Repository hosted by TU Delft Library

Home · Contact · About · Disclaimer ·
 

MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe

Author: Sofiev, M. · Berger, U. · Prank, M. · Vira, J. · Arteta, J. · Belmonte, J. · Bergmann, K.C. · Chéroux, F. · Elbern, H. · Friese, E. · Galan, C. · Gehrig, R. · Khvorostyanov, D. · Kranenburg, R. · Kumar, U. · Marécal, V. · Meleux, F. · Menut, L. · Pessi, A.M. · Robertson, L. · Ritenberga, O. · Rodinkova, V. · Saarto, A. · Segers, A. · Severova, E. · Sauliene, I. · Siljamo, P. · Steensen, B.M. · Teinemaa, E. · Thibaudon, M. · Peuch, V.H.
Type:article
Date:2015
Publisher: Copernicus GmbH
Source:Atmospheric Chemistry and Physics, 14, 15, 8115-8130
Identifier: 527790
doi: doi:10.5194/acp-15-8115-2015
Keywords: Climate · Aerobiology · Aerosol · Ensemble forecasting · Flowering · Modeling · Pollen · Europe · Environment & Sustainability · Urbanisation · Urban Mobility & Environment · CAS - Climate, Air and Sustainability · ELSS - Earth, Life and Social Sciences

Abstract

This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a record-low pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting. Author(s) 2015.