Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5583-2021
https://doi.org/10.5194/gmd-14-5583-2021
Development and technical paper
 | 
10 Sep 2021
Development and technical paper |  | 10 Sep 2021

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

Annika Vogel and Hendrik Elbern

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Cited articles

Auger, L. and Tangborn, A.: A wavelet-based reduced rank Kalman filter for assimilation of stratospheric chemical tracer observations, Mon. Weather Rev., 132, 1220–1237, https://doi.org/10.1175/1520-0493(2004)132<1220:AWRRKF>2.0.CO;2, 2004. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. a
Boynard, A., Beekmann, M., Foret, G., Ung, A., Szopa, S., Schmechtig, C., and Coman, A.: An ensemble assessment of regional ozone model uncertainty with an explicit error representation, Atmos. Environ., 45, 784–793, https://doi.org/10.1016/j.atmosenv.2010.08.006, 2011. a
Buizza, R.: Introduction to the special issue on “25 years of ensemble forecasting”, Q. J. Roy. Meteor. Soc., 145, 1–11, https://doi.org/10.1002/qj.3370, 2019. a
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Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions hamper an efficient uncertainty estimation. This study presents a novel approach to efficiently sample these uncertainties by extracting dominant dependencies and correlations. Applying the algorithm to biogenic emissions, their uncertainties can be estimated from a low number of dominant components. This states the capability of an efficient treatment of parameter uncertainties in atmospheric models.
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