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

Origial figure, before suggestions resulting in the graph above. Note that neither figure yet includes historical data from 2000-2005. Revision requests included asking for confidence intervals, a greater level of significance, and a nonnegative domain.

Summary

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.

Script to create

Later revision: The cost of extreme weather is rising rapidly and could reach 350 billion U.S. 2001 dollars per year by 2025. source data: IPCC, 2001. Some of the cost increase is due to added exposure such as building on the coast.

Application and source code

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

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

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