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Inter-comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visu-alization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, for example, simulations of photosynthesis and respiration, using(More)
Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative(More)
Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization(More)
Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land-atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO 2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to(More)
Key Points: • Models largely agree on the sign of the carbon flux response to climate extremes • Models are uncertain in the carbon flux response to heat waves in boreal forests • Droughts and heat waves strongly compound each other in their impact on C fluxes Citation: Zscheischler, J., et al. (2014), Impact of large-scale climate extremes on biospheric(More)