A periodic mixed linear state-space model to monthly long-term temperature data

Abstract

In recent decades, the world has been confronted with the consequences of global warming; however, this phenomenon is not reflected equally in every part of the globe. Thus, the warming phenomenon must be monitored in a more regional or local scale. This paper analyzes monthly long-term time series of air temperatures in three Portuguese cities: Lisbon, Oporto and Coimbra. We propose a periodic state space framework, associated with a suitable version of the Kalman filter; which allows for the estimation of monthly warming rates taking into account the seasonal behavior and serial correlation. Results about the monthly mean of the daily mid-range temperature time series show that there are different monthly warming rates. The greatest annual mean rise was found in Oporto with 2.17◦C whereas, in Lisbon and Coimbra, it was respectively, 0.62ºC and 0.55ºC, per century.