ICONICS

IMPRESSIONS | Impacts and Risks from High-End Scenarios: Strategies for Innovative Solutions

Website

IMPRESSIONS is an EU Framework Program 7 project that aims to advance understanding of the implications of high-end climate change, involving temperature increases above 2°C, and to help decision-makers apply such knowledge within integrated adaptation and mitigation strategies.The IAM group is participating as an unfunded partner by providing guidance and iPETS simulations related to global scenarios.

There is widespread acceptance that the climate is changing. Although the United Nations Framework Convention on Climate Change warns that the increase in global temperature should be below 2°C to avoid severe impacts, projections based on current emission trends point to much more substantial warming, with possible increases of 4°C or more in the long-term unless there is radical action to cut emissions.

Despite the increasing plausibility of these high-end scenarios, there are few studies that assess their potential impacts and the options available for reducing the risks. Existing modelling tools and methods fail to account for potential tipping points, the need to cope with radical rather than gradual change, the complex interactions between sectors and the synergies and trade-offs between adaptation and mitigation actions. It is vital that decision-makers have access to reliable scientific information on these uncertain, but potentially high-risk, scenarios of the future, so that they can make effective adaptation and mitigation plans.

IMPRESSIONS aims to advance understanding of the implications of high-end climate change, involving temperature increases above 2°C, and to help decision-makers apply such knowledge within integrated adaptation and mitigation strategies.

The climate scenarios will be integrated with underlying socio-economic storylines, determined through consultation with decision-makers so that the scenarios are credible and useful. The secnarios will reflect the high levels of variability and uncertainty that characterise high-end climate change futures, as well as the potential for non-linear effects, climate tipping points, such as ice-sheet collapse, and socio-economic shocks, such as large scale migration of climate refugees.

In the overall framework of high-end scenarios, we will use both RCps and SSPs, and a combination of the two in a matrix. A number of cells (RCPxSSP combinations) will be selected for which we will discuss adaptation measures, and transformative learning. For the moment, we have not made the final selection, but:

RCP: RCP4 and RCP8.5

SSP: SSP2 was excluded

Project Contact: Paula Harrison, paulaharrison@ceh.ac.uk