Spatial and Temporal Variability in Oceanographic and Meteorologic Forcing along Central California: 1980-2002
- Curt Storlazzi
United States Geological Survey (USGS)
In the past two decades, our understanding of important large-scale phenomena (El Nino, upwelling, California current, etc) that drive physical, chemical, and biological processes along the US West Coast has greatly improved. However, our ability to predict the influence of annual and inter-annual events on a regional scale still remains limited. High-resolution hourly data from 8 NOAA buoys deployed since the early 1980ís off Central California were analyzed to improve our understanding of spatial and temporal variability of oceanographic and meteorologic forcing along the coastline. Seasonal to inter-annual trends in wave height, wave period, wave direction, sea-level barometric pressure, sea-surface temperature, wind speed, and wind direction were identified, as were significant departures in these trends during El Nino and La Nina periods.
Summary to DateOver 20 years of hourly deepwater buoy data from off Central California were analyzed to investigate long-term trends and compute statistically significant probability estimates of the behavior of the measured oceanographic and meteorologic data during different climatic regimes (Figure 1). Significantly different trends were observed in the data during El Nino and La Nina months, reinforcing the long-held but relatively unsupported theories on the difference in oceanographic and meteorologic forcing along Central California during different phases of the El Nino-Southern Oscillation (Figure 2).
- Statistically significant differences were observed in the monthly mean significant wave height, dominant wave period, sea-level barometric pressures, sea-surface water temperature, and wind speed and direction during Normal, La Nina and El Nino months.
- In addition to these monthly differences, we observed statistically significant long-term trends in monthly mean significant wave height, dominant wave period, sea-level barometric pressures, sea-surface water temperature, and wind speed.
- sea-level barometric pressure
Study MethodsIn order to desample and increase the robustness of the hourly NDBC buoy data, monthly means, minimums, maximums, and standard deviations were computed for each parameter during each month of the entire data record. Gaps in the data due to buoy failure and/or maintenance range from several weeks to entire seasons at a time. When computing these monthly statistics, it was important to have sufficient data in which to compare various buoys. In order to obtain equally weighted calculations, months with less than 480 hours (20 days) worth of data were discarded and not analyzed.
Monthly exceedence values, in terms of the percentage of time the parameter was observed to exceed a given value, were determined to identify periods of sustained extreme conditions whose cumulative effects are important for certain physical processes and might not be adequately described by the mean and standard deviation.
With extreme values and recurrence interval projections, it is possible to estimate the average time between events of a given magnitude (Carter et al., 1986). For example, a two-year recurrence interval for significant wave height suggests that the probability of an occurrence of a given wave height of extreme magnitude is once every two years. The inverse, or reciprocal of the recurrence interval, is the probability of such an occurrence equaling or exceeding the given magnitude (NALMS, 2004). Return magnitudes were based on the Fisher-Tippet Type-I distributions (Carter, 1986) of monthly maximum values. Two-, 10-, 50-, and 100- year projections were computed.
In order to understand how these oceanographic and meteorologic parameters were influenced by ENSO, monthly statistics were broken down into categories based on the corresponding Multivariate ENSO Index (MEI; Wolter and Timlin, 1998) values for the specific month. These categories included: All months of the study period, El Nino months, and La Nina months. MEI monthly values before 1993 were normalized in order to have an average of zero and a standard deviation of 1.0 (CDC, 2004). Cutoff values were used to separate index values of El Nino/La Nina months with normal months. The monthly mean, standard deviation of the mean, and mean of the standard deviation of all parameters were matched with their corresponding monthly MEI values. Any month with an index value greater than 1.0 was defined as an El Nino month, and any month with an index value lower than -0.5 was defined a La Nina month. In this way, it was possible to assign monthly numeric MEI intensities with concurrent buoy data in order to categorize the oceanic and atmospheric parameters into El Nino months, La Nina months, or months when neither El Nino nor La Nina conditions were observed (-0.5 < MEI < 1.0).
Figures and Images
Figure 1: General wave patterns along central California. This figure was produced by synthesizing more than 2,800,000 hourly observations of significant wave height, dominant wave period, wave direction, wind speed and wind direction from the NDBC Buoy 46042 (Monterey Bay) collected over the years 1993-2002 when concurrent directional wave and wind data were available. The bands delineate the typical range of directions for a given wave pattern while the arrows show the mean direction of a given wave pattern.
Figure 2: Distribution of top 10% largest significant wave heights by wave direction for NDBC Buoy 46042 (Monterey Bay) when directional wave data were available (1993-2002) for El Nino events, La Nina events and the change in wave direction between the two end-members. El Nino events have a relatively higher proportion of largest signicant wave heights out of the west and southwest as compared to La Nina events, when more of the largest significant wave heights are out of the northwest.