Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

다국어 초록 (Multilingual Abstract)

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport that are responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares nine global AR detection algorithms from the perspective of AR lifecycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980–2017. Uncertainties in AR lifecycle characteristics, including event number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum AR origin (termination) frequency is located over the western (eastern) portion of ocean basins, significant disagreements appear in regional distribution. Spreads are large in AR lifetime and intensity. The number of landfalling AR events produced by the algorithms can vary from 16 to 80 events per year, although the agreement improves for stronger ARs. By examining the ARs' connections with the Madden‐Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfall activity) to climate variability are consistent among algorithms.
Atmospheric rivers (ARs) are one of the major mechanisms by which water vapor is transported from the tropics to high latitudes and, therefore, dictate water resource availability in many coastal regions such as the west coast of North America. As such, many studies have developed detection algorithms to isolate and study the characteristics of ARs. However, conclusions from these studies may differ because of algorithm design assumptions. Here we select nine detection algorithms that have been applied to a common data set which describes the meteorological conditions of the recent past. This analysis framework enables us to identify the disagreements in AR characteristics across algorithms including AR size, event number, lifetime, intensity, and landfall activity. Results suggest that basic AR characteristics vary significantly depending on the detection algorithm. However, algorithm differences may be ameliorated when AR behavior is analyzed over intraseasonal and interannual time scales.



Detection algorithms introduce uncertainties in the number, lifetime, and intensity of atmospheric rivers (AR) lifecycles

Agreement in landfall activity across detection algorithms increases with stronger ARs

Uncertainties may be smoothed out when investigating AR activity at intraseasonal and interannual time scales


Detection algorithms introduce uncertainties in the number, lifetime, and intensity of atmospheric rivers (AR) lifecycles
Agreement in landfall activity across detection algorithms increases with stronger ARs
Uncertainties may be smoothed out when investigating AR activity at intraseasonal and interannual time scales