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

Abstract:

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 in the input data streams. We demonstrate 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 timeseries 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 and remove the climatological seasonal cycle by calculating the anomaly of the histograms from the seasonally varying time average. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis-induced. We present an example that shows that 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.