A recent multidisciplinary compilation of studies on changes in the Siberian environment details how climate is changing faster than most places on Earth with exceptional warming in the north and increased aridity in the south. Impacts of these changes are rapid permafrost thaw and melt of glaciers, increased flooding, extreme weather events leading to sudden changes in biodiversity, increased forest fires, more insect pest outbreaks, and increased emissions of CO 2 and methane. These trends interact with sociological changes leading to land-use change, globalisation of diets, impaired health of Arctic Peoples, and challenges for transport. Local mitigation and adaptation measures are likely to be limited by a range of public perceptions of climate change that vary according to personal background. However, Siberia has the possibility through land surface feedbacks to amplify or suppress climate change impacts at potentially global levels. Based on the diverse studies presented in this Ambio Special Issue, we suggest ways forward for more sustainable environmental research and management. Supplementary Information The online version contains supplementary material available at 10.1007/s13280-021-01626-7.

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At present, mainly GIS software is used for exploration and visualization of spatially distributed data. At the same time an approach based on the combination of advanced web technologies along with the standard rules of information-computational system development is more suitable for performing tasks requiring more sophisticated data analysis.In this report, functional capabilities of the information-computational system created for meteorological and climatic data processing and online visualization are introduced. The system represents a dedicated web-interface, which allows performing of mathematical and statistical operations on the diverse observational and model data and to determine characteristics of global and regional climate changes.Currently such datasets as NCEP/NCAR Reanalysis, ECMWF ERA-40 Reanalysis, etc. are available for processing. In particular, the system allows calculating of temporal average and extremum values, time trends, etc. at arbitrary spatial and temporal ranges for different meteorological parameters. The possibility of online intercomparison of meteorological characteristics calculated for different datasets is also realized in the system. The final version of the system being developed is supposed to find application in meteorological and climatological investigations and should help researchers to save time during performing routine analytical tasks by simplifying handling of huge arrays of spatially distributed meteorological data.