How well do climate modes explain precipitation variability?
Sanaa Hobeichi, Gab Abramowitz, Alex Sen Gupta, Andréa S. Taschetto, Doug Richardson, Neelesh Rampal, Hooman Ayat, Lisa V. Alexander, A. J. Pitman
Abstract
Abstract Large-scale modes of climate variability, such as the El Niño-Southern Oscillation, North Atlantic Oscillation, and Indian Ocean Dipole, show significant regional correlations with seasonal weather conditions, and are routinely forecast by meteorological agencies attempting to anticipate seasonal precipitation patterns. Here, we use machine learning together with more traditional approaches to quantify how much precipitation variability can be explained by large-scale modes of variability, and to understand the degree to which these modes interact non-linearly. We find that the relationship between climate modes and precipitation is predominantly non-linear. In some regions and seasons climate modes can explain up to 80% of precipitation variability. However, variability explained is below 10% for more than half of the land surface, and only 1% of the land shows values above 50%. This outcome provides a clear rationale to limit expectations of predictability from modes of variability in all but a few select regions and seasons.