A new simple method of multivariable maximum covariance analysis (MMCA) for extracting
common variability from multiple (more than two) datasets, that expands the singular value
decomposition analysis method, is proposed. Two approaches of the method are proposed, one
using the extreme of a summation of covariances (sum MMCA) and the other using the product of covariances (product MMCA).
Both approaches are demonstrated by successfully extracting the variability related to the Arctic Oscillation from three monthly-mean meteorological datasets (e.g., Fig. 1).
The method is useful because it is easily programmed and is computationally inexpensive.