Multi-model ensemble simulations of olive pollen distribution in Europe in 2014

Other literature type, Article English OPEN
Sofiev, Mikhail ; Ritenberga, Olga ; Albertini, Roberto ; Arteta, Joaquim ; Belmonte, Jordina ; Bonini, Maira ; Celenk, Sevcan ; Damialis, Athanasios ; Douros, John ; Elbern, Hendrik ; Friese, Elmar ; Galan, Carmen ; Gilles, Oliver ; Hrga, Ivana ; Kouznetsov, Rostislav ; Krajsek, Kai ; Parmentier, Jonathan ; Plu, Matthieu ; Prank, Marje ; Robertson, Lennart ; Steensen, Birthe Marie ; Thibaudon, Michel ; Segers, Arjo ; Stepanovich, Barbara ; Valdebenito, Alvaro M. ; Vira, Julius ; Vokou, Despoina (2017)
  • Publisher: EGU
  • Journal: (issn: 1680-7324, eissn: 1680-7324)
  • Related identifiers: doi: 10.5194/acp-2016-1189
  • Subject:
    • ddc: ddc:550

A 6-models strong European ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run through the season of 2014 computing the olive pollen dispersion in Europe. The simulations have been compared with observations in 6 countries, members of the European Aeroallergen Network. Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimized combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season start was reported too early, by 8 days but for some models the error mounted to almost two weeks. For the season end, the disagreement between the models and the observations varied from a nearly perfect match up to two weeks too late. A series of sensitivity studies performed to understand the origin of the disagreements revealed crucial role of ambient temperature, especially systematic biases in its representation by meteorological models. A simple correction to the heat sum threshold eliminated the season shift but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous-days observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and shown to remain nearly optimal throughout the whole period.
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