final_program_abstracts[1]

11 IMSC Session Program
Constructing ensembles of climate change scenarios based
on statistical downscaling
Tuesday - Parallel Session 5
Radan Huth 1 , Stanislava Kliegrová 2 and Ladislav Metelka 2
1 Institute of Atmospheric Physics, Prague, Czech Republic
2
Czech Hydrometeorological Institute, Regional Office, Hradec Králové, Czech
Republic
Probabilistic framework is now accepted as a standard for formulating future climate
change scenarios based on global climate model (GCM) outputs. However, the use of
statistical downscaling (SDS) as a tool for transferring climate information from large
to local scales introduces additional uncertainties that should be taken into account in
climate change scenarios. The SDS-related uncertainties arise from a considerable
sensitivity of SDS outputs to the selection of the statistical model and its parametres,
as well as of the set of predictors. In this contribution, we aim to assess the SDS
uncertainty alone, that is, for a single emission scenario of a single GCM, thereby
keeping most of the other uncertainty factors constant. We calculate climate change
response for daily temperature at a network of European stations (data coming from
the ECA&D database) by various SDS methods, including linear regression and
neural networks, and for different sets of predictors. We discuss whether the
individual SDS outputs forming the ensemble should be weighted, and which weights
should be used. We argue that the weights of a particular SDS-based scenario should
reflect (i) the ability of the SDS model to reproduce the predictand, (ii) the ability to
reproduce past climatic trends, (iii) the ability of the driving GCM to reproduce the
SDS predictors, (iv) the multiplicity (or mutual dependence) of SDS models, and (v)
the stability of the predictor-predictand relationship. The final pdf of anticipated
temperature change is constructed bz the Gaussian kernal algorithm. Examples of the
probabilistic climate change scenarios constructed in this way are shown.
Abstracts 138