A new diagram for performance evaluation of complex models

Abstract

AbstractIn the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor’s diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor’s diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor’s diagram. In this paper a new diagram is proposed, referred to as Aras’ diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor’s diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space.