Abstract ID: 144
Correlation between predictability in Sub-seasonal to Seasonal (S2S) timescales and performance of mean state
Lead Author: Ryu Jihun
GIST, Republic of Korea
Keywords: Sub-seasonal to Seasonal, predictability in S2S timesclases, mean state
Abstract: Despite much effort, predictability in the Sub-seasonal to Seasonal (S2S) time scale remains a challenge. For instance, the predictability of the Madden-Julian Oscillation (MJO), considered as a crucial point in the S2S timescale, varies greatly from two to four weeks. Additionally, the predictability of extreme weather events such as 2010 Russian heat wave, tropical cyclones YASI, and July 2015 West-European heat wave is also limited. Proper evaluation of the predictability in the S2S time scale requires hindcast, equal to large amount of computing resources. Therefore, simpler diagnostic metrics such as performance of mean state has been used as a guidance or an indicator for the model’s predictability. However, relationship between simpler metrics and the predictability is not clear. Therefore, it is focused on establishing the relationship between the predictability and mean state of forecast model. To address this issue, an evaluation index that includes model’s climatology, annual cycle, and semi-annual patterns was created. Based on this index, models were divided into a few groups, and it was found that the simulated performance of the mean state is relatively well related to seasonal predictability in the S2S time scale. Furthermore, this relationship is clear within three weeks. With this in mind, it is expected that by improving the mean state, predictability of the S2S time scale can be improved when developing S2S models.
Co-authors:
Jin-Ho Yoon (GIST)
