the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How to trace the origins of short-lived atmospheric species in the Arctic
Abstract. The origins of particles and trace gases involved in the rapidly changing polar climates remain unclear, limiting the reliability of climate models. This is especially true for particles involved in aerosol-cloud interactions with polar clouds. As detailed chemical fingerprinting measurements are difficult and expensive in polar regions, backtrajectory modeling is often used to identify the sources of observed atmospheric compounds. However, the accuracy of these methods is not well quantified. This study provides a first evaluation of these analysis protocols, by combining backtrajectories from the FLEXible PARTicle dispersion model (FLEXPART) with simulations of tracers from the Weather Research and Forecast model including chemistry (WRF-Chem). Knowing the exact modeled tracer emission sources in WRF-Chem enables precise quantification of the source detection accuracy. The results show that commonly used backtrajectory analysis are unreliable in identifying emissions sources. After exploring parameter sensitivities thanks to our simulation framework, we present an updated and rigorously evaluated backtrajectory analysis protocol for tracing the origins of atmospheric species from measurement data. Two tests of the improved protocol on actual aerosol data from Arctic campaigns demonstrate its ability to correctly identify known sources of methane sulfonic acid and black carbon. Our results reveal that traditional backtrajectory methods often misidentify emission source regions. Therefore, we recommend using the method described in this study for future efforts to trace the origins of measured atmospheric species.
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RC1: 'Comment on egusphere-2024-2839', Anonymous Referee #1, 19 Dec 2024
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The submitted paper, titled "How to trace the origins of short-lived atmospheric species in the Arctic", investigates the origins of particles and trace gases in rapidly changing polar climates, with a focus on aerosol-cloud interactions. The authors highlight the limitations of current backtrajectory models like FLEXPART in identifying emission sources accurately, emphasizing the need for improvement due to the impact of aerosols on polar clouds and climate modeling.
To address this, the study combines backtrajectories from FLEXPART with tracer simulations from WRF-Chem, enabling a precise evaluation of source detection methods. They present a new approach based on backtrajectory analysis, to improve source identification accuracy through parameter sensitivity studies and validations using Arctic aerosol campaign data. Their results demonstrate the flaws of traditional backtrajectory analysis and the skills of the revised method in correctly identifying sources of methane sulfonic acid and black carbon.
The methodology presented in this paper appears robust and well-developed, addressing key challenges in tracing the origins of short-lived atmospheric species in polar regions. The combination of WRF-Chem tracer simulations and FLEXPART backtrajectory analysis represents a significant step forward in improving source identification accuracy. The results are convincingly validated with observational data, making this study a valuable contribution to the field of atmospheric sciences.
I recommend this paper for publication, subject to the authors addressing the minor comments outlined below
1) The choice of a 50 km × 50 km grid resolution for FPES calculations might limit the method's ability to resolve emissions from localized or highly dynamic sources such as ship traffic. Given the transient and narrow spatial footprint of such sources, the averaging approach inherent in the method could dilute the contribution of mobile emissions and introduce overlap with nearby stationary sources. Have you tested the sensitivity of your method with a 25x25km or smaller grid?
2) The optimized cutting threshold on FPES is set to 2%, which the authors appropriately note in the discussion cannot be generalized to other regions or surface sources. To enhance the potential for generalizing these results, I suggest also to translate the FPES values into a quantifiable number of trajectories contributing to a given FPES value. This would provide a more universally interpretable metric for future applications. When stating that the purpose of filtering the FPES is to remove isolated backtrajectories, could the authors clarify what constitutes an 'isolated trajectory'? Does ir refer to a single trajectory, or a minimal number of trajectories in a given grid cell?
3) Could the authors elaborate on how their method would perform in isolating the impact of ship traffic, particularly in areas where shipping lanes are adjacent to other emission sources, such as coastal or industrial regions? Would finer grid resolutions or additional filtering parameters improve the reliability of source detection for such cases?"
Citation: https://doi.org/10.5194/egusphere-2024-2839-RC1
Model code and software
Origin detection tools for atmospheric species: FLEXPART-WRF post-processing scripts for the Ratio Method Anderson Da Silva https://doi.org/10.5281/zenodo.13902693
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