Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis

Yang, Qike; Zhao, Chun; Feng, Jiawang; Li, Gudongze; Gu, Jun; Xia, Zihan; Xu, Mingyue; Yang, Zining

doi:https://doi.org/10.5194/gmd-2024-229

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https://doi.org/10.5194/gmd-2024-229

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Submitted as: model evaluation paper

24 Jan 2025

Submitted as: model evaluation paper |

| 24 Jan 2025

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

Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis

Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang

Abstract. Regular latitude-longitude grids in global simulations encounter polar singularities in the Arctic and Antarctic regions. In contrast, unstructured meshes have the potential to overcome this issue; however, so far, the performance of unstructured meshes in polar areas is barely investigated. This study examined the efficacy of unstructured meshes over Antarctica using the integrated Atmospheric Model Across Scales (iAMAS, v1.0) with multi-source observations. Four mesh configurations of the iAMAS model were assessed, varying in resolutions (120 km, 60 km, 16 km, and 4 km) over the Antarctic region. The study evaluates the iAMAS simulation performance for both surface layer and upper meteorological fields (temperature, pressure, specific humidity, and wind speed), by comparing simulations against the fifth-generation ECMWF reanalysis (ERA5) data and measurements from automatic weather stations and radiosondes. The results indicate that the iAMAS model does not exhibit the polar singularity issue observed in ERA5, where the ERA5 with regular latitude-longitude grids significantly underestimates wind speeds at the polar grid center (i.e., the South Pole at 90° S). In the relatively flat region of East Antarctica, all four iAMAS experiments at various resolutions demonstrate comparable and even superior performance in simulating temperature and wind speed when compared to ERA5. In regions with complex terrain, such as near the Transantarctic Mountains, the iAMAS model (particularly at coarse grid resolutions like 120 km) exhibits a cold bias and stronger wind speeds, consistent with biases identified in other Antarctic simulations using regional models with latitude-longitude grids. Notably, mesh refinement at 4 km in complex terrains significantly enhance iAMAS’s accuracy in simulating the meteorological fields for both the surface layer and upper atmosphere, suggesting that a grid resolution of 4 km (or even higher) is optimal in such regions. Conversely, in flatter areas, like the high East Antarctic plateau, increases in grid resolution yield minimal improvements in simulation accuracy, and a 60-km grid resolution appears sufficient.

Received: 02 Dec 2024 – Discussion started: 24 Jan 2025

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Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang

Status: final response (author comments only)

CEC1:
'Comment on gmd-2024-229', Juan Antonio Añel, 13 Feb 2025

Dear authors,
After checking your manuscript, we would like to request you to store the data that you have used to perform your work in a permanent repository, currently it is stored only in websites that do not offer enough long-term archival security.
You can find in our Code and Data policy examples of potential repositories to use: https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
Juan A. Añel
Geosci. Model Dev. Executive Editor

Citation: https://doi.org/10.5194/gmd-2024-229-CEC1
- AC1:
  'Reply on CEC1', Qike Yang, 15 Feb 2025
  
  Dear Executive Editor, Geoscientific Model Development,
  Thank you for reviewing our manuscript. We have provided citations for the persistent public archives containing the precise versions of all the data associated with our manuscript, specifically the DOIs. Below we listed the revisions to be implemented in the "Data Availability" section.
  Replace:
  “The surface layer measurements are obtained from the Antarctic Meteorological Research Center and the Automatic Weather Station program (ftp://amrc.ssec.wisc.edu/pub/aws/q3h/2015, last access: 16 July 2023). The meteorological parameters measured by the radiosondes at McMurdo and South Pole are available at the Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub, last access: 16 July 2023), while the meteorological fields at Dome C are available at the Antarctic Meteo-Climatological Observatory (http://www.climantartide.it, last access: 16 July 2023). ERA5 data are obtained (https://cds.climate.copernicus.eu, last access: 16 July 2023).”
  With:
  “The surface layer measurements are obtained from the Antarctic Meteorological Research Center and the Automatic Weather Station program (https://doi.org/10.48567/1hn2-nw60, last access: 16 July 2023). The radiosonde measurements for meteorological parameters at McMurdo and South Pole are available at the Antarctic Meteorological Research Center (https://doi.org/10.48567/ka0n-n046, last access: 16 July 2023), while the meteorological fields at Dome C are available at the Antarctic Meteo-Climatological Observatory (https://doi.org/10.12910/DATASET2022-004, last access: 16 July 2023). Surface-layer ERA5 data can be accessed at https://doi.org/10.24381/cds.adbb2d47 (last access: 16 July 2023), and upper-layer ERA5 data are available from https://doi.org/10.24381/cds.bd0915c6 (last access: 16 July 2023).”
  Should you have any questions, please feel free to contact me. Thank you very much.
  Best regards,
  
  Dr. Qike Yang
  
  University of Science and Technology of China
  
  No. 96, JinZhai Road, Baohe District, Hefei, Anhui, 230026, P.R. China
  
  Email: yangqike@ustc.edu.cn
  
  Citation: https://doi.org/10.5194/gmd-2024-229-AC1
  - CEC2: 'Reply on AC1', Juan Antonio Añel, 15 Feb 2025
    
    Dear authors,
    I am sorry, but we can not accept the AMRDC Data Repository to host the assets of your manuscript. Please, store the data in one of the acceptable repositories listed in our policy, and reply to this comment with the necessary information, link and DOI.
    Juan A. Añel
    Geosci. Model Dev. Executive Editor
    
    Citation: https://doi.org/10.5194/gmd-2024-229-CEC2
    
    AC2: 'Reply on CEC2', Qike Yang, 16 Feb 2025
    
    Geosci. Model Dev. Executive Editor
    I apologize for the previous revisions not meeting the requirements of your journal. Since the AMRDC Data Repository cannot be used to host the data associated with our manuscript, we have instead stored the data in one of the acceptable repositories listed in your policy, namely Zenodo. The link and DOI have been updated accordingly. Below, we have listed the revisions to be implemented in the "Data Availability" section.
    Replace:
    “The surface layer measurements are obtained from the Antarctic Meteorological Research Center and the Automatic Weather Station program (ftp://amrc.ssec.wisc.edu/pub/aws/q3h/2015, last access: 16 July 2023). The meteorological parameters measured by the radiosondes at McMurdo and South Pole are available at the Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub, last access: 16 July 2023), while the meteorological fields at Dome C are available at the Antarctic Meteo-Climatological Observatory (http://www.climantartide.it, last access: 16 July 2023). ERA5 data are obtained (https://cds.climate.copernicus.eu, last access: 16 July 2023).”
    With:
    “The Antarctic measurements used in this study are archived on Zenodo (https://doi.org/10.5281/zenodo.14876867, last access: 16 February 2025). Surface-layer ERA5 data can be accessed at https://doi.org/10.24381/cds.adbb2d47 (last access: 16 July 2023), and upper-layer ERA5 data are available at https://doi.org/10.24381/cds.bd0915c6 (last access: 16 July 2023).”
    Should you have any questions, please feel free to contact me. Thank you very much.
    Best regards,
    Dr. Qike Yang
    University of Science and Technology of China
    No. 96, JinZhai Road, Baohe District, Hefei, Anhui, 230026, P.R. China
    Email: yangqike@ustc.edu.cn
    
    Citation: https://doi.org/10.5194/gmd-2024-229-AC2
    
    CEC3: 'Reply on AC2', Juan Antonio Añel, 16 Feb 2025
    
    Dear authors,
    Many thanks for your reply. We can consider now your manuscript in compliance with the policy of the journal.
    Juan A. Añel
    Geosci. Model Dev. Executive Editor
    
    Citation: https://doi.org/10.5194/gmd-2024-229-CEC3
RC1:
'Comment on gmd-2024-229', Anonymous Referee #1, 02 Mar 2025
In this paper, the authors employ the integrated Atmospheric Model Across Scales (iAMAS) with four unstructured mesh configurations (resolutions of 120 km, 60 km, 16 km, and 4 km) to simulate Antarctic meteorological conditions and compare the results against both ERA5 reanalysis data and in situ measurements (AWS and radiosondes). Their findings highlight that unstructured meshes avoid the “polar singularity” problem seen in ERA5 at the South Pole, where ERA5 substantially underestimates wind speeds. In relatively flat Antarctic regions, iAMAS at coarse resolutions (e.g., 60 km) already achieves performance comparable to—or better than—ERA5, suggesting that high-resolution meshes offer minimal additional benefits there. Conversely, over complex terrain such as the Transantarctic Mountains, higher-resolution grids (around 4 km) significantly improve the simulation of temperature, pressure, specific humidity, and wind speed, reducing the systematic cold and wind biases found in coarser configurations. Overall, this study underscores the advantages of unstructured meshes for polar modeling, particularly in resolving complex topography, while also showing that moderate resolutions may suffice in more uniform terrain.
The paper is well written and organized. It would be a contribution to the journal GMD. However, there are some minor issues where further explanation is needed to improve the manuscript’s quality. I suggest minor revisions to the manuscript before it is accepted for publication.
Major comments
One of my main concerns is that the authors did not show any comparison regarding snowfall estimation. As mentioned in the introduction, ice sheet simulation is why accurate polar atmospheric simulations are needed. Then it is necessary to understand how different model configurations affect the amount and distribution of snow. If precipitation observation is available, the authors should compare their simulation results with the observation. Even if the observation data do not have a precipitation record, analyzing simulation results and how different resolutions affect the precipitation pattern and intensity would still be helpful.

The second point I think the author should elaborate on is the stratospheric wind bias (Fig. 8). I don’t feel the authors’ explanation is convincing enough. ERA5 clearly does not have similar issues. Is the cause the relatively low model top? It appears to me that a high-top model like WACCM is necessary if we want to get the stratosphere simulation correct. I hope the authors can provide more explanations and references in this section.

Minor comments:
Line 45: It is better to mention cubed sphere grid as an alternative option. Are there polar simulation studies using the cubed sphere (like FV3)?

Line 97-98: Why is the center of the 4-km grid mesh not centered at the south pole? What is the justification? What is unique about 80 degree S and 160 degree E?

Line 192-194: It is good that iAMAS exhibits more accurate temperature result. However, what is the physical reason for the better performance? The author should give some explanation, or at least speculation here.

Line 244: What does AMPS stand for? It should be defined before the first use.

Line 245: Interesting point. What is the potential effect of this missing process on temperature bias in Noah MP?

Line 300: the unit is probably wrong here. Should it be K/km instead of K/m?
Citation: https://doi.org/10.5194/gmd-2024-229-RC1
RC2:
'Comment on gmd-2024-229', Anonymous Referee #2, 07 Apr 2025
General Comments:
This is an interesting manuscript that investigates the performance of the MPAS model (see below) over Antarctica in relation to in-situ observations and the ERA5 global reanalysis. The sensitivity of the model results to the model grid spacing is emphasized, and the results are in line with expectations. It is to my knowledge the first manuscript describing the performance of MPAS over Antarctica in the refereed literature. However, there are many aspects to the manuscript that need major improvement to reflect reality.
First, the iAMAS model that is run here without the chemistry component activated is actually the MPAS model available from NCAR. After looking at Gu et al. (2022), the changes (apart from the atmospheric chemistry component) are focused on efficient execution on an HPC platform. The atmospheric physics options used here are those available with MPAS (https://mpas-dev.github.io/atmosphere/atmosphere.html) that originate from various versions of WRF.
Second, the manuscript needs to consider the Antarctic Mesoscale Prediction System (AMPS) much more carefully than is done. AMPS has been run routinely for many years, starting with MM5 before moving on to the current WRF model. Here is the best reference: doi: 10.1175/BAMS-D-11-00186.1. It is run on a polar stereographic projection, i.e., no pole point problem, with the finest grid around Ross Island currently being 0.89 km grid spacing. In addition, AMPS has been running MPAS for a number of years, although this is not well known and unfortunately there is no peer reviewed manuscript. However, the following conference presentation should be referred to and discussed to provide a quantitative context for your results. https://amrc.ssec.wisc.edu/presentations_2017/Day1/powers_mpas.pdf. More recent summaries of the status of MPAS in AMPS are provided here: https://polarmet.osu.edu/WAMC_2024/pdf/WAMC_2.08.pdf and https://polarmet.osu.edu/WAMC_2024/pdf/WAMC_2.09.pdf
Third, the polar version of WRF (PWRF) has been extensively used over and validated for Antarctica. The most recent peer reviewed publication is https://doi.org/10.1007/s11707-022-0971-8 that considers a full annual cycle and considers both surface and upper air observations. PWRF with a few modifications is used in AMPS. The extensive PWRF literature provides guidance on the Antarctic performance of available physics options in WRF. You should quantitatively contrast your results with those presented by Xue et al. in the above reference.
Fourth, a lot is known about ERA5 performance in the Antarctic, yet none is referenced here. Here is a good reference to consult and incorporate into this manuscript. https://doi.org/10.1175/JCLI-D-19-0030.1
Fifth, we checked one day of data for ERA5 for the average wind speed at 89.5, 89.75, and 90°S (from the square root (u² + v²)) from 600 hPa and above. The first two values were close in magnitude (3-12 m/s) while the value at the pole point was nearly zero. We also checked at the North Pole and found the same problem. Thus, the pole problem in ERA5 for wind speed reported here is supported.
Specific Comments:
Line 155: The scheduled frequency of radiosonde launches at McMurdo and South Pole varies during the year with generally 1 per day during the colder months.

Lines 169-171: What roughness length did you use in the logarithmic wind profile to adjust the 10 m wind speeds down to 3 m?

Figure 3 for January: It looks like there is a problem with the snow albedo used in iAMAS for interior Antarctica. It should be no smaller than 0.8. Otherwise, large warm biases in the 2-m temperature will be simulated. See Xue et al. (2022).

Lines 188-190: This discussion refers to the ERA-Interim reanalysis and not ERA5.

Table 3: The 2-m air temperature results one gets depends a lot on the land surface model. How did you initialize the Noah LSM?

Table 4: It is very challenging to measure atmospheric moisture content at low air temperatures because of the tiny amounts of water vapor present.

Line 260 “enhancing the barrier effect of air flowing over it”. This language requires change because the barrier effect you are talking about refers to the low-level blocking/barrier effect on air encountering an obstacle causing some or all of the air to blow around rather than over the obstacle.

Line 261: O’Connor. Fix also in the references.

For your comparisons, you should add the numbers of observing stations involved in the comparisons.

Figure 8 for October: The model cold biases above ~ 15 km are probably related to the stratospheric ozone treatment in MPAS. ERA5 puts a lot of effort into correctly handling the stratospheric ozone concentration.

Section 3.2.2: Comparisons such as these usually use geopotential height as this variable on constant pressure surfaces is used for atmospheric analysis.

Figures 10 and 12 for October: If your temperature field is in error in the stratosphere then so will the specific humidity and wind fields.

Line 413: Many/most regional models for Antarctica use a polar stereographic projection not a latitude-longitude grid because of the singularity at the Pole.

References: Golledge et al. (2015), Kannemadugu et al. (2023), Kessenich et al. (2023), and Zhang et al. (2023) contain repetitious information.
Citation: https://doi.org/10.5194/gmd-2024-229-RC2
RC3: 'Comment on gmd-2024-229', Anonymous Referee #2, 08 Apr 2025

The issue of the low wind speeds at South Pole as reported in this manuscript has been known at ECMWF since January 2019
and is posted on this ECMWF discussion page for ERA5:
https://confluence.ecmwf.int/pages/viewpage.action?pageId=129134800
The problem occurs for both the north and south poles. ECMWF Advice: "please use winds at neighbouring locations at 89.75 (N/S) and
89.5 (N/S) for Reanalysis and Ensemble members winds, respectively."

Citation: https://doi.org/10.5194/gmd-2024-229-RC3

Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang

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https://doi.org/10.5194/gmd-2024-229-supplement

Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang

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Short summary

This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.


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