Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach

Climate Research

Jean P. Palutikof¹  Ricardo M. Trigo¹ 

关键词: Downscaling;    Artificial neural networks;    Climate change scenarios;    Portugal;   

DOI  :  10.3354/cr013045

来源: Inter-Research Science Publishing

【 摘 要 】

ABSTRACT: Methods to assess the impact of global warming on the temperature regime of a single site are explored with reference to Coimbra in Portugal. The basis of the analysis is information taken from a climate change simulation performed with astate-of-the-art general circulation model (the Hadley Centre model). First, it is shown that the model is unable to reproduce accurately the statistics of daily maximum and minimum temperature at the site. Second, using a re-analysis data set,downscaling models are developed to predict site temperature from large-scale free atmosphere variables derived from the sea level pressure and 500 hPa geopotential height fields. In particular, the relative performances of linear models and non-linearartificial neural networks are compared using a set of rigorous validation techniques. It is shown that even a simple configuration of a 2-layer non-linear neural network significantly improves on the performance of a linear model. Finally, the non-linearneural network model is initialised with general circulation model output to construct scenarios of daily temperature at the present day (1970-79) and for a future decade (2090-99). These scenarios are analysed with special attention to the comparison ofthe frequencies of heat waves (days with maximum temperature greater than 35°C) and cold spells (days with minimum temperature below 5°C).

【 授权许可】

Unknown