Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Preprints
https://doi.org/10.5194/gmd-2020-317
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-317
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 19 Oct 2020

Submitted as: model evaluation paper | 19 Oct 2020

Review status
This preprint is currently under review for the journal GMD.

A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1

Johannes Horak1, Marlis Hofer1, Ethan Gutmann2, Alexander Gohm1, and Mathias W. Rotach1 Johannes Horak et al.
  • 1Universität Innsbruck, Department of Atmospheric and Cryospheric Sciences, Innsbruck, Austria
  • 2Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract. The verification of models in general is a non-trivial task and can, due to epistemological and practical reasons, never be considered as complete. As a consequence, a model may yield correct results for the wrong reasons, i.e. by a different chain of processes than found in observations. While in the atmospheric sciences guidelines and strategies exist to maximize the chances that models are correct for the right reasons, these are mostly applicable to full-physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field atmospheric quantities, such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full-physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt-Väisälä frequencies are calculated in accordance to linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary different to the standard zero gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations as well as the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates a large shift in the precipitation maximum for the ICAR simulation employing the developed recommendations in contrast to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such analysis may not reflect the skill of the model in capturing atmospheric processes such as gravity waves and cloud formation.

Johannes Horak et al.

Interactive discussion

Status: open (until 25 Dec 2020)
Status: open (until 25 Dec 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Johannes Horak et al.

Data sets

Data set - Idealized ridge simulations with ICAR and WRF J. Horak https://doi.org/10.5281/zenodo.3609954

Model code and software

johanneshorak/icar: ICAR-N E. Gutmann, T. Eidhammer, P. Bohlinger, J. Horak, J. Vano, K. Rasouli, and L. Scaff https://doi.org/10.5281/zenodo.4042992

Johannes Horak et al.

Viewed

Total article views: 193 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
154 36 3 193 4 4
  • HTML: 154
  • PDF: 36
  • XML: 3
  • Total: 193
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 19 Oct 2020)
Cumulative views and downloads (calculated since 19 Oct 2020)

Viewed (geographical distribution)

Total article views: 138 (including HTML, PDF, and XML) Thereof 137 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 01 Dec 2020
Publications Copernicus
Download
Short summary
The presented process based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
The presented process based evaluation of the atmospheric model ICAR is conducted to derive...
Citation