Variability of Surface Air Temperature

Patterns and Variability of Surface Air Temperature: Comparative Analysis Using Empirical Orthogonal Function and Eigen Microstate Approaches Surface Air Temperature (SAT) is one of the most fundamental indicators of climate variability and change. Its spatial and temporal patterns reflect the interaction of atmospheric circulation, ocean-atmosphere processes, land-surface characteristics, and large-scale climate phenomena. Understanding these patterns is therefore essential for revealing the dominant modes of climate variability and their implications for a changing climate. This book presents a comprehensive comparative analysis of Surface Air Temperature patterns and variability using two complementary data-driven approaches: Empirical Orthogonal Function (EOF) and Eigen Microstate Analysis (EMA). While EOF has long been established as a powerful technique for identifying dominant spatial modes and their associated temporal variability, EMA offers a different perspective by characterizing recurrent atmospheric states and their transitions. Bringing these approaches together provides a broader framework for understanding the structure, complexity, and dynamics of temperature variability. The book explores the mathematical foundations, methodological procedures, spatial patterns, temporal evolution, explained variance, and physical interpretation of the dominant modes identified by EOF and EMA. Particular attention is given to the similarities and differences between the two approaches, including how each method represents dominant temperature structures and captures distinct aspects of climate variability. Beyond methodological comparison, the analysis connects the identified temperature patterns with large-scale climate drivers and atmospheric-oceanic processes. Spectral and temporal analyses are also introduced to investigate the characteristic timescales associated with the dominant modes. Through this integrated framework, readers are encouraged to view climate variability not simply as a collection of individual observations, but as a dynamic system characterized by interacting spatial structures, temporal fluctuations, and recurrent climate states.