Complicated ENSO models do not significantly outperform very simple ENSO models

Halide, Halmar; Ridd, Peter V.

doi:10.1002/joc.1519

Cited by 35 publications

(6 citation statements)

References 75 publications

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“…Models for forecasting variations in climate are not an exception to this rule. Halide and Ridd (2007) compared predictions of El Niño-Southern Oscillation events from a simple univariate model with those from other researchers' complex models. Some of the complex models were dynamic causal models incorporating laws of physics.…”

Section: Keep Forecasting Methods Simple (Principle 71)mentioning

confidence: 99%

Global Warming: Forecasts by Scientists Versus Scientific Forecasts

Green

Armstrong

2007

Energy & Environment

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In 2007, the Intergovernmental Panel on Climate Changes Working Group One, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme, issued its updated, Fourth Assessment Report, forecasts. The Report was commissioned at great cost in order to provide policy recommendations to governments. It included predictions of dramatic and harmful increases in average world temperatures over the next 92 years. Using forecasting principles as our guide we asked, are these forecasts a good basis for developing public policy? Our answer is "no." To provide forecasts of climate change that are useful for policy-making, one would need to forecast (1) global temperature, (2) the effects of any temperature changes, (3) the effects of alternative policies, and (4) whether the best policy would be successfully implemented. Proper forecasts of all four are necessary for rational policy making. The IPCC Report was regarded as providing the most credible long-term forecasts of global average temperatures by 31 of the 51 scientists and others involved in forecasting climate change who responded to our survey. We found no references to the primary sources of information on forecasting methods despite the fact these are easily available in books, articles, and websites. In our audit of Chapter 8 of the IPCC's WG1 Report, we found enough information to make judgments on 89 out of a total of 140 forecasting principles. The forecasting procedures that were described violated 72 principles. Many of the violations were, by themselves, critical. We concluded that the forecasts in the Report were not the outcome of scientific procedures. In effect, they were the opinions of scientists transformed by mathematics and obscured by complex writing. Research on forecasting has shown that experts' predictions are not useful. Instead, policies should be based on forecasts from scientific forecasting methods. We have been unable to identify any scientific forecasts of global warming. Claims that the Earth will get warmer have no more credence than saying that it will get colder. Using forecasting principles as our guide we asked, are these forecasts a good basis for developing public policy? Our answer is "no." To provide forecasts of climate change that are useful for policy-making, one would need to forecast (1) global temperature, (2) the effects of any temperature changes, (3) the effects of alternative policies, and (4) whether the best policy would be successfully implemented. Proper forecasts of all four are necessary for rational policy making.The IPCC Report was regarded as providing the most credible long-term forecasts of global average temperatures by 31 of the 51 scientists and others involved in forecasting climate change who responded to our survey. We found no references to the primary sources of information on forecasting methods despite the fact these are easily available in books, articles, and websites. In our audit of Chapter 8 of the IPCC's WG1 Report, we found eno...

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Section: Keep Forecasting Methods Simple (Principle 71)mentioning

confidence: 99%

Global Warming: Forecasts by Scientists Versus Scientific Forecasts

Green

Armstrong

2007

Energy & Environment

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“…Interestingly, this appears to coincide with the philosophy of Halide & Ridd (2008): increased model complexify does not guarantee greater model efficacy. More variables may not necessarily lead to greater accuracy.…”

Section: Discussionmentioning

confidence: 94%

A control chart for severity index to detect drought compares favourably to logistic regression

Townsley

Chimka

2011

Hydrology Research

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We describe the discovery of how a traditional control chart for the Palmer Drought Severity Index (PDSl) to detect drought compares favourably to a theoretically appropriate statistical (logistic regression) model of drought as a function of PDSl. Our empirical results are based on monthly observations of PDSl, precipitation and temperature made in Kansas since 1895. Results from the study suggest that a relatively simple statistical approach based on Shewhart control charts may provide a more accessible method for relevant government agencies to predict droughts, improving resource management and preparation. Moreover, utilizing such an approach over more sophisticated methods may come at little expense regarding prediction errors.

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“…Moreover, the effectiveness of prediction models developed through complex dynamic methods has often found as questionable. This is due to the failure of complex dynamic methods to outperform simple statistical prediction models (Halide and Ridd, 2008). Therefore, simple statistical climate prediction models often become the first choice to many researchers due to its simplicity, cost effective and easy to implement characteristics.…”

Section: Introductionmentioning

confidence: 99%

Analysing the effect of lagged climate indices on rainfall predictability for Western Australia's North Coast Region

Islam¹,

Imteaz²,

Rasel³

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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In last few decades, Australia experiences several extreme floods, bushfires and droughts. The consequences of these natural calamities have great social, environmental and economic impacts. Therefore, having a reliable seasonal forecasting of rainfall is highly important to withstand these situations as rainfall is mostly correlated with extreme flood, bushfire and drought incidents. Over decades, scientists and researchers are looking for reliable forecasting systems, which could provide definite predictions of rainfall that is essential for water management, agricultural production and flood management systems and mitigating such destructive situations by proper planning and management. Rainfall forecasting using climate indices has become one of the best ways to predict rainfall variability in many parts of the world. This strategy has also been proved as an effective tool to determine such variability. As Australia is affected by many different weather systems, several studies have been conducted to identify potential climate indices like Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), Southern Annular Mode (SAM), Blocking highs, MODOKI and their interactions with rainfalls in different parts of Australia. Different regions of Australia have been exposed to different climate indices, so it is often recommended to study different regions separately to understand the effects of climate indices and their effectiveness to predict long-term rainfall. This study explores the seasonal variability of rainfall in Western Australia (WA) considering the effects of dominant climatic indices responsible for this region and develop a rainfall model for predicting Western Australia seasonal rainfall using potential climate indices. Very few studies have ever been found which had a primary concentration in determining rainfall variability in WA. Moreover, most of these studies has evaluated concurrent relationship between rainfall and climate indices. While a very few studies were conducted on concentration of lagged relationship between climate indices and seasonal rainfall. This study explores the significant correlation among lagged climate indices with autumn rainfall for north coast region of Western Australia (NCWA). To do so, two stations (Mingenew and Northampton) from NCWA were considered for this study. From the single statistical correlation analysis, it was found that DMI (IOD indicator), SOI, Nino3.4 and Nino4 (ENSO indicators) and ENSO Modoki Index (EMI) have shown significant correlation with autumn rainfall for these two stations. Several multiple regression analysis were conducted using lagged ENSO and DMI and lagged DMI and EMI as a combined predictor of autumn rainfall. Results showed that multiple regression has significantly increased the correlation between autumn rainfall and climate indices than single correlation analysis. Lagged DMI-Nino4 model showed highest correlation (0.38) for Mingenew and (0.44) for Northampton. Developed models were tested with out of sample dataset and stati...

show abstract

Complicated ENSO models do not significantly outperform very simple ENSO models

Cited by 35 publications

References 75 publications

Global Warming: Forecasts by Scientists Versus Scientific Forecasts

Global Warming: Forecasts by Scientists Versus Scientific Forecasts

A control chart for severity index to detect drought compares favourably to logistic regression

Analysing the effect of lagged climate indices on rainfall predictability for Western Australia's North Coast Region

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