Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging

TitleAssessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging
Publication TypeJournal Article
Year of Publication2018
JournalJournal of Advances in Modeling Earth Systems
Volume9
Number7
Pages2753-2770
Date Published04/2018
Abstract / Summary

With increasing computational capabilities, cumulus parameterizations that are adaptable to the smaller grid spacing and temporal interval for high-resolution climate model simulations are needed. In this study, we propose a method to improve the resolution adaptability of the Zhang-McFarlane (ZM) scheme, by implementing spatial and temporal averaging to the CAPE tendency. This method allows for a more consistent application of the quasi-equilibrium (QE) hypothesis at high spatial and temporal resolu-tions. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with spatiotemporal averaging at 4–32 km grid spacings are assessed using the Weather Research and Forecasting (WRF) model by comparing to cloud resolving model (CRM) simulation results coarse-grained to these same grid spacings. We show the original ZM scheme has poor resolution adaptability, with spatiotemporally averaged subgrid convective transport and convective precipitation increasing signif-icantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves total transport and total precipitation. Temporal averaging further improves the resolution adaptability of the scheme. With better constrained (although smoothed) convec-tive transport and precipitation, both the spatial distribution and time series of total precipitation at 4 and 8 km grid spacings are improved with the averaging methods. The results could help develop resolution adaptability for other cumulus parameterizations that are based on the QE assumption.

URLhttp://dx.doi.org/10.1002/2017ms001035
DOI10.1002/2017ms001035
Journal: Journal of Advances in Modeling Earth Systems
Year of Publication: 2018
Volume: 9
Number: 7
Pages: 2753-2770
Date Published: 04/2018

With increasing computational capabilities, cumulus parameterizations that are adaptable to the smaller grid spacing and temporal interval for high-resolution climate model simulations are needed. In this study, we propose a method to improve the resolution adaptability of the Zhang-McFarlane (ZM) scheme, by implementing spatial and temporal averaging to the CAPE tendency. This method allows for a more consistent application of the quasi-equilibrium (QE) hypothesis at high spatial and temporal resolu-tions. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with spatiotemporal averaging at 4–32 km grid spacings are assessed using the Weather Research and Forecasting (WRF) model by comparing to cloud resolving model (CRM) simulation results coarse-grained to these same grid spacings. We show the original ZM scheme has poor resolution adaptability, with spatiotemporally averaged subgrid convective transport and convective precipitation increasing signif-icantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves total transport and total precipitation. Temporal averaging further improves the resolution adaptability of the scheme. With better constrained (although smoothed) convec-tive transport and precipitation, both the spatial distribution and time series of total precipitation at 4 and 8 km grid spacings are improved with the averaging methods. The results could help develop resolution adaptability for other cumulus parameterizations that are based on the QE assumption.

DOI: 10.1002/2017ms001035
Citation:
Yun, Y, J Fan, H Xiao, G Zhang, S Ghan, K Xu, P Ma, and W Gustafson.  2018.  "Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging."  Journal of Advances in Modeling Earth Systems 9(7): 2753-2770, pp. 2753-2770.  https://doi.org/10.1002/2017ms001035.