Cross Correlation

This script is for completeing cross correlation for the Diss Mere record and TraCE temp and AMOC.

The data has been interpolated to match the chronolgy of the TraCE datasets.

data <- read.csv("Data.csv")

Setting up the differnet variables

time <- data$Age_BP
AMOC <- data$AMOC
varve <- data$Varve
temp <- data$Temp

First lets plot the different timeseries

par(mfrow = c(3,1))
plot(time, varve, type = 'l', col='blue')
plot(time, AMOC, type = 'l', col='red')
plot(time, temp, type = 'l', col='grey')

par(mfrow = c(1,1))

Here we are going to compute the CCF and plot them both individually

Varve_AMOC = ccf(AMOC,varve)

Varve_Temp = ccf(temp, varve)

Now printing out the results AMOC

Varve_AMOC
## 
## Autocorrelations of series 'X', by lag
## 
##   -26   -25   -24   -23   -22   -21   -20   -19   -18   -17   -16   -15   -14 
## 0.407 0.446 0.444 0.431 0.423 0.420 0.436 0.442 0.443 0.423 0.446 0.420 0.406 
##   -13   -12   -11   -10    -9    -8    -7    -6    -5    -4    -3    -2    -1 
## 0.441 0.449 0.423 0.429 0.450 0.439 0.462 0.428 0.426 0.448 0.451 0.421 0.453 
##     0     1     2     3     4     5     6     7     8     9    10    11    12 
## 0.449 0.442 0.447 0.489 0.469 0.465 0.464 0.446 0.452 0.424 0.474 0.492 0.460 
##    13    14    15    16    17    18    19    20    21    22    23    24    25 
## 0.455 0.448 0.457 0.435 0.461 0.437 0.456 0.469 0.405 0.456 0.478 0.417 0.460 
##    26 
## 0.485

Varve

Varve_Temp
## 
## Autocorrelations of series 'X', by lag
## 
##   -26   -25   -24   -23   -22   -21   -20   -19   -18   -17   -16   -15   -14 
## 0.490 0.485 0.496 0.508 0.494 0.479 0.474 0.465 0.485 0.492 0.495 0.501 0.493 
##   -13   -12   -11   -10    -9    -8    -7    -6    -5    -4    -3    -2    -1 
## 0.490 0.501 0.502 0.512 0.507 0.514 0.512 0.494 0.492 0.501 0.508 0.502 0.522 
##     0     1     2     3     4     5     6     7     8     9    10    11    12 
## 0.529 0.525 0.521 0.528 0.544 0.546 0.548 0.544 0.537 0.526 0.516 0.543 0.531 
##    13    14    15    16    17    18    19    20    21    22    23    24    25 
## 0.542 0.550 0.545 0.528 0.542 0.531 0.521 0.531 0.539 0.532 0.539 0.524 0.527 
##    26 
## 0.532

Now lets plot them together

par(mfrow = c(1,2))
plot(Varve_AMOC)
plot(Varve_Temp)

par(mfrow = c(1,1))

What we can see from these plots is that they are both highly correlated even at lag 26. There does seem to be some seasonality here, however the correlation is high.

The varve and AMOC highest correlation at lag 3 = 0.489, lag 11 is higher, however lags above 10 I think are not normally included (depending on the dataset)

The varve and temp highest correlation at lag 6 0.548.