A height dependent evaluation of wind and temperature over Europe in the CMIP5 Earth System Models

Climate Research

Matthias Demuzere¹  Nicole P. M. van Lipzig¹  Annemarie Devis¹ 

关键词: Earth System Models;    Evaluation;    Probability density function;    Wind speed;    Temperature;    Near-surface;    CMIP5;   

DOI  :  10.3354/cr01242

来源: Inter-Research Science Publishing

【 摘 要 】

ABSTRACT: To date, evaluation studies of global circulation models (GCMs) generally focus on large-scale circulation variables. However, since the resolution of GCMs is increasing and their interaction with the surface is improving, GCM representations of near-surface variables are becoming increasingly realistic. For downscaling practices, this implies that near-surface variables might become suitable as predictors in statistical models, and might increase the added value of dynamical models. This study focuses on the representation of wind and temperature in the lowest 1.5 km of the atmosphere over Europe as simulated by 6 of the earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 5. The evaluation is based on the representation of the variables’ probability density functions (PDFs) using ERA-Interim reanalysis data as the reference. The PDF biases are analyzed according to their scale and origin. Above coastal bays and capes, small-scale biases in the ESMs result in unskillful wind speed PDFs up to 400 m. High orography affects wind speeds throughout the lowest 1.5 km of the atmosphere, especially during summer and night. Apart from these small-scale biases, the surface wind speed PDFs north of 45°N are well represented by all the ESMs. Therefore, these PDFs can be considered skillful inputs for statistical downscaling practices. South of 45° N, winds are affected by a large-scale bias originating from errors in the representation of the large-scale circulation, especially during winter. For temperature, near-surface levels as well as upper-atmospheric levels are affected by small-scale and large-scale biases. Large-scale biases are adopted by the downscaling models, underlining the importance of model evaluation before downscaling.

【 授权许可】

Unknown