San José State University

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Thayer Watkins
Silicon Valley
USA

 The Effect of Autocorrelated Heat Flows on the Spectrum of Temperature

The point is made elsewhere that the temperature of the Earth is the cumulative sum of the net energy inflows. The net flows have a stochastic element. As a result the temperature statistics will appears to be following trends and/or cycles even if there are no such long term patterns. The following simulation illustrates this phenomenon

However the net heat flows are not strictly white noise. They are autocorrelated. To illustrate the possible effects of autocorrelations consider the special case in which a moving average of a white noise is taken of white noise and this is used as the net heat inflow to generate a temperature series. This simulation is shown below for the case of the moving average of length four.

Generally the effect of the moving averaging is to make the trends to appear more persistent, more like some underlying development is been played out.

## Analysis

### The Case of a Simple Moving Average

Suppose u(t) is white noise and the net heat inflow is h(t) = (1/H)∫t-Htu(s)ds. Then

#### T(t) = (1/C)∫0th(s)ds

where T is the temperature and C is the heat capacity.

The spectrum of a series is the absolute value of its Fourier transform, which generally is a complex-valued function of frequency.

There are some general properties of the Fourier transform that show relationships. Consider a simple moving average for a series.

#### h(t) = (1/H)∫t-½Ht+½Hu(s)ds which can be represented as h(t) = (1/H)[∫0t+½Hu(s)ds − ∫0t-½Hu(s)ds]

The Fourier transform of h(t) is

#### Fh(ω) = (1/H)[(1/iω)(e-ωH/2Fu − e+ωH/2Fu] which reduces to Fh(ω) = (2/H)(1/ω)sin(½ωH)Fu

where i is √-1.

Since T(t)=(1/C)∫0th(s)ds

#### FT = (1/C)(1/iω)Fh = −(2/(CHω²)sin(½ωH)Fu(ω)

The spectrum of u is a constant k over a range of frequencies. Therefore

#### |FT| = (1/C)(1/iω)|Fh| = (2/(CHω²)|sin(½ωH)|k or, equivalently |FT| = (2H/(C(ωH)²)|sin(½ωH)|k

The |sin(½ωH)| portion of the above product looks like the following. The division by ω² has such a powerful effect that the fluctuations in |sin(½ωH)| are obscured. However if the spectrum is plotted at different scales over different ranges the peaks are revealed.  ### The General Case

Let the net heat flow h(t) be a weighted sum of a stochastic variable u(t); i.e.,

#### h(t) = ∫0tg(s)u(t-s)ds

It then follows from the Convolution Theorem for Fourier transforms that

#### Fh(ω) = Fg(ω)*Fu(ω)

Then for u(t) being white noise of strength k and T(t) = ∫0th(s)ds

#### FT(ω) = (1/iω)Fh(ω) = (k/iω)Fg(ω) and hence |FT(ω)| = (k/ω)|Fg(ω)|

So the spectrum of T is the spectrum of the function g multiplied by the factor (k/ω).

(To be continued.)