A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models

Abstract

Despite the existing myriad of tools and models to assess atmospheric source–receptor relationships, their uncertainties remain largely unexplored and arguably stem from the scarcity of observations available for validation. Yet, Lagrangian models are increasingly used to determine the origin of precipitation and atmospheric heat by scrutinizing the changes in moisture and temperature along air parcel trajectories. Here, we present a unified framework for the process-based evaluation of atmospheric trajectories to infer source–receptor relationships of both moisture and heat. The framework comprises three steps: (i) diagnosing precipitation, surface evaporation, and sensible heat from the Lagrangian simulations and identifying the accuracy and reliability of flux detection criteria; (ii) establishing source–receptor relationships through the attribution of sources along multi-day backward trajectories; and (iii) performing a bias correction of source–receptor relationships. Applying this framework to simulations from the Lagrangian model FLEXPART, driven with ERA-Interim reanalysis data, allows us to quantify the errors and uncertainties associated with the resulting source–receptor relationships for three cities in different climates (Beijing, Denver, and Windhoek). Our results reveal large uncertainties inherent in the estimation of heat and precipitation origin with Lagrangian models, but they also demonstrate that a source and sink bias correction acts to reduce this uncertainty. The proposed framework paves the way for a cohesive assessment of the dependencies in source–receptor relationships.