Subseasonal Weather Prediction in a Global Convection-Permitting Model

Abstract

AbstractAlthough accurate weather and climate prediction beyond one to two weeks is of great value to society, the skill of such extended prediction is limited in current operational global numerical models, whose coarse horizontal grid spacing necessitates the parameterization of atmospheric processes. Of particular concern is the parameterization of convection and specifically convection in the tropics, which impacts global weather at all time scales through atmospheric teleconnections. Convection-permitting models, which forego convective parameterization by explicitly resolving cumulus-scale motions using fine (1?4 km) horizontal grid spacing, can improve global prediction at extended time scales by more faithfully simulating tropical convection and associated teleconnections. This study demonstrates that convection-permitting resolution in a global numerical model can improve both the statistical features of tropical precipitation and extended predictive skill in the tropics and midlatitudes. Comparing four monthlong global simulations with 3-km grid spacing to coarser-resolution simulations that parameterize convection reveals that convection-permitting simulations improve tropical precipitation rates and the diurnal cycle of tropical convection. The propagation of the Madden?Julian oscillation was better predicted in three of the four 3-km simulations; these three runs also featured more skillful prediction of weekly extratropical circulation anomalies, particularly during week 3 of each forecast. These results, though based on a small sample of four cases, demonstrate that convection-permitting global modeling can benefit extended atmospheric prediction and offers the potential for improved operational subseasonal forecast skill.