Quantifying the relationship between global circulation model behavior and dynamical cores using geospatial statistics

The behavior of atmospheric models is sensitive to the algorithms that are used to represent the equations of motion. Typically, comprehensive models are conceived in terms of the resolved fluid dynamics (i.e. the dynamical core) and subgrid, unresolved physics represented by parameterizations. This research focuses on the how the choice of dynamical core impacts the representation of precipitation; this brings attention to the interaction of the resolved and the parameterized components of the model. Two dynamical cores are considered within the Community Atmosphere Model. These are the Spectral (Eulerian) and the Finite Volume (FV) dynamical cores. A fundamental difference between the two dynamical cores is that the spectral dynamical core relies on global basis functions and the finite volume core uses only local information. Previous publications have shown that the formulation of the dynamical core strongly impacts the representation of meteorological features near steep topography and coastlines.

We introduce the concept of "meteorological realism;" that is, do local representations of large-scale phenomena, for example, fronts and orographic precipitation, look like the observations? A follow on question is, does the representation of these phenomena improve with resolution? Our approach to quantify meteorological realism starts with methods of geospatial statistics. Specifically, we employ variography, which is a geostatistical method which is used to measure the spatial continuity of a regionalized variable, and principle component analysis which is an efficient method to extract trends in a dataset. We pose that these methods intrinsically link local, weather-scale phenomena to important climatological features and provide a quantitative bridge between weather and climate.