16 / 18
Page 79
Journal of Earth Science & Climatic Change | ISSN: 2157-7617 | Climate 2018 | Volume: 9
5
th
World Conference on
May 23-24, 2018 | New York, USA
Climate Change and Global Warming
Impacts of climate model parametric uncertainty in an MPC implementation of the DICE integrated
assessment model
Salman Hafeez
1,2
, Steven R Weller
1
and
Christopher M Kellett
1
1
University of Newcastle, Australia
2
University of Essex, UK
I
ntegrated assessment models (IAMs) are a key tool in studying the interdependence of the global economy and the climate system.
For example, the dollar value of carbon dioxide emissions due to anthropogenic climate damages, known as the social cost of carbon
(SCC), can be computed using the widely used DICE (Dynamic Integrated model of Climate and the Economy) IAM by solving an
open-loop optimal control problem. The results of such an open-loop decision-making strategy, however, do not fully reflect the
impacts of uncertainty in the dynamic response of the global climatic system to radiative forcing. An implementation of the DICE
IAM based on model predictive control (MPC) is proposed. This MPC-based approach draws a clear distinction between the climate
model used by DICE for mitigation planning purposes, and the “true" global climate captured by a low-order emulation of a model
drawn from a state-of-the-art climate model ensemble (CMIP5, the fifth phase of the Coupled Model Intercomparison Project). The
closed-loop control methodology quantifies the impact of parametric climate model uncertainty (plant-model mismatch).
c3197107@uon.edu.auJ Earth Sci Clim Change 2018, Volulme: 9
DOI: 10.4172/2157-7617-C1-040