Dahoui, M., Bormann, N., and Isaksen, L.: Automatic checking of observations at ECMWF, ECMWF Newsletter No. 140, 21–24,
https://doi.org/10.21957/kuwqjp5y, 2014.
a,
b
Dahoui, M., Isaksen, L., and Radnoti, G.: Assessing the impact of observations using observation-minus-forecast residuals, ECMWF Newsletter No. 152, 27–31,
https://doi.org/10.21957/51j3sa, 2017.
a
Dahoui, M., Bormann, N., Isaksen, L., and McNally, T.: Recent developments in the automatic checking of Earth system observations, ECMWF Newsletter No. 162, 27–31, Winter 2019/20,
https://doi.org/10.21957/9tys2md61a, 2020.
a
Dakos, V., Carpenter, S., Brock, W., Ellison, A., Guttal, V., Ives, A., Kefi, S., Livina, V., Seekell, D., van Nes, E., and Scheffer, M.: Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data, PLoS ONE, 7, e41010,
https://doi.org/10.1371/journal.pone.0041010, 2012.
a
Domonkos, P., Guijarro, J. A., Venema, V., Brunet, M., and Sigro, J.: Efficiency of Time Series Homogenization: Method Comparison with 12 Monthly Temperature Test Datasets, J. Climate, 34, 2877–2891,
https://doi.org/10.1175/JCLI-D-20-0611.1, 2021.
a
Dunn, R.: HadISD version 3: monthly updates, Hadley Centre Technical Note, N103,
https://library.metoffice.gov.uk/Portal/DownloadImageFile.ashx?objectId=1110&ownerType=0&ownerId=635758 (last access: 17 November 2025), 2019. a
Dunn, R. J. H., Willett, K. M., Thorne, P. W., Woolley, E. V., Durre, I., Dai, A., Parker, D. E., and Vose, R. S.: HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011, Clim. Past, 8, 1649–1679,
https://doi.org/10.5194/cp-8-1649-2012, 2012.
a
Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473–491,
https://doi.org/10.5194/gi-5-473-2016, 2016.
a
Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Difference, Little Brown, ISBN 10 0316346624, 2000. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
a
Killick, R., Jolliffe, I., and Willett, K.: Benchmarking the performance of homogenization algorithms on synthetic daily temperature data, International Journal of Climatology, 42, 3968–3986,
https://doi.org/10.1002/joc.7462, 2022.
a
Lenton, T., Held, H., Kriegler, E., Hall, J., Lucht, W., Rahmstorf, S., and Schellnhuber, H.: Tipping elements in the Earth's climate system, Proceedings of the National Academy of Sciences USA, 105, 1786–1793,
https://doi.org/10.1073/pnas.0705414105, 2008.
a,
b,
c
Livina, V. N., Kwasniok, F., and Lenton, T. M.: Potential analysis reveals changing number of climate states during the last 60 kyr, Clim. Past, 6, 77–82,
https://doi.org/10.5194/cp-6-77-2010, 2010.
a,
b
Livina, V., Kwasniok, F., Lohmann, G., Kantelhardt, J., and Lenton, T.: Changing climate states and stability: from Pliocene to present, Climate Dymamics, 37, 2437–2453,
https://doi.org/10.1007/s00382-010-0980-2, 2011.
a,
b
Livina, V., Ditlevsen, P., and Lenton, T.: An independent test of methods of detecting system states and bifurcations in time-series data, Physica A, 391, 485–496,
https://doi.org/10.1016/j.physa.2011.08.025, 2012.
a,
b
Livina, V., Lohmann, G., Mudelsee, M., and Lenton, T.: Forecasting the underlying potential governing the time series of a dynamical system, Physica A, 392, 3891–3902,
https://doi.org/10.1016/j.physa.2013.04.036, 2013.
a,
b
Livina, V., Barton, E., and Forbes, A.: Tipping point analysis of the NPL footbridge, Journal of Civil Structural Health Monitoring, 4, 91–98,
https://doi.org/10.1007/s13349-013-0066-z, 2014.
a
Livina, V., Lewis, A., and Wickham, M.: Tipping point analysis of electrical resistance data with early warning signals of failure for predictive maintenance, Journal of Electronic Testing, 36, 569–576,
https://doi.org/10.1007/s10836-020-05899-w, 2020.
a
Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanley, H., and Goldberger, A.: Mosaic organization of DNA nucleotides, Phys. Rev. E, 49, 1685–1689,
https://doi.org/10.1103/PhysRevE.49.1685, 1994.
a
Peterson, T., Easterling, D., Karl, T., Groisman, P., Nicholls, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, Forland, E., Hanssen-Bauer, I., Alexandersson, H., Jones, P., and Parker, D.: Homogeneity adjustments of in situ atmospheric climate data: a review, Int. J. Climatol., 18, 1493–1517,
https://doi.org/10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T, 1998.
a
Reeves, S., Chen, J., Wang, J., Lund, X., and Lu, Q.: A review and comparison of changepoint detection techniques for climate data, J. Appl. Meteorol. Clim., 46, 900–915,
https://doi.org/10.1175/JAM2493.1, 2007.
a
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent Developments and Partnerships, Bulletin of the American Meteorological Society, 92, 704–708,
https://doi.org/10.1175/2011BAMS3015.1, 2011.
a
Smith, T., Zotta, R.-M., Boulton, C. A., Lenton, T. M., Dorigo, W., and Boers, N.: Reliability of resilience estimation based on multi-instrument time series, Earth Syst. Dynam., 14, 173–183,
https://doi.org/10.5194/esd-14-173-2023, 2023.
a
Todling, R., Semane, N., Anthes, R., and Healy, S.: The Relationship Between Two Methods for Estimating Uncertainties in Data Assimilation, Quarterly Journal of the Royal Meteorological Society, 148, 2942–2954,
https://doi.org/10.1002/qj.4343, 2022.
a
Vaz Martins, T., Livina, V. N., Majtey, A., and Toral, R.: Resonance induced by repulsive interactions in a model of globally coupled bistable systems, Phys. Rev. E, 81, 041103,
https://doi.org/10.1103/PhysRevE.81.041103, 2010.
a
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data, Clim. Past, 8, 89–115,
https://doi.org/10.5194/cp-8-89-2012, 2012.
a
Waller, J.: Estimating the full observation error covariance matrix, Tackling Technical Challenges in Land Data Assimilation, AIMES Group (Analysis, Integration, and Modelling of the Earth System), 14–16 June 2021,
https://aimesproject.org/wp-content/uploads/2021/08/Waller_AIMES_Workshop.pdf (last access: 17 November 2025), 2021. a
Waller, J., Dancea, S., and Nichols, N.: Theoretical insight into diagnosing observation error correlations using observation-minus-background andobservation-minus-analysis statistics, Q. J. R. Meteorol. Soc., 142, 418–431,
https://doi.org/10.1002/qj.2661, 2015.
a
Willett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983–2006,
https://doi.org/10.5194/cp-10-1983-2014, 2014.
a
Yang, W., Reis, M., Borodin, V., Juge, M., and Roussy, A.: An interpretable unsupervised Bayesian network model for fault detection and diagnosis, Control Engineering Practice, 127, 105304,
https://doi.org/10.1016/j.conengprac.2022.105304, 2022.
a
Zhao, K., Wulder, M., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X., and Brown, M.: Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm, Remote Sensing of Environment, 232, 111181,
https://doi.org/10.1016/j.rse.2019.04.034, 2019.
a,
b
