Towards understanding the effect of parametric aerosol uncertainty on climate using a chemical transport model perturbed parameter ensemble.

Aerosols in the climate system have a direct link to the Earth’s energy balance. Aerosols interact directly with the solar radiation through scattering and absorption; and indirectly by changing cloud properties. The effect aerosols have on climate is one of the major causes of radiative forcing (RF) uncertainty in global climate model simulations. Thus, reducing aerosol RF uncertainty is key to improving climate prediction. The objective of this work is to understand the magnitude and causes of aerosol uncertainty in the chemical transport model TM5.

Perturbed Parameter Ensembles (PPEs) are a set of model runs created by perturbing an ensemble of parameters. Parameters are model inputs, for this study we focus on parameters describing aerosol emissions, properties and processes, such as dry deposition, aging rate, emissions to aerosols microphysics. PPEs vary theses parameters over their uncertainty range all at once to study their combine effect on TM5.

Varying these parameters along with others through their value range, will reflect on TM5 outputs. The TM5 outputs parameters we are using in our sensitivity study are the cloud droplet number concentration and the ambient aerosol absorption optical thickness at 550nm.

Here we discuss the design of the PPE, and one-at-a-time sensitivity studies used in this process. The PPE samples the parameter space to enable us to use emulation. Emulating is a machine learning technique that uses a statistical surrogate model to replace the chemical transport model. The aim is to provide output data with more dense sampling throughout the parameter space. We will be using a Gaussian process emulator, which has been shown to be an efficient technique for quantifying parameter sensitivity in complex global atmospheric models.

We also describe plans to extend this work to emulate an aerosol PPE for EC-Earth. The PPE for EC-Earth will also contain cloud parameters that will vary over their uncertainty range together with the aerosol parameters to examine the influence of aerosol parametric uncertainty on RF.