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

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

doi:https://doi.org/10.5194/hess-24-2817-2020

Articles | Volume 24, issue 5

https://doi.org/10.5194/hess-24-2817-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-2817-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 5

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

Supplement

https://doi.org/10.5194/hess-24-2817-2020-supplement

Data sets

CRUNCEP Version 7 – Atmospheric Forcing Data for the Community Land Model N. Viovy http://rda.ucar.edu/datasets/ds314.3

ecmwf-api-client ECMWF – European Centre for Medium-Range Weather Forecasts https://pypi.org/project/ecmwf-api-client/

Model code and software

ToE_tools: A non-parametric method to calculate the Time of Emergence of climate signals E. Pohl https://github.com/pohleric/toe_tools

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.