Energy and infrequent fluctuations of temperature related to atmospheric mechanisms for various climate change scenarios

Understanding, modeling, and predicting complex systems such as climate require coupling distinct phenomena, acting at different space/temporal scales:  wavelike features, and turbulent cascade, with different regimes, crucial for mixing and dissipation. In addition, turbulent statistics appear to be correlated with the long-time (large-scale) filtered field of the same quantity. For example, local and strong temperature fluctuations are most likely related to daily, seasonal, and sometimes interdecadal phenomena.  This contribution aims to provide physical arguments of this conditioning by investigating turbulent statistics at each scale and for a particular time/phase of the large-scale, long-time phenomena.  

The methodology uses transport equations for second and fourth-order moments of temperature,  filtered at different space/time scales.  Data originate from experimental measurements performed at the level of the ground in Hong Kong. The effect of daily and annual periodicity over one-and-two point statistics has been assessed by resorting to the theoretical framework based on the advection-diffusion for scalar fluctuations.  It is shown that extreme/rare temperature fluctuations are related to the enhancement of temperature cascade and the large-scale, meandering,  temperature gradient. Further extensions of this approach deal with improved modeling of extremes, such as heavy rainfall, dry spells, in the context of large-scale climate change and variability.