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Upwelling: Winds and SST<\/strong> Wind stress was calculated from wind speed following Large and Pond (1981), and the alongshore component was calculated using the orientation of the principal axis of the wind, which largely follows the coastal orientation (details in Garc\u00eda-Reyes and Largier 2010). \u00a0We compared these variables to the upwelling index (UI) provided by NOAA (Schwing and others 1996). \u00a0The UI quantifies the offshore Ekman transport caused by alongshore wind stress, but is calculated from cross-shore gradients in sea level pressure. \u00a0While UI data is provided at 3\u00b0 intervals along the Northeast Pacific coast, the scale of the cross-shore sea level pressure used in its calculation is 6\u00b0. \u00a0Here, we considered data (http:\/\/www.pfeg.noaa.gov\/products\/las.html<\/a>) from 33 to 48\u00b0N across the 1988 to 2010 interval shared by the buoy data.\u00a0<\/p><\/div>","@type":"rdf:HTML"}],"http:\/\/ocean-data.org\/schema\/hasBriefDescription":[{"@value":"Upwelling index along the north-central California Current, 1988-2010","@language":"en-US"}],"http:\/\/www.w3.org\/2000\/01\/rdf-schema#label":[{"@value":"California Current winter climate reconstruction - upwelling index","@type":"xsd:string"}],"http:\/\/ocean-data.org\/schema\/hasProcessingDescription":[{"@value":" To investigate variability in upwelling with respect to season, we computed the monthly mean, standard deviation and coefficient of variation for each physical variable (Uw, SST, and UI). \u00a0Subsequently, we conducted a Principal Component Analysis (PCA) to explore shared patterns of variability among these time series, which were extracted and used as multivariate indices of the CCE. \u00a0Given that upwelling varies among locations, among season and among years (Garc\u00eda-Reyes and Largier 2012; Thompson and others 2012), we arranged each physical data variable (Uw, SST and UI) into a three-dimensional matrix consisting of 12 locations x 12 months x 23 years; resulting components were labeled as PCUw, PCSST, and PCUI. \u00a0Each column was normalized (zero mean and variance equal to 1 standard deviation) before calculating the PCA. \u00a0Next we ran a PCA that combined Uw and SST data by arranging their data arrays into a single matrix with dimensions: 24 locations (12 Uw locations + 12 SST locations) x 12 months x 23 years. \u00a0Resulting PCs, labeled as PCenv, captured the dominant and sub-dominant seasonal \"modes\" or patterns in upwelling and their interannual variability. \u00a0PCs (scores) from the three physical variables (PCUw, PCSST, and PCUI) were compared to one another as well as to PCenv using Spearman ranked correlations. \u00a0PCs with Eigenvalues < 1 and explaining < 10% of the variability in the data set were dropped from further analysis (Jolliffe 2002).\u00a0<\/p>\n The 15 biological indicators included in the study were cross-correlated with one another to generally assess the extent to which they shared common patterns. \u00a0Subsequently, we conducted a PCA (resulting components labeled as PCbio) using the nine longest (1982-2006) biological indicators. \u00a0Biological indicators excluded from the PCA were correlated against the PCbio components as a measure of their agreement with dominant patterns in the longer datasets. \u00a0Those that were significant at the p < 0.05 level were retained. \u00a0To summarize physical-biological interactions, the scores of the environmental PCs (PCUw, PCSST, PCUI and PCenv), the biological indices, and biological PCs (PCbio) were compared using Spearman correlations.<\/p>\n Biological Principal Component Sources and Loading (PDF)<\/a><\/p>\n
\nWe quantified local upwelling using two variables: 1) an estimate of offshore Ekman transport (Uw) calculated from buoy winds and 2) SST as an integrated estimate of ocean conditions that reflects the shoaling and mixing of deep water in response to upwelling winds, alongshore and cross-shore transport, and air-sea heat fluxes as well as variability in water column characteristics that could impact the upwelling process. \u00a0Hourly wind data, from 1988 to 2010, were obtained from 12 NOAA buoys located from 34 to 47\u00b0N along the continental shelf of the U.S. west coast, available at\u00a0http:\/\/www.ndbc.noaa.gov<\/a>. \u00a0Missing data ranged from 4% at buoy 46025 to 26% at buoy 46041, with an average of 15% in all buoys. \u00a0Gaps were 77 days long on average, with the longest gaps ranging from 4 months at buoy 46025 to 1.8 years at buoy 46011. \u00a0Data were highly correlated (r > 0.9 for wind and > 0.75 for SST, except for buoy 46025), so neighboring buoys could be used to fill the gaps via linear regressions. \u00a0<\/p>\n