Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters
By Lindsay A. Lee, K. Carslaw, K. Pringle, G. Mann, Dominick V. Spracklen. 2011
182 citations recorded by the source corpus.
Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/bfd8304953db83a410d17405d823bf1de19300eb
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, unknown): “global atmospheric aerosol dynamics (Lee et al., 2011), denitrification and decomposition processes (Qin et al.”
- Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity (2014, background): “Lee et al. (2011) considered the effect on simulated CCN concentrations of co-varying eight parameters in a global aerosol microphysics model, showing that in the European free troposphere, simulated CCN concentrations are highly sensitive to parameters associated with the treatment of nucleation sc”
- Bounding Global Aerosol Radiative Forcing of Climate Change (2020, background): “Parametric uncertainty can be quantified us-637 ing Perturbed Parameter Ensembles (PPEs) (L. A. Lee et al., 2011; Carslaw et al., 2013;638 Regayre et al., 2018).”