Difference in boreal winter predictability between two dynamical cores of Community Atmosphere Model 5

Abstract

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FVSE) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001-2002 to 2017-2018, are validated using Japanese 55-year reanalysis data. It turns out that the SE (-4.27℃) has a smaller cold bias in boreal-winter surface air temperature than the FV (-5.17℃) and FVSE ( -5.29℃), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant negative North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter surface air temperature, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FVSE), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FVSE and FV. Conversely, in East Asia, FV and FVSE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.