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Are Cut-off Lows simulated better in CMIP6 compared to CMIP5?

Pinheiro, H., Ambrizzi, T., Hodges, K., Gan, M., Andrade, K. and Garcia, J. (2022) Are Cut-off Lows simulated better in CMIP6 compared to CMIP5? Climate Dynamics, 59. pp. 2117-2136. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-022-06200-9


This is the first study to show the global Cut-off Low (COL) activity in 23 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 17 models from Phase 6 (CMIP6). The COL historical simulations for the period 1979-2005 obtained from the CMIP5 and CMIP6 models and their ensembles are compared with the ERA5 reanalysis using an objective feature-tracking algorithm. The results show that the CMIP6 models simulate the spatial distribution of COLs more realistically than the CMIP5 models. Some improvements include reduced equatorward bias and underestimation over regions of high COL density. Reduced biases in CMIP6 are mainly attributed to the improved representation of the zonal wind due to the poleward shift of the subtropical jet streams. The CMIP5 models systematically underestimate the COL intensity as measured by the T42 vorticity at 250 hPa. In CMIP6, the intensity is still underestimated in summer, but overestimated in winter in part due to increased westerlies. The overestimation is enhanced by the finer spatial resolution models that identify more of the strong systems compared to coarser resolution models. Other aspects of COLs such as their temporal and lifetime distributions are modestly improved in CMIP6 compared to CMIP5. Finally, the predictive skill of climate models is evaluated using five variables and the Taylor diagram. We find that 10 out of the 15 best CMIP5-CMIP6 models belong to CMIP6, and this highlights the overall improvement compared to its predecessor CMIP5. Despite this, the use of the multi-model ensemble average seems to be better in simulating COLs than individual models.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:98201


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