Time series analysis in environmental science and applications to climate change

The purpose of this event is to gather scientists from a large range of disciplines in Earth Sciences based on regular and constant in-situ measurements, and provide a discussion forum in the field of time series analysis and forecasting. The presentations will show how observations can help detecting climate change and its impacts focusing on both the mathematical modelling, statistics, signal processing (non-stationarity, gaps in series, extremes, etc.), and the environmental scientific results. This conference is part of a series of conferences gathering a wide community to be integrated in the ESONET-Vi (-the vision) consortium that builds upon ESONET, EuroSITES, EMSO, FixO³ and ENVRI^PLUS partners, extending worldwide.

Who can attend: Based on in-situ and remote data analysis and modelling, this conference will gather senior and young researchers (post-doctoral, doctoral and master students) to share their experience in time series interpretation across several scientific fields. Starting by a 2-day training session and followed by a conference part during the two following days, this event will promote the transfer of knowledge to younger or less experienced scientists and between researchers from several research fields. Both training courses and scientific talks will be mainly based on application examples and case studies.

Conference themes:

- Marine environment and connections with land and atmosphere (sea ice, atmospheric measurement, foraminifera, biogeochemistry)

- Sea level

- Methane measurements and analysis

- Ocean carbon cycle

- Mathematical tools to understand climate change

Click through for program and registration. Deadline September 25.

Training and conference location:

The training will be held at the University of Tromsø at the Naturfagbygget building. The conference will be held at the Scandic Ishavshotel (Fredrik Langes gate 2, 9008 Tromsø) in the city center where we reserved rooms with a preferential price.