27 Jun 2022
27 Jun 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Reducing errors on estimates of the carbon uptake period based on time series of atmospheric CO2

Theertha Kariyathan1,2, Wouter Peters2, Julia Marshall3, Ana Bastos1, Pieter Tans4, and Markus Reichstein1 Theertha Kariyathan et al.
  • 1Max Planck Institute for Biogeochemistry
  • 2Wageningen University and Research
  • 3Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 4Climate Monitoring Laboratory, National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, CO80305, USA

Abstract. Long, high-quality time series measurements of atmospheric greenhouse gases show interannual variability in the measured seasonal cycles. These changes can be analyzed to better understand the carbon cycle and the impact of climate drivers. However, nearly all discrete measurement records contain gaps and have noise due to the influence of local fluxes or synoptic variability. To facilitate analysis, filtering and curve-fitting techniques are often applied to these time series. Previous studies have recognized that there is inherent uncertainty associated with this curve fitting and the choice of a given mathematical method might introduce biases. Since uncertainties are seldom propagated to the metrics under study, this can lead to misinterpretation of the signal. In this study, we present a novel curve fitting method and an ensemble-based approach that allows the uncertainty of the metrics to be quantified. We apply it here to the Northern Hemisphere CO2 dry air mole fraction time series. We use this ensemble-based approach to analyze different seasonal cycle metrics, namely the onset, termination, and duration of the carbon uptake period (CUP), i.e., the time of the year when the CO2 uptake is greater than the CO2 release. Previous studies have diagnosed CUP based on the dates on which the detrended, zero-centered seasonal cycle curve switches from positive to negative (the downward zero-crossing date) and vice versa (upward zero-crossing date). However, we find that the upward zero-crossing date is sensitive to the skewness of the CO2 seasonal cycle during the net carbon release period. Hence, we propose an alternative method to estimate the onset and termination of the CUP based on a threshold defined in terms of the first-derivative of the CO2 seasonal cycle (First-derivative threshold (FDT) method). Further, using the ensemble-based approach and an additional curve fitting algorithm, we show that (a) the uncertainty of the studied metrics is smaller using the FDT method than when estimated using the timing of the zero-crossing dates, and (b) the onset and termination dates derived with the FDT-method provide more robust results, irrespective of the curve-fitting method applied to the data. The code is made freely available under a Creative Commons-BY license, along with the documentation in this paper.

Theertha Kariyathan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-179', Anonymous Referee #1, 08 Jul 2022
    • AC1: 'Reply on RC1', Theertha Kariyathan, 15 Dec 2022
  • RC2: 'Comment on amt-2022-179', Anonymous Referee #2, 08 Oct 2022
    • AC2: 'Reply on RC2', Theertha Kariyathan, 15 Dec 2022

Theertha Kariyathan et al.

Theertha Kariyathan et al.


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Short summary
We introduce a novel methodology for curve-fitting discrete CO2 time-series data and an ensemble-based approach for quantifying the uncertainty in the metrics derived from it. We then propose an alternate method for estimating the timing and the duration of the carbon uptake period called the “First-Derivative Threshold” method (FDT method). And we use the ensemble-based approach to show that the FDT method provides robust estimates relative to the method used in previous studies.