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

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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.
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.

Contents

[edit] 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.

Later inferrior 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.
Later inferrior 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.

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

[edit] Script to create

[edit] 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")

[edit] Thanks

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


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