Long Term Temperature Variability of Santa Barbara Coutny By Courtney Keeney and Leila M.V. Carvalho

Abstract Background Data sources and making the time series plots Detrending and excel charts Comparison of PDO and monthly time series plots Conclusion Future work

Abstract Is there evidence that Santa Barbara County is experiencing the effects of climate change due to a long term increase in magnitude of the maximum and minimum daily temperatures or are there underlying long term or decadal variability, such as the Pacific Decadal Oscillation, that accounts for these disturbances in our climate?

Background The Pacific Decadal Oscillation or PDO is an El Nino like pattern of climate variability PDO events persists every years while El Nino persists for 6-18 months Fingerprints are most visible on the North Pacific/North American region and then in the tropics, the opposite is true of El Nino Causes of PDO are not unknown and predicting PDO is difficult because of the relatively new discovery of this event

PDO Cycles “cool" PDO regimes prevailed from and again from , while "warm" PDO regimes dominated from and from 1977 through (at least) the mid- 1990’s warm eras have seen enhanced coastal ocean biological productivity in Alaska and inhibited productivity off the west coast of the contiguous United States, while cold PDO eras have seen the opposite north- south pattern of marine ecosystem productivity Warm Phase Cold Phase

Making the Time Series Plots time series of the mean daily temperature in a 12 month time period (January to December) was created for Santa Barbara County from 1895 to 2010 are examined A 10 year running mean of the time series was calculated to smooth out the inter annual variability caused by the El Nino- Southern Oscillation (ENSO) the time series for the maximum and minimum temperatures is also investigated

Detrending and Single Months A linear trend like was adjusted to the 10 years running mean To verify the long term variability that is not related to the linear trend the time series was detrended by subtracting the linear fit from the observed values using the trend line linear function. Plotting single months rather than whole years allows to determine which months have the greatest variability and are causing the most change overall. I arbitrarily chose to plot the maximum temperatures for these individual months.

Time Series Plots and Detrended Plots

By comparing the 12 month time series of the 10 year running mean, the month of March, July, and October we show that the transition months between seasons have the most variability and are causing the largest fluctuations on the changes in temperature in Santa Barbara.

Conclusions Seeing the detrended time series plots side by side with the PDO fluctuations, there is evidence that it accounts for some of the long term variations experienced in Santa Barbara County. Although an analysis of the mean minimum temperatures of individual months in Santa Barbara was not included the fluctuations are expected to look the similar.

Future Research Many other important oceanic, anthropogenic, and atmospheric cycles and disturbances play a role in the climate of Santa Barbara. Continuing research on the variations between the spring and fall seasons may reveal the significance of these changes to our climate. A look at the other weather phenomena or natural disasters may explain some of the variations seen on the time series plots. An investigation of the changes in precipitation during this same time period would be interesting to compare with the fluctuations in the minimum and maximum temperatures.