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  • (2014) Lorenz, Ruth; Pitman, Andrew; Donat, Markus; Hirsch, Annette; Kala, Jatin; Kowalczyk, E; Law, R; Srbinovsky, J
    Journal Article
    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 (T-MIN) during nights are generally too warm and daily maximum temperatures (T-MAX) 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 T-MIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in T-MAX 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 T-MAX 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.

  • (2013) Lunt, D; Abe-Ouchi, A; Bakker, P; Berger, A; Braconnot, P; Charbit, S; Fischer, N; Herold, N; Jungclaus, J; Khon, V; Krebs-Kanzow, U; Langebroek, P; Lohmann, G; Nisancioglu, K; Otto-Bliesner, B; Park, W; Pleiffer, M; Phipps, Steven; Prange, M; Rachmayani, R; Renssen, H; Rosenbloom, N; Schneider, B; Stone, E; Takahashi, K; Wei, W; Yin, Q; Zhang, Z
    Journal Article
    The last interglaciation (similar to 130 to 116 ka) is a time period with a strong astronomically induced seasonal forcing of insolation compared to the present. Proxy records indicate a significantly different climate to that of the modern, in particular Arctic summer warming and higher eustatic sea level. Because the forcings are relatively well constrained, it provides an opportunity to test numerical models which are used for future climate prediction. In this paper we compile a set of climate model simulations of the early last interglaciation (130 to 125 ka), encompassing a range of model complexities. We compare the simulations to each other and to a recently published compilation of last interglacial temperature estimates. We show that the annual mean response of the models is rather small, with no clear signal in many regions. However, the seasonal response is more robust, and there is significant agreement amongst models as to the regions of warming vs cooling. However, the quantitative agreement of the model simulations with data is poor, with the models in general underestimating the magnitude of response seen in the proxies. Taking possible seasonal biases in the proxies into account improves the agreement, but only marginally. However, a lack of uncertainty estimates in the data does not allow us to draw firm conclusions. Instead, this paper points to several ways in which both modelling and data could be improved, to allow a more robust model-data comparison.