Deliverable 3.1: Sampling strategies for improved prediction of relevant Arctic land variables

The Arctic is a region of special concern because of its highly vulnerable environment which is facing profound environmental changes because of global warming which in this region is even amplified (also known as Arctic Amplification) compared to the rest of the globe. To better understand and predict these changes coordinated and accessible observations and information services in combination with models simulating the Arctic environment are needed. Arctic PASSION addresses this urgent need for the Arctic region and aims to pave the way forward towards a fully integrated Pan-Arctic Observing System of Systems.

Improving the prediction of relevant Arctic land variables requires sampling strategies that ensure high impact observations when combined with appropriate modelling systems. To assess the added value of observations and such sampling strategies we employ the Quantitative Network Design (QND) methodology. This methodology quantifies the added value of altered observations and their characteristics in terms of uncertainty reductions in selected target quantities.

Here, we have chosen the QND framework to quantify the added value of an observing system in terms of uncertainty reduction in permafrost extent by simulating the active layer depth with a process-based dynamic global vegetation model. The observing system analysed is based on in-situ soil temperature observations as obtained by borehole soil temperature measurements. Permafrost, commonly defined as ground remaining frozen for two consecutive years or more, is widespread in the Arctic, covering approximately 25% of the Northern Hemisphere's land surface. However, it has undergone rapid changes in recent decades, and will probably continue to do so in the future, with consequences for the natural environment and human activities.