/TiPES_statistical_toolbox

Statistical Toolbox for the analysis of (past) abrupt climate change

Primary LanguageR

Statistical Toolbox for the analysis of (past) abrupt climate change

All code comprised in this repository was developed in the context of the TiPES ('Tipping points in the Earth System') project which was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 820970.

This toolbox aims at facilitating the investigation of past abrupt climate change as recorded in climate proxy data. All contributing authors were involved in the TiPES project and accordingly were involved in the assessment of paleoclimate proxy data with respect to abrupt shifts.


Content

transition_characterization (python) MCMC-based

A baysian ramp fit to characterize previously detected and isolated transitions. Applicable to abrupt changes of the mean of a time series. The method returns uncertainty sensitive estimates of the transition's starting point, duration, amplitude and initial value.

TransitionDetection (julia)

A transition detection algorithm based on the comparison of the local mean calculated over two adjacent sliding running windows. Applicable to univariate time series. Returns the time points of potential abrupt shifts in the local mean of the time series.

TransitionDetectionSisalv2 (julia)

Readily implemented of the above TransitionDetection algorithm to the Sisalv2 data base that comprises an extensive collection of speleothem records. The implementation automatically downloads the Sisalv2 data base and runs the TransitionDetection on a selection of records.

KS_detection (Matlab)

Transition detection algorithm based on comparing the KS statistic of two adjacent running windows. Returns the time points of detected transitions.

RQA_detected (Matlab)

Transition detection algorithm based recurrence plot analysis. Returns the time points of detected transitions.

NGRIP_datinguncertainty (R)

Algorithm to efficiently sample realizations of the GICC05 chronology for the NGRIP ice core. The method relies on a comprehensive statistical model for the respective dating uncertainties.


Related Publications

  • Comas-Bru, L., Rehfeld, K., Roesch, C., Amirnezhad-Mozhdehi, S., Harrison, S. P., Atsawawaranunt, K., Ahmad, S. M., Brahim, Y. A., Baker, A., Bosomworth, M., Breitenbach, S. F. M., Burstyn, Y., Columbu, A., Deininger, M., Demény, A., Dixon, B., Fohlmeister, J., Hatvani, I. G., Hu, J., Kaushal, N., Kern, Z., Labuhn, I., Lechleitner, F. A., Lorrey, A., Martrat, B., Novello, V. F., Oster, J., Pérez-Mejías, C., Scholz, D., Scroxton, N., Sinha, N., Ward, B. M., Warken, S., Zhang, H., and SISAL Working Group members: SISALv2: a comprehensive speleothem isotope database with multiple age–depth models, Earth Syst. Sci. Data, 12, 2579–2606, https://doi.org/10.5194/essd-12-2579-2020, 2020.

  • Riechers, K. & Boers, N.: Significance of uncertain phasing between the onsets of stadial-interstadial transitions in different Greenland ice core proxies. Clim. Past 17, 1751–1775, https://doi.org/10.5194/cp-17-1751-2021, 2021.

  • Myrvoll-Nilsen, E., Riechers, K., Rypdal, M. W. & Boers, N.: Comprehensive uncertainty estimation of the timing of Greenland warmings in the Greenland ice core records. Clim. Past 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, 2022.

  • Bagniewski, W., Ghil, M. & Rousseau, D. D.: Automatic detection of abrupt transitions in paleoclimate records. Chaos 31, https://doi.org/10.1063/5.0062543, 2021.

  • Fohlmeister, J., Voarintsoa, N.R.G., Lechleitner, F.A., Boyd, M., Brandtstätter, S., Jacobson, M.J., Oster, J.: Global controls on the stable carbon isotope composition of speleothems, Geochimica et Cosmochimica Acta, 279, 67-87, https://doi.org/10.1016/j.gca.2020.03.042, 2020.


Main Authors:

  • Witold Bagniewski
  • Eirik Myrvoll-Nilsen
  • Jens Fohlmeister
  • Keno Riechers