Model sensitivity across scales: a case study of simulating an offshore low-level jet

Abstract. In this study, a seven-member ensemble of mesoscale-to-microscale simulations with varying sea surface temperature (SST) is conducted for a case in which an offshore low-level jet was observed via floating lidar. The performance of each SST setup in reproducing physical characteristics of the observed low-level jet is compared across the mesoscale and microscale domains. It is shown that the representation of low-level shear, jet nose height, and hub-height wind speed are improved when moving from mesoscale to microscale. Specifically, low-level shear is improved in the microscale by reducing near-surface wind speeds and lowering the jet nose height to be closer to that observed. Counterintuitively, the sensible heat flux on the mesoscale domains is more negative than on the microscale domains, which would indicate a more stable boundary layer with higher shear; however, the low-level shear in the mesoscale is weaker than that of the microscale domains. This indicates over-mixing of the PBL scehme in the mesoscale domains and/or over-prediction of surface drag in the microscale domain.

We analyze performance considering a real-world scenario in which the computational burden of running an ensemble of LES limits a study to performing a mesoscale ensemble to select the best model setup that will drive a single LES run. In the context of this study, the best model setup is subjective and weighs model performance in the physical representation of the low-level jet as well as the model surface forcing through the temperature gradient between air and sea. The expectation of this approach is that the best performing setup of the mesoscale simulations will produce the best result for the microscale simulations. It is shown that there are large fundamental changes in the characteristics of the low-level jet as well as in the surface forcing conditions between the mesoscale and microscale domains. This results in a non-linear ranking of performance between the mesoscale domains and the microscale domains. While the best performing mesoscale setup is also deemed to produce the best results on the microscale, the second best performing mesoscale setup produces the worst results on the microscale.