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dc.contributor.authorJones, Todd
dc.contributor.authorRandall, David
dc.coverage.spatialglobal
dc.date.accessioned2019-05-30T18:52:31Z
dc.date.available2019-05-30T18:52:31Z
dc.descriptionContents of the dataset (purpose and scope, time period, areas of investigation): Daily-mean history file output in netcdf format for 30 simulated years from three different CAM simulations: 1) MP-CAM (multiple instance super-parameterized community atmospheric model), 2) SP-CAM (super-parameterized community atmospheric model), and 3) standard CAM. ** simulation name prefixes ** MP-CAM = spcam_ensemble_branch - mix of multiple records per file and just one record per file; SP-CAM = spcamtest - all files contain 30 records per file; and standard CAM = stdcamtest - all files contain 30 records per file. For the MP-CAM history files, we used the netcdf operator (NCO) NCWA (netcdf weighted average) to take means over the number of ensembles (crm_num_ensb=10) and the number of cloud-permitting columns in each grid cell (crm_nx=32).
dc.description.abstractWe have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model, SP-CAM. The new configuration, called the multiple-instance superparameterized-CAM, or MP-CAM, uses the average of 10 independent two-dimensional cloud-permitting models (CPMs) in each grid column of the global model, instead of the conventional single CPM. The 10 CPMs start from slightly different initial conditions, and simulate alternative realizations of the convective cloud system. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud system, and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy and the degree of mesoscale organization. It is strongly controlled by the large-scale circulation. As discussed in a companion paper, the global atmospheric circulations simulated by SP-CAM and MP-CAM are somewhat different.
dc.description.sponsorshipNational Science Foundation Science and Technology Center for Multi-Scale Modeling of Atmospheric Processes, managed by Colorado State University under cooperative agreement No. ATM-0425247.
dc.description.sponsorshipNational Oceanic and Atmospheric Administration under grant NA16OAR4590230 to Colorado State University.
dc.identifier.urihttps://hdl.handle.net/10217/195181
dc.identifier.urihttp://dx.doi.org/10.25675/qcy5-2v72
dc.languageEnglish
dc.relation.ispartofData - Colorado State University
dc.relation.isreferencedbyJones, T. R., Randall, D. A., & Branson, M. (2019). Multiple‐Instance Superparameterization: 1. Concept, and Predictability of Precipitation. Journal of Advances in Modeling Earth Systems, 11, 3497-3520. https://doi.org/10.1029/2019MS001610
dc.titleDataset associated with "Multiple‐Instance Superparameterization, Part 1: Concept, and Predictability of Precipitation"
dc.typeDataset


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