Clear-air turbulence trends over the North Atlantic in high-resolution climate models

Smith, I. H. ORCID: https://orcid.org/0000-0002-7512-8370, Williams, P. D. ORCID: https://orcid.org/0000-0002-9713-9820 and Schiemann, R. ORCID: https://orcid.org/0000-0003-3095-9856 (2023) Clear-air turbulence trends over the North Atlantic in high-resolution climate models. Climate Dynamics, 61 (7-8). pp. 3063-3079. ISSN 1432-0894 doi: 10.1007/s00382-023-06694-x

Abstract/Summary

Clear-air turbulence (CAT) has a large impact on the aviation sector. Our current understanding of how CAT may increase with climate change in future is largely based on simulations from CMIP3 and CMIP5 global climate models (GCMs). However, these models have now been superseded by high-resolution CMIP6 GCMs, which for the first time have grid lengths at which individual turbulence patches may start to be resolved. Here we use a multi-model approach to quantify projected moderate CAT changes over the North Atlantic using CMIP6 models. The influence of the model resolution on CAT projections is analysed. Twenty-one CAT diagnostics are used, in order to represent uncertainties in CAT production mechanisms. Each diagnostic responds differently in time, but the majority display an increase in moderate CAT between 1950 and 2050. Although winter is historically the most turbulent season, there is strong multi-model agreement that autumn and summer will have the greatest overall relative increase in CAT frequency. By 2050, summers are projected to become as turbulent as 1950 winters and autumns. The global-mean seasonal near-surface temperature is used as a comparative metric. For every 1 °C of global near-surface warming, autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively. Our results confirm that the aviation sector should prepare for a more turbulent future.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/111047
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Springer
Download/View statistics	View download statistics for this item