Quantitative Sensitivity Analysis of Physical Parameterizations for Cases of Deep Convection in the NASA GEOS-5

Parameterization of processes that occur on length scales too small to resolve on a computational grid is a major source of uncertainty in global climate models. This study investigates the relative importance of a number of parameters used in the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model, focusing on cloud, convection, and boundary layer parameterizations. Latin hypercube sampling is used to generate a few hundred sets of 19 candidate physics parameters, which are subsequently used to generate ensembles of single-column model realizations of cloud content, precipitation, and radiative fluxes for four different field campaigns. A Gaussian process model is then used to create a computationally inexpensive emulator for the simulation code that can be used to determine a measure of relative parameter sensitivity by sampling the response surface for a very large number of input parameter sets. Parameter sensitivities are computed for different geographic locations and seasons to determine whether the intrinsic sensitivity of the model parameterizations changes with season and location. The results indicate the same subset of parameters collectively control the model output across all experiments, independent of changes in the environment. These are the threshold relative humidity for cloud formation, the ice fall speeds, convective and large-scale autoconversion, deep convection relaxation time scale, maximum convective updraft diameter, and minimum ice effective radius. However, there are differences in the degree of parameter sensitivity between continental and tropical convective cases, as well as systematic changes in the degree of parameter influence and parameter–parameter interaction.