Abstract

The time at which climate change signal can be clearly identified from its noise is known as Time of Emergence (ToE), and is typically detected by a General Circulation Model (GCM) signal-to-noise ratio exceeding a threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locallyproposes a methodology for estimating ToE for both precipitation and temperature at the river basin scale. The methodology identifies ToE for both variables by assessing the probability of having significant differences between climate series with natural variability and series that also incorporate trend percentiles extracted from a group of GCMs. The significant difference between the changing climate against the stationary one is identified by using the statistical power of a Kolmogorov-Smirnov hypothesis test. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo and Maule), whereas for precipitation, which reduce in the future for the three basins, an earlier ToE is clearly identified for the Maule basin. The method is sensitive to the selection of the GCM trend percentile, and thus there are different levels of uncertainty in the ToE identification for a specific location. Thus, the uncertainty in the ToE of precipitation for the Limari is the largest, while for Maule basin is the smallest. These results suggest that local risk assessment and adaptation measures should be implemented first in the Maule basin.