87 research outputs found

    Assessment of the tropical Indo-Pacific climate in the SINTEX CGCM

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    A new coupled GCM (SINTEX) has been developed. The model is formed by the atmosphere model ECHAM-4 and the ocean model ORCA. The atmospheric and oceanic components are coupled through OASIS. The domain is global and no flux correction is applied. In this study, we describe the ability of the coupled model to simulate the main features of the observed climate and its dominant modes of variability in the tropical Indo-Pacific. Three long experiments have been performed with different horizontal resolution of the atmospheric component in order to assess a possible impact of the atmosphere model resolution onto the simulated climate. Overall, the mean state is captured reasonably well, though the simulated SST tends to be too warm in the tropical Eastern Pacific and there is a model tendency to produce a double ITCZ. The model gives also a realistic representation of the temperature structure at the equator in the Pacific and Indian Ocean. The slope and the structure of the equatorial thermocline are well reproduced. Compared to the observations, the simulated annual cycle appears to be underestimated in the eastern equatorial Pacific, whereas a too pronounced seasonal variation is found in the Central Pacific. The main basic features of the interannual variability in the tropical Indo-Pacific region are reasonably well reproduced by the model. In the Indian Ocean, the characteristics of the simulated interannual variability are very similar to the results found from the observations. In the Pacific, the modelled ENSO variability appears to be slightly weaker and the simulated period a bit shorter than in the observations. Our results suggest that, both the simulated mean state and interannual variability are generally improved when the horizontal resolution of the atmospheric mode component is increased

    ENSO in a changing climate

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    The El Niño–Southern Oscillation (ENSO) phenomenon is a naturally occurring climate fluctuation, which originates in the tropical Pacific region and affects ecosystems, agriculture, freshwater supplies, hurricanes and other severe weather events worldwide (Goddard and Dilley 2005; McPhaden et al. 2006). Despite considerable progress in our understanding of the impact of climate change on many of the processes that contribute to ENSO variability (e.g., Collins et al. 2010), it is not yet possible to say whether ENSO activity will be enhanced or damped, or if the frequency or character of events will change in the coming decades (Vecchi and Wittenberg 2010). As changes in ENSO have the potential to be one of the largest manifestations of anthropogenic climate change, this status has profound impacts on the reliability of regional attribution of climate variability and change. Two main reasons can be invoked for these shortcomings. First there is a lack of long and comprehensive enough observations of the various ENSO processes to be able to detect past changes. It may be that we need to observe ENSO for another several decades to detect and attribute significant ENSO changes (Wittenberg 2009; Stevenson et al. 2012). Second, as ENSO involves a complex interplay of numerous ocean and atmospheric processes, accurately modeling this climate phenomenon with CGCMs, an

    Development and exploitation of a controlled vocabulary in support of climate modelling

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    There are three key components for developing a metadata system: a container structure laying out the key semantic issues of interest and their relationships; an extensible controlled vocabulary providing possible content; and tools to create and manipulate that content. While metadata systems must allow users to enter their own information, the use of a controlled vocabulary both imposes consistency of definition and ensures comparability of the objects described. Here we describe the controlled vocabulary (CV) and metadata creation tool built by the METAFOR project for use in the context of describing the climate models, simulations and experiments of the fifth Coupled Model Intercomparison Project (CMIP5). The CV and resulting tool chain introduced here is designed for extensibility and reuse and should find applicability in many more projects
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