John Tannahill

Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5

We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and Quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75–90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions.

The parametric sensitivity of CAM5's MJO

We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden-Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100-member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large-scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low-level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5s parameterization suite are needed in order to simultaneously well simulate the MJO and mean-state climate.