the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2025
Isentropic Mixing vs. Convection in CLaMS-3.0/MESSy: Evaluation Using Satellite Climatologies and In Situ Carbon Monoxide Observations
Abstract. Lagrangian modeling of transport, as implemented in the Chemical Lagrangian Model of the Stratosphere (CLaMS), connects the advective (reversible) component of transport along 3D trajectories with mixing, the irreversible component. Here, we investigate the interplay between strongly localized convective uplifts and large-scale flow dynamics in the upper troposphere and lower stratosphere (UTLS). We revisit the Lagrangian formulation of convection in CLaMS-3.0/MESSy, driven by ECMWF’s ERA5 reanalysis, and further develop the model. These developments include refining spatial resolution in the Planetary Boundary Layer (PBL) and decoupling the frequency of the adaptive grid procedure—which captures isentropic mixing and redefines Lagrangian air parcels—from the parameterization of convection.
To improve the model’s UTLS transport representation, particularly from the PBL over days to weeks, we derive zonally and seasonally resolved climatologies of CO partial columns (XCO, spanning 147 to 68 hPa) and compare them with Microwave Limb Sounder (MLS) and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations, as well as in situ data. Incorporating a parameterization for unresolved convection significantly improves CO anomaly representation in the UTLS, particularly in capturing seasonal and spatial patterns. While the simulated absolute XCO values align better with ACE-FTS, the model reproduces MLS anomalies more accurately, suggesting MLS better represents CO variability. In situ observations in the boreal polar region generally support lower ACE-FTS CO values, while MLS better represents CO enhancements in air affected by the Asian summer monsoon above 10 km.
Competing interests: Author Marc von Hobe is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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RC1: 'Comment on egusphere-2025-1155', Anonymous Referee #1, 21 Apr 2025
Review of “Isentropic Mixing vs. Convection in CLaMS-3.0/MESSy: Evaluation
Using Satellite Climatologies and In Situ Carbon Monoxide
Observations” by Konopka et al.
This is a very well written paper which is easy to understand and I recommend it be accepted after very minor corrections noted below.
The hybrid grid used in the tropospheric portion of CLaMS blends kinematic and isentropic trajectory schemes to get around the conservation problems with potential temperature (q) in the troposphere and surface issues. The advantage of the isentropic schemes is that they are immune to gravity wave biases whereas kinematic schemes can suffer from these biases. For example, the vertical motion of a partially resolved gravity wave or convective complex can dominate the smaller motions associated with larger scale waves. This issue is discussed in the text, but the authors should also discuss how the model would work for orographic gravity waves. For convection, an alternate approach to using a hybrid coordinate is to use qe which automatically includes the latent heat energy. Convective parcels rise roughly to the q level that equivalent to qe at the surface. I was surprised that this idea wasn’t mentioned.
CLaMS, unlike most trajectory models, employs a mixing scheme where nearby parcels are ‘mixed’ as might occur in the actual atmosphere. The scheme also includes creation of new parcels as part of an entropy conserving regridding. The authors note that the regridding step is computationally costly since it involves identifying and estimating distance to the nearest neighbors. I have implemented regridding schemes using the Lyapunov approach (similar to the authors) and since my code was highly parallelized, the computational cost of regridding was larger than the cost just accumulating parcels. However, the cost of accumulating parcels for multi-year runs is also cost prohibitive. The authors do a good job describing their approach and are honest about the computational cost which can be reduced by adjusting the frequency of regridding.
The discussion of the three versions of CLaMS was very interesting and enlightening. Clearly CLaMS 3.0 is a step forward from early versions (Fig. 4, 5).
The test with CO simulations is also very interesting and a little confounding in that MLS and ACE-FTS don’t agree on the partial columns. The CLaMS simulation looks good when boundary CO is increased (an interesting result in itself). Comparison with in situ data is also useful showing the improvements in 3.0.
Overall this is an excellent paper. I have only two minor suggestions.
- Comment on how CLaMS hybrid coordinate deals with orographic gravity waves. This is probably the worst case scenarios for hybrid coordinates. I suggest an experiment where hybrid and isentropic calculations are compared near mountain ranges.
- Add black dots to the caption in Fig. 8.
Citation: https://doi.org/10.5194/egusphere-2025-1155-RC1 -
RC2: 'Comment on egusphere-2025-1155', Anonymous Referee #2, 28 Jul 2025
General Comment This manuscript presents an update of the Chemical Lagrangian Model of the Stratosphere (CLaMS), focusing on improvements in the parameterization of convection and the adaptive grid procedure used in CLaMS-3.0/MESSy. The authors evaluate the impact of these modifications on the simulation of CO partial columns in the UTLS using satellite observations (MLS, ACE-FTS), as well as CO profiles in the troposphere and UTLS using various airborne campaigns.
While the paper is well written and technically sound, I have reservations regarding its suitability for Atmospheric Chemistry and Physics. My concerns are twofold: (1) the lack of scientific novelty beyond incremental model development; and (2) the scope of the study, which limits its relevance to a broader atmospheric chemistry or physics audience, making it primarily of interest to users and developers of the CLaMS model.
The manuscript presents valuable technical developments that merit publication, but it would be more appropriately considered for Geoscientific Model Development (GMD), where the scope and format better match the nature of this work.
Major Comments
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The convection parameterization implemented here builds on earlier work (Konopka et al., 2012, 2019, 2022). The improvements presented in this paper include the use of CAPE as a convective trigger, increased resolution in the PBL, and the decoupling of the convective time step (6 h) from the mixing step (24 h). These are technical refinements rather than conceptual breakthroughs.
While they do lead to improved agreement with satellite and in situ data compared to CLaMS v1, these improvements do not lead to new insights into atmospheric chemistry or physics, and are best characterized as model engineering updates. In my view, they fall more within the scope of Geoscientific Model Development (GMD), which encourages detailed model evaluations and technical advancements.
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The authors use CO as a passive tracer of transport from the PBL to the UTLS. The results are consistent with expectations: including a convective parameterization enhances vertical transport and improves agreement with observed CO anomalies. I would encourage the authors to consider evaluating other tracers such as ozone, particularly in regions where convective transport plays a dominant role—e.g., the upper troposphere over the Maritime Continent, where ozone minima are observed due to the lofting of ozone-poor marine boundary layer air into the UTLS.
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The evaluation relies mainly on zonal and seasonal climatologies. While this approach is adequate for large-scale diagnostics, it limits the ability to assess the performance of the convective scheme under specific meteorological conditions. Including a specific case study (e.g., a well-observed convective event from one of the airborne campaign used in the paper) would provide a more convincing and concrete evaluation of the scheme’s performance.
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Section 2.4 raises the issue of mass conservation. However, the discussion remains qualitative. Could the authors provide a quantitative assessment of mass conservation errors, ideally as a function of region or meteorological regime? I suspect the largest errors may occur in regions with strong convection. This is important for assessing the robustness of tracer transport in the model.
Technical Comment
Figure 8: There appears to be a unit error. The CO mixing ratio should be expressed in ppbv, not ppmv.
Citation: https://doi.org/10.5194/egusphere-2025-1155-RC2 -
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