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. Atmospheric chemical forecasts heavily rely on various model parameters, which are often insufficiently known, such as emission rates and deposition velocities.However, a reliable estimation of resulting uncertainties with an ensemble of forecasts is impaired by the high dimensionality of the system.This study presents a novel approach, which substitutes the problem into a low-dimensional subspace spanned by the leading uncertainties.It is based on the idea that the forecast model acts as a dynamical system inducing multivariate correlations of model uncertainties.This enables an efficient perturbation of high-dimensional model parameters according to their leading coupled uncertainties.The specific algorithm presented in this study is designed for parameters that depend on local environmental conditionsand consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen–Loéve expansion. Due to their perceived simulation challenge,the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions.Rapidly decreasing eigenvalues state that highly correlated uncertainties of regional biogenic emissions can be represented by a low number of dominant components.Depending on the required level of detail, leading parameter uncertainties with dimensions of (106) can be represented by a low number of about 10 ensemble members.This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.

geoscientific model development

Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5