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1. Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs

   https://doi.org/10.5194/gmd-15-6259-2022  

   Sridhar, Akshay ; Tissaoui, Yassine ; Marras, Simone ; Shen, Zhaoyi ; Kawczynski, Charles ; Byrne, Simon ; Pamnany, Kiran ; Waruszewski, Maciej ; Gibson, Thomas H. ; Kozdon, Jeremy E. ; et al ( January 2022 , Geoscientific Model Development)

   Abstract. We introduce ClimateMachine, a new open-source atmosphere modeling framework which uses the Julia language and is designed to be scalable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LESs). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows using a total energy-conserving nodal discontinuous Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics, and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modeling tool for limited-area LES flow configurations. 

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2. Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer

   https://doi.org/10.1016/j.jweia.2018.04.004  

   Lamberti, Giacomo ; García-Sánchez, Clara ; Sousa, Jorge ; Gorlé, Catherine ( June 2018 , Journal of Wind Engineering and Industrial Aerodynamics)
3. ENSO’s impacts on the tropical Indian and Atlantic Oceans via tropical atmospheric processes: observations versus CMIP5 simulations

   https://doi.org/10.1007/s00382-020-05247-w  

   He, Shan ; Yu, Jin-Yi ; Yang, Song ; Fang, Shih-Wei ( June 2020 , Climate Dynamics)
4. Constraints on Arctic Atmospheric Chlorine Production through Measurements and Simulations of Cl 2 and ClO

   https://doi.org/10.1021/acs.est.6b03909  

   Custard, Kyle D. ; Pratt, Kerri A. ; Wang, Siyuan ; Shepson, Paul B. ( October 2016 , Environmental Science & Technology)

   null (Ed.)
5. An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learned from Five Large Ensembles and Multiple CMIP5 Climate Simulations

   https://doi.org/10.1175/JCLI-D-19-0803.1  

   Topál, Dániel ; Ding, Qinghua ; Mitchell, Jonathan ; Baxter, Ian ; Herein, Mátyás ; Haszpra, Tímea ; Luo, Rui ; Li, Qingquan ( September 2020 , Journal of Climate)

   null (Ed.)

   Abstract Arctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June–August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the east-central tropical Pacific Ocean. Here, we utilize five available large “initial condition” Earth system model ensembles and 31 CMIP5 models’ preindustrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability in summer on interannual to interdecadal time scales in preindustrial, historical, and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the magnitude of the observed local atmosphere–sea ice coupling and its sensitivity to remote tropical SST variability in the past four decades. These biases call for caution in the interpretation of existing models’ simulations and fresh thinking about models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying the causes of these model limitations may provide implications for alleviating the biases and improving interannual- and decadal-time-scale sea ice prediction and future sea ice projection. 

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