How Large-Scale Atmospheric Dynamics Shape Observed Midlatitude Temperature Probability Distributions

Abstract:

The shape of temperature probability distributions describes the frequency of heat and cold events. To understand what physically sets the shape of temperature distributions in observations, we use a temperature-percentile based conditional mean framework to quantify the role of horizontal temperature advection relative to other processes (e.g. convection, radiation) in shaping the temperature distribution (Linz et al. 2020). We apply the framework to 850-hPa ERA5 reanalysis data during 1979-2019 and find that midlatitude temperature distributions can be largely explained by the conditional mean of horizontal temperature advection, while other processes are parameterized by a Newtonian relaxation process. The framework is then used to explore how different stationary and transient components of horizontal temperature advection affect the moments of temperature distributions during JJA. We find that the anomalous advection of the stationary temperature gradient has a dominant effect in influencing temperature variance, while both that term and the covariance between anomalous wind and anomalous temperature have significant effects on temperature skewness. We use clustering analysis to identify regions with different advection-temperature distribution relationships. Understanding the physics of temperature distributions under this conditional mean framework can help us gain more confidence in the prediction of its future changes, and enable more robust prediction in extreme temperature events with global warming.

Linz, M., Chen, G., Zhang, B., & Zhang, P. (2020). A framework for understanding how dynamics shape temperature distributions. Geophysical Research Letters, e2019GL085684. https://doi.org/10.1029/2019GL085684