the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China
Chao Gao
Xuelei Zhang
Aijun Xiu
Qingqing Tong
Hongmei Zhao
Shichun Zhang
Guangyi Yang
Mengduo Zhang
Shengjin Xie
Abstract. In the eastern China region, two-way coupled meteorology and air quality models have been applied aiming to more realistically simulate meteorology and air quality by accounting for the aerosol‒radiation‒cloud interactions. There have been numerous related studies being conducted, but the performances of multiple two-way coupled models simulating meteorology and air quality under equivalent configurations have not been compared in this region. In this study, we systematically evaluated annual and seasonal meteorological and air quality variables simulated by three open-source and widely used two-way coupled models (i.e., WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) by validating the model results with surface and satellite observations for eastern China during 2017. Our comprehensive model evaluations showed that all three two-way coupled models simulated the annual spatiotemporal distributions of meteorological and air quality variables reasonably well, especially the surface temperature (with R up to 0.97) and fine particular matter (PM2.5) concentrations (with R up to 0.68). The model results of winter PM2.5 and summer ozone compared better with observations and WRF-CMAQ exhibited the best overall performance. The aerosol feedbacks affected model results of meteorology and air quality in various ways and turning on aerosol-radiation interactions made the PM2.5 and surface shortwave radiation simulations better, but worse for T2 and Q2. The impacts of aerosol-cloud interactions (ACI) on model performances’ improvements were limited and several possible improvements on ACI representations in two-way coupled models are further discussed and proposed. When sufficient computational resources become available, two-way coupled models including the aerosol-radiation-cloud interactions should be applied for more accurate air quality prediction and timely warning of air pollution events in atmospheric environmental management.
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Chao Gao et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2023-21', Anonymous Referee #1, 26 Apr 2023
Review of “Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1, and WRF v3.7.1-CHIMERE v2020r1) in eastern China,” by Gao et al., submitted to Geoscientific Model Development.
This paper intercompares several two-way nested coupled meteorological and air quality models in eastern China. This is the first such comparison that I am aware of. The paper is thorough and should be published following some modest revisions.
Introduction. “The feedbacks of aerosols to meteorology have been widely investigated by two-way coupled meteorology and air quality models in the past two decades.” Two-way coupled meteorological and air quality models have been developed and applied for almost three decades (Jacobson, 1994; 1997; 1998, 2001).
Table 1. what is the vertical resolution of the boundary layer in each model (how many layers in the bottom 1 km and what is the bottom-layer thickness?
Table 1. How many aerosol size bins and components per bin? Do you use a modal or discrete bin approach?
Table 1. Does photolysis account for clouds? How are clouds treated for radiative transfer calculations?
Table 1. What height is the model top and how are model-top boundary conditions treated?
The authors evaluate with RMSE, which is an absolute quantity for each variable. However, normalized gross error (absolute value of differences between model and data, divided by data, summed over all locations and normalized by the number of locations, is a more useful metric since it gives error relative to the data values rather than an absolute amount. It is similar to NMB, but with absolute values taken, since NMB cancels out large errors of the opposite sign. Also, it would be useful to see some time-series plots of model results versus data.
A lot of comparisons are performed, but what are the most relevant comparisons with data? Ozone and PM2.5 calculations? Please focus the discussion of results more. Right now the results section is crammed with lots of information that is not easy to determine from what is important and not important.
References.
Jacobson, M. Z., Developing, coupling, and applying a gas, aerosol, transport, and radiation model to study urban and regional air pollution. Ph. D. Thesis, Dept. of Atmospheric Sciences, University of California, Los Angeles, 436 pp., 1994.
Jacobson, M. Z., Development and application of a new air pollution modeling system. Part III: Aerosol-phase simulations, Atmos. Environ., 31A, 587–608, 1997.
Jacobson, M. Z., Studying the effects of aerosols on vertical photolysis rate coefficient and temperature profiles over an urban airshed, J. Geophys. Res., 103, 10,593-10,604, 1998.
Jacobson, M. Z., GATOR-GCMM: A global through urban scale air pollution and weather forecast model. 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow, J. Geophys. Res., 106, 5385-5401, 2001.
Citation: https://doi.org/10.5194/gmd-2023-21-RC1 -
CEC1: 'Comment on gmd-2023-21', Juan Antonio Añel, 05 May 2023
Dear authors,Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlI have to highlight that if you do not fix this problem in a prompt manner, we will have to reject your manuscript for publication in our journal. I should note that, actually, your manuscript should not have been accepted in Discussions, given this lack of compliance with our policy. Therefore, the current situation with your manuscript is irregular.First, you have archived the code of your models on GitHub and another server: "polytechnique.fr". Neither of them are acceptable repositories for scientific publication. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo. Therefore, you must publish your code in one of the appropriate repositories.Also, you state in your manuscript that input and output files are available upon request. Our policy is clear about the fact that we can not accept that it is necessary to contact the authors to get access to the assets of a manuscript. Therefore, again, you must publish the data in one of the repositories in compliance with our policy.You must reply to this comment with the relevant information for the new repositories (link and DOI) as soon as possible, as it should be available for the Discussions stage.Also, you must include in a potentially reviewed version of your manuscript the modified 'Code and Data Availability' section with the DOIs of the new repositories.Juan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/
10.5194/gmd-2023-21-CEC1 -
AC1: 'Reply on CEC1', Chao Gao, 23 May 2023
Dear Dr. Juan A. Añel
We really appreciate your valuable suggestion on our manuscript entitled “Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China” (Manuscript ID: GMD-2023-21). To comply with the GMD’s policy, we have uploaded the source codes of WRF-CMAQ, WRF-Chem, WRF-CHIMERE used in our simulations at https://doi.org/10.5281/zenodo.7901682 (Gao et al., 2023a; link: https://zenodo.org/record/7901682). The meteorological initial and boundary conditions (ICs and BCs) can be obtained at https://doi.org/10.5281/zenodo.7925012 (Gao et al., 2023b; link: https://zenodo.org/record/7925012). The chemical ICs and BCs used for WRF-CMAQ, WRF-Chem and WRF-CHIMERE are available at https://doi.org/10.5281/zenodo.7932390 (Gao et al., 2023; link: https://zenodo.org/record/7932390), https://doi.org/10.5281/zenodo.7932936 (Gao et al., 2023d; link: https://zenodo.org/record/7932936), and https://doi.org/10.5281/zenodo.7933641 (Gao et al., 2023e; link: https://zenodo.org/record/7933641), respectively. The emission data used for WRF-CMAQ, WRF-Chem and WRF-CHIMERE can be downloaded from https://doi.org/10.5281/zenodo.7932430 (Gao et al., 2023f; link: https://zenodo.org/record/7932430), https://doi.org/10.5281/zenodo.7932734 (Gao et al., 2023g; link: https://zenodo.org/record/zenodo.7932734), and https://doi.org/10.5281/zenodo.7931614 (Gao et al., 2023h; link: https://zenodo.org/record/zenodo.7931614), respectively. Please note that due to the size of output data files and the size limit (50 GB) for data files on Zenodo, an output file of annual simulation from each model under each scenario needs to be divided into 3-4 parts. The DOIs and links for all the outputs uploaded on Zenodo are added in the revised reference list.
We revised the manuscript accordingly and all the relevant changes (in blue and italic) made in the revised manuscript. More detailed information is presented in Supplement.
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AC1: 'Reply on CEC1', Chao Gao, 23 May 2023
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RC2: 'Comment on gmd-2023-21', Anonymous Referee #2, 05 May 2023
I have some major concerns of using FDDA for a study like this and try to objectively learn from model performance. When using FDDA, nudging is forcing the model to the same observations that are being used for evaluation (NCEP reanalysis includes probably all observations that are using for evaluation). Additionally, FDDA makes conclusions for feedback studies very problematic, since the physics parameterizations react in very different ways. Furthermore, you are using different physics and chemistry routines in all models. This makes this an "apples to oranges" comparison. Do you know what happens in Morrison microphysics when the mix-activation routine is not called (no chemistry)? My first thought was to reject the paper, however, the authors have done an immense amount of work and present some useful results that can be used by some of the developers. In turn I will propose accepting but with major revisions. These major revisions should be focused on the interpretation of the results. Abstract and conclusions should clearly say that this work is NOT to decide which model is better or worse since employed setups are very different and furthermore, it is not clear how FDDA runs influences feedback studies. Also, the authors need to be clear on what is used by Radiation (R) and MicroPhysics (MP) if feedback is off versus on. Are you just using a constant droplet number? A climatology? WRF-Chem has a lot of options, how come you decided to use different physics than in WRF-CMAQ? Chimere is way behind in the WRF version used, which makes that one even harder to compare. I am not asking you to rerun this monster simulation, but you will need to rephrase some of your abstract, conclusion, and results description. Since this reviewer is not asking for additional runs, this should not be a major effort. I really appreciate the work you folks put into this paper!
It would be interesting, maybe in a later additional paper, to compare the feedbacks in the different models for a shorter run that does not use FDDA. Maybe picking one or several interesting 5 day periods from the long runs that you used for this paper.
Citation: https://doi.org/10.5194/gmd-2023-21-RC2
Chao Gao et al.
Chao Gao et al.
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