Identifying Shifts in Modes of Low-Frequency Circulation Variability Using the 20CR Renalysis Ensemble
Abstract
Principal component analysis (PCA) is a widely used technique to identify modes of low-frequency variabil-ity of atmospheric circulation and their spatial changes. However, it turns out that PCA is highly sensitive to the period an-alyzed and the length of the time window used.
Its results can vary considerably if the period is shifted by even 1 year. We present temporal variability of modes from the late nineteenth century using moving PCA of winter (DJF) monthly mean 500-hPa height anomalies for 20-50-yr moving periods with 1-yr step.
We employ the congruence coefficient to compare spatial patterns of the modes and identify their substantial changes. Shorter moving periods are more susceptible to sudden fluctuations in mode patterns from one period to the next, while longer periods yield more stable results.
We strongly rec-ommend applying a moving PCA to detect spatial changes in modes of low-frequency variability, as it unveils any hidden sudden changes in the modes. These changes can be influenced by many aspects, such as data quality, sampling variability, and length of the analyzed period.
Spatial patterns of the Atlantic-European modes are more stable across ensemble mem-bers than those over the Pacific and North America, especially before the 1920s. During this period, North Atlantic and European modes explain more variance in the ensemble mean than in ensemble members, while the reverse holds for Pacific and North American modes.
In data-sparse regions, modes in ensemble members exhibit greater variability. The process of averaging then leads to weaker modes in the ensemble mean, explaining less variance compared to ensemble members.