Supporting an intelligent AOSS through model-based impact assessments
Lead: Frank Kauker, Ocean Atmosphere Systems and Harald Schyberg, Norwegian Meteorological Institute
The main objective of WP3 is to assess the limitations in our modelling capability with respect to monitoring and forecasting in the Arctic and how new observations and observing technology can provide improvements. Here we will look at numerical models for ocean and sea ice, atmosphere and weather, vegetation and permafrost, as well as glaciers.
The overall objective of WP3 is to support the cost-efficient extension of Arctic monitoring and forecasting capabilities, through dedicated modelling activities. We will
- Quantify the benefit of several observational scenarios for safer and more cost-efficient navigation in ice-infested waters of the Arctic.
- Assess and analyse the cost-efficiency of different ways of extending the overall set of earth surface and atmospheric observations for Arctic short-range weather prediction and climate reanalysis.
- Quantify the benefit of additional and new (or even hypothetical) satellite and in-situ data streams for monitoring environmental changes (e.g. burned area, tundra shrubification, permafrost extent) and climate change drivers (e.g. greenhouse gas emissions from biomass burning and permafrost thawing, methane releases from wetlands, increased CO2 sink capacity by tundra shrubification and tree-line advance).
- Assess current capabilities, and their limitations, for monitoring the state of glaciers and ice caps and the Greenland Ice Sheet.
OASys will focus on ocean and sea ice modeling in support of safe navigation of the Arctic. MET Norway focusses on weather forecasting and atmospheric climate monitoring. University of Lund will use a vegetation model for land cover variables such as vegetation and permafrost in their investigations. University of Bristol will assess modeling and monitoring of Arctic glaciers. iLab will contribute with their mathematical-statistical expertise in mission/network design.