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Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments

Journal Article · · Journal of Geophysical Research. Atmospheres
DOI:https://doi.org/10.1029/2024jd042222· OSTI ID:2511363
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [16];  [17];  [18];  [19];  [20] more »;  [20];  [21];  [20];  [22];  [23] « less
  1. Santa Clara Univ., CA (United States)
  2. Yale Univ., New Haven, CT (United States)
  3. Univ. of Massachusetts, Lowell, MA (United States)
  4. Yale Univ., New Haven, CT (United States); Princeton Univ., NJ (United States)
  5. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  6. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of California, Davis, CA (United States)
  7. Indiana Univ., Bloomington, IN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  8. Spanish National Research Council and Univ. of Cantabria, Santander (Spain). Instituto de Física de Cantabria (CSIC‐UC)
  9. Univ. of California, Los Angeles, CA (United States); California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL)
  10. Univ. of Wisconsin, Madison, WI (United States)
  11. Univ. of California, Davis, CA (United States)
  12. Pennsylvania State Univ., University Park, PA (United States)
  13. Institute for Basic Science (IBS), Busan (Korea, Republic of); Pusan National Univ., Busan (Korea, Republic of)
  14. Karlsruhe Inst. of Technology (KIT) (Germany). Institute of Meteorology and Climate Research Troposphere Research (IMKTRO)
  15. Univ. of Melbourne, Parkville, VIC (Australia). ARC Centre of Excellence for 21st Century Weather
  16. Univ. of California, Irvine, CA (United States)
  17. Universidade de Lisboa (Portugal)
  18. Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland); Institut des Géosciences de l'Environnement (IGE), Saint-Martin-d-Heres (France); Centre National de la Recherche Scientifique (CNRS), Saint-Martin-d-Heres (France); Univ. of Grenoble Alpes, Saint-Martin-d-Heres (France); Institut de Recherche pour le Développement (IRD), Saint-Martin-d-Heres (France); Institut Polytechnique de Grenoble (G‐INP), Saint-Martin-d-Heres (France)
  19. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  20. Univ. of California, San Diego, CA (United States)
  21. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  22. Hong Kong University of Science and Technology (HKUST) (Hong Kong)
  23. Indiana Univ., Bloomington, IN (United States)
+ Show Author Affiliations
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). Here, we compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial 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.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
Australian Research Council (ARC); National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); US Department of Energy; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), Climate and Environmental Sciences Division (SC-23.1 ); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS)
Grant/Contract Number:
AC02-05CH11231; AC05-76RL01830; AC52-07NA27344; SC0022070; SC0023519
OSTI ID:
2511363
Alternate ID(s):
OSTI ID: 2564239
OSTI ID: 2573775
Report Number(s):
LLNL--JRNL-866574; 1101451
Journal Information:
Journal of Geophysical Research. Atmospheres, Journal Name: Journal of Geophysical Research. Atmospheres Journal Issue: 1 Vol. 130; ISSN 2169-897X
Publisher:
American Geophysical Union; WileyCopyright Statement
Country of Publication:
United States
Language:
English

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Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments
Journal Article · Wed Jan 15 19:00:00 EST 2025 · Journal of Geophysical Research: Atmospheres · OSTI ID:2564239

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