TurboMeter: an attribution framework for aviation turbulence events in a changing climate

Abstract. Understanding the influence of climate change on aviation turbulence is critical for ensuring flight safety and developing adaptive strategies. Thus, we present TurboMeter, an attribution framework that combines reanalysis data with a conditional attribution methodology to assess whether the present-day atmosphere, under similar large-scale synoptic configurations, can lead to stronger aviation turbulence than in a cooler historical climate. Turbulence diagnostics – namely the Turbulence Index (TI1) and its two components, vertical wind shear and total deformation – are used to characterize cruise-level turbulence events reported in 2024 over four key flight corridors: the North Atlantic, the North Pacific, East Asia, and the Contiguous United States (CONUS). Our results highlight enhanced vertical wind shear and deformation in the present period, with increases ranging from 20 % to 50 % relative to historical analogues. Attribution diagnostics indicate that all events are at least partially influenced by anthropogenic climate change, with approximately 68 % (2142 of 3123 reports) classified as being mainly strengthened by anthropogenic forcing, particularly over the North Atlantic, CONUS, and East Asia. A smaller but non-negligible share (about 32 %) is associated with a combination of climate change and natural variability. In contrast, over the North Pacific, the majority of events (81 %) fall into this latter category, highlighting a comparatively stronger role of internal climate variability in that region, consistent with previous studies showing a regionally dependent interplay between anthropogenic forcing and internal climate variability. These findings are further supported by the spatial patterns of increased turbulence-favourable conditions and underscore the growing relevance of climate-informed diagnostics for aviation risk planning by considering both anthropogenic climate change and internal climate variability in projecting future turbulence risk.