Evidence-Based Forecasting for Climate Change
cost benefit analysis
Policy Design, Analysis, and Evaluation
Statistics and Probability
Following Green, Armstrong and Soon’s (IJF 2009) (GAS) naïve extrapolation, Fildes and Kourentzes (IJF 2011) (F&K) found that each of six more-sophisticated, but inexpensive, extrapolation models provided forecasts of global mean temperature for the 20 years to 2007 that were more accurate than the “business as usual” projections provided by the complex and expensive “General Circulation Models” used by the U.N.’s Intergovernmental Panel on Climate Change (IPCC). Their average trend forecast was .007°C per year, and diminishing; less than a quarter of the IPCC’s .030°C projection. F&K extended previous research by combining forecasts from evidence-based short-term forecasting methods. To further extend this work, we suggest researchers: (1) reconsider causal forces; (2) validate with more and longer-term forecasts; (3) adjust validation data for known biases and use alternative data; and (4) damp forecasted trends to compensate for the complexity and uncertainty of the situation. We have made a start in following these suggestions and found that: (1) uncertainty about causal forces is such that they should be avoided in climate forecasting models; (2) long term forecasts should be validated using all available data and much longer series that include representative variations in trend; (3) when tested against temperature data collected by satellite, naïve forecasts are more accurate than F&K’s longer-term (11-20 year) forecasts; and (4) progressive damping improves the accuracy of F&K’s forecasts. In sum, while forecasting a trend may improve the accuracy of forecasts for a few years into the future, improvements rapidly disappear as the forecast horizon lengthens beyond ten years. We conclude that predictions of dangerous manmade global warming and of benefits from climate policies fail to meet the standards of evidence-based forecasting and are not a proper basis for policy decisions.