The effect of experiment conditioning on estimates of human influence on extreme weather

Abstract

Many methods and climate/weather modelling tools have been used over the past decade for assessment of the role of anthropogenic emissions in recent specific weather events (“event attribution”). Differences in the methods and models often correspond to differences in the characterisation, or conditioning, of an observed extreme event within a model, and this might be expected to affect any attribution statement. In practice, however, it may not always be feasible or practical to use the most appropriate method for the question at hand, or to use multiple methods so as to arrive at a generic conclusion. This is especially true given the growing interest in making rapid assessments of extreme events within operational forecast centres. How transferable are conclusions across methods, hence allowing the substitution of one method or modelling tool for another? In this paper we investigate differences in event attribution conclusions across a wide range of experiment designs, running from free-running simulations of atmosphere–ocean climate models, through to weather forecasts constrained to reproduce the nature of the event quite closely. Across a number of recent extreme weather events over Aotearoa New Zealand, we find no systematic differences in conclusions across the various experiment setups. This surprising result offers hope that attribution statements may be transferable across methods, because errors that arise when transferring results across methods are overshadowed by other errors and uncertainties given current technology.

Keywords

• Detection and attribution
• Event attribution
• Climate models