Michael Nathan Levy

Aquaplanet Experiments Using CAM’s Variable-Resolution Dynamical Core

AbstractA variable-resolution option has been added within the spectral element (SE) dynamical core of the U.S. Department of Energy (DOE)–NCAR Community Atmosphere Model (CAM). CAM-SE allows for static refinement via conforming quadrilateral meshes on the cubed sphere. This paper investigates the effect of mesh refinement in a climate model by running variable-resolution (var-res) simulations on an aquaplanet. The variable-resolution grid is a 2° (~222 km) grid with a refined patch of 0.25° (~28 km) resolution centered at the equator. Climatology statistics from these simulations are compared to globally uniform runs of 2° and 0.25°.A significant resolution dependence exists when using the CAM version 4 (CAM4) subgrid physical parameterization package across scales. Global cloud fraction decreases and equatorial precipitation increases with finer horizontal resolution, resulting in drastically different climates between the uniform grid runs and a physics-induced grid imprinting in the var-res simulation...

A methodology for evaluating the impact of data compression on climate simulation data

High-resolution climate simulations require tremendous computing resources and can generate massive datasets. At present, preserving the data from these simulations consumes vast storage resources at institutions such as the National Center for Atmospheric Research (NCAR). The historical data generation trends are economically unsustainable, and storage resources are already beginning to limit science objectives. To mitigate this problem, we investigate the use of data compression techniques on climate simulation data from the Community Earth System Model. Ultimately, to convince climate scientists to compress their simulation data, we must be able to demonstrate that the reconstructed data reveals the same mean climate as the original data, and this paper is a first step toward that goal. To that end, we develop an approach for verifying the climate data and use it to evaluate several compression algorithms. We find that the diversity of the climate data requires the individual treatment of variables, and, in doing so, the reconstructed data can fall within the natural variability of the system, while achieving compression rates of up to 5:1.