Preprints
https://doi.org/10.5194/gmd-2021-180
https://doi.org/10.5194/gmd-2021-180

Submitted as: methods for assessment of models 29 Jun 2021

Submitted as: methods for assessment of models | 29 Jun 2021

Review status: this preprint is currently under review for the journal GMD.

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

Jessica Keune, Dominik L. Schumacher, and Diego G. Miralles Jessica Keune et al.
  • Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, 9000, Belgium

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.

Jessica Keune et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-180', Anonymous Referee #1, 30 Jul 2021
    • AC1: 'Reply on RC1', Jessica Keune, 04 Oct 2021
  • RC2: 'Comment on gmd-2021-180', Anonymous Referee #2, 23 Aug 2021
    • AC2: 'Reply on RC2', Jessica Keune, 04 Oct 2021
  • RC3: 'Comment on gmd-2021-180', Harald Sodemann, 27 Aug 2021
    • AC3: 'Reply on RC3', Jessica Keune, 04 Oct 2021

Jessica Keune et al.

Data sets

Datasets for "A holistic framework to estimate the origins of atmospheric moisture and heat using a Lagrangian model" Jessica Keune, Dominik L. Schumacher, Diego G. Miralles http://doi.org/10.5281/zenodo.5025802

Model code and software

h-cel/hamster v1.0.0 Jessica Keune, Dominik L. Schumacher, Diego G. Miralles http://doi.org/10.5281/zenodo.4889311

Source code for "A holistic framework to estimate the origins of atmospheric moisture and heat using a Lagrangian model" Jessica Keune, Dominik L. Schumacher, Diego G. Miralles http://doi.org/10.5281/zenodo.5031398

Jessica Keune et al.

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Short summary
Air is transporting moisture and heat, shaping the weather we experience. When and where was this air moistened and warmed by the surface? To address this question, atmospheric models trace the history of air parcels in space and time. However, their uncertainties remain unexplored, which hinders their utility and application. Here, we present a framework that sheds light on these uncertainties. Our approach sets a new standard in the assessment of atmospheric moisture and heat trajectories.