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Global Water Futures
Global Water Futures Observatories (GWFO)
Global Water Futures (GWF)
Global Institute for Water Security (GIWS)
International Network of Alpine Research Catchment Hydrology (INARCH 2)
Legacy Research Programs
Changing Cold Regions Network (CCRN)
Drought Research Initiative (DRI)
International Network of Alpine Research Catchment Hydrology (INARCH 1)
Improving Processes & Parameterization for Prediction in Cold Regions Hydrology (IP3)
The Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS)
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A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1 (in review see 2021)
Section 1: Publication
Horak, J., Hofer, M., Gutmann, E., Gohm, A., and Rotach, M. W.
A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1
Geosci. Model Dev. Discuss.
Horak, J., Hofer, M., Gutmann, E., Gohm, A., and Rotach, M. W. 2020: A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1, Geosci. Model Dev. Discuss.,
, in review.
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
5 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
10 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
15 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
20 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
25 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.
Plain Language Summary
Section 2: Additional Information
GWF: Global Water Futures
INARCH: International Network of Alpine Research Catchment Hydrology
GWF-AWF: Agricultural Water Futures
INARCH1: International Network of Alpine Research Catchment Hydrology (Phase 1)
Section 3: Download
T-2021-11-12-v1IDwu0u9nEiM9T6MtlTp1w Publication 1.0