A holistic framework to estimate the origins of atmospheric moisture and heat using a Lagrangian model

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, scrutinizing the changes in moisture and temperature along air parcel trajectories. Here, we present a holistic 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) the diagnosis of moisture and heat from Lagrangian trajectories using multi-objective criteria to evaluate the accuracy and reliability of the fluxes, (ii) the attribution of sources following mass- and energy-conserving algorithms in order to establish source–receptor relationships, and (iii) the bias correction of diagnosed fluxes and the corresponding 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 the synergistic impacts of source- and sink bias-corrections. The proposed framework paves the way for a cohesive assessment of the dependencies in source–receptor relationships.