Abstract
Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and amplifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.
Highlights
Climate extremes, including heat waves, heavy precipitation events or droughts have important effects on ecosystems and society (Easterling, 2000; Ciais et al, 2005; Pall et al, 2011)
First we present the seasonal averages of TMAX, TMIN and PTOT
We examined the biases in net longwave (LWNET) and net shortwave (SWNET) from ACCESS1.3b to explain the biases in temperature
Summary
Climate extremes, including heat waves, heavy precipitation events or droughts have important effects on ecosystems and society (Easterling, 2000; Ciais et al, 2005; Pall et al, 2011). Given the impact of extremes, it is important to understand their causes, how they might change in the future and the role of potential interacting processes and feedbacks that might amplify them. This is urgent given that some extremes appear to be increasing in frequency (Alexander et al, 2006; Coumou and Rahmstorf, 2012; Donat and Alexander, 2012; Perkins et al, 2012) and that extremes are considered to be a challenging aspect of climate change adaptation (IPCC, 2012). Temperature variability is affected by land-surface processes (Seneviratne et al, 2006) and Jaeger and Seneviratne (2010) found a tendency towards a greater impact of land-surface
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