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

Horak, Johannes; Hofer, Marlis; Gutmann, E. D.; Gohm, Alexander; Rotach, Mathias W.

doi:10.5194/gmd-14-1657-2021

Cited by 9 publications

(6 citation statements)

References 34 publications

(66 reference statements)

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“…(2018) for their WRF runs. Importantly, ICAR/HICAR allows the use of a coarser vertical grid than WRF (Horak et al, 2021).…”

Section: Gridded Datasetsmentioning

confidence: 99%

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale

Reynolds¹,

Gutmann

Kruyt

et al. 2023

Preprint

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Abstract. High resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere-atmospheric processes. While this approach has yielded insightful results, studies over annual time-scales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a High-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle-flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 118x fewer computational resources than the WRF atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data is often required at hectometer resolution for whole basins at seasonal time scales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver, and coupling of the model with an intermediate complexity snow model.

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“…(2018) for their WRF runs. Importantly, ICAR/HICAR allows the use of a coarser vertical grid than WRF (Horak et al, 2021).…”

Section: Gridded Datasetsmentioning

confidence: 99%

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale

Reynolds¹,

Gutmann

Kruyt

et al. 2023

Preprint

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“…Process-based validation can help us to understand and correct biases in seasonal forecasts (Eyring et al, 2019;Horak et al, 2021). Here, we propose to use causal discovery to perform a process-based validation (Nowack et al, 2020) provided by seasonal forecasts.…”

Section: Introductionmentioning

confidence: 99%

Validation of boreal summer tropical-extratropical causal links in seasonal forecasts

Capua

Coumou

Hurk

et al. 2022

Preprint

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Abstract. Much of the forecast skill in the mid-latitudes on seasonal timescales originates from deep convection in the tropical belt. For boreal summer, such tropical-extratropical teleconnections are less well understood as compared to winter. Here we validate the representation of boreal tropical – extratropical teleconnections in a general circulation model in comparison with observational data. To characterise variability between tropical convective activity and mid-latitude circulation, we identify the South Asian monsoon (SAM) – circumglobal teleconnection (CGT) pattern and the western North Pacific summer monsoon (WNPSM) – North Pacific high (NPH) pairs as the leading modes of tropical-extratropical coupled variability in both reanalysis (ERA5) and seasonal forecast (SEAS5) data. We calculate causal maps, an application of the PCMCI causal discovery algorithm which identifies causal links in a 2D field, to show the causal effect of each of these patterns on circulation and convection in the Northern Hemisphere. The spatial patterns and signs of the causal links in SEAS5 closely resemble those seen in ERA5, independent of the initialization date of SEAS5. However, the strength of causal links in SEAS5 is often too weak (about two thirds of those in ERA5). By performing a subsampling (over time) experiment, we identify those regions for which SEAS5 data well reproduce ERA5 values, e.g. South-eastern US, and highlight those where the bias is more prominent, e.g. North Africa. We demonstrate that different El Niño – Southern Oscillation phases have only a marginal effect on the strength of these links. Finally, we discuss the potential role of model mean-state biases in explaining differences between SEAS5 and ERA5 causal links.

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“…The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model that balances the complexity of more accurate, high-order modeling schemes with the resources required to model larger scale scenarios [1]. It is currently in use at the National Center for Atmospheric Research (NCAR) in the United States and has been used for simulations such as a year-long precipitation simulation of a 169 × 132 km section of the Rocky Mountains and precipitation patterns in the European Alps and the South Island of New Zealand [2][3][4]. ICAR has a three-dimensional model that handles a three-dimensional wind field, advection of heat and moisture.…”

Section: Introductionmentioning

confidence: 99%

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

et al. 2021

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This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image’s boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles.

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

Cited by 9 publications

References 34 publications

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale

Validation of boreal summer tropical-extratropical causal links in seasonal forecasts

Fortran Coarray Implementation of Semi-Lagrangian Convected Air Particles within an Atmospheric Model

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