Climate model emulation in an integrated assessment framework: a case study for mitigation policies in the electricity sector
- 1Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge, CB3 9EP, UK
- 2Environment, Earth and Ecosystems, Open University, Milton Keynes, MK7 6AA, UK
- 3Cambridge Econometrics Ltd, Covent Garden, Cambridge, CB1 2HT, UK
- anow at: Department of Geography, Environment and Development Studies, Birkbeck, University of London, 32 Tavistock Square, London, WC1H 9EZ, UK
- bnow at: Department of Environmental Sciences, Radboud University, Nijmegen, the Netherlands
Abstract. We present a carbon-cycle–climate modelling framework using model emulation, designed for integrated assessment modelling, which introduces a new emulator of the carbon cycle (GENIEem). We demonstrate that GENIEem successfully reproduces the CO2 concentrations of the Representative Concentration Pathways when forced with the corresponding CO2 emissions and non-CO2 forcing. To demonstrate its application as part of the integrated assessment framework, we use GENIEem along with an emulator of the climate (PLASIM-ENTSem) to evaluate global CO2 concentration levels and spatial temperature and precipitation response patterns resulting from CO2 emission scenarios. These scenarios are modelled using a macroeconometric model (E3MG) coupled to a model of technology substitution dynamics (FTT), and represent different emissions reduction policies applied solely in the electricity sector, without mitigation in the rest of the economy. The effect of cascading uncertainty is apparent, but despite uncertainties, it is clear that in all scenarios, global mean temperatures in excess of 2 °C above pre-industrial levels are projected by the end of the century. Our approach also highlights the regional temperature and precipitation patterns associated with the global mean temperature change occurring in these scenarios, enabling more robust impacts modelling and emphasizing the necessity of focusing on spatial patterns in addition to global mean temperature change.