in natural, managed, and human systems and aims to explore the diversity of methods employed across disciplines and schools of thought. It also covers a broad range of applications, case studies, current challenges of the field, and avenues for expanding the attribution research community. It specifically also includes studies that focus on the influence of specific externally forced changes as well as separating, quantifying, and understanding internal variability as both constitute a key uncertainty in climate attribution.
Presentations will cover common and new methodologies (improved statistical methods, statistical causality, Artificial Intelligence) using single climate realisations, large ensembles, or other methods to derive counterfactuals, on single climate variable or compound/cascading events, on impacts on natural, managed, or human systems.