A stratospheric sudden warming is perhaps one of the most radical changes of weather observed in our planet. As numerical weather prediction models have been improved, including better representation of the stratosphere, an extensive amount of studies have been investigating forecasts for major stratospheric sudden warmings (MSSWs), which affect all layers of the atmosphere, changing wind circulation patterns and space weather effects like the aurora.

Whereas most previous studies employed single systems for a limited number of MSSWs, a new study published in Advances in Atmospheric Sciences (AAS) sought to verify multi-system MSSW forecasts using hindcasts of four systems archived in the subseasonal-to-seasonal prediction project database. AAS is published by Springer and hosted by the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences (CAS).

"The target hindcast period extended from 1998/99 to 2012/13, including 12 MSSWs," said the author, Prof. Masakazu TAGUCHI, from the Department of Earth Science, Aichi University of Education in Japan, "the results show that all four systems can be judged to be skillful for five-day MSSW forecasts when averaged across all available MSSWs."

For longer lead times, such as 15 or 20 days, however, some systems are skillful, but others are not. Prof. TAGUCHI found that it is more difficult to forecast MSSWs where the polar vortex splits into two or greatly stretches, as compared to MSSWs where the vortex just shifts away from the pole, although a statistically significant difference was not obtained for almost all cases (systems and verification measures).

The polar vortex usually encompasses the polar region in the winter stratosphere, but sometimes shifts markedly away from its normal position and distorts during stratospheric sudden warmings (SSWs). The cover is a schematic illustration of such a situation, in which the polar vortex is characterized by the counterclockwise flow. (Image by AAS)

"This study could be extended in a future line of research to better unravel the characteristics of MSSW forecasts, e.g., in terms of case-to-case variations in predictable lead time, and their determinant (i.e., source of the predictability)," said Prof. TAGUCHI. "It will also be useful to identify connections between specific MSSWs and anomalous weather conditions in both the real world and in forecasts."