Estimating impact likelihoods by combining probabilistic projections of climate and socio-economic change with impact response surfaces

14:00 Wednesday 29 May


Room S13


Stefan Fronzek (Finland) 1; Nina Pirttioja (Finland) 1; Martina Flörke (Germany) 2; Yasushi Honda (Japan) 3; Akihiko Ito (Japan) 4; João Pedro Nunes (Portugal) 5; Jouni Räisänen (Finland) 6; Kiyoshi Takahashi (Japan) 4; Akemi Tanaka (Japan) 7; Florian Wimmer (Germany) 2; Minoru Yoshikawa (Japan) 8; Timothy R. Carter (Finland) 1

1 - Finnish Environment Institute; 2 - University of Kassel; 3 - The University of Tsukuba; 4 - National Institute for Environmental Studies; 5 - University of Lisbon; 6 - Institute for Atmosheric and Earth System Research, University of Helsinki; 7 - Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization; 8 - Mizuho Information and Research Institute

The impacts of climate change on various sectors can be estimated using impact models. These models are typically applied by taking scenarios of climate change and socio-economic conditions as inputs and simulating the effects of these scenario conditions on the system being modelled. Each estimate is then tied closely to the scenario being used and, hence, many scenarios are needed to explore a plausible range of alternative future conditions.

In this paper, an alternative approach to impact modelling has been used that changes the order of analysis. First, impact models are used to construct impact response surfaces (IRSs), which show responses systematically across a range of conditions. Second, any scenario of those changed conditions can then be defined and located in the appropriate region of the IRS to determine the estimated response. This ïscenario-neutral’ approach has some advantages over the conventional scenario-based approach:

  • it allows a more rigorous testing of impact models (across many possible future conditions);
  • it can improve understanding of the basic model behaviour;
  • it facilitates the systematic evaluation of simulated adaptation options; and
  • if drivers can be quantified probabilistically, the impact analysis can be conducted in a risk framework.

Here, building on work presented by Fronzek et al. (2018), we present IRSs constructed from sensitivity analyses of impact models illustrating responses of indicators in a range of sectors for regions in Europe. The indicators are: heat-related mortality, river discharge and water scarcity index, crop yields and net primary production of terrestrial ecosystems, with estimates for the Iberian Peninsula, Hungary and Scotland. Simulations were conducted with respect to changes in temperature, precipitation, atmospheric CO2 levels and population, with some estimates including representation of adaptation. The IRSs were then combined with probabilistic projections of climate change and of population to estimate the likelihood of crossing critical impact thresholds.

The analysis was still ongoing at the time of writing, but preliminary results showed an increased risk of severe water scarce conditions in the Iberian Peninsula, decreased crop yields in Hungary and increased heat-related mortality in all regions. Overall, the study demonstrates a novel method of using probabilistic projections of climate change and/or of socio-economic drivers directly in impact modelling as an alternative to undertaking potentially demanding model simulations based on projections from ensembles of opportunity.


Fronzek et al. (2018) Determining sectoral and regional sensitivity to climate and socio-economic change in Europe using impact response surfaces, Regional Environmental Change, doi: 10.1007/s10113-018-1421-8