Histogram Anomaly Time Series: A Compact Graphical Representation of Spatial Time Series Data Sets

Abstract

We introduce a method that graphically reveals subtle changes in a spatial time series by displaying histograms of each map’s values as anomalies from its seasonal cycle.We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from the MERRA-2 reanalysis and from GPCP 1DD precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land-ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis-induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.