DOI: 10.5061/dryad.vk8ms62
title: Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias

description: There is more useful information in the time series of satellite-derived
 column-averaged carbon dioxide (XCO2) than is typically characterized.
 Often, the entire time series is treated at once without considering
 detailed features at shorter timescales, such as nonstationary changes in
 signal characteristics – amplitude, period and phase. In many instances,
 signals are visually and analytically differentiable from other portions
 in a time series. Each rise (increasing) and fall (decreasing) segment in
 the seasonal cycle is visually discernable in a graph of the time series.
 The rise and fall segments largely result from seasonal differences in
 terrestrial ecosystem production, which means that the segment's
 signal characteristics can be used to establish observational benchmarks
 because the signal characteristics are driven by similar underlying
 processes. We developed an analytical segmentation algorithm to
 characterize the rise and fall segments in XCO2 seasonal cycles. We
 present the algorithm for general application of the segmentation analysis
 and emphasize here that the segmentation analysis is more generally
 applicable to cyclic time series. We demonstrate the utility of the
 algorithm with specific results related to the comparison between
 satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large
 bioregions across the globe. We found a seasonal amplitude gradient of
 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with
 similar gradients for rise and fall segments. This translates to a
 south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the
 gradient in seasonal amplitude based on surface site in situ CO2 data
 (∼19 ppm). The latitudinal gradients in the period of the
 satellite-derived seasonal cycles were of opposing sign and magnitude
 (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for
 rise segments) and suggest that a specific latitude (∼2∘ N) exists that
 defines an inversion point for the period asymmetry. Before (after) the
 point of asymmetry inversion, the periods of rise segments are lesser
 (greater) than the periods of fall segments; only a single model could
 reproduce this emergent pattern. The asymmetry in amplitude and the period
 between rise and fall segments introduces a novel pattern in seasonal
 cycle analyses, but, while we show these emergent patterns exist in the
 data, we are still breaking ground in applying the information for science
 applications. Maybe the most useful application is that the segmentation
 analysis allowed us to decompose the model biases into their correlated
 parts of biases in amplitude, period and phase independently for rise and
 fall segments. We offer an extended discussion on how such information
 about model biases and the emergent patterns in satellite-derived seasonal
 cycles can be used to guide future inquiry and model development.
description: Segmentation algorithm
 for seasonal cycle analysesThis file contains a
 permanent version of the R code for the segmentation algorithm for
 seasonal cycle analyses of atmospheric carbon dioxide. A PDF manual with
 descriptions is available in the subdirectory ./man. Code is heavily
 annotated and available in the subdirectory ./R. Install instructions are
 in the README.md file. Any additional updates to the algorithm will be
 available in GitHub at , but a
 version is available in this repository for posterity.segmentTS_0.0.1.0000.tar.gzsegmentTSUpdates to segmentTS will be available in GitHub at