Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more?
Authors: Galmarini Stefano , Kioutsioukis Ioannis , Solazzo Efisio , Alyuz Ummugulsum , Balzarini Alessandra , Bellasio Roberto , Benedictow Anna M. K. , Bianconi Roberto , Bieser Johannes , Brandt Joergen , Christensen Jesper H. , Colette Augustin , Curci Gabriele , Davila Yanko , Dong Xinyi , Flemming Johannes , Francis Xavier , Fraser Andrea , Fu Joshua , Henze Daven K. , Hogrefe Christian , Im Ulas , Garcia Vivanco Marta , Jiménez-Guerrero Pedro , Jonson Jan Eiof , Kitwiroon Nutthida , Manders Astrid , Mathur Rohit , Palacios-Peña Laura , Pirovano Guido , Pozzoli Luca , Prank Marie , Schultz Martin , Sokhi Rajeet S. , Sudo Kengo , Tuccella Paolo , Takemura Toshihiko , Sekiya Takashi , Unal Alper 
Published: June 2018

Abstract. In this study we introduce a hybrid ensemble
consisting of air quality models operating at both the global and regional
scale. The work is motivated by the fact that these different types of models
treat specific portions of the atmospheric spectrum with different levels of
detail, and it is hypothesized that their combination can generate an ensemble
that performs better than mono-scale ensembles. A detailed analysis of the
hybrid ensemble is carried out in the attempt to investigate this hypothesis
and determine the real benefit it produces compared to ensembles constructed
from only global-scale or only regional-scale models. The study utilizes
13 regional and 7 global models participating in the Hemispheric Transport of
Air Pollutants phase 2 (HTAP2)–Air Quality Model Evaluation International
Initiative phase 3 (AQMEII3) activity and
focuses on surface ozone concentrations over Europe for the year 2010.
Observations from 405 monitoring rural stations are used for the evaluation
of the ensemble performance. The analysis first compares the modelled and
measured power spectra of all models and then assesses the properties of the
mono-scale ensembles, particularly their level of redundancy, in order to
inform the process of constructing the hybrid ensemble. This study has been
conducted in the attempt to identify that the improvements obtained by the
hybrid ensemble relative to the mono-scale ensembles can be attributed to its
hybrid nature. The improvements are visible in a slight increase of the
diversity (4 % for the hourly time series, 10 % for the daily maximum
time series) and a smaller improvement of the accuracy compared to diversity.
Root mean square error (RMSE) improved by 13–16 % compared to G and by 2–3 % compared to R.
Probability of detection (POD) and false-alarm rate (FAR) show a remarkable improvement, with a steep increase in the largest
POD values and smallest values of FAR across the concentration ranges. The
results show that the optimal set is constructed from an equal number of
global and regional models at only 15 % of the stations. This implies that
for the majority of the cases the regional-scale set of models governs the
ensemble. However given the high degree of redundancy that characterizes the
regional-scale models, no further improvement could be expected in the
ensemble performance by adding yet more regional models to it. Therefore the
improvement obtained with the hybrid set can confidently be attributed to the
different nature of the global models. The study strongly reaffirms the
importance of an in-depth inspection of any ensemble of opportunity in order
to extract the maximum amount of information and to have full control over
the data used in the construction of the ensemble.