Skip to main content

A glimpse into the Arctic future: equipping a unique natural experiment for next-generation ecosystem research

Periodic Reporting for period 1 - FutureArctic (A glimpse into the Arctic future: equipping a unique natural experiment for next-generationecosystem research)

Reporting period: 2019-06-01 to 2021-05-31

How much carbon will escape from the (sub)Arctic in future climate?
How do the multitude of ecosystem processes, driven by plant growth, microbial activities and soil characteristics, interact to determine (sub)Arctic soil carbon storage capacity?
These are the central research questions of FutureArctic.

They are addressed at a unique location. The ForHot (www.forhot.is) site in Iceland offers a geothermally controlled soil temperature warming gradient, where Subarctic ecosystem processes are affected by temperature increases as expected through climate change. Given the strong urgency of tackling the climate challenge and the particularly important role herein of (sub)Arctic ecosystems, a rapid assessment of the ecosystem and ambient processes at the ForHot site will provide potentially crucial insight in future carbon cycling.

If this knowledge is to be applied in the climate change challenge, significant advances should be made on short time scales, including an unprecedented search for unknown interactions within the ecosystem. FutureArctic will achieve this challenge by adopting the fast advances made in the field of machine learning and artificial intelligence (AI), unmanned aerial vehicles (UAV) and (remote) sensor technology into environmental research at the ecosystem scale, into a new concept of an ‘ecosystem-of-things’.

FutureArctic embeds this research challenge in an inter-sectoral training initiative, aiming to form ‘ecosystem-of-things’ scientists and engineers, in order to:

- Pave the way for generalized permanently connected data acquisition systems for key environmental variables. Technological advances in scientific instrumentation in regard to both sensor capabilities and connectedness are necessary to achieve permanent analysis and data integration. The interaction with industry partners to achieve these advances is a key to success (WP2).

- Initiate a new machine-learning approach to analyse large environmental data-streams. ForHot, with its unique and large subarctic warming gradients, becomes a pioneer project for the ‘ecosystem-of-things’. Machine learning analysis procedures will be implemented (WP3) to complement hypothesis-based environmental research (WP1). Will big data and machine learning algorithms inspire new ways of thinking into environmental science and ecology, complementing traditional hypothesis-based-research that does not make full use of valuable information contained in data?

- Create an ethical / philosophical framework for the implementation of machine-learning/artificial intelligence approaches into environmental science. What are the potential implications for traditional science, what are the caveats and pitfalls?
Overall FutureArctic is still on schedule and has met the objectives as set out in the original work plan. The first Networkwide training event has taken place in Month 12 as planned. Due to Covid 19 travel restrictions the training switched to an online event.

There have been many discussions to achieve an optimal coordination of the field work in Iceland at the end of June 2020. A common research scheme has been developed, summarizing all planned analyses and experiments. Due to Covid-19, travelling to Iceland is not straightforward and the planning for the initial field work has been adapted. All WP1 ESRs have however at least partly been able to start their field work, and ample attention is paid to achieving as good as possible the sampling coordination plan outline in Deliverable 1.1. In WP2, prototype development is well on track. Still, prototype installations have been delayed due to the covid-related travel restrictions. This will have an impact on the timing of future objectives as ESRs depend on each other for data delivery, especially the ESRs in WP3. In part B of the Technical Report, the progress of each ESR, and the potential changes to the initial plans, are outlined in detail.
FutureArctic aims for a strong breakthrough in the assessment of climate change on ecosystems, where currently even plant, soil and atmosphere science is mostly uncoupled at the ecosystem scale. We propose an innovative approach where latest achievements in machine learning-based statistics, sensor technology and efficient digital data management are combined, to assess the ecosystem feedbacks at spatial and temporal scales.
Each ESR trained in FutureArctic will be ideally placed to make a significant contribution to 21st century earth sciences. FutureArctic will create the foundation in Europe for a new “Ecosystem Science 2.0”, where optimal balance is achieved between field research, permanent ecosystem sensing, novel sensing technique development and machine learning assisted data analysis. Main breakthroughs:

• Insights in interactions between soil microbial community, vegetation and climate change drivers
-> all WP1 ESR’s are cooperating intensely to achieve this
• An ecosystem-health based assessment of subarctic ecosystem functioning in a warming world
• Prediction of future alterations in carbon budget of subarctic ecosystems
-> Combined results already show novel insights in the impact of soil warming on the ecosystem
• Development of novel ready-to-market techniques in ecosystem science
-> WP2 ESR’s are on track to develop prototypes as planned
• The introduction of machine learning in ecosystem model development
-> WP3 ESR’s are working on multiple ecological datasets to achieve this goal
• The creation of a science-society-ethics framework for machine-assisted environmental science and modelling
-> ESR15 has already performed multiple interviews as planned

FutureArctic will achieve an enduring impact not only through its lasting effect on earth system model development and improved datasets of detailed (sub)Arctic ecosystem functioning, but also through the strong involvement of inter-sectoral partners. This is achieved through the development of ready-to-market solutions for equipping new research sites for an ecosystem-of-things setup. Industry beneficiaries VSI, SVARMI and DMR all work on the development of sensors, while IMEC and Microsoft will develop ready-to-use algorithms for detecting ecosystem process interactions. This can pave the way for self-learning model algorithms that automatically adapt ecosystem models to the newest insights from most recent datasets. The deep inter-sectoral cooperation in FutureArctic will open new horizons for the environmental modelling community. Secondments within FutureArctic are defined to optimize the inter-sectoral interaction, where ESRs from NAP (non-academic partner) engage in explorative secondments to identify current gaps in technology directly with top environmental scientists, and all ESRs from AP (academic partner) engage in secondments to develop market-ready technological solutions, to explore digital frontlines in assisting dataset analysis or to assess the unknown synergies between technologies used in multiple science disciplines. ESRs in FutureArctic will thus develop unique skill sets and job profiles that are currently rare or even unavailable to environmental science, yet are key to a coordinated scientific and societal responses to some of the most pressing ecological challenges, including climate change.
Project Workflow