This paper derives the seasonal factors from the US temperature, gasoline price, and fresh food price data sets using the Kalman state smoother and the principal component analysis. Seasonality in this paper is modeled by the autoregressive process and added to the random component of the time series. The derived seasonal factors show a common feature: their volatilities have increased over the last four decades. Climate change is undoubtedly reflected in the temperature data. The three data sets' similar patterns from the 1990s suggest that climate change may have affected the prices' volatility behavior.