Emergence of multivariate climate change signals

Abstract

Abstract The emergence of a climate change signal relative to background variability is a useful metric for understanding local changes and their consequences. Studies have identified emergent signals of climate change, particularly in temperature-based indices with weaker signals found for precipitation metrics. In this study, we adapt climate analogue methods to examine multivariate climate change emergence over the historical period. We use seasonal temperature and precipitation observations and apply a sigma dissimilarity method to demonstrate that large local climate changes may already be identified, particularly in low-latitude regions. The multivariate methodology brings forward the time of emergence by several decades in many areas relative to analysing temperature in isolation. We observed particularly large departures from an early-20th century climate in years when the global warming signal is compounded by an El Niño-influence. The latitudinal dependence in the emergent climate change signal means that lower-income nations have experienced earlier and stronger emergent climate change signals than the wealthiest regions. Analysis based on temperature and precipitation extreme indices finds weaker signals and less evidence of emergence but is hampered by lack of long-running observations in equatorial areas. The framework developed here may be extended to attribution and projections analyses.