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 midrange 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 0.62°C and 0.55°C per century.

On the persistence of near‐surface temperature dynamics in a warming world

“…56 Several studies applied the state-space model to average near-surface air temperature and related series. [57][58][59] However, as far as we know, no existing study investigated the persistence of temperature after controlling for trend and measurement errors at a geographical grid level.…”

Change Point Detection in a State Space Framework Applied to Climate Change in Europe

“…The application of state space models to long temperature time series in Lisbon, Coimbra, and Porto, in [6], allowed us to conclude that the latter city has a very different growth rate per century than the others. In [5], the application of state space models to long series of air temperature, together with cluster procedures, let the identification of temperature growth rates patterns in several cities in Europe.…”

Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe

“…From another perspective, some works focus on a single or on a few temperature time series in a limited analysis, in general related to a country [5] or a time series modeling of a single series [6]. At a regional level, ref.…”

“…A mixedeffect state-space model was considered to model both historical climate data as well as dynamically derived mean climate change projection information obtained from global climate models in [17]. The state space modeling, in a periodic framework, was performed to long temperature time series in Portuguese cities (Lisbon, Coimbra and Porto) in a study by [5], where it was concluded that Porto has a temperature growth rate, per century, substantially different from the other cities. In [18], the application of state space models to long series of air temperature, together with cluster procedures, enabled the identification of temperature growth rates patterns in several cities in Europe.…”