Assessing the Uncertainty of Tropical Cyclone Simulations in NCAR's Community Atmosphere Model

Abstract

The paper explores the impact of the initial-data, parameter and structural model uncertainty on the simulation of a tropical cyclone-like vortex in the National Center for Atmospheric Research's (NCAR) Community Atmosphere Model (CAM). An analytic technique is used to initialize the model with an idealized weak vortex that develops into a tropical cyclone over ten simulation days. A total of 78 ensemble simulations are performed at horizontal grid spacings of 1.0&deg, 0.5&deg and 0.25&deg using two recently released versions of the model, CAM 4 and CAM 5. The ensemble members represent simulations with random small-amplitude perturbations of the initial conditions, small shifts in the longitudinal position of the initial vortex and runs with slightly altered model parameters. The main distinction between CAM 4 and CAM 5 lies within the physical parameterization suite, and the simulations with both CAM versions at the varying resolutions assess the structural model uncertainty. At all resolutions storms are produced with many tropical cyclone-like characteristics. The CAM 5 simulations exhibit more intense storms than CAM 4 by day 10 at the 0.5&deg and 0.25&deg grid spacings, while the CAM 4 storm at 1.0&deg is stronger. There are also distinct differences in the shapes and vertical profiles of the storms in the two variants of CAM. The ensemble members show no distinction between the initial-data and parameter uncertainty simulations. At day 10 they produce ensemble root-mean-square deviations from an unperturbed control simulation on the order of 1--5 m s^-1 for the maximum low-level wind speed and 2--10 hPa for the minimum surface pressure. However, there are large differences between the two CAM versions at identical horizontal resolutions. It suggests that the structural uncertainty is more dominant than the initial-data and parameter uncertainties in this study. The uncertainty among the ensemble members is assessed and quantified.

Keywords

• General Circulation Modeling
• Tropical Cyclones