CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model

Dennis, John M.; Edwards, Jim; Evans, Katherine J.; Guba, Oksana; Lauritzen, P. H.; Mirin, A.A.; St-Cyr, Amik; Taylor, Mark A.; Worley, Patrick H

doi:10.1177/1094342011428142

Cited by 346 publications

(370 citation statements)

References 54 publications

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“…The simulations include output from the tropical-channel WRF and CAM climate models. For the WRF model, only the Eulerian mass coordinate dycore is used, while the CAM simulations are based upon four different dycores including the Eulerian spectral transform dycore (Neale et al, 2010), the spectral element dycore (HOMME) (Dennis et al, 2012a), the finite volume (FV) dycore (Lin, 2004), and the Model for Prediction Across Scales atmospheric (MPAS-A) dycore (Rauscher et al, 2012). The WRF model has been modified to use the parameterizations of the CAM4 physics package, so that the dynamical core is the main difference between the CAM4 and WRF simulations presented here.…”

Section: Scale-incognizance In Multiple Dycoresmentioning

confidence: 99%

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

O’Brien¹,

Li²,

Collins

et al. 2013

J. Climate

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We use observations of robust scaling behavior in clouds and precipitation to derive constraints on how partitioning of precipitation should change with model resolution. Our analysis indicates that 90-99% of stratiform precipitation should occur in clouds that are resolvable by contemporary climate models (e.g., with 200 km or finer grid spacing). Furthermore, this resolved fraction of stratiform precipitation should increase sharply with resolution, such that effectively all stratiform precipitation should be resolvable above scales of ∼50 km. We show that the Community Atmosphere Model (CAM) and the Weather Research and Forecasting (WRF) model also exhibit the robust cloud and precipitation scaling behavior that is present in observations, yet the resolved fraction of stratiform precipitation actually decreases with increasing model resolution. A suite of experiments with multiple dynamical cores provides strong evidence that this 'scale-incognizant' behavior originates in one of the CAM4 parameterizations. An additional set of sensitivity experiments rules out both convection parameterizations, and by a process of elimination these results implicate the stratiform cloud and precipitation parameterization. Tests with the CAM5 physics package show improvements in the resolution-dependence of resolved cloud fraction and resolved stratiform precipitation fraction.

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Section: Scale-incognizance In Multiple Dycoresmentioning

confidence: 99%

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

O’Brien¹,

Li²,

Collins

et al. 2013

J. Climate

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“…The variable-resolution capability is implemented into the spectral element (SE) dynamic core of CAM5. The SE dynamic core uses a continuous Galerkin spectral finiteelement method designed for fully unstructured quadrilateral meshes and has demonstrated near-optimal (close to linear) parallel scalability on tens of thousands of cores (Dennis et al, 2012). This enables the model to run efficiently on decadal to multi-decadal timescales.…”

Section: Model and Experimental Designmentioning

confidence: 99%

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

Liu

Lin

et al. 2018

Atmos. Chem. Phys.

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Abstract. The deposition of light-absorbing aerosols (LAAs), such as black carbon (BC) and dust, onto snow cover has been suggested to reduce the snow albedo and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined highresolution (0.125 • ) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period 1981-2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations in the Rocky Mountain region. Although the model underestimates near-surface atmospheric BC concentrations, the model overestimates BC-in-snow concentrations by 35 % on average. The regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6-1.7 W m −2 in spring, and dust contributes to about 21-42 % of total SRE. Due to positive snow albedo feedbacks induced by the LAA SRE, snow water equivalent is reduced by 2-50 mm and snow cover fraction by 5-20 % in the two regions around the mountains (eastern Snake River Plain and southwestern Wyoming), corresponding to an increase in surface air temperature by 0.9-1.1 • C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15-1.00 mm day −1 in April-May and reductions of 0.04-0.18 mm day −1 in June-July in the mountainous regions. Of all the mountainous regions, the Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snowmelt (i.e., June and July). Compared to previous studies based on field observations, our estimation of dust-induced SRE is generally 1 order of magnitude smaller in the Southern Rockies, which is ascribed to the omission of larger dust particles (with the diameter > 10 µm) in the model. This calls for the inclusion of larger dust particles in the model to reduce the discrepancies. Overall these results highlight the potentially important role of LAA interactions with snowpack and the subsequent impacts on the hydrologic cycles across the Rocky Mountains.

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“…It is formulated in an unstructured quadrilateral grid system on the cubed sphere. It has an excellent scalability and can be free of polar singularity of latitude-longitude grid system (DENNIS et al 2012).…”

Section: Forecast Modelmentioning

confidence: 99%

The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

Shin

Kang

2016

Pure Appl. Geophys.

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Abstract-We develop an ensemble data assimilation system using the four-dimensional local ensemble transform kalman filter (LEKTF) for a global hydrostatic numerical weather prediction (NWP) model formulated on the cubed sphere. Forecast-analysis cycles run stably and thus provide newly updated initial states for the model to produce ensemble forecasts every 6 h. Performance of LETKF implemented to the global NWP model is verified using the ECMWF reanalysis data and conventional observations. Global mean values of bias and root mean square difference are significantly reduced by the data assimilation. Besides, statistics of forecast and analysis converge well as the forecast-analysis cycles are repeated. These results suggest that the combined system of LETKF and the global NWP formulated on the cubed sphere shows a promising performance for operational uses.

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CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model

Cited by 346 publications

References 54 publications

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

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