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Validation and forecasting accuracy in models of climate change
Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.
Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.ORCID iD: 0000-0003-0211-5218
2011 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 27, no 4, p. 968-995Article in journal (Refereed) Published
Abstract [en]

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.

Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 27, no 4, p. 968-995
Keywords [en]
Decadal prediction, DePreSys, Encompassing, Environmental modelling, Global circulation models, Long range forecasting, Neural networks, Simulation models, Validation
National Category
Probability Theory and Statistics Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:his:diva-18260DOI: 10.1016/j.ijforecast.2011.03.008ISI: 000295429400002Scopus ID: 2-s2.0-80052149882OAI: oai:DiVA.org:his-18260DiVA, id: diva2:1398734
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved

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Kourentzes, Nikolaos

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