Effects of Cloud Model Formulation on Precipitation at Global and Local Scales


Predicting future climate change remains a high priority as well as a complex challenge for science. Insufficient physical understanding and relatively coarse grid resolution limit the ability of global circulation models (GCMs) in this endeavor. Despite increased computational power enabling higher resolution, GCMs still must rely on parameterizations (computational methods to simplify complex physical processes) to represent the subgrid variability of clouds, aerosols, and their interactions. In research led by Department of Energy scientists at Pacific Northwest National Laboratory, scientists investigated the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to dozens of uncertain parameters mainly related to cloud and aerosol processes in the Community Atmosphere Model (CAM version 5). They found that extreme precipitation characteristics are sensitive to a fewer number of parameters, precipitation does not always respond monotonically to parameter change, and the influence of individual parameters does not depend on sampling approaches or related parameters selected. The study was a fast-process investigation responding to parameter perturbation in the current climate, over a 5-year period with prescribed sea surface temperatures. The study better explains the CAM5 model behavior associated with parameter uncertainties and will guide the next step to reducing model uncertainty in precipitation via calibration of the most uncertain model parameters and developing new parameterizations.


Qian, Y., H. Yan, Z. Hou, G. Johannesson, S. A. Klein, D. Lucas, R. Neale, P. J. Rasch, L. P. Swiler, J. Tannahill, H. Wang, M. Wang, and C. Zhao. 2015.  “Parametric Sensitivity Analysis of Precipitation at Global and Local Scales in the Community Atmosphere Model CAM5,” Journal of Advanced Modeling Earth Systems 7(2), 382–411. DOI: 10.1002/2014MS000354.