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Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments
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  • William Davis Rush,
  • Juan Manuel Lora,
  • Christopher Skinner,
  • Sofia Menemenlis,
  • Christine A Shields,
  • Paul A. Ullrich,
  • Travis Allen O'Brien,
  • Swen Brands,
  • Bin Guan,
  • Kyle S. Mattingly,
  • Elizabeth McClenny,
  • Kyle M. Nardi,
  • Arjun Babu Nellikkattil,
  • Alexandre M. Ramos,
  • Kimberley Jane Reid,
  • Eric Jay Shearer,
  • Ricardo Tomé,
  • Jonathan Wille,
  • L. Ruby Leung,
  • F. Martin Ralph,
  • Jonathan J Rutz,
  • Michael F Wehner,
  • Zhenhai Zhang,
  • Mengqian Lu,
  • Kwesi Twentwewa Quagraine
William Davis Rush
Santa Clara University

Corresponding Author:[email protected]

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Juan Manuel Lora
Yale University
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Christopher Skinner
University of Massachusetts Lowell
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Sofia Menemenlis
Princeton University
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Christine A Shields
National Center for Atmospheric Research (UCAR)
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Paul A. Ullrich
Lawrence Livermore National Laboratory
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Travis Allen O'Brien
Indiana University Bloomington
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Swen Brands
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria
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Bin Guan
University of California Los Angeles
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Kyle S. Mattingly
University of Wisconsin-Madison
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Elizabeth McClenny
University of California, Davis
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Kyle M. Nardi
Pennsylvania State University
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Arjun Babu Nellikkattil
IBS Center for Climate Physics
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Alexandre M. Ramos
Karlsruhe Institute of Technology
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Kimberley Jane Reid
Monash University
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Eric Jay Shearer
University of California, Irvine
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Ricardo Tomé
Instituto Dom Luiz
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Jonathan Wille
ETH Zurich
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L. Ruby Leung
PNNL
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F. Martin Ralph
SIO
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Jonathan J Rutz
Center for Western Weather and Water Extremes, Scripps Institute of Oceanography, University of California San Diego
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Michael F Wehner
Lawrence Berkeley National Laboratory (DOE)
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Zhenhai Zhang
UC San Diego
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Mengqian Lu
The Hong Kong University of Science and Technology
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Kwesi Twentwewa Quagraine
Indiana University Bloomington
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Abstract

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.
23 Aug 2024Submitted to ESS Open Archive
24 Aug 2024Published in ESS Open Archive