10:45 〜 12:15
[AAS02-P03] Improved MJO prediction using a multi-member subseasonal to seasonal forecast system of NUIST (NUIST CFS 1.1)
キーワード：MJO prediction, initialization schemes, model deficiency
The Madden-Julian Oscillation (MJO) provides an important source of global subseasonal-to-seasonal (S2S) predictability, while its prediction remains great challenges. Based on an atmosphere-ocean coupled model and the widely-used nudging method, suitable initialization and ensemble schemes are explored toward an improved MJO prediction. It is found that strong but not excessive relaxation strength for the divergence and vorticity, and a mild relaxation for the air temperature are appropriate to generate good atmospheric initial conditions. Additionally, the ensemble strategy with perturbed atmospheric nudging coefficients conduces to adequate ensemble spread and hence improves the prediction skill.
Here, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead[WJ1] , which outperforms a majority of current models in the S2S project but is far from the estimated potential predictability (~47 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are largely owing to the model’s shortcoming in representing MJO-related physical processes. The model underestimates the diabatic heating of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. These biases limit the MJO prediction in the NUIST CFS1.1, prompting further efforts to improve the model physics.
Here, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead[WJ1] , which outperforms a majority of current models in the S2S project but is far from the estimated potential predictability (~47 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are largely owing to the model’s shortcoming in representing MJO-related physical processes. The model underestimates the diabatic heating of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. These biases limit the MJO prediction in the NUIST CFS1.1, prompting further efforts to improve the model physics.