Few ocean stations have been in place long enough to observe decadal ocean cycles, but Ocean Station Papa (OSP) is one of those few. With a time series that spans over 60 years, OSP is an ideal data set for linking decadal and multi-decadal changes in the climate and ocean cycles. The purpose of this study is to determine if long term (decadal) climate variations significantly relate to and potentially impact biogeochemical variables in the open ocean north Pacific surface waters. One outstanding problem is that the OSP time series has highly variable sampling effort over time for different ocean variables. Such time gaps and irregular sampling make it difficult to do standard statistical time series analyses. Hence, the first part of this thesis is a statistical reconstruction of the original OSP data onto a regular monthly time grid. This is done using a state space model and the Kalman smoother algorithm. Its central idea is to estimate missing observations in seven ocean variable time series (temperature, salinity, nitrate, phosphate, silicate, chlorophyll, and oxygen) by using empirical relationships between the variables, as well as making use of the fact that some of these variables (e.g. temperature and salinity) are available for the entire analysis period. Specifically, a period of high-density sampling is first used to establish the relationship between the variables, which is then used to reconstruct the seven ocean variable time series with the Kalman smoother algorithm. The second part of this thesis aims to relate the reconstructed OSP variables to modes of climate variability. The reconstructed OSP time series are first smoothed to remove seasonal variations. They are then compared to four climate modes (Pacific Decadal Oscillation, North Pacific Gyre Oscillation, Southern Oscillation Index, and Multivariate ENSO Index) using cross-correlation and cross-spectral analyses. The cross-correlations between the ocean state variables and climate modes show that NPGO has the greatest number of significant correlations as the leading variable. The cross-spectral analyses show that PDO has the least amount of influence on seven ocean state variables, and that NPGO is the climate mode with the most influence on the ocean state variables. Using this method, the influence of climate variability on physical, biogeochemical, and biological ocean variables could potentially be used on any ocean time series.