When Will Humanity Notice Its Influence on Atmospheric Rivers?

Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium‐to‐high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50‐km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next‐generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR‐day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time‐independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first‐order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member. An “Atmospheric River” (AR) is a weather phenomenon characterized by strong, narrow moisture transport that brings heavy rainfall to land. They serve as a critical water resource but also can cause damaging flash floods and high winds. Thus, knowing how AR activity will change in the future climate can help us to mitigate potential AR‐related disasters and promote effective water resource management. However, this task is challenging due to uncertainty in how fast the climate will warm and in how much the noise of natural climate variability can obscure the signal from global warming. In addition, several different definitions of AR exist, which raises questions about the sensitivity of AR changes to AR definition. In this study, we use a next‐generation global climate model to evaluate the influence of these uncertainties and to determine the time when humanity will notice a discernible change in AR activity. We find that the response to global warming can be robustly identified by 2060 across all explored methods of computation. We further examine a less expensive approach, which enables us to quantify the uncertainty in warming signals and estimate the time of signal emergence in a single realization of nature. Increases in atmospheric river (AR) frequency emerge from the noise on internal variability by 2060 in simulations with a global climate modelA computationally efficient method is developed for determining the time of emergence (ToE) of AR responses to global warmingThe AR ToE is not sensitive to the criteria of AR detection algorithm or shared Socioeconomic pathways Increases in atmospheric river (AR) frequency emerge from the noise on internal variability by 2060 in simulations with a global climate model A computationally efficient method is developed for determining the time of emergence (ToE) of AR responses to global warming The AR ToE is not sensitive to the criteria of AR detection algorithm or shared Socioeconomic pathways