Articles | Volume 11, issue 4
https://doi.org/10.5194/gmd-11-1653-2018
https://doi.org/10.5194/gmd-11-1653-2018
Methods for assessment of models
 | 
27 Apr 2018
Methods for assessment of models |  | 27 Apr 2018

Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study

Ksenia Aleksankina, Mathew R. Heal, Anthony J. Dore, Marcel Van Oijen, and Stefan Reis

Related authors

Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
Ksenia Aleksankina, Stefan Reis, Massimo Vieno, and Mathew R. Heal
Atmos. Chem. Phys., 19, 2881–2898, https://doi.org/10.5194/acp-19-2881-2019,https://doi.org/10.5194/acp-19-2881-2019, 2019
Short summary

Related subject area

Atmospheric sciences
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024,https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024,https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024,https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024,https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024,https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary

Cited articles

Aleksankina, K.: Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study [Data set], Zenodo, https://doi.org/10.5281/zenodo.1145852, 2018.
Appel, K. W., Gilliland, A. B., Sarwar, G., and Gilliam, R. C.: Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance, Atmos. Environ., 41, 9603–9615, https://doi.org/10.1016/j.atmosenv.2007.08.044, 2007.
AQEG: Linking Emission Inventories and Ambient Measurements, available at: https://uk-air.defra.gov.uk/assets/documents/reports/cat11/1508060906_ DEF-PB14106_Linking_Emissions_ Inventories_And_Ambient_ Measurements_Final.pdf (last access: 9 March 2018), 2015.
Bergin, M. S., Noblet, G. S., Petrini, K., Dhieux, J. R., Milford, J. B., and Harley, R. A.: Formal Uncertainty Analysis of a Lagrangian Photochemical Air Pollution Model, Environ. Sci. Technol., 33, 1116–1126, https://doi.org/10.1021/es980749y, 1999.
Blatman, G. and Sudret, B.: A comparison of three metamodel-based methods for global sensitivity analysis: GP modelling, HDMR and LAR-gPC, Procedia – Soc. Behav. Sci., 2, 7613–7614, https://doi.org/10.1016/j.sbspro.2010.05.143, 2010.
Download
Short summary
Atmospheric chemistry transport models are widely used to underpin policy decisions. We present a global sensitivity and uncertainty analysis approach to understand how uncertainty in input emissions of SO2, NOx, and NH3 drives uncertainties in model outputs, using the FRAME model as an example. We interpret results for input emissions uncertainty ranges reported by the national emissions inventory. Variance-based measures of sensitivity were used to apportion model output uncertainty.