Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session Q33: Geophysical Fluid Dynamics: General
1:25 PM–3:22 PM,
Monday, November 21, 2022
Chair: Ching-Yao Lai, Princeton University
Abstract: Q33.00006 : Spatio-temporal interactions between large-scale climate oscillations and extreme-temperature events
2:30 PM–2:43 PM
(Université de Rouen)
(University of Rouen Normandy M2C)
(Université de Rouen)
(University of California, Los Angeles)
In these planetary fluids, turbulent statistics at smaller scales appear to be correlated with the long-time and large-scale fields of the same quantity. For example, local, strong temperature fluctuations are affected by daily, seasonal, and interdecadal phenomena. This contribution aims to provide physical arguments for this conditioning of the smaller scales by the largest ones. To achieve this aim, we investigate turbulent statistics at each scale for several phases of the large-scale, long-time phenomena.
Our methodology uses transport equations for second- and fourth-order moments of the temperature field, filtered at different space and time scales. The emphasis is on the interaction between the temperature gradient’s large-scale dynamics, which acts as a forcing, and the temporal evolution of the second and fourth-order moments – namely the energy and the flatness, or kurtosis – of the temperature field at smaller scales. The theoretical results are verified against ERA5 reanalysis data for the wind velocity and temperature fields over the Euro-Atlantic region at the 500 hPa pressure level.
Our results show that the flatness factor increases as the scales become smaller. These values correlate with the local flux of temperature fluctuations and the large-scale temperature gradients. The extreme values of the temperature fluctuations are related to an enhancement of the temperature cascade and the temperature gradients. Further extensions of this approach deal with improved modeling of extremes in other fields, such as heavy rainfall or dry spells, in the context of large-scale climate change and variability.
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