Published November 4, 2024 | Version v1
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Vapor kinetic energy for the detection and understanding of atmospheric rivers

  • 1. Argonne National Laboratory
  • 2. University of Chicago

Description

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

Data availability

The MERRA-2 data used in this study are available in Goddard Earth Sciences Data and Information Services Center (GES DISC) at https://doi.org/10.5067/QBZ6MG944HW0, https://doi.org/10.5067/SUOQESM06LPK, https://doi.org/10.5067/CWV0G3PPPWFW, and https://doi.org/10.5067/A9KWADY78YHQ. The ERA5 data used in this study are available in Research Data Archive at the National Center for Atmospheric Research at https://doi.org/10.5065/BH6N-5N20. The tropical cyclone track used in this study aiding the AR regression region selection are visualized at https://coast.noaa.gov/hurricanes/ while the data are available in NOAA National Centers for Environmental Information at https://doi.org/10.25921/82ty-9e16. The data generated for Figs. 1–5 are provided in the Source Data file. Source data are provided with this paper.

The TempestExtremes version 2.2.1 algorithm for the AR detection is available in GitHub at https://github.com/ClimateGlobalChange/tempestextremes. The AR detection algorithm of Mundhenk, Barnes, and Maloney is available in Digital Collections of Colorado at http://hdl.handle.net/10217/170619. The NCAR Command Language (NCL) version 6.6.2 built-in functions for the numerical analyses are available at https://doi.org/10.5065/D6WD3XH5. Custom scripts for applying NCL and TempestExtremes are available at https://doi.org/10.5281/zenodo.13883900.

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Additional details

Identifiers

DOI
10.1038/s41467-024-53369-0
Other
oai:uchicago.tind.io:13913

Funding

Packard Fellowship

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Geophysical Sciences