Comparing Australian heat waves in the CMIP5 models through cluster analysis. Issue 6 (21st March 2017)

• Record Type: Journal Article
• Authors: Gibson, Peter B.; Perkins‐Kirkpatrick, Sarah E.; Alexander, Lisa V.; Fischer, Erich M.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 6(2017)
• Page Start: 3266
• Page End: 3281
• Publication Date: 2017-03-21
• Subjects: heat extremes -- heat waves -- model evaluation -- hierarchical cluster analysis -- model independence -- multimodel mean; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD025878
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Quantitative projections of climate extremes on local to regional scales are highly valuable for planners and decision makers and necessary for effective local climate change adaptation. However, in contrast to the model robustness of simulated extremes at the global scale, the robustness in simulating past and future extremes often diminishes over finer spatial scales. In this study we analyze heat waves simulated by state‐of‐the‐art global climate models over the Australian region. For the first time we present results explicitly detailing the model spread in simulated heat wave trends and climatology for this region for the recent past (1958–2005). As expected, large intermodel spread is observed at the local to regional scale for both heat wave trends and climatology. By analyzing multiple initial condition runs from individual models, we show that model internal variability strongly influences the spatial patterns of heat wave trends, while intermodel differences in heat wave climatology appear more influenced by model uncertainty. From a model evaluation perspective, cluster analysis is shown to be useful in characterizing robust spatial features of heat waves simulated by the models. In contrast to the multimodel mean, where uncorrelated spatial features tend to be averaged out, cluster composites preserve these features. Since previous examinations have tended to focus on the multimodel mean the extent of model spread may have been overlooked. Further examination of the processes that lead to model differences and biases is needed. Key Points: A wide range of spatial patterns is found in the heat wave climatology and trends of CMIP5 models These spatial patterns are compared and characterized through cluster analysis Internal variability strongly influences historical heat wave trend patterns in the models