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Multivariate stochastic downscaling models for generating precipitation and temperature scenarios of climate change based on atmospheric circulation

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Pages :
263 - 272

Panagoulia D., Bardossy A. and Lourmas G.
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The spatial resolution of General Circulation Models (GCMs) is too coarse to represent
regional climate changes at the scales required for environmental impact assessment.
Therefore, downscaling of precipitation and temperature has to be carried out from the GCM
grids to smaller scales of a few square kilometres. Daily precipitation and temperature are
modelled as stochastic processes coupled to atmospheric circulation. Precipitation is linked to
circulation patterns (CPs) using conditional model parameters. Temperature is modelled
using a simple autoregressive model conditioned on atmospheric circulation and local daily
precipitation. The models use an automated objective classification of daily atmospheric
circulation patterns based on optimized fuzzy rules. Both temperature and precipitation are
downscaled to several locations taking into account the CP dependent spatial correlation. The
models were applied to the Mesochora medium-sized mountainous catchment in Central
Greece for validation using observed precipitation and temperature and observed classified
geo-potential heights (at 700 hPa). GCM scenarios of the ECHAM4 model for 1xCO2 and
2xCO2 cases were used to make climate change predictions (by using classified GCM geopotential
heights). Simulated values agree fairly well with historical data. Most of the GCM
results (incl. mean daily values, renewal process probabilities, spell lengths) under the 2xCO2
case reflect a somewhat wetter and a more variable precipitation regime over the Mesochora
catchment with significantly increased daily mean temperatures.

atmospheric circulation, automated objective classification, autoregressive model, fuzzy rules, global warming, multivariate stochastic downscaling, precipitation, temperature