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A Generalized Lagrangian Attribution Tool for Hydrometeorological Extremes

delete2025-08-20
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PRE
AI
S
Shenglong Zhang
J
Jonathon S. Wright *
M
Mengqian Lu
DOI:10.1029/2025JD043314delete
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Abstract

Abstract

En 中文
We introduce a novel generalized Lagrangian attribution tool for hydrometeorological extremes (LATHE) designed to trace the origins and processes that contribute to moisture and heat (i.e., specific enthalpy) anomalies. The approach is based on a strict conceptual framework in which the influences of turbulent evaporation and sensible heat fluxes are separated from the effects of latent heat release and radiative cooling by the boundary layer top. In its default form, LATHE distinguishes sensible heating, latent heating (assigned to where conversion takes place), and conversion from upstream moisture (assigned to the initial evaporative source) as contributions to anomalous atmospheric heat content, along with a standard moisture source algorithm. This procedure can be applied flexibly to a wide range of moist thermodynamic metrics. We apply LATHE to two extreme events, one extreme precipitation event and one severe heatwave, to demonstrate the added information relative to traditional analyses. We then conduct a series of sensitivity tests to identify key parameters, missing pieces, and sources of uncertainty. The results highlight the importance of shallow convection acting to deepen the moisture source (by venting humid air from the boundary layer) and intensify sensible heating in the boundary layer (by enhancing entrainment), leading us to propose an extension to the conceptual model. Finally, we note that many key uncertainties are intrinsic not to the method itself but to the reanalysis products we rely on to apply it. To make best use of LATHE and similar tools, we will need to confront these uncertainties directly.

Journal

J
Journal of Geophysical Research and Atmospheres
IF:
3.4
Papers:
2.2W
Citations:
7.7W

Organization

T
tsinghua university
Scholars:
11.5W
Papers: 9.9W
Citations: 137
T
The Hong Kong University of Science and Technology
Scholars:
1.6K
Papers: 826
Citations: 4
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