Optimal weighting of data to detect climatic change: Application to the carbon dioxide problem

Open Access
Publisher Website
Google Scholar
Abstract
Increasing carbon dioxide concentration in the atmosphere is expected to lead to warming of the surface of the earth. Detection of the warming is difficult because it must be distinguished from natural variability of temperatures due to daily weather changes. It is shown that a weighting of surface temperature data using information about the expected level of warming in different seasons and geographical regions and statistical information about the amount of natural variability in surface temperature can improve the chances of early detection of the warming. Surface temperature data are conventionally averaged over the surface of the earth, weighted according to the geographical area represented by the data. A preliminary analysis of the optimal weighting method suggests that it is 25% more effective in revealing a surface warming than the conventional weighting method, in the sense that 25% more data analyzed in the conventional way are needed to have the same chance of detecting the climatic warming. The possibility of detecting the warming in data already available is examined. A rough calculation suggests that the warming ought to have already been detected if the only sources of significant variability in surface temperature had time scales less than 1 year.