34
Karmacharya, J., Konare, A., Martinez, D., da Rocha, R. P., Sloan, L. C., &
Steiner, A. (2007). The ICTP RegCM3 and RegCNET: Regional climate modeling for the developing world. Bulletin of the American Meteorological Society,
88, 1395–1409.
Ryu,Y.-H., Baik, J.-J., Kwak, K.-H., Kim, S., & Moon, N. (2013). Impacts of urban
land-surface forcing on ozone air quality in the Seoul metropolitan area.
Atmospheric Chemistry and Physics, 13, 2177–2194.
Simmons, A., Uppala, S., Dee, D., & Kobayashi, S. (2007). ERAinterim: New
ECMWF reanalysis products from 1989 onwards. Newsletter, 110(Winter
2006/07), ECMWF, Reading.
Timothy, M. & Lawrence, M. G. (2007). The influence of megacities on global
atmospheric chemistry: A modeling study. Environment and Chemistry, 6,
219–225.
1.3.5 Statistical Downscaling Techniques Applied
to ENSEMBLES GCMs: Bologna-Modena Case Study
Rodica Tomozeiu and Lucio Botarelli
ARPA Emilia-Romagna, Bologna, Italy
rtomozeiu@arpa.emr.it
1.3.5.1 Introduction
Another tool used by the scientific community in order to construct future climate
projections is the statistical downscaling techniques (SDs). One of the main advantages of this technique is that it produces information at local scale, station or grid
points and it is not expensive in terms of computational time. One major problem
for all tools that produce climate change scenario is to quantify and reduce the
uncertainties that appear in modelling processes. Particular attention has been paid
on this problem and many projects have been focused on this issue. One of this is
Ensembles project (http://www.ensembles-eu.org/), where it was recommended use
of a range of models over the same area and construction of an ensemble mean
(EM). This technique has been applied in the present work, in order to produce
climate change scenario over Bologna-Modena case study selected in the project.
1.3.5.2 Data and Methods
The SDs model developed by ARPA-SIMC, is a multivariate regression based on
Perfect–Prog approach, built using observed local fields at station level, and large
scale fields derived from re-analysis data set. A set of 75 stations distributed over
N-Italy, including Bologna station, that measure minimum and maximum
J. Fallmann et al.
Karmacharya, J., Konare, A., Martinez, D., da Rocha, R. P., Sloan, L. C., &
Steiner, A. (2007). The ICTP RegCM3 and RegCNET: Regional climate modeling for the developing world. Bulletin of the American Meteorological Society,
88, 1395–1409.
Ryu,Y.-H., Baik, J.-J., Kwak, K.-H., Kim, S., & Moon, N. (2013). Impacts of urban
land-surface forcing on ozone air quality in the Seoul metropolitan area.
Atmospheric Chemistry and Physics, 13, 2177–2194.
Simmons, A., Uppala, S., Dee, D., & Kobayashi, S. (2007). ERAinterim: New
ECMWF reanalysis products from 1989 onwards. Newsletter, 110(Winter
2006/07), ECMWF, Reading.
Timothy, M. & Lawrence, M. G. (2007). The influence of megacities on global
atmospheric chemistry: A modeling study. Environment and Chemistry, 6,
219–225.
1.3.5 Statistical Downscaling Techniques Applied
to ENSEMBLES GCMs: Bologna-Modena Case Study
Rodica Tomozeiu and Lucio Botarelli
ARPA Emilia-Romagna, Bologna, Italy
rtomozeiu@arpa.emr.it
1.3.5.1 Introduction
Another tool used by the scientific community in order to construct future climate
projections is the statistical downscaling techniques (SDs). One of the main advantages of this technique is that it produces information at local scale, station or grid
points and it is not expensive in terms of computational time. One major problem
for all tools that produce climate change scenario is to quantify and reduce the
uncertainties that appear in modelling processes. Particular attention has been paid
on this problem and many projects have been focused on this issue. One of this is
Ensembles project (http://www.ensembles-eu.org/), where it was recommended use
of a range of models over the same area and construction of an ensemble mean
(EM). This technique has been applied in the present work, in order to produce
climate change scenario over Bologna-Modena case study selected in the project.
1.3.5.2 Data and Methods
The SDs model developed by ARPA-SIMC, is a multivariate regression based on
Perfect–Prog approach, built using observed local fields at station level, and large
scale fields derived from re-analysis data set. A set of 75 stations distributed over
N-Italy, including Bologna station, that measure minimum and maximum
J. Fallmann et al.