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Uncertainties in Atmospheric River Life Cycles by Detection Algorithms: Climatology and Variability
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  • Yang Zhou,
  • Travis O'brien,
  • Paul Ullrich,
  • William Collins,
  • Christina Patricola,
  • Alan Rhoades
Yang Zhou
Lawrence Berkeley National Lab

Corresponding Author:[email protected]

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Travis O'brien
Indiana University
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Paul Ullrich
University of California
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William Collins
University of California
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Christina Patricola
Iowa State University
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Alan Rhoades
Lawrence Berkeley National Lab
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Abstract

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport 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 eight global AR detection algorithms from the perspective of AR life cycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980-2017. Uncertainties related to lifecycle characteristics, including 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 origin (termination) frequency locates over the northwestern (northeastern) Pacific, significant disagreements occur 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 78 events per cool season, i.e. by almost a factor of five, although the agreement improves for stronger ARs. By examining the AR’s connection 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 landfalling activity) to low-frequency climate variabilities are consistent among algorithms.