Assessing the effect of uncertainty in input emissions on atmospheric chemistry transport model outputs

Ksenia Aleksankina*, Mathew R. Heal, Anthony J. Dore, Massimo Vieno, Stefan Reis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Atmospheric Chemistry Transport Models (CTMs) provide important scientific support for effective policy development. It is therefore important to have a quantitative understanding of the level of uncertainty associated with model outputs. Conventionally, model assessment studies direct attention to uncertainties in parameter values and model-specific structural and computational errors. Here, we investigate uncertainty in model outputs as a function of the uncertainty in model inputs, such as emissions of primary pollutants. The Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model provides the basis for the development of an uncertainty estimation framework. The study utilises local and global sensitivity analysis techniques. The impact on model outputs of variation in the input emissions of SO2, NOx, and NH3 individually within a ±30% range is assessed using sensitivity coefficients (local method). The propagation of uncertainty in all emissions together is investigated using a Latin hypercube sampling (LHS) global sensitivity analysis. Preliminary results show variability in the uncertainty ranges for different output species and different spatial distribution of these ranges. We present further detail on the development and application of the sensitivity analysis framework for assessment of the effect of input uncertainties on CTMs used for policy support.

Original languageEnglish
Title of host publicationSpringer Proceedings in Complexity
PublisherSpringer-Verlag
Pages111-116
Number of pages6
DOIs
Publication statusPublished - 2018

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Keywords

  • Sensitivity analysis
  • Uncertainty assessment

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