Open AccessSeasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques
Author(s) -
Dippe Tina,
Greatbatch Richard J.,
Ding Hui
Publication year - 2019
Publication title -
atmospheric science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 45
ISSN - 1530-261X
DOI - 10.1002/asl.898
Subject(s) - hindcast , initialization , climatology , sea surface temperature , environmental science , forecast skill , mode (computer interface) , heat flux , climate model , climate change , oceanography , geology , computer science , heat transfer , physics , thermodynamics , programming language , operating system
Due to strong mean state‐biases most coupled models are unable to simulate equatorial Atlantic variability. Here, we use the Kiel Climate Model to assess the impact of bias reduction on the seasonal prediction of equatorial Atlantic sea surface temperature (SST). We compare a standard experiment (STD) with an experiment that employs surface heat flux correction to reduce the SST bias (FLX) and, in addition, apply a correction for initial errors in SST. Initial conditions for both experiments are generated in partially coupled mode, and seasonal hindcasts are initialized at the beginning of February, May, August and November for 1981–2012. Surface heat flux correction generally improves hindcast skill. Hindcasts initialized in February have the least skill, even though the model bias is not particularly strong at that time of year. In contrast, hindcasts initialized in May achieve the highest skill. We argue this is because of the emergence of a closed Bjerknes feedback loop in boreal summer in FLX that is a feature of observations but is missing in STD.