Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

Leibovici, Didier G.; Quegan, Shaun; Comyn-Platt, Edward; Hayman, Garry; Val Martin, Maria; Guimberteau, Mathieu; Druel, Arsène; Zhu, Dan; Ciais, Philippe

doi:https://doi.org/10.5194/bg-17-1821-2020

Articles | Volume 17, issue 7

https://doi.org/10.5194/bg-17-1821-2020

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

https://doi.org/10.5194/bg-17-1821-2020

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

Articles | Volume 17, issue 7

Research article

|

03 Apr 2020

Research article |

| 03 Apr 2020

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

Didier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Garry Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, and Philippe Ciais

Data sets

Net Primary Production (NPP) estimates from 4 Land Surface Modelling (LSM) in Fennoscandia 1997-2013 see paper https://doi.org/10.5281/zenodo.3648070

Model code and software

PTAk: Principal Tensor Analysis on k Modes (R package) Didier G. Leibovici https://doi.org/10.18637/jss.v034.i10

Short summary

Analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, may require land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from. The paper proposes a multivariate spatio-temporal data science method to understand the inherent uncertainties in four LSMs and the variations between them in Nordic areas for the net primary production.