抄録
We propose the use of a high speed spherical self-organizing map (HSS-SOM) to visualize climate variability as a complementary and alternative method to empirical orthogonal function (EOF) analysis. In the fields of meteorology and climatology, the EOF analysis, which is the same as the principal component analysis, is often used to extract leading patterns of climate variability. However, since EOF analysis obtains a linear mapping only, sometimes we can not obtain any meaningful result. On the other hand, because of computational limitation, it is difficult to apply conventional self-organizing map to huge climate datasets. Recently, one of the authors has developed HSS-SOM with dynamically growing neurons to reduce computational time. First, we validate our method using observational climate data, and show the effectiveness of the HSS-SOM as a complementary and alternative way to the EOF. Next, we extract dominant atmospheric circulation patterns from huge climate data in the general circulation model at the first time, in which both present climatology and future climate are reproduced. These patterns correspond to those obtained in the previous studies, which suggest that the HSS-SOM is usable for climate research.
