Hohenegger, C., L. Kornblueh, D. Klocke, T. Becker, G. Cioni, J. F. Engels, U. Schulzweida, and B. Stevens, 2020: Climate statistics in global simulations of the atmosphere, from 80 to 2.5 km grid spacing. J. Meteor. Soc. Japan, 98, 73-91.
Special Edition on DYAMOND: The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains
https://doi.org/10.2151/jmsj.2020-005 Graphical Abstract with highlights
Plain Language Summary: General Circulation Models (GCMs) are complex tools embodying physical principles to represent the statistics of the climate system. Limitations in computer resources impose constraints on the resolution of such models and hence on the scales of the atmospheric processes that such models can explicitly represent. Both the chosen grid spacing and the employed model formulation affect the quality of a simulation. In this study, we examine the convergence behavior of a GCM by systematically varying its grid spacing. We objectively assess the convergence by comparing differences resulting from changes in grid spacing to differences resulting from using distinct model formulations. For the investigated statistics of the climate system, our study thus indicates at which grid spacing model formulation becomes more important than grid spacing.
- Forty-day global simulations have been performed with grid spacings down to 2.5 km and compared to an ensemble of eight distinct global storm-resolving models using kilometer-scale grid spacing.
- Using our convergence metric, we find that at least a grid spacing of 5 km is sufficient to capture 26 out of the 27 investigated climate statistics.
- Refining the grid spacing moves the simulations closer to observations, but climate statistics exhibiting weaker resolution dependencies are not necessarily associated with smaller biases.