File:Cost-of-storms-by-decade.jpg

The cost of extreme weather is rising rapidly and could reach four trillion dollars by 2020. source data: IPCC. Some of the increase is due to greater exposure such as building on the coast.

Note that the underlying cause (excess heat trapped in atmosphere by greenhouse gases) more closely fits a sigmoid curve. The logistic function or its functional neighbors (e.g., the Gompertz function) might produce a better extrapolation.

R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.0 2005-10-06....

decade <- c(1950, 1960, 1970, 1980, 1990)

billions <- c(3.5, 5, 7.5, 13, 40)

# from http://www.ipcc.ch/present/graphics/2001syr/large/08.17.jpg

new <- data.frame(decade = seq(1950, 2050, 1)) # for graph

lb <- log(billions) # enter log domain for nonnegative data

pm <- lm(lb ~ poly(decade, 2))

summary(pm)

"... on 2 degrees of freedom, adjusted R-squared: 0.9839, p-value: 0.00804"

clim <- predict(pm, new, interval = "confidence") # calculate confidence intervals

eclim <- exp(clim) # exit log domain

matplot(new$decade, eclim, lty = c(1, 3, 3), col = c("black", "brown", "brown"), type = "l", ylab = "billions", ylim=c(0,4000), xlab = "decade", xlim=c(1950,2020), main="average yearly inflation-adjusted U.S. dollar

cost of extreme weather events worldwide")

Thanks to Marc Schwartz for the R commands to create graphs with confidence intervals.

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