### Archive

Archive for the ‘GIStemp’ Category

## GISTEMP: Build Notes

 I received an email request to assist in a linux build of GISTEMP. Looking at my old notes and directories, I realized that I did not have an end-to-end instruction set. And as far as I can tell, no one else does either. So here they are. The build has become much easier, primarily due to bug fixes over the last 2 years or so, many of them initiated by findings from Barnes and Jones at Clear Climate Code.

## Lines, Sines, and Curve Fittings 17 – More Morlets

I combine my Hemispheric Land-Ocean Fourier hacks with the previous Morlet Wavelet imagery.

## Natural Variability 1 – One World, Two Cuts

This is a sparse post. It just a test spin on my way to looking for regional trends. Lots of tables, little code.

## Lines, Sines, and Curve Fittings 14 – Two Halves

Assume a spherical cow. Slice in two. Broil one half. Bake the other. Bones go into the stew.

## Lines, Sines, and Curve Fittings 13 – Fourier

To date, I have used two methods to examine the apparent sine wave in the annualized surface temperature date. I looked for a best fit to the residuals of detrended (linear, exponential) data. I have also just a priori added a sine component to the best fits of the data (manual fits, nls). Also, while working through this, I have at times withheld some of the data to check the fits a posteriori. This has yielded a range of possible periodicities:

Its time to bring a new tool to the problem: Fast Fourier Transform. Specifically, we will use the spectrum function in R which is wrapper for spec.pgram. The latter is “calculates the periodogram using a fast Fourier transform, and optionally smooths the result with a series of modified Daniell smoothers (moving averages giving half weight to the end values).”

## Lines, Sines, and Curve Fittings 12 – heteroskedasticity 1

In statistics, a sequence of random variables is heteroscedastic, or heteroskedastic, if the random variables have different variances. The term means “differing variance” and comes from the Greek “hetero” (‘different’) and “skedasis” (‘dispersion’). In contrast, a sequence of random variables is called homoscedastic if it has constant variance.

http://en.wikipedia.org/wiki/Heteroscedasticity

## Lines, Sines, and Curve Fitting 11 – more extrapolation

I’ve mostly been working through GISTEMP in this series, but the exp+sine results were interesting enough that I wanted to pause and look at both line+sine and exp+sine in all three data sets.

## Lines, Sines, and Curve Fitting 10 – nls

The “nonlinear least squares” (nls) function is part of the core of R. John Fox wrote an introduction to it: Nonlinear Regression and Nonlinear Least Squares. This function will in a few dozen iterations return a better fit than my brain-dead looping around parameter space a few tens of thousands of times.

## Lines, Sines, and Curve Fitting 8 – D'Agostino

The eyeball and quick sigma population checks in the previous post provided some confidence that the global temperature anomalies are normally distributed over the mean. But there are more formal tests, including D’Agostino normality test.