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

Journal Article · Thu Jan 16 00:00:00 EST 2025 · Journal of Geophysical Research: Atmospheres

DOI:https://doi.org/10.1029/2024JD042222· OSTI ID:2564239

Rush, William ^[1]; Lora, Juan ^[2]; Skinner, Christopher ^[3]; Menemenlis, Sofia ^[4]; Shields, Christine A. ^[5]; Ullrich, Paul ^[6]; O'Brien, Travis A. ^[7]; Brands, Swen ^[8]; Guan, Bin ^[9]; Mattingly, Kyle ^[10]; Mcclenny, Elizabeth ^[11]; Nardi, Kyle M. ^[12]; Nellikkattil, Arjun ^[13]; Ramos, Alexandre M. ^[14]; Reid, Kimberley ^[15]; Shearer, Eric J. ^[16]; Tome, Ricardo ^[17]; Willie, Jonathan ^[18]; Leung, Lai-Yung ^[19]; Ralph, Martin ^[20] ; Rutz, Jonathan J. ^[21]; Wehner, Michael F. ^[22]; Zhang, Zhenhai ^[23]; Lu, Mengqian ^[24]; Quagraine, Kwesi ^[25]

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1. Santa Clara University
2. Yale University
3. University of Massachusetts
4. Princeton University
5. National Center for Atmospheric Research
6. LLNL
7. INDIANA UNIVERSITY
8. MeteoGalicia–Xunta de Galicia, Santiago de Compostela, Spain
9. University of California, Los Angeles
10. University of Wisconsin-Madison
11. University of California, Davis
12. Pennsylvania State University
13. Pusan National University
14. Karlsruhe Institute of Technology
15. University of Melbourne
16. University of California, Irvine
17. Universidade de Lisboa, Portugal
18. ETH Zurich
19. BATTELLE (PACIFIC NW LAB)
20. University of California, San Diego
21. National Oceanic and Atmospheric Administration
22. Lawrence Berkeley National Laboratory
23. Scripps Institution of Oceanography
24. Hong Kong University of Science and Technology
25. Indiana University-Bloomington

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 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: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2564239
• Report Number(s): PNNL-SA-207306
• Journal Information: Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Journal Issue: 1 Vol. 130
• Country of Publication: United States
• Language: English

Related Subjects

Atmospheric river
paleoclimate