Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2817-2020
https://doi.org/10.5194/hess-24-2817-2020
Research article
 | 
29 May 2020
Research article |  | 29 May 2020

Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama

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Cited articles

Beermann, F., Langer, M., Wetterich, S., Strauss, J., Boike, J., Fiencke, C., Schirrmeister, L., Pfeiffer, E.-M., and Kutzbach, L.: Permafrost Thaw and Liberation of Inorganic Nitrogen in Eastern Siberia, Permafrost Periglac. Process., 28, 605–618, https://doi.org/10.1002/ppp.1958, 2017. 
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Bianchi, M.: Bandwidth Selection in Density Estimation, in: XploRe: An Interactive Statistical Computing Environment, Springer New York, NY, 101–112, 1995. 
Boike, J., Grau, T., Heim, B., Günther, F., Langer, M., Muster, S., Gouttevin, I., and Lange, S.: Satellite-derived changes in the permafrost landscape of central Yakutia, 2000–2011: Wetting, drying, and fires, Global Planet. Change, 139, 116–127, https://doi.org/10.1016/j.gloplacha.2016.01.001, 2016. 
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Short summary
Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.