Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

Abstract

The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

“…The ability of reanalyses and models in simulating station anomaly distributions was assessed with the Kuiper goodness-of-fit metric (Kuiper 1960) and the Perkins metric (Perkins et al 2007). The two metrics were considered to check whether they provided consistent conclusions.…”

“…The Perkins metric is defined as the overlap between the two empirical probability density functions (EPDF) and has been used in evaluating temperature and precipitation series simulated by GCMs (Perkins et al 2007(Perkins et al , 2012Maxino et al 2008;Pitman and Perkins 2009;) as well as RCMs (Kjellstrom et al 2010;Kabela and Carbone 2015;Boberg et al 2010). Here we extend its use by evaluating climate index anomaly distributions.…”

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

“…Both 40-km models are able to reproduce rainfall rates up to 108 mm day 21 , while the reanalysis does not capture the higher precipitation amounts due to the low spatial resolution of the ERA-40 data. To provide a measure of similarity between observed and modeled frequencies, the Perkins skill score (PSS) has been calculated (Perkins et al 2007):…”

“…8, right). Perkins et al (2007) use probability density functions (PDFs) for the evaluation of simulated daily precipitation over Australia from 14 different climate models. Similarly to the density curves of ALADIN and CNRM, the PDFs in Perkins et al show for all models an overestimation of ''drizzle,'' with most models overestimating the observed density of rainfall in the 1-2 mm day 21 range by 2-3 times.…”

Regionalizing global climate models

“…Perkins et al, 2007), the challenge of providing robust and coincidence of regionally important processes that affect regional climate with human society. We suggest that via enhancing the modelling of these processes, explicitly within the global climate models, regional prediction can be improved, by forming a stronger foundation for dynamical and statistical downscaling or by directly improving regional simulations.…”