Dataset associated with "Multiple‐instance superparameterization, Part 1: Concept, and predictability of precipitation"

Jones, Todd; Randall, David

Dataset associated with "Multiple‐instance superparameterization, Part 1: Concept, and predictability of precipitation"

dc.contributor.author	Jones, Todd
dc.contributor.author	Randall, David
dc.coverage.spatial	Global	en_US
dc.date.accessioned	2019-05-30T18:52:31Z
dc.date.available	2019-05-30T18:52:31Z
dc.date.issued	2019
dc.description	Contents 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).	en_US
dc.description	Department of Atmospheric Science
dc.description.abstract	We 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.	en_US
dc.description.sponsorship	National Science Foundation Science and Technology Center for Multi-Scale Modeling of Atmospheric Processes, managed by Colorado State University under cooperative agreement No. ATM-0425247.	en_US
dc.description.sponsorship	National Oceanic and Atmospheric Administration under grant NA16OAR4590230 to Colorado State University.	en_US
dc.format.medium	ZIP
dc.format.medium	NetCDF
dc.identifier.uri	https://hdl.handle.net/10217/195181
dc.identifier.uri	http://dx.doi.org/10.25675/qcy5-2v72
dc.language	English
dc.language.iso	eng
dc.publisher	Colorado State University. Libraries
dc.relation.ispartof	Research Data
dc.relation.isreferencedby	Jones, 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.title	Dataset associated with "Multiple‐instance superparameterization, Part 1: Concept, and predictability of precipitation"	en_US
dc.type	Dataset