Date of Award


Level of Access Assigned by Author

Campus-Only Thesis

Degree Name

Master of Science (MS)




Andrew C. Thomas

Second Committee Member

Andrew Pershing

Third Committee Member

Fei Chai


Thirteen years (1998-2010) of satellite-measured chlorophyll a (CHL) quantify spatial patterns in climatological phytoplankton biomass seasonality across the California Current System (CCS) and its interannual variability. Multivariate clustering divides the study area based on the shape of the local climatological seasonal cycle into four cycle groups: two with spring-summer maxima representing the coastal upwelling zones, one with a summer minimum offshore in mid-latitudes and a fourth with very weak seasonality in between. Multivariate clustering on the individual seasonal cycles from all thirteen years provides a view of interannual variability in seasonal biogeography. Our resulting seasonal cycles are similar to, and appear in relatively similar locations as the climatological clusters. However, strong interannual variability in the geography of the seasonal cycles is evident across the CCS, including changes associated with the 1997- 1999 El Nino-Southern Oscillation (ENSO) signal as well as the 2005 delayed spring transition off the Oregon and northern and central California coasts. We quantify linear trends over the study period in the seasonal timing of the two seasonal cycles that represent the biologically productive coastal upwelling zones. In the northern upwelling region, the date of the spring maximum is delaying and the central tendency of the summer elevated period is advancing. In the southern coastal upwelling region, both the initiation and cessation of the spring maximum are delaying and the maximum is increasing in duration over the study period. Connections between shifts in phytoplankton seasonality and physical forcing expressed as either basin-scale climate signals or local forcing show phytoplankton seasonality in the CCS to be strongly influenced by the seasonality of the wind mixing power offshore and coastal upwelling in the near-shore regions; large-scale patterns in local winds in the CCS are often driven by climate signals such as ENSO, PDO and NPGO.