Skip to main content

Advertisement

SpringerLink
Log in

Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data

  • Thematic Issue
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

The Earth’s atmosphere and oceans are largely determined by periodic patterns of solar radiation, from daily and seasonal, to orbital variations over thousands of years. Dynamical processes alter these cycles with feedbacks and delays, so that the observed climate response is a combination of cyclical features and sudden regime changes. A primary example is the shift from a glacial (ice age) state to interglacial, which is driven by a 100-thousand year orbital cycle, while the transition occurs over a period of hundreds of years. Traditional methods of statistical analysis such as Fourier and wavelet transforms are very good at describing cyclical behavior, but lack any characterization of singular events and regime changes. More recently, researchers have tested techniques in the statistical discipline of change point detection. This paper explores the unique advantages of a piecewise linear regression change point detection algorithm to identify events, regime shifts, and the direction of cyclical trends in geophysical data. It evaluates the reasons for choosing this particular change detection algorithm over other techniques by applying the technique to both observational and model data sets. A comparison of the proposed change detection algorithm to the more established statistical techniques shows the benefits and drawbacks of each method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ahrens J, Jourdain S, O’Leary P, Patchett J, Rogers DH, Petersen M (2014) An image-based approach to extreme scale in situ visualization and analysis. In: Proceedings of the international conference for high performance computing, networking, storage and analysis. IEEE Press, pp 424–434

  • Banesh D, Schoonover JA, Ahrens JP, Hamann B (2017) Extracting, visualizing and tracking mesoscale ocean eddies in two-dimensional image sequences using contours and moments. In: Rink K, Middel A, Zeckzer D, Bujack R (eds) Workshop on visualisation in environmental sciences (EnvirVis). The Eurographics Association. https://doi.org/10.2312/envirvis.20171103

  • Basseville M, Nikiforov IV et al (1993) Detection of abrupt changes: theory and application, vol 104. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Bereiter B, Eggleston S, Schmitt J, Nehrbass-Ahles C, Stocker TF, Fischer H, Kipfstuhl S, Chappellaz J (2015) Revision of the epica dome c co2 record from 800 to 600 kyr before present. Geophys Res Lett 42(2):542–549

    Article  Google Scholar 

  • Chaigneau A, Gizolme A, Grados C (2008) Mesoscale eddies off peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns. Prog Oceanogr 79(2–4):106–119

    Article  Google Scholar 

  • Chelton DB, Schlax MG, Samelson RM, de Szoeke RA (2007) Global observations of large oceanic eddies. Geophys Res Lett 34:L15606

  • Chelton DB, Gaube P, Schlax MG, Early JJ, Samelson RM (2011) The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science 334(6054):328–332

    Article  Google Scholar 

  • Chen G, Hou Y, Chu X (2011) Mesoscale eddies in the south china sea: Mean properties, spatiotemporal variability, and impact on thermohaline structure. J Geophys Res Oceans 116:C06018

  • Chen J, Gupta AK (2011) Parametric statistical change point analysis: with applications to genetics, medicine, and finance. Springer Science & Business Media, Berlin

    Google Scholar 

  • CRAN (2017) The comprehensive R archive network. https://cran.r-project.org/web/packages/ecp/index.html

  • Doglioli A, Blanke B, Speich S, Lapeyre G (2007) Tracking coherent structures in a regional ocean model with wavelet analysis: application to cape basin eddies. J Geophys Res Oceans 112:C05043

  • Goela PC, Cordeiro C, Danchenko S, Icely J, Cristina S, Newton A (2016) Time series analysis of data for sea surface temperature and upwelling components from the southwest coast of Portugal. J Mar Syst 163:12–22

    Article  Google Scholar 

  • Golaz JC, Caldwell PM, Van Roekel LP, Petersen MR, Tang Q, Wolfe JD, Abeshu G, Anantharaj V, Asay-Davis XS, Bader DC, Baldwin SA, Bisht G, Bogenschutz PA, Branstetter M, Brunke MA, Brus SR, Burrows SM, Cameron-Smith PJ, Donahue AS, Deakin M, Easter RC, Evans KJ, Feng Y, Flanner M, Foucar JG, Fyke JG, Griffin BM, Hannay C, Harrop BE, Hunke EC, Jacob RL, Jacobsen DW, Jeffery N, Jones PW, Keen ND, Klein SA, Larson VE, Leung LR, Li HY, Lin W, Lipscomb WH, Ma PL, Mahajan S, Maltrud ME, Mametjanov A, McClean JL, McCoy RB, Neale RB, Price SF, Qian Y, Rasch PJ, Reeves Eyre JJ, Riley WJ, Ringler TD, Roberts AF, Roesler EL, Salinger AG, Shaheen Z, Shi X, Singh B, Tang J, Taylor MA, Thornton PE, Turner AK, Veneziani M, Wan H, Wang H, Wang S, Williams DN, Wolfram PJ, Worley PH, Xie S, Yang Y, Yoon JH, Zelinka MD, Zender CS, Zeng X, Zhang C, Zhang K, Zhang Y, Zheng X, Zhou T, Zhu Q (2019) The DOE E3SM coupled model version 1: overview and evaluation at standard resolution. J Adv Model Earth Syst 11(ja):2089–2129. https://doi.org/10.1029/2018MS001603

    Article  Google Scholar 

  • Hurrell JW (1995) Decadal trends in the North Atlantic oscillation: regional temperatures and precipitation. Science 269(5224):676–679

    Article  Google Scholar 

  • Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) The North Atlantic oscillation: climatic significance and environmental impact. In: Washington DC American geophysical union geophysical monograph series, vol 134. https://doi.org/10.1029/GM134

    Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts (ISBN-13: 978-0987507112)

  • Hyun S, G’Sell M, Tibshirani RJ (2016) Exact post-selection inference for changepoint detection and other generalized lasso problems. arXiv preprint arXiv:160603552

  • Imbrie J, Berger A, Boyle E, Clemens S, Duffy A, Howard W, Kukla G, Kutzbach J, Martinson D, Mcintyre A (1993) On the structure and origin of major glaciation cycles 2. The 100,000-year cycle. Paleoceanography 8:699–735

    Article  Google Scholar 

  • Jones PD, Jonsson T, Wheeler D (1997) Extension to the North Atlantic oscillation using early instrumental pressure observations from gibraltar and south-west iceland. Int J Climatol 17(13):1433–1450

    Article  Google Scholar 

  • Jouzel J, Masson-Delmotte V et al (2007) Orbital and millennial Antarctic climate variability over the past 800,000 years. Science 317(5839):793–796. https://doi.org/10.1126/science.1141038, http://science.sciencemag.org/content/317/5839/793, http://science.sciencemag.org/content/317/5839/793.full.pdf

    Article  Google Scholar 

  • Lisiecki LE, Raymo ME (2005) A pliocene-pleistocene stack of 57 globally distributed benthic \(\delta \)18o records. Paleoceanography 20:PA1003

    Article  Google Scholar 

  • McManus JF, Francois R, Gherardi JM, Keigwin LD, Brown-Leger S (2004) Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes. Nature 428(6985):834

    Article  Google Scholar 

  • McWilliams JC (2008) The nature and consequences of oceanic eddies. In: Ocean modeling in an Eddying regime. Washington, DC, pp 5–15 (ISBN-13: 978-0875904429)

  • Milankovitch M (1941) Canon of insolation and the iceage problem. In: Koniglich Serbische Akademice Beograd Special Publication, vol 132 (ISBN: 9788617066190)

  • MPAS-Developers (2013) MPAS. http://mpas-dev.github.io/. Accessed 20 Jun 2016

  • Myers K, Lawrence E, Fugate M, Bowen CM, Ticknor L, Woodring J, Wendelberger J, Ahrens J (2016) Partitioning a large simulation as it runs. Technometrics 58(3):329–340

    Article  Google Scholar 

  • NAO (2019) North Atlantic oscillation. https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/nao.long.data

  • NCEI (2018) Antarctic ice cores revised 800kyr co2 data. https://www1.ncdc.noaa.gov/pub/data/paleo/icecore/antarctica/antarctica2015co2.xlss

  • NinoData (2018) Nino 3.4. https://www.esrl.noaa.gov/psd/data/correlation/nina34.data

  • Oceanic Nino Index (2018) Equatorial pacific sea surface temperatures. https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/

  • Petersen M, Williams S, Maltrud M, Hecht M, Hamann B (2013) A three-dimensional eddy census of a high-resolution global ocean simulation. J Geophys Res (Oceans) 118:1759–1774. https://doi.org/10.1002/jgrc.20155

    Article  Google Scholar 

  • Petersen MR, Williams SJ, Maltrud ME, Hecht MW, Hamann B (2013) A three-dimensional eddy census of a high-resolution global ocean simulation. J Geophys Res (Oceans) 118(4):1759–1774

    Article  Google Scholar 

  • Petersen MR, Jacobsen DW, Ringler TD, Hecht MW, Maltrud ME (2015) Evaluation of the arbitrary Lagrangian-Eulerian vertical coordinate method in the MPAS-Ocean model. Ocean Model 86:93–113. https://doi.org/10.1016/j.ocemod.2014.12.004, http://www.sciencedirect.com/science/article/pii/S1463500314001796

    Article  Google Scholar 

  • Petersen MR, Asay-Davis XS, Berres AS, Chen Q, Feige N, Hoffman MJ, Jacobsen DW, Jones PW, Maltrud ME, Price SF, Ringler TD, Streletz GJ, Turner AK, Van Roekel LP, Veneziani M, Wolfe JD, Wolfram PJ, Woodring JL (2019) An evaluation of the ocean and sea ice climate of e3sm using mpas and interannual core-ii forcing. J Adv Mode Earth Syst 11(5):1438–1458. https://doi.org/10.1029/2018MS001373

    Article  Google Scholar 

  • Quan J, Zhan W, Chen Y, Wang M, Wang J (2016) Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands. J Geophys Res Atmos 121(6):2638–2657

    Article  Google Scholar 

  • Ray BK, Tsay RS (2002) Bayesian methods for change-point detection in long-range dependent processes. J Time Ser Anal 23(6):687–705

    Article  Google Scholar 

  • Reeves J, Chen J, Wang XL, Lund R, Lu QQ (2007) A review and comparison of changepoint detection techniques for climate data. J Appl Meteorol Climatol 46(6):900–915

    Article  Google Scholar 

  • Ringler T, Petersen M, Higdon RL, Jacobsen D, Jones PW, Maltrud M (2013) A multi-resolution approach to global ocean modeling. Ocean Model 69(Supplement C):211–232. https://doi.org/10.1016/j.ocemod.2013.04.010, http://www.sciencedirect.com/science/article/pii/S1463500313000760

    Article  Google Scholar 

  • Samsel F, Petersen M, Geld T, Abram G, Wendelberger J, Ahrens J (2015) Colormaps that improve perception of high-resolution ocean data. In: Proceedings of the 33rd annual ACM conference extended abstracts on human factors in computing systems. ACM, New York, CHI EA ’15, pp 703–710. https://doi.org/10.1145/2702613.2702975

  • Sigman DM, Hain MP, Haug GH (2010) The polar ocean and glacial cycles in atmospheric CO2 concentration. Nature 466:47–55

    Article  Google Scholar 

  • Souza JMACD, De Boyer Montegut C, Le Traon PY (2011) Comparison between three implementations of automatic identification algorithms for the quantification and characterization of mesoscale eddies in the South Atlantic ocean. Ocean Sci 7(3):317–334

    Article  Google Scholar 

  • Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (2013) Climate change 2013: The physical science basis. Tech. rep., Cambridge University, p 1535

  • Talley LD (2011) Descriptive physical oceanography: an introduction. Academic Press, Amsterdam, pp 167–172

    Google Scholar 

  • Thomson RE, Emery WJ (2014) Data analysis methods in physical oceanography. Newnes, Massachusetts, USA, pp 505–546 (ISBN-13: 978-0123877826)

    Chapter  Google Scholar 

  • Tibshirani RJ et al (2014) Adaptive piecewise polynomial estimation via trend filtering. Ann Stat 42(1):285–323

    Article  Google Scholar 

  • Tomé A, Miranda P (2004) Piecewise linear fitting and trend changing points of climate parameters. Geophys Res Lett 31:L02207

  • Turton TL, Ware C, Samsel F, Rogers DH (2017) A crowdsourced approach to colormap assessment. In: Lawonn K, Smit N, Cunningham D (eds) EuroVis workshop on reproducibility, verification, and validation in visualization (EuroRV3). The Eurographics Association. https://doi.org/10.2312/eurorv3.20171106

  • Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114(1):106–115

    Article  Google Scholar 

  • Volkov DL, Lee T, Fu LL (2008) Eddy-induced meridional heat transport in the ocean. Geophys Res Lett 35:L20601

  • von Storch H, Zwiers FW (2002) Statistical analysis in climate research. Cambridge, United Kingdom, pp 197–199 (ISBN-13: 978-0521450713)

  • Wang C, Deser C, Yu JY, DiNezio P, Clement A (2017) El niño and southern oscillation (enso): a review. In: Coral reefs of the eastern tropical pacific. Springer, pp 85–106

  • Ware C, Turton TL, Bujack R, Samsel F, Shrivastava P, Rogers DH (2018) Measuring and modeling the feature detection threshold functions of colormaps. IIEEE Trans Vis Comput Graph 25(9):2777–2790

    Article  Google Scholar 

  • Williams S, Petersen M, Bremer PT, Hecht M, Pascucci V, Ahrens J, Hlawitschka M, Hamann B (2011) Adaptive extraction and quantification of geophysical vortices. IEEE Trans Vis Comput Graph 17(12):2088–2095

    Article  Google Scholar 

  • Woodring J, Petersen M, Schmei\(\beta \)er A, Patchett J, Ahrens J, Hagen H, (2015) In situ eddy analysis in a high-resolution ocean climate model. IEEE Trans Vis Comput Graph 22(1):857–866. https://doi.org/10.1109/TVCG.2015.2467411

    Article  Google Scholar 

  • Wunsch C (2015) Modern observational physical oceanography: understanding the global ocean. Princeton University Press, Princeton. Appendix A2

Download references

Acknowledgements

We would like to thank the DOE, NNSA and ASC for funding this work at Los Alamos National Laboratory (LANL). M. Petersen was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. MPAS-Ocean simulations were conducted at LANL Institutional Computing, under US DOE NNSA (DE-AC52-06NA25396). We would also like to thank Terece Turton for valuable feedback.

Author information

Authors and Affiliations

  1. University of California, Davis, Davis, USA

    D. Banesh & B. Hamann

  2. Los Alamos National Laboratory, New Mexico, USA

    D. Banesh, M. Petersen, J. Wendelberger & J. Ahrens

Authors
  1. D. Banesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Wendelberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Ahrens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. B. Hamann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Banesh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of a Topical Collection in Environmental Earth Sciences on “Visual Data Exploration”, guest edited by Karsten Rink, Roxana Bujack, Stefan Jänicke, and Dirk Zeckzer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banesh, D., Petersen, M., Wendelberger, J. et al. Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data. Environ Earth Sci 78, 623 (2019). https://doi.org/10.1007/s12665-019-8636-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-019-8636-y

Keywords

Search

Navigation