The Arctic climate is changing. Permafrost is warming, hydrological processes are changing and biological and social systems are also evolving in response to these changing conditions. Knowing how the structure and function of arctic terrestrial ecosystems are responding to recent and persistent climate change is paramount to understanding the future state of the Earth system and how humans will need to adapt. Our holistic review presents a broad array of evidence that illustrates convincingly; the Arctic is undergoing a system-wide response to an altered climatic state. New extreme and seasonal surface climatic conditions are being experienced, a range of biophysical states and processes influenced by the threshold and phase change of freezing point are being altered, hydrological and biogeochemical cycles are shifting, and more regularly human sub-systems are being affected. Importantly, the patterns, magnitude and mechanisms of change have sometimes been unpredictable or difficult to isolate due to compounding factors. In almost every discipline represented, we show Climatic Change (2005) 72: 251-298 how the biocomplexity of the Arctic system has highlighted and challenged a paucity of integrated scientific knowledge, the lack of sustained observational and experimental time series, and the technical and logistic constraints of researching the Arctic environment. This study supports ongoing efforts to strengthen the interdisciplinarity of arctic system science and improve the coupling of large scale experimental manipulation with sustained time series observations by incorporating and integrating novel technologies, remote sensing and modeling.

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Slums are home to a large fraction of urban residents in cities of developing nations, but little attempt has been made to go beyond a simple slum/non-slum dichotomy, nor to identify slums more quantitatively than through local reputation. We use census data from Accra, Ghana, to create an index that applies the UN-Habitat criteria for a place to be a slum. We use this index to identify neighborhoods on a continuum of slum characteristics and on that basis are able to locate the worst slums in Accra. These do include the areas with a local reputation for being slums, lending qualitative validation to the index. We show that slums also have footprints that can be identified from data classified from satellite imagery. However, variability among slums in Accra is also associated with some variability in the land cover characteristics of slums.

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