the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Nov 2024
Evaluating Weather and Chemical Transport Models at High Latitudes using MAGIC2021 Airborne Measurements
Abstract. Methane (CH4) fluxes emitted by wetlands at high latitudes remain one of the largest sources of uncertainties in global methane budgets. At these latitudes, flux estimation approaches, such as atmospheric inversions, are impacted by improper characterisation of atmospheric transport due to challenging meteorological conditions and a lack of measurements. Here, we assess the performances of ERA5 reanalysis, mesoscale simulations from WRF-Chem, and various atmospheric transport models from several global and regional inversion systems using meteorological and CH4 in-situ measurements collected during the MAGIC2021 campaign near Kiruna, Sweden. Over six measurements days in August 2021, ERA5 exhibited better agreement with observations than WRF-Chem thanks to data assimilation. Nevertheless, WRF-Chem demonstrated proficiency in simulating local atmospheric dynamics. Among global simulations of atmospheric concentrations of CH4, inversion-optimised simulations of CH4 concentrations yielded the best performances, particularly near the surface, with CAMS v21r1 marginally outperforming PYVAR-LMDz-SACS ensemble inversions. WRF-Chem regional simulations revealed performance disparities among CH4 products, with positive biases in the boundary layer indicative of an overestimation of wetland emissions by selected wetland flux models. All transport models exhibited a vertically delayed gradient of CH4 mixing ratios near the tropopause, resulting in a positive bias in the stratosphere. The high vertical resolution of CAMS hlkx facilitated a better representation of the vertical structure of CH4 profiles in the stratosphere. Despite the limited spatiotemporal scope of MAGIC2021, we were able to identify the best performing transport models and to evaluate fluxes from different biogeochemical model parametrisations using the MAGIC2021 high-resolution dataset.
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RC1: 'Comment on egusphere-2024-3559', Danilo Custódio, 16 Dec 2024
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Reviewer Comments for the Article "Evaluating Weather and Chemical Transport Models at High Latitudes using MAGIC2021 Airborne Measurements"
The manuscript focus on the ability of atmospheric composition models in reproducing observed CH4 mixing ratios. As well, to asses the compliance of the meteorological variables used to drive atmospheric transport comparing it to meteorological variables measured at high latitude. The article addresses critical issues in the field of atmospheric modeling, particularly at high latitudes, which are underrepresented in global atmospheric monitoring and modeling efforts. The relevance of such an evaluation cannot be overstated, as high-latitude regions are crucial for understanding key atmospheric processes, including CH4 emission and transport.
The study makes a valuable attempt to bridge the gap between observations and model simulations by combining state-of-the-art airborne measurements with model inter-comparisons. Confronting model results with high-resolution observations is a cornerstone of atmospheric science, as it is necessary to validate, refine, and benchmark modeling frameworks. The findings have the potential to contribute significantly to the atmospheric modeling community, offering insights into model performance under challenging conditions and emphasizing the importance of improving CH4 in polar regions.
However, while the study holds promise, the manuscript in its current form is not an easy read and has significant shortcomings in its presentation, structure, and overall clarity. I recommend major revisions before the article is considered for publication.
If the authors address the weaknesses in presentation and analysis, this work could substantially contribute to the scientific discourse on atmospheric modeling in high-latitude environments.
- 1. Major Concerns
- Clarity and Presentation
The manuscript is difficult to follow due to unclear wording, undued wording, and overly dense descriptions. Some key points are buried in the text, making it challenging for readers to extract the central findings and their implications. Additionally, the plots are mazy and visually overwhelming, detracting from their effectiveness in conveying the results.
- Metrics and Model Performance Assessment
While the study evaluates model performance, the choice of metrics is not optimal. The authors should consider employing more comprehensive and widely accepted set of statistical metrics for model evaluation. Correlation Coefficients and Root Mean Square Error are good; however, I would recommend bias.
Additionally, the manuscript could discus the implications of the metrics used. For example, while some metrics may show agreement, others may reveal discrepancies, which are worth exploring.
- Figures
The figures and tables are a central issue. While they contain a wealth of information, they are too crowded and difficult to interpret. Each figure should serve a clear purpose and convey specific insights. To improve:
- Use clean background, simplify the layout and make sure that the data are visible.
- Use color schemes that are easy to distinguish, particularly for readers with color vision deficiencies.
- Add concise and informative captions that explain the key takeaways from each figure.
- 2. Others Comments
The manuscript's Figure 1 is confusing and requires clarification:
- Scope of Flights: Does Figure 1 intend to show the entire MAGIC2021 campaign or only flights over Kiruna? The caption does not make this clear.
- Flight over Norway: Why the flight over Norway is not included in the figure? Is it not part of the MAGIC2021 campaign?
- Unexplained Elements: The color blocks in the middle of Figure 1 are not explained in the caption or the text. This information seems to appear "out of the blue," and the figure lacks sufficient annotation to guide the reader. Please ensure that every feature in the figure is fully explained in the caption and supported by the text.
The introduction of AirCore measurements is presented in a very shallow manner. While AirCore is a critical part of the study, its role and methodology are not sufficiently explained. Readers who are unfamiliar with AirCore technology willgrasp it.
The division of the atmosphere into three layers based on pressure ranges—P > 800 hPa, 300 < P < 800 hPa, and P < 300 hPa—is arbitrary and does not align with commonly accepted atmospheric definitions. The chosen pressure thresholds do not accurately correspond to the planetary boundary layer (PBL), free troposphere (FT), or lower stratosphere (LS). A more scientifically sound approach would involve:
- Using PBL height (PBLH) to define the boundary layer.
- Defining the tropopause to separate the troposphere from the stratosphere.
This approach would ensure that the results are more meaningful and interpretable, especially for discussions of CH₄ transport dynamics across these atmospheric layer
The table captions should be placed at the top of the tables, following standard formatting conventions. Additionally, the table labels should succinctly describe the contents of the table. For instance, it does not make sense to include information about what is not in the table. Ensure that the captions are clear and concise, helping the reader to quickly understand the data presented.
The manuscript refers to "four statistic," which is an unclear and incorrect phrasing. Likely, the authors mean "four metrics used to evaluate model performance." The use of appropriate and precise terminology is critical for clarity. This error is indicative of broader language issues in the subsection "Statistics," which should be rewritten to ensure proper English usage and a professional tone.
The caption for Figure 2 is insufficient to help readers understand the plot. Captions should summarize the key information conveyed in the figure and provide any necessary context for interpretation. In its current state, the caption leaves too much ambiguity and fails to assist the reader in navigating the content.
The manuscript's discussion of wind fields is constrained solely to advection (horizontal transport), which provides an incomplete picture. The vertical component of wind, which is critical for transport processes and atmospheric mixing, is entirely missing. Vertical transport are among the most significant challenges in atmospheric modeling. Without addressing these, the discussion remains superficial. The authors could evaluate turbulence representation and vertical wind components in the models, as these are critical to understanding transport processes.
Figure 5 is visually confusing and "weird" in its current presentation. The layout, formatting, and choice of visualization make it difficult to follow and interpret. Clearer design and simpler representations would greatly enhance the reader's understanding of this figure. Ensure that key messages are apparent and not lost in the visual clutter.
The content of subsection 3.3 is difficult to follow due to poor organization and unclear visualizations. The comparisons presented in this section lack coherence in terms of visual representation, metrics used, and overall wording. It is essential to streamline the presentation of comparisons to make them more reader-friendly and effective.
The comparison of meteorological data between models and observations is superficial, merely reporting which model or dataset is closer to observations. This approach fails to provide meaningful insights or a deeper understanding of model inter-comparisons. Readers expect a more insightful analysis of model performance, including:
- Identifying potential reasons for discrepancies.
- Explaining how differences in parametrizations or data assimilation processes contribute to observed biases or differences.
- Suggesting ways to improve model representation of meteorological processes.
The manuscript must go beyond simply reporting agreement or disagreement to provide a more nuanced and insightful evaluation.
The vertical profiles presented in the manuscript are overly complicated and lack clarity. The plots are "mazy," and the text does not provide sufficient guidance to help the reader interpret them. The analysis of vertical profiles should do more than report which model performs better in specific atmospheric regions (which, as noted above, were not properly defined). A thorough discussion of the physical processes contributing to vertical variations in CH₄ and meteorological variables would enrich the article.
The conclusion that all models overestimate CH₄ at the upper troposphere-lower stratosphere (UTLS) boundary is interesting but could be influenced by the interpolation method used for data colocation. In addition:
- TM3 does not have the resolution to accurately resolve the tropopause.
- While IFS has more vertical level, it still struggles with tropopause representation.
The lack of proper selection for the lower-most stratosphere in this study further compounds this issue. A more refined methodology is required to draw robust conclusions about model biases in the UTLS region.
The association of the overall positive CH₄ bias in the boundary layer to wetland emissions is an important finding. However, this conclusion seems premature without further testing. A sensitivity test maybe could strengthen this claim and ensure that this conclusion is robust.
The spatial and temporal limitations of this model evaluation could be addressed by incorporating data from the CoMet 2.0 campaign over Canada in the summer of 2022. While the MAGIC2021 campaign provides valuable observations, supplementing this with additional datasets could offer a more comprehensive evaluation of model performance.
I hope the authors do not feel disheartened by this review. The effort and dedication evident in this work are truly impressive. I believe that addressing these points will unlock the full potential of the manuscript, making it clearer, more robust, and significantly more impactful for the atmospheric modelling community.
Citation: https://doi.org/10.5194/egusphere-2024-3559-RC1
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