Abstract
Abstract. 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 conditions and 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 𝒪(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.
Highlights
Due to highly nonlinear properties of the atmosphere, including its chemistry, forecast uncertainties vary significantly in space and time and among variables
The following description of the results focuses on emissions of isoprene, which is the most abundantly emitted biogenic trace gas
In the results presented in this study, this has been demonstrated for a set of biogenic emissions with dimension 2 × 106
Summary
Due to highly nonlinear properties of the atmosphere, including its chemistry, forecast uncertainties vary significantly in space and time and among variables. During the last few decades, increasing effort has been put into estimating forecast uncertainties induced by different error sources. In this context, the method for generating an ensemble of forecasts is crucial as it determines the forecast probability distribution. The major challenge is the generation of ensembles that sufficiently sample the forecast uncertainty within manageable computational efforts. This renders ensemble forecasting one of the most challenging research areas in atmospheric modeling (e.g., Bauer et al, 2015; Buizza, 2019)
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