convincing correlations

#1
I have two time series. The first is very well sampled, it's an 6-year-long time series of hourly wind data. My second time series is an oceanographic measurement that was sampled ~2 times a year for the same six years that I have wind data.

How can I convincingly correlate these two time series? My hypothesis is that a year of high winds should lead to time-lagged oceanographic changes at 1000 m depth. But with only 12 in-situ oceanographic measurements I'm concerned that the statistical significance of any correlation would be low.

What methods would you use to correlate an effectively continuous signal with a few discrete measurements? Are EOFs appropriate here? I am sadly lacking in statistical background and I don't have a good sense of what to try first, what to try second if the first method doesn't work, etc.
 
#2
Thomas, R., Vaughan, I. & Lello, J., 2013. Data Analysis with R Statistical Software. A Guidebook for Scientists, Newport: Eco-explore.

I find this book quite good for commands for time-series code and also can examine the associations between multiple time-series

acf(time.series1, time.series2) # Auto-correlation function for two time series.
acf(time.series1, time.series2, type ="p") # Partial auto-correlation