Estimating the Local Time of Emergence of Climatic Variables Using an Unbiased Mapping of GCMs: An Application in Semiarid and Mediterranean Chile

Abstract

The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin.

Relative Importance of Internal Climate Variability versus Anthropogenic Climate Change in Global Climate Change

“…The ACC is usually estimated as the evolution of multiyear mean change (Giorgi and Bi 2009;Hawkins and Sutton 2012;King et al 2015;Lehner et al 2017;Nguyen et al 2018;King and Harrington 2018) by using linear (e.g., Zhuan et al 2018;Gaetani et al 2020) or nonlinear equations (e.g., Frame et al 2019;Hawkins et al 2020;Gaetani et al 2020) to fit the time series. The ensemble mean of multiple climate model simulations has also commonly been used in many studies (e.g., Mahlstein et al 2011;Diffenbaugh and Scherer 2011;Chadwick et al 2019), since the intermodel uncertainty and climate variability are largely averaged out by using the ensemble mean. Even though a single climate model simulations may be biased with respect to reproducing the real-world climate, the use of a multimodel ensemble mean can be considered to be more reliable (Mahlstein et al 2011;Maraun 2013).…”

A Moderate Mitigation Can Significantly Delay the Emergence of Compound Hot Extremes

“…In addition, the results of ToE are sensitive to many other factors, such as the definition of hot extremes, threshold level of S / N and number of consecutive years beyond historical variability (Chadwick et al., 2019; Hawkins et al., 2014; IPCC, 2021; Raymond et al., 2020; Sui et al., 2014; Tan et al., 2018). ChotEs reflect the combination of univariate hot days and hot nights, so we also investigated the corresponding ToEs for hot days and hot nights respectively.…”

Climate Projections for the Southern Ocean Reveal Impacts in the Marine Microbial Communities Following Increases in Sea Surface Temperature

“…The Time of Emergence (ToE) is defined as the time at which the signal of a forced response emerges from the noise of internal variability (Hawkins and Sutton, 2012), thus providing an indicator of the human-induced climate change for several climate variables (Chadwick et al, 2019). The ToE was computed for each model separately as the year when the SST time series at each grid point exceeds two standard deviations of the monthly mean SST from the piControl experiment, similar to previous studies (Hawkins and Sutton, 2012;Bordbar et al, 2015;Lyu et al, 2020;Ferrero et al, 2021).…”