A Statistical Evaluation of Atmosphere-Ocean General Circulation Models: Complexity vs. Simplicity

The principal tools used to model future climate change are General Circulation Models which are deterministic high resolution bottom-up models of the global atmosphere-ocean system that require large amounts of supercomputer time to generate results. But are these models a cost-effective way of predicting future climate change at the global level? In this paper we use modern econometric techniques to evaluate the statistical adequacy of three general circulation models (GCMs) by testing three aspects of a GCM's ability to reconstruct the historical record for global surface temperature: (1) how well the GCMs track observed temperature; (2) are the residuals from GCM simulations random (white noise) or are they systematic (red noise or a stochastic trend); (3) what is the explanatory power of the GCMs compared to a simple alternative time series model, which assumes that temperature is a linear function of radiative forcing. The results indicate that three of the eight experiments considered fail to reconstruct temperature accurately; the GCM errors are either red noise processes or contain a systematic error, and the radiative forcing variable used to simulate the GCM's have considerable explanatory power relative to GCM simulations of global temperature. The GFDL model is superior to the other models considered. Three out of four Hadley Centre experiments also pass all the tests but show a poorer goodness of fit. The Max Planck model appears to perform poorly relative to the other two models. It does appear that there is a trade-off between the greater spatial detail and number of variables provided by the GCMs and more accurate predictions generated by simple time series models. This is similar to the debate in economics regarding the forecasting accuracy of large macro-economic models versus simple time series models.

Will oil prices decline over the long run?

At present, oil markets appear to be behaving in a fashion similar to that in the late 1970s and early 1980s when oil prices rose sharply over an extended period. Furthermore, like at that time, analysts are split on whether such increases will persist or reverse, and if so by how much. The present paper argues that the similarities between the two episodes are not as strong as they might appear at first sight, and that the likelihood of sharp reversals in prices is not particularly great. There are a number of reasons in support of the view that it is unlikely that the first two decades of this century will mimic the last two decades of the previous century. First, oil demand is likely to grow significantly in line with strong economic growth in non-OECD countries. Second, on the supply side, OPEC is likely to enhance its control over markets over the next two decades, as supply increases in newly opened areas will only partially offset declining rates of production in other geologically mature non-OPEC oil regions. Moreover, while concerns about climate change will spur global efforts to reduce carbon emissions, these efforts are not expected to reduce oil demand. Finally, although there is much talk about alternative fuels, few of these are economically viable at the prices currently envisioned, and given the structural impediments, there is a reduced likelihood that the market will be able to generate sufficient quantities of these alternative fuels over the forecast horizon. The above factors imply that oil prices are likely to continue to exceed the USD 70 to USD 90 range over the long term. JEL Classification: Q41, Q42, Q43Oil prices, Oil supply, Oil demand, Alternative fuels, Climate Change Policy

Essential role of driver education in the EMR curriculum

The four-phase program of driver education (Classroom, Simulation, Range and Behind The i~heel) as we know it today produces the best product. Due to the high instructional and maintenance costs for the four-phase program, we find it, as a rule, only in use in the more diversified school systems. In this paper we will concern ourselves primarily with the classroom or theory and the behind-the-wheel phases of instruction which are so commonly found elsewhere

Assessing the factors behind oil price changes

The rapid rise in the price of crude oil between 2004 and the summer of 2006 are the subject of debate. This paper investigates the factors that might have contributed to the oil price increase in addition to demand and supply for crude oil, by expanding a model for crude oil prices to include refinery utilization rates, a non-linear effect of OPEC capacity utilization, and conditions in futures markets as explanatory variables. Together, these factors allow the model to perform well relative to forecasts implied by the far month contracts on the New York Mercantile Exchange and are able to account for much of the rise in crude oil prices between 2004 and 2006. JEL Classification: C53, Q41Oil prices, OPEC, Refinery industry

Is there a global warming signal in hemispheric temperature series?

Global and hemispheric temperatures, greenhouse gas concentrations, solar irradiance, and anthropogenic sulfate aerosols all have increased during the last one hundred and fifty years. Classical linear regression techniques will indicate a positive relationship among such series whether or not such a relation exists. Such standard techniques cannot, therefore, show whether observed temperature increases are the result of anthropogenic climate change. However, recent developments in econometrics allow for the analysis of relationships between statistically nonstationary data. We apply some of these recently developed tests in order to uncover the presence of stochastic trends in global climate change variables. These tests indicate that the greenhouse gases are characterized by I(2) stochastic trends while they fail to find evidence of an I(2) stochastic trend in hemispheric temperature series. This would mean that there is no simple long-run equilibrium relationship between radiative forcing and temperature. We then use a multivariate structural time series model to decompose Northern and Southern Hemisphere temperatures into stochastic trends and autoregressive noise processes. This method does not suffer from some of the disadvantages of the standard tests. The results show that there are two independent stochastic trends. The first is I(2) and is shared by the Northern and Southern Hemisphere temperatures. It may be related to the to the radiative forcing variables and represent a global warming signal. The second trend is I(1) and is only present in Northern Hemisphere temperatures. This trend seems closely related to the radiative forcing due to tropospheric sulfates

Estimates of Global Anthropogenic Sulfate Emissions 1860-1993

This paper updates estimates of global anthropogenic sulfate emissions through 1993 and provides a time series of estimates for each year. We extend the methodology developed by Hameed and Dignon (1989) to include emissions from copper smelting and use estimates for US emissions between 1900 and 1940 which were not previously available. Emissions since 1986 show a slight rise due to an increase in the US followed by a slight decline and a continuing decline in emissions from the other OECD countries. Emissions from the rest of the world peak in 1989 and show a steep decline associated with recession and economic restructuring in Eastern Europe. The various emissions series are consistent with the historical record for the atmospheric concentration of non sea sulfates that is reconstructed from an ice core recovered from Greenland

Estimates of Global Anthropogenic Methane Emissions 1860-1993

This paper provides the first time series estimates of global anthropogenic methane emissions from the mid-19th century to the present. Our purpose is to provide time series estimates of anthropogenic methane emissions for global climate models estimated or calibrated using historical time series data. Previous estimates of methane emissions include "top-down" (deconvolution) estimates of total emissions, estimates of global anthropogenic emissions for the 16th century, and various estimates of anthropogenic and natural emissions in the 1980s and 1990s. This study uses previously published point estimates for the 16th century and the 1980s and early 90s and a variety of historical time series of proxy variables to estimate a time series of global anthropogenic methane emissions. We find that anthropogenic methane emissions have increased from about 80 million tonnes per annum in 1860 to close to 380 million tonnes today. The relative importance of various activities in generating methane emissions has changed over time and continues to change. The rate of increase now may be slowing. A comparison with the estimates generated by Khalil and Rasmussen suggests that natural sources of methane have declined over the period. There are, however, great uncertainties in these estimates which future research may be able to reduce

Time Series Properties of Global Climate Variables: Detection and Attribution of Climate Change

Several time series investigations of global climate change have been published, but the time series properties of the variables has received little attention with a few exceptions in the case of global temperature series. We focus on the presence or absence of stochastic trends. We use three different tests to determine the presence of stochastic trends in a selected group of global climate change data for the longest time series available. The test results indicate that the radiative forcing due to changes in the atmospheric concentrations of CO2, CH4, CFCs, and N2O, emissions of SOX, CO2, CH4, and CFCs and solar irradiance contain a unit root while most tests indicate that temperature does not. The concentration of stratospheric sulfate aerosols emitted by volcanoes is stationary. The radiative forcing variables cannot be aggregated into a deterministic trend which might explain the changes in temperature. Taken at face value our statistical tests would indicate that climate change has taken place over the last 140 years but that this is not due to anthropogenic forcing. However, the noisiness of the temperature series makes it difficult for the univariate tests we use to detect the presence of a stochastic trend. We demonstrate that multivariate cointegration analysis can attribute the observed climate change directly to natural and anthropogenic forcing factors in a statistically significant manner between 1860 and 1994