Testing the Sensitivity of Extratropical Cyclones to Variations in Environmental Conditions

Extratropical cyclones are a main driver of mid-latitude weather conditions, continually interacting with their synoptic and mesoscale environment. These systems are a product of the cyclogenetic environment in which they develop, and their associated circulation, latent heating, and radiative heating in turn exert significant influence on the near and far-field dynamic and thermodynamic state. With the projected warming to our climate system, the environments in which mid-latitude cyclones develop are changing, as are the controlling influences on storm characteristics: temperature, moisture content, jet strength, and baroclinicity. Feedbacks between changes in the initial environment and changes in extratropical cyclone properties represent a challenge to our ability to characterize the effects of changes in climate on the winds and rainfall produced by these storms.

In this presentation, we consider how extratropical cyclones might respond to simultaneous changes in multiple environmental factors. We utilize an idealized version of the Weather Research and Forecasting model (WRF), allowing for systematic control of environmental conditions. We perform a comprehensive ensemble analysis by tracking the variations in extratropical cyclone properties as a function of the changes in the surrounding environment, with the aim of identifying key controls on cyclone characteristics. We consider the socially relevant impacts of changes in dynamics and precipitation, as well as considering the climatologically relevant impacts of changes in cloud and radiative properties. We identify and implement tunable variables best approximating changes in temperature, moisture content, jet strength, and baroclinicity. Examining the effects of each variable with single-variable sensitivity tests, we document the effect of each variable alone, before filling out a multivariate parameter space by combining variations of two or more variables. In reviewing the multivariate results, we use the single-variable functions to untangle potential feedbacks in the system, teasing out relationships and interactions between our tunable variables and their effects on the entire system.