Articles | Volume 14, issue 5
https://doi.org/10.5194/esd-14-955-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-955-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Weather persistence on sub-seasonal to seasonal timescales: a methodological review
Alexandre Tuel
CORRESPONDING AUTHOR
Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Olivia Martius
Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Mobiliar Lab for Natural Risks, University of Bern, Bern, Switzerland
Related authors
Alexandre Tuel and Olivia Martius
Weather Clim. Dynam., 5, 263–292, https://doi.org/10.5194/wcd-5-263-2024, https://doi.org/10.5194/wcd-5-263-2024, 2024
Short summary
Short summary
Warm and cold spells often have damaging consequences for agriculture, power demand, human health and infrastructure, especially when they occur over large areas and persist for a week or more. Here, we split the Northern Hemisphere extratropics into coherent regions where 3-week warm and cold spells in winter and summer are associated with the same large-scale circulation patterns. To understand their physical drivers, we analyse the associated circulation and temperature budget anomalies.
This article is included in the Encyclopedia of Geosciences
Pauline Rivoire, Olivia Martius, Philippe Naveau, and Alexandre Tuel
Nat. Hazards Earth Syst. Sci., 23, 2857–2871, https://doi.org/10.5194/nhess-23-2857-2023, https://doi.org/10.5194/nhess-23-2857-2023, 2023
Short summary
Short summary
Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. In this article, we assess the capacity of the precipitation forecast provided by ECMWF to predict heavy precipitation events on a subseasonal-to-seasonal (S2S) timescale over Europe. We find that the forecast skill of such events is generally higher in winter than in summer.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
Short summary
Short summary
River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel, Nabil El Moçayd, Moulay Driss Hasnaoui, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 26, 571–588, https://doi.org/10.5194/hess-26-571-2022, https://doi.org/10.5194/hess-26-571-2022, 2022
Short summary
Short summary
Snowmelt in the High Atlas is critical for irrigation in Morocco but is threatened by climate change. We assess future trends in High Atlas snowpack by modelling it under historical and future climate scenarios and estimate their impact on runoff. We find that the combined warming and drying will result in a roughly 80 % decline in snowpack, a 5 %–30 % decrease in runoff efficiency and 50 %–60 % decline in runoff under a business-as-usual scenario.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel and Olivia Martius
Nat. Hazards Earth Syst. Sci., 21, 2949–2972, https://doi.org/10.5194/nhess-21-2949-2021, https://doi.org/10.5194/nhess-21-2949-2021, 2021
Short summary
Short summary
Extreme river discharge may be triggered by large accumulations of precipitation over short time periods, which can result from the successive occurrence of extreme-precipitation events. We find a distinct spatiotemporal pattern in the temporal clustering behavior of precipitation extremes over Switzerland, with clustering occurring on the northern side of the Alps in winter and on their southern side in fall. Clusters tend to be followed by extreme discharge, particularly in the southern Alps.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel and Olivia Martius
Weather Clim. Dynam., 5, 263–292, https://doi.org/10.5194/wcd-5-263-2024, https://doi.org/10.5194/wcd-5-263-2024, 2024
Short summary
Short summary
Warm and cold spells often have damaging consequences for agriculture, power demand, human health and infrastructure, especially when they occur over large areas and persist for a week or more. Here, we split the Northern Hemisphere extratropics into coherent regions where 3-week warm and cold spells in winter and summer are associated with the same large-scale circulation patterns. To understand their physical drivers, we analyse the associated circulation and temperature budget anomalies.
This article is included in the Encyclopedia of Geosciences
Pauline Rivoire, Olivia Martius, Philippe Naveau, and Alexandre Tuel
Nat. Hazards Earth Syst. Sci., 23, 2857–2871, https://doi.org/10.5194/nhess-23-2857-2023, https://doi.org/10.5194/nhess-23-2857-2023, 2023
Short summary
Short summary
Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. In this article, we assess the capacity of the precipitation forecast provided by ECMWF to predict heavy precipitation events on a subseasonal-to-seasonal (S2S) timescale over Europe. We find that the forecast skill of such events is generally higher in winter than in summer.
This article is included in the Encyclopedia of Geosciences
Jérôme Kopp, Agostino Manzato, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 16, 3487–3503, https://doi.org/10.5194/amt-16-3487-2023, https://doi.org/10.5194/amt-16-3487-2023, 2023
Short summary
Short summary
We present the first study of extended field observations made by a network of 80 automatic hail sensors from Switzerland. The sensors record the exact timing of hailstone impacts, providing valuable information about the local duration of hailfall. We found that the majority of hailfalls lasts just a few minutes and that most hailstones, including the largest, fall during a first phase of high hailstone density, while a few remaining and smaller hailstones fall in a second low-density phase.
This article is included in the Encyclopedia of Geosciences
S. Mubashshir Ali, Matthias Röthlisberger, Tess Parker, Kai Kornhuber, and Olivia Martius
Weather Clim. Dynam., 3, 1139–1156, https://doi.org/10.5194/wcd-3-1139-2022, https://doi.org/10.5194/wcd-3-1139-2022, 2022
Short summary
Short summary
Persistent weather can lead to extreme weather conditions. One such atmospheric flow pattern, termed recurrent Rossby wave packets (RRWPs), has been shown to increase persistent weather in the Northern Hemisphere. Here, we show that RRWPs are also an important feature in the Southern Hemisphere. We evaluate the role of RRWPs during south-eastern Australian heatwaves and find that they help to persist the heatwaves by forming upper-level high-pressure systems over south-eastern Australia.
This article is included in the Encyclopedia of Geosciences
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
Short summary
Short summary
The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
Short summary
Short summary
River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
This article is included in the Encyclopedia of Geosciences
Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann
EGUsphere, https://doi.org/10.5194/egusphere-2022-92, https://doi.org/10.5194/egusphere-2022-92, 2022
Preprint archived
Short summary
Short summary
We assess the performance of various fire weather indices to predict wildfire occurrence in Northern Switzerland. We find that indices responding readily to weather changes have the best performance during spring; in the summer and autumn seasons, indices that describe persistent hot and dry conditions perform best. We demonstrate that a logistic regression model trained on local historical fire activity can outperform existing fire weather indices.
This article is included in the Encyclopedia of Geosciences
Lisa-Ann Kautz, Olivia Martius, Stephan Pfahl, Joaquim G. Pinto, Alexandre M. Ramos, Pedro M. Sousa, and Tim Woollings
Weather Clim. Dynam., 3, 305–336, https://doi.org/10.5194/wcd-3-305-2022, https://doi.org/10.5194/wcd-3-305-2022, 2022
Short summary
Short summary
Atmospheric blocking is associated with stationary, self-sustaining and long-lasting high-pressure systems. They can cause or at least influence surface weather extremes, such as heat waves, cold spells, heavy precipitation events, droughts or wind extremes. The location of the blocking determines where and what type of extreme event will occur. These relationships are also important for weather prediction and may change due to global warming.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel, Nabil El Moçayd, Moulay Driss Hasnaoui, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 26, 571–588, https://doi.org/10.5194/hess-26-571-2022, https://doi.org/10.5194/hess-26-571-2022, 2022
Short summary
Short summary
Snowmelt in the High Atlas is critical for irrigation in Morocco but is threatened by climate change. We assess future trends in High Atlas snowpack by modelling it under historical and future climate scenarios and estimate their impact on runoff. We find that the combined warming and drying will result in a roughly 80 % decline in snowpack, a 5 %–30 % decrease in runoff efficiency and 50 %–60 % decline in runoff under a business-as-usual scenario.
This article is included in the Encyclopedia of Geosciences
Hélène Barras, Olivia Martius, Luca Nisi, Katharina Schroeer, Alessandro Hering, and Urs Germann
Weather Clim. Dynam., 2, 1167–1185, https://doi.org/10.5194/wcd-2-1167-2021, https://doi.org/10.5194/wcd-2-1167-2021, 2021
Short summary
Short summary
In Switzerland hail may occur several days in a row. Such multi-day hail events may cause significant damage, and understanding and forecasting these events is important. Using reanalysis data we show that weather systems over Europe move slower before and during multi-day hail events compared to single hail days. Surface temperatures are typically warmer and the air more humid over Switzerland and winds are slower on multi-day hail clusters. These results may be used for hail forecasting.
This article is included in the Encyclopedia of Geosciences
Timothy H. Raupach, Andrey Martynov, Luca Nisi, Alessandro Hering, Yannick Barton, and Olivia Martius
Geosci. Model Dev., 14, 6495–6514, https://doi.org/10.5194/gmd-14-6495-2021, https://doi.org/10.5194/gmd-14-6495-2021, 2021
Short summary
Short summary
When simulated thunderstorms are compared to observations or other simulations, a match between overall storm properties is often more important than exact matches to individual storms. We tested a comparison method that uses a thunderstorm tracking algorithm to characterise simulated storms. For May 2018 in Switzerland, the method produced reasonable matches to independent observations for most storm properties, showing its feasibility for summarising simulated storms over mountainous terrain.
This article is included in the Encyclopedia of Geosciences
Alexandre Tuel and Olivia Martius
Nat. Hazards Earth Syst. Sci., 21, 2949–2972, https://doi.org/10.5194/nhess-21-2949-2021, https://doi.org/10.5194/nhess-21-2949-2021, 2021
Short summary
Short summary
Extreme river discharge may be triggered by large accumulations of precipitation over short time periods, which can result from the successive occurrence of extreme-precipitation events. We find a distinct spatiotemporal pattern in the temporal clustering behavior of precipitation extremes over Switzerland, with clustering occurring on the northern side of the Alps in winter and on their southern side in fall. Clusters tend to be followed by extreme discharge, particularly in the southern Alps.
This article is included in the Encyclopedia of Geosciences
Jérôme Kopp, Pauline Rivoire, S. Mubashshir Ali, Yannick Barton, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 5153–5174, https://doi.org/10.5194/hess-25-5153-2021, https://doi.org/10.5194/hess-25-5153-2021, 2021
Short summary
Short summary
Episodes of extreme rainfall events happening in close temporal succession can lead to floods with dramatic impacts. We developed a novel method to individually identify those episodes and deduced the regions where they occur frequently and where their impact is substantial. Those regions are the east and northeast of the Asian continent, central Canada and the south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and north of Argentina and south of Bolivia.
This article is included in the Encyclopedia of Geosciences
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3577–3594, https://doi.org/10.5194/hess-25-3577-2021, https://doi.org/10.5194/hess-25-3577-2021, 2021
Short summary
Short summary
This study analyses changes in magnitude, frequency, and seasonality of moderate low and high flows for 93 catchments in Switzerland. In lower-lying catchments (below 1500 m a.s.l.), moderate low-flow magnitude (frequency) will decrease (increase). In Alpine catchments (above 1500 m a.s.l.), moderate low-flow magnitude (frequency) will increase (decrease). Moderate high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments.
This article is included in the Encyclopedia of Geosciences
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3071–3086, https://doi.org/10.5194/hess-25-3071-2021, https://doi.org/10.5194/hess-25-3071-2021, 2021
Short summary
Short summary
Runoff regimes in Switzerland will change significantly under climate change. Projected changes are strongly elevation dependent with earlier time of emergence and stronger changes in high-elevation catchments where snowmelt and glacier melt play an important role. The magnitude of change and the climate model agreement on the sign increase with increasing global mean temperatures and stronger emission scenarios. This amplification highlights the importance of climate change mitigation.
This article is included in the Encyclopedia of Geosciences
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
Short summary
Short summary
Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
This article is included in the Encyclopedia of Geosciences
Peter Stucki, Moritz Bandhauer, Ulla Heikkilä, Ole Rössler, Massimiliano Zappa, Lucas Pfister, Melanie Salvisberg, Paul Froidevaux, Olivia Martius, Luca Panziera, and Stefan Brönnimann
Nat. Hazards Earth Syst. Sci., 18, 2717–2739, https://doi.org/10.5194/nhess-18-2717-2018, https://doi.org/10.5194/nhess-18-2717-2018, 2018
Short summary
Short summary
A catastrophic flood south of the Alps in 1868 is assessed using documents and the earliest example of high-resolution weather simulation. Simulated weather dynamics agree well with observations and damage reports. Simulated peak water levels are biased. Low forest cover did not cause the flood, but such a paradigm was used to justify afforestation. Supported by historical methods, such numerical simulations allow weather events from past centuries to be used for modern hazard and risk analyses.
This article is included in the Encyclopedia of Geosciences
Juan José Gómez-Navarro, Christoph C. Raible, Denica Bozhinova, Olivia Martius, Juan Andrés García Valero, and Juan Pedro Montávez
Geosci. Model Dev., 11, 2231–2247, https://doi.org/10.5194/gmd-11-2231-2018, https://doi.org/10.5194/gmd-11-2231-2018, 2018
Short summary
Short summary
We carry out and compare two high-resolution simulations of the Alpine region in the period 1979–2005. We aim to improve the understanding of the local mechanisms leading to extreme events in this complex region. We compare both simulations to precipitation observations to assess the model performance, and attribute major biases to either model or boundary conditions. Further, we develop a new bias correction technique to remove systematic errors in simulated precipitation for impact studies.
This article is included in the Encyclopedia of Geosciences
Related subject area
Topics: Atmosphere | Interactions: Ocean/atmosphere interactions | Methods: Mathematical geosciences and physical models
Nonlinear time series analysis of coastal temperatures and El Niño–Southern Oscillation events in the eastern South Pacific
Berenice Rojo-Garibaldi, Manuel Contreras-López, Simone Giannerini, David Alberto Salas-de-León, Verónica Vázquez-Guerra, and Julyan H. E. Cartwright
Earth Syst. Dynam., 14, 1125–1164, https://doi.org/10.5194/esd-14-1125-2023, https://doi.org/10.5194/esd-14-1125-2023, 2023
Short summary
Short summary
Our study focuses on Chile, which is affected by the prevailing ocean currents, upwellings and El Niño; these generate the weather in Chile. That is why we conducted a study on the dynamics of this system using spectral and nonlinear analysis techniques. We obtained periodicities related to internal and external forcing; we find that the dynamics is not chaotic but nonlinear. Finally, the northern part presents a strong correlation between ONI and LLE due to regional characteristics.
This article is included in the Encyclopedia of Geosciences
Cited articles
Adeniji, A. E., Olusola, O. I., and Njah, A. N.: Comparative study of chaotic
features in hourly wind speed using recurrence quantification analysis, Aip.
Adv., 8, 025102, https://doi.org/10.1063/1.4998674, 2018. a
Ali, S. K., Aydam, Z. M., and Rashed, B. M.: Similarity metrics for
classification: A Review, Iop. Conf. Ser.-Mat. Sci., 928, 032052,
https://doi.org/10.1088/1757-899X/928/3/032052, 2020. a
Ali, S. M., Röthlisberger, M., Parker, T., Kornhuber, K., and Martius, O.: Recurrent Rossby waves and south-eastern Australian heatwaves, Weather Clim. Dynam., 3, 1139–1156, https://doi.org/10.5194/wcd-3-1139-2022, 2022. a
Altmann, E. G. and Kantz, H.: Recurrence time analysis, long-term correlations,
and extreme events, Phys. Rev. E, 71, 056106,
https://doi.org/10.1103/PhysRevE.71.056106, 2005. a, b
Ault, T. R., Cole, J. E., Overpeck, J. T., Pederson, G. T., and Meko, D. M.:
Assessing the Risk of Persistent Drought Using Climate Model Simulations and
Paleoclimate Data, J. Clim., 27, 7529–7549,
https://doi.org/10.1175/JCLI-D-12-00282.1, 2014. a
Barnes, E. A. and Hartmann, D. L.: Dynamical Feedbacks and the Persistence of
the NAO, J. Atmos. Sci., 67, 851–865, https://doi.org/10.1175/2009JAS3193.1, 2010. a
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and
Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather
Rev., 115, 1083–1126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2,
1987. a, b
Barton, Y., Giannakaki, P., von Waldow, H., Chevalier, C., Pfahl, S., and
Martius, O.: Clustering of Regional-Scale Extreme Precipitation Events in
Southern Switzerland, Mon. Weather Rev., 144, 347–369,
https://doi.org/10.1175/MWR-D-15-0205.1, 2016. a, b, c, d
Barton, Y., Rivoire, P., Koh, J., Ali, M. S., Kopp, J., and Martius, O.: On
the temporal clustering of European extreme precipitation events and its
relationship to persistent and transient large-scale atmospheric drivers,
Weather Clim. Extrem., 38, 100518, https://doi.org/10.1016/j.wace.2022.100518,
2022. a, b, c, d
Baur, F.: Extended-Range Weather Forecasting, American
Meteorological Society, Boston, MA, 814–833, https://doi.org/10.1007/978-1-940033-70-9_66, 1951. a
Beran, J.: Statistics for Long-Memory Processes, Routledge, 315 pp.,
https://doi.org/10.1201/9780203738481, 2017. a
Berkovic, S. and Raveh-Rubin, S.: Persistent warm and dry extremes over the
eastern Mediterranean during winter: The role of North Atlantic blocking and
central Mediterranean cyclones, Q. J. Roy. Meteor. Soc., 148, 2384–2409,
https://doi.org/10.1002/qj.4308, 2022. a, b
Besio, G., Briganti, R., Romano, A., Mentaschi, L., and De Girolamo, P.: Time clustering of wave storms in the Mediterranean Sea, Nat. Hazards Earth Syst. Sci., 17, 505–514, https://doi.org/10.5194/nhess-17-505-2017, 2017. a
Bevacqua, E., Zappa, G., and Shepherd, T. G.: Shorter cyclone clusters
modulate changes in European wintertime precipitation extremes, Environ. Res.
Lett., 15, 124005, https://doi.org/10.1088/1748-9326/abbde7, 2020. a, b, c, d
Black, E., Blackburn, M., Harrison, G., Hoskins, B., and Methven, J.: Factors
contributing to the summer 2003 European heatwave, Weather, 59, 217–223,
https://doi.org/10.1256/wea.74.04, 2004. a, b
Blanchet, J., Stalla, S., and Creutin, J.-D.: Analogy of multiday sequences of
atmospheric circulation favoring large rainfall accumulation over the French
Alps, Atmos. Sci. Lett., 19, e809, https://doi.org/10.1002/asl.809, 2018. a
Blender, R., Raible, C. C., and Lunkeit, F.: Non-exponential return time
distributions for vorticity extremes explained by fractional Poisson
processes, Q. J. Roy. Meteor. Soc., 141, 249–257, https://doi.org/10.1002/qj.2354, 2015. a
Bloomberg: Extreme Heat to Persist in India for Third Straight Month,
https://www.bloomberg.com/ (last access: 8 September 2023),
2022. a
Bray, M. T. and Cavallo, S. M.: Characteristics of long-track tropopause polar
vortices, Weather Clim. Dynam., 3, 251–278,
https://doi.org/10.5194/wcd-3-251-2022, 2022. a
Bunde, A., Havlin, S., Koscielny-Bunde, E., and Schellnhuber, H. J.:
Atmospheric Persistence Analysis: Novel Approaches and Applications, pp.
170–191, Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-56257-0_5, 2002. a
Bunde, A., Büntgen, U., Ludescher, J., Luterbacher, J., and von Storch, H.: Is
there memory in precipitation?, Nat. Clim. Change, 3, 174–175,
https://doi.org/10.1038/nclimate1830, 2013. a, b, c
Büeler, D., Ferranti, L., Magnusson, L., Quinting, J. F., and Grams, C. M.:
Year-round sub-seasonal forecast skill for Atlantic–European weather
regimes, Q. J. Roy. Meteor. Soc., 147, 4283–4309, https://doi.org/10.1002/qj.4178, 2021. a
Cassou, C., Terray, L., and Phillips, A. S.: Tropical Atlantic Influence on
European Heat Waves, J. Clim., 18, 2805–2811, https://doi.org/10.1175/JCLI3506.1,
2005. a
Chapman, C. C., Monselesan, D. P., Risbey, J. S., Feng, M., and Sloyan, B. M.:
A large-scale view of marine heatwaves revealed by archetype analysis, Nat.
Commun., 13, 7843, https://doi.org/10.1038/s41467-022-35493-x, 2022. a
Charney, J. G. and DeVore, J. G.: Multiple Flow Equilibria in the Atmosphere
and Blocking, J. Atmos. Sci, 36, 1205–1216,
https://doi.org/10.1175/1520-0469(1979)036<1205:MFEITA>2.0.CO;2, 1979. a
Corral, A.: Scaling in the timing of extreme events, Chaos Soliton Fract., 74,
99–112, https://doi.org/10.1016/j.chaos.2015.01.011, 2015. a
Cox, D. and Isham, V.: Point Processes, Routledge, 188 pp.,
https://doi.org/10.1201/9780203743034, 1980. a
De las Nieves López García, M. and Requena, J. P. R.: Different
methodologies and uses of the Hurst exponent in econophysics, Estud. Econ.,
37, 96–108, 2019. a
De Luca, P., Harpham, C., Wilby, R. L., Hillier, J. K., Franzke, C. L. E.,
and Leckebusch, G. C.: Past and Projected Weather Pattern Persistence with
Associated Multi-Hazards in the British Isles, Atmosphere, 10, 577,
https://doi.org/10.3390/atmos10100577, 2019. a, b
Degenhardt, L. and Ólafsson, H.: Persistence of observed air
temperatures in Iceland, Int. J. Climatol., 39, 1262–1275,
https://doi.org/10.1002/joc.5875, 2019. a
Demuzere, M., Kassomenos, P., and Philipp, A.: The COST733 circulation type
classification software: an example for surface ozone concentrations in
Central Europe, Theor. Appl. Climatol., 105, 143–166,
https://doi.org/10.1007/s00704-010-0378-4, 2011. a
Deni, S. M., Jemain, A. A., and Ibrahim, K.: The best probability models for
dry and wet spells in Peninsular Malaysia during monsoon seasons, Int. J.
Climatol., 30, 1194–1205, https://doi.org/10.1002/joc.1972, 2010. a
Desgraupes, B.: clusterCrit: An R Package for Computing Clustering Quality
Indices, R
package version 1.2.8, https://CRAN.R-project.org/package=clusterCrit (last access: 8 September 2023), 2018. a
Di Capua, G., Sparrow, S., Kornhuber, K., Rousi, E., Osprey, S., Wallom, D.,
van den Hurk, B., and Coumou, D.: Drivers behind the summer 2010 wave train
leading to Russian heatwave and Pakistan flooding, npj Clim.
Atmos. Sci., 4, 55, https://doi.org/10.1038/s41612-021-00211-9, 2021. a, b
Di Lorenzo, E. and Mantua, N.: Multi-year persistence of the 2014/15 North
Pacific marine heatwave, Nat. Clim. Change, 6, 1042–1047,
https://doi.org/10.1038/nclimate3082, 2016. a
Dixon, P. M.: Ripley's K Function, in: Wiley StatsRef: Statistics Reference
Online, John Wiley & Sons, Ltd, Chichester, UK,
https://doi.org/10.1002/9781118445112.stat07751, 2014. a
Dole, R. M. and Gordon, N. D.: Persistent Anomalies of the Extratropical
Northern Hemisphere Wintertime Circulation: Geographical Distribution and
Regional Persistence Characteristics, Mon. Weather Rev., 111, 1567–1586,
https://doi.org/10.1175/1520-0493(1983)111<1567:PAOTEN>2.0.CO;2, 1983. a, b, c
Domeisen, D. I., White, C. J., Afargan-Gerstman, H., Muñoz, Á. G.,
Janiga, M. A., Vitart, F., Wulff, C. O., Antoine, S., Ardilouze, C.,
Batté, L., Bloomfield, H. C., Brayshaw, D. J., Camargo, S. J.,
Charlton-Pérez, A., Collins, D., Cowan, T., del Mar Chaves, M.,
Ferranti, L., Gómez, R., González, P. L., González
Romero, C., Infanti, J. M., Karozis, S., Kim, H., Kolstad, E. W., LaJoie,
E., Lledó, L., Magnusson, L., Malguzzi, P., Manrique-Suñén,
A., Mastrangelo, D., Materia, S., Medina, H., Palma, L., Pineda, L. E.,
Sfetsos, A., Son, S.-W., Soret, A., Strazzo, S., and Tian, D.: Advances in
the subseasonal prediction of extreme events: Relevant case studies across
the globe, B. Am. Meteorol. Soc.,
E1473–E1501, https://doi.org/10.1175/BAMS-D-20-0221.1, 2022. a
Drouard, M. and Woollings, T.: Contrasting Mechanisms of Summer Blocking Over
Western Eurasia, Geophys. Res. Lett., 45, 12,040–12,048,
https://doi.org/10.1029/2018GL079894, 2018. a, b, c
Du, H., Alexander, L. V., Donat, M. G., Lippmann, T., Srivastava, A., Salinger,
J., Kruger, A., Choi, G., He, H. S., Fujibe, F., Rusticucci, M.,
Nandintsetseg, B., Manzanas, R., Rehman, S., Abbas, F., Zhai, P., Yabi, I.,
Stambaugh, M. C., Wang, S., Batbold, A., Oliveira, P. T., Adrees, M., Hou,
W., Zong, S., Santos e Silva, C. M., Lucio, P. S., and Wu, Z.:
Precipitation From Persistent Extremes is Increasing in Most Regions and
Globally, Geophys. Res. Lett., 46, 6041–6049, https://doi.org/10.1029/2019GL081898,
2019. a
Du, H., Donat, M. G., Zong, S., Alexander, L. V., Manzanas, R., Kruger, A.,
Choi, G., Salinger, J., He, H. S., Li, M.-H., Fujibe, F., Nandintsetseg, B.,
Rehman, S., Abbas, F., Rusticucci, M., Srivastava, A., Zhai, P., Lippmann,
T., Yabi, I., Stambaugh, M. C., Wang, S., Batbold, A., de Oliveira, P. T.,
Adrees, M., Hou, W., e Silva, C. M. S., Lucio, P. S., and Wu, Z.: Extreme
Precipitation on Consecutive Days Occurs More Often in a Warming Climate, B.
Am. Meteorol. Soc., 103, E1130–E1145, https://doi.org/10.1175/BAMS-D-21-0140.1, 2022. a
Economou, T., Stephenson, D. B., Pinto, J. G., Shaffrey, L. C., and Zappa, G.:
Serial clustering of extratropical cyclones in a multi-model ensemble of
historical and future simulations, Q. J. Roy. Meteor. Soc., 141, 3076–3087,
https://doi.org/10.1002/qj.2591, 2015. a
Eichner, J. F., Koscielny-Bunde, E., Bunde, A., Havlin, S., and Schellnhuber,
H.-J.: Power-law persistence and trends in the atmosphere: A detailed study
of long temperature records, Phys. Rev. E, 68, 046133,
https://doi.org/10.1103/PhysRevE.68.046133, 2003. a
Eichner, J. F., Kantelhardt, J. W., Bunde, A., and Havlin, S.: Statistics of
return intervals in long-term correlated records, Phys. Rev. E, 75, 011128,
https://doi.org/10.1103/PhysRevE.75.011128, 2007. a
Faranda, D., Messori, G., Alvarez-Castro, M. C., and Yiou, P.: Dynamical
properties and extremes of Northern Hemisphere climate fields over the past
60 years, Nonlinear Proc. Geophys., 24, 713–725, https://doi.org/10.5194/npg-24-713-2017,
2017a. a, b, c, d
Faranda, D., Alvarez-Castro, M. C., Messori, G., Rodrigues, D., and Yiou, P.:
The hammam effect or how a warm ocean enhances large scale atmospheric
predictability, Nat. Commun., 10, 1316, https://doi.org/10.1038/s41467-019-09305-8,
2019. a
Ferro, C. A. T. and Segers, J.: Inference for clusters of extreme values,
J. Roy. Stat. Soc. Ser. B,
65, 545–556, https://doi.org/10.1111/1467-9868.00401, 2003. a, b
Floodlist: Australia? More Floods in Queensland After Widespread Heavy
Rainfall,
https://floodlist.com/australia/queensland-floods-may-2022 (last access: 8 September 2023),
2022. a
Fraedrich, K. and Larnder, C.: Scaling regimes of composite rainfall time
series, Tellus A, 45, 289–298, https://doi.org/10.3402/tellusa.v45i4.14893, 1993. a, b
Francis, J. A., Skific, N., and Vavrus, S. J.: North American Weather Regimes
Are Becoming More Persistent: Is Arctic Amplification a Factor?, Geophys. Res.
Lett., 45, 11414–11422, https://doi.org/10.1029/2018GL080252, 2018. a, b, c, d
Franzke, C., Majda, A. J., and Vanden-Eijnden, E.: Low-Order Stochastic Mode
Reduction for a Realistic Barotropic Model Climate, J. Atmos. Sci., 62,
1722–1745, https://doi.org/10.1175/JAS3438.1, 2005. a
Franzke, C., Crommelin, D., Fischer, A., and Majda, A. J.: A Hidden Markov
Model Perspective on Regimes and Metastability in Atmospheric Flows, J.
Clim., 21, 1740–1757, https://doi.org/10.1175/2007JCLI1751.1, 2008. a
Franzke, C., Woollings, T., and Martius, O.: Persistent Circulation Regimes
and Preferred Regime Transitions in the North Atlantic, J. Atmos. Sci., 68,
2809–2825, https://doi.org/10.1175/JAS-D-11-046.1, 2011. a, b, c, d
Franzke, C. L. E., Barbosa, S., Blender, R., Fredriksen, H.-B., Laepple, T.,
Lambert, F., Nilsen, T., Rypdal, K., Rypdal, M., Scotto, M. G., Vannitsem,
S., Watkins, N. W., Yang, L., and Yuan, N.: The Structure of Climate
Variability Across Scales, Rev. Geophys., 58, e2019RG000657, https://doi.org/10.1029/2019RG000657,
2020. a, b
Gálfi, V. M., Lucarini, V., and Wouters, J.: A large deviation
theory-based analysis of heat waves and cold spells in a simplified model of
the general circulation of the atmosphere, J. Stat. Mech.-Theory E, 2019,
033404, https://doi.org/10.1088/1742-5468/ab02e8, 2019. a
García-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J., and Fischer,
E. M.: A Review of the European Summer Heat Wave of 2003, Crit. Rev. Env. Sci.
Tec., 40, 267–306, https://doi.org/10.1080/10643380802238137, 2010. a
Gershunov, A. and Barnett, T. P.: ENSO Influence on Intraseasonal Extreme
Rainfall and Temperature Frequencies in the Contiguous United States:
Observations and Model Results, J. Clim., 11, 1575–1586,
https://doi.org/10.1175/1520-0442(1998)011<1575:EIOIER>2.0.CO;2, 1998. a
Ghil, M. and Robertson, A. W.: “Waves” vs. “particles” in the atmosphere's
phase space: A pathway to long-range forecasting?, P. Natl. Acad. Sci. USA, 99,
2493–2500, https://doi.org/10.1073/pnas.012580899, 2002. a
Goswami, B.: A Brief Introduction to Nonlinear Time Series Analysis and
Recurrence Plots, Vibration, 2, 332–368, https://doi.org/10.3390/vibration2040021,
2019. a
Guilbert, J., Betts, A. K., Rizzo, D. M., Beckage, B., and Bomblies, A.:
Characterization of increased persistence and intensity of precipitation in
the northeastern United States, Geophys. Res. Lett., 42, 1888–1893,
https://doi.org/10.1002/2015GL063124, 2015. a
Haines, K. and Hannachi, A.: Weather Regimes in the Pacific from a GCM, J.
Atmos. Sci., 52, 2444–2462,
https://doi.org/10.1175/1520-0469(1995)052<2444:WRITPF>2.0.CO;2, 1995. a
Hamidieh, K., Stoev, S., and Michailidis, G.: On the Estimation of the
Extremal Index Based on Scaling and Resampling, J. Comput. Graph. Stat., 18,
731–755, https://doi.org/10.1198/jcgs.2009.08065, 2009. a
Hannachi, A.: Low-Frequency Variability in a GCM: Three-Dimensional Flow
Regimes and Their Dynamics, J. Clim., 10, 1357–1379,
https://doi.org/10.1175/1520-0442(1997)010<1357:LFVIAG>2.0.CO;2, 1997. a
Hannachi, A.: A New Set of Orthogonal Patterns in Weather and Climate:
Optimally Interpolated Patterns, J. Clim., 21, 6724–6738,
https://doi.org/10.1175/2008JCLI2328.1, 2008. a, b, c
Hannachi, A.: On the Origin of Planetary-Scale Extratropical Winter
Circulation Regimes, J. Atmos. Sci., 67, 1382–1401,
https://doi.org/10.1175/2009JAS3296.1, 2010. a
Hannachi, A.: Intermittency, autoregression and censoring: a first-order AR
model for daily precipitation, Meteorol. Appl., 21, 384–397,
https://doi.org/10.1002/met.1353, 2014. a
Hannachi, A.: Patterns Identification and Data Mining in Weather and Climate,
Springer Nature, https://doi.org/10.1007/978-3-030-67073-3, 2021. a, b, c
Hannachi, A. and Trendafilov, N.: Archetypal Analysis: Mining Weather and
Climate Extremes, J. Clim., 30, 6927–6944,
https://doi.org/10.1175/JCLI-D-16-0798.1, 2017. a
Hannachi, A., Woollings, T., and Fraedrich, K.: The North Atlantic jet stream:
a look at preferred positions, paths and transitions, Q. J. Roy. Meteor. Soc.,
138, 862–877, https://doi.org/10.1002/qj.959, 2012. a, b
Hastie, T. J.: Generalized Additive Models, in Statistical Models in S, Routledge, 59 pp.,
https://doi.org/10.1201/9780203738535-7, 1992. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, 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. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a, b
Hodges, K. I.: Feature Tracking on the Unit Sphere, Mon. Weather Rev., 123,
3458–3465, https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2, 1995. a
Holešovský, J. and Fusek, M.: Improved interexceedance-times-based estimator
of the extremal index using truncated distribution, Extremes, 25, 695–720,
https://doi.org/10.1007/s10687-022-00444-8, 2022. a
Holmberg, E., Messori, G., Caballero, R., and Faranda, D.: The link between European warm-temperature extremes and atmospheric persistence, Earth Syst. Dynam., 14, 737–765, https://doi.org/10.5194/esd-14-737-2023, 2023. a, b
Horel, J. D.: Persistence of the 500 mb Height Field during Northern
Hemisphere Winter, Mon. Weather Rev., 113, 2030–2042,
https://doi.org/10.1175/1520-0493(1985)113<2030:POTMHF>2.0.CO;2, 1985a. a
Horel, J. D.: Persistence of Wintertime 500 mb Height Anomalies over the
Central Pacific, Mon. Weather Rev., 113, 2043–2048,
https://doi.org/10.1175/1520-0493(1985)113<2043:POWMHA>2.0.CO;2, 1985b. a
Horton, P., Jaboyedoff, M., and Obled, C.: Global Optimization of an Analog
Method by Means of Genetic Algorithms, Mon. Weather Rev., 145, 1275–1294,
https://doi.org/10.1175/MWR-D-16-0093.1, 2017. a
Hoskins, B. and Woollings, T.: Persistent Extratropical Regimes and Climate
Extremes, Curr. Clim. Change Rep., 1, 115–124,
https://doi.org/10.1007/s40641-015-0020-8, 2015. a
Huguenin, M. F., Fischer, E. M., Kotlarski, S., Scherrer, S. C., Schwierz, C.,
and Knutti, R.: Lack of Change in the Projected Frequency and Persistence of
Atmospheric Circulation Types Over Central Europe, Geophys. Res. Lett., 47, e2019GL086132,
https://doi.org/10.1029/2019GL086132, 2020. a, b, c
Huntingford, C., Marsh, T., Scaife, A. A., Kendon, E. J., Hannaford, J., Kay,
A. L., Lockwood, M., Prudhomme, C., Reynard, N. S., Parry, S., Lowe, J. A.,
Screen, J. A., Ward, H. C., Roberts, M., Stott, P. A., Bell, V. A., Bailey,
M., Jenkins, A., Legg, T., Otto, F. E. L., Massey, N., Schaller, N., Slingo,
J., and Allen, M. R.: Potential influences on the United Kingdom's floods of
winter 2013/14, Nat. Clim. Change, 4, 769–777, https://doi.org/10.1038/nclimate2314,
2014. a, b
Hurst, H. E.: Long-Term Storage Capacity of Reservoirs, T. Am. Soc. Civ. Eng.,
116, 770–799, https://doi.org/10.1061/TACEAT.0006518, 1951. a
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M.,
Kyselý, J., and Tveito, O. E.: Classifications of Atmospheric Circulation
Patterns, Ann. Ny. Acad. Sci., 1146, 105–152, https://doi.org/10.1196/annals.1446.019,
2008. a, b
Kalra, D. S. and Santhanam, M. S.: Inferring long memory using extreme events,
Chaos, 31, 113131, https://doi.org/10.1063/5.0064432, 2021. a
Khare, S., Bonazzi, A., Mitas, C., and Jewson, S.: Modelling clustering of natural hazard phenomena and the effect on re/insurance loss perspectives, Nat. Hazards Earth Syst. Sci., 15, 1357–1370, https://doi.org/10.5194/nhess-15-1357-2015, 2015. a, b, c
Kimoto, M. and Ghil, M.: Multiple Flow Regimes in the Northern Hemisphere
Winter. Part I: Methodology and Hemispheric Regimes, J. Atmos. Sci., 50, 2625–2644, https://doi.org/10.1175/1520-0469(1993)050<2625:MFRITN>2.0.CO;2, 1993. a
Kolstad, E. W., Sobolowski, S. P., and Scaife, A. A.: Intraseasonal
Persistence of European Surface Temperatures, J. Clim., 28, 5365–5374,
https://doi.org/10.1175/JCLI-D-15-0053.1, 2015. a, b
Kolstad, E. W., Barnes, E. A., and Sobolowski, S. P.: Quantifying the role of
land-atmosphere feedbacks in mediating near-surface temperature persistence,
Q. J. Roy. Meteor. Soc., 143, 1620–1631, https://doi.org/10.1002/qj.3033, 2017. a, b
Kooperberg, C. and O'sullivan, F.: Predictive Oscillation Patterns: A
Synthesis of Methods for Spatial-Temporal Decomposition of Random Fields, J.
Am. Stat. Assoc., 91, 1485–1496, https://doi.org/10.1080/01621459.1996.10476716, 1996. a
Kopp, J., Rivoire, P., Ali, S. M., Barton, Y., and Martius, O.: A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods, Hydrol. Earth Syst. Sci., 25, 5153–5174, https://doi.org/10.5194/hess-25-5153-2021, 2021. a, b, c, d, e, f, g, h, i
Kornhuber, K. and Tamarin-Brodsky, T.: Future Changes in Northern Hemisphere
Summer Weather Persistence Linked to Projected Arctic Warming, Geophys. Res. Lett., 48, e2020GL091603, https://doi.org/10.1029/2020GL091603, 2021. a, b
Kornhuber, K., Osprey, S., Coumou, D., Petri, S., Petoukhov, V., Rahmstorf, S.,
and Gray, L.: Extreme weather events in early summer 2018 connected by a
recurrent hemispheric wave-7 pattern, Environ. Res. Lett., 14, 054002,
https://doi.org/10.1088/1748-9326/ab13bf, 2019. a
Koscielny-Bunde, E., Bunde, A., Havlin, S., Roman, H. E., Goldreich, Y., and
Schellnhuber, H.-J.: Indication of a Universal Persistence Law Governing
Atmospheric Variability, Phys. Rev. Lett., 81, 729–732,
https://doi.org/10.1103/PhysRevLett.81.729, 1998. a
Kossin, J. P.: A global slowdown of tropical-cyclone translation speed,
Nature, 558, 104–107, https://doi.org/10.1038/s41586-018-0158-3, 2018. a
Koutsoyiannis, D.: Climate change, the Hurst phenomenon, and hydrological
statistics, Hydrol. Sci. J., 48, 3–24, https://doi.org/10.1623/hysj.48.1.3.43481,
2003. a
Kučerová, M., Beck, C., Philipp, A., and Huth, R.: Trends in
frequency and persistence of atmospheric circulation types over Europe
derived from a multitude of classifications, Int. J. Climatol., 37, 2502–2521,
https://doi.org/10.1002/joc.4861, 2017. a
Kumar, S., Merwade, V., Kinter, J. L., and Niyogi, D.: Evaluation of
Temperature and Precipitation Trends and Long-Term Persistence in CMIP5
Twentieth-Century Climate Simulations, J. Clim., 26, 4168–4185,
https://doi.org/10.1175/JCLI-D-12-00259.1, 2013. a
Kyselý, J. and Domonkos, P.: Recent increase in persistence of
atmospheric circulation over Europe: comparison with long-term variations
since 1881, Int. J. Climatol., 26, 461–483, https://doi.org/10.1002/joc.1265, 2006. a
Lawrence, Z. D., Perlwitz, J., Butler, A. H., Manney, G. L., Newman, P. A.,
Lee, S. H., and Nash, E. R.: The Remarkably Strong Arctic Stratospheric
Polar Vortex of Winter 2020: Links to Record-Breaking Arctic Oscillation and
Ozone Loss, J. Geophys. Res.-Atmos, 125, e2020JD033271, https://doi.org/10.1029/2020JD033271, 2020. a
Legras, B. and Ghil, M.: Persistent Anomalies, Blocking and Variations in
Atmospheric Predictability, J. Atmos. Sci., 42, 433–471,
https://doi.org/10.1175/1520-0469(1985)042<0433:PABAVI>2.0.CO;2, 1985. a
Liu, P., Zhu, Y., Zhang, Q., Gottschalck, J., Zhang, M., Melhauser, C., Li, W.,
Guan, H., Zhou, X., Hou, D., Peña, M., Wu, G., Liu, Y., Zhou, L., He, B.,
Hu, W., and Sukhdeo, R.: Climatology of tracked persistent maxima of 500-hPa
geopotential height, Clim. Dynam., 51, 701–717,
https://doi.org/10.1007/s00382-017-3950-0, 2018. a, b
Liu, Q.: On the definition and persistence of blocking, Tellus A, 46,
286–298, https://doi.org/10.1034/j.1600-0870.1994.t01-2-00004.x, 1994. a, b
Lorenz, E. N.: Can chaos and intransitivity lead to interannual variability?,
Tellus A, 42, 378–389, https://doi.org/10.3402/tellusa.v42i3.11884, 1990. a
Lorenz, R., Jaeger, E. B., and Seneviratne, S. I.: Persistence of heat waves
and its link to soil moisture memory, Geophys. Res. Lett., 37, L09703,
https://doi.org/10.1029/2010GL042764, 2010. a, b
Lucarini, V., Faranda, D., Freitas, A. C. G. M. M., de Freitas, J. M. M.,
Holland, M., Kuna, T., Nicol, M., Todd, M., and Vaienti, S.: Extremes and
Recurrence in Dynamical Systems, John Wiley and Sons, Inc, Hoboken, NJ,
USA, https://doi.org/10.1002/9781118632321, 2016. a
Mallakpour, I., Villarini, G., Jones, M. P., and Smith, J. A.: On the use of
Cox regression to examine the temporal clustering of flooding and heavy
precipitation across the central United States, Global Planet. Change, 155,
98–108, https://doi.org/10.1016/j.gloplacha.2017.07.001, 2017. a
Mallapaty, S.: Why are Pakistan’s floods so extreme this year?,
https://www.nature.com/articles/d41586-022-02813-6 (last access: 7 September 2023), 2022. a
Mandelbrot, B. B. and Wallis, J. R.: Some long-run properties of geophysical
records, Water Resour. Res., 5, 321–340, https://doi.org/10.1029/WR005i002p00321, 1969. a
Mann, M. E., Rahmstorf, S., Kornhuber, K., Steinman, B. A., Miller, S. K.,
Petri, S., and Coumou, D.: Projected changes in persistent extreme summer
weather events: The role of quasi-resonant amplification, Sci. Adv.,
4, eaat3272, https://doi.org/10.1126/sciadv.aat3272, 2018. a
Marwan, N.: A historical review of recurrence plots, Europ. Phys.
J. Spec. Top., 164, 3–12, https://doi.org/10.1140/epjst/e2008-00829-1, 2008. a
Marwan, N., Carmen Romano, M., Thiel, M., and Kurths, J.: Recurrence plots
for the analysis of complex systems, Phys. Rep., 438, 237–329,
https://doi.org/10.1016/j.physrep.2006.11.001, 2007. a, b, c
Meehl, G. A., Richter, J. H., Teng, H., Capotondi, A., Cobb, K., Doblas-Reyes,
F., Donat, M. G., England, M. H., Fyfe, J. C., Han, W., Kim, H., Kirtman,
B. P., Kushnir, Y., Lovenduski, N. S., Mann, M. E., Merryfield, W. J.,
Nieves, V., Pegion, K., Rosenbloom, N., Sanchez, S. C., Scaife, A. A., Smith,
D., Subramanian, A. C., Sun, L., Thompson, D., Ummenhofer, C. C., and Xie,
S.-P.: Initialized Earth System prediction from subseasonal to decadal
timescales, Nat. Rev. Earth Environ., 2, 340–357,
https://doi.org/10.1038/s43017-021-00155-x, 2021. a
Meng, L., Ford, T., and Guo, Y.: Logistic regression analysis of drought
persistence in East China, Int. J. Climatol., 37, 1444–1455,
https://doi.org/10.1002/joc.4789, 2017. a, b
Mohr, S., Wilhelm, J., Wandel, J., Kunz, M., Portmann, R., Punge, H. J.,
Schmidberger, M., Quinting, J. F., and Grams, C. M.: The role of large-scale
dynamics in an exceptional sequence of severe thunderstorms in Europe
May–June 2018, Weather Clim. Dynam., 1, 325–348,
https://doi.org/10.5194/wcd-1-325-2020, 2020. a
Moon, H., Gudmundsson, L., and Seneviratne, S. I.: Drought Persistence Errors
in Global Climate Models, J. Geophys. Res.-Atmos., 123, 3483–3496,
https://doi.org/10.1002/2017JD027577, 2018. a, b
Mukhin, D., Hannachi, A., Braun, T., and Marwan, N.: Revealing recurrent
regimes of mid-latitude atmospheric variability using novel machine learning
method, Chaos, 32, 113105, https://doi.org/10.1063/5.0109889, 2022. a, b, c
Mumby, P. J., Vitolo, R., and Stephenson, D. B.: Temporal clustering of
tropical cyclones and its ecosystem impacts, P. Natl. Acad. Sci. USA, 108,
17626–17630, https://doi.org/10.1073/pnas.1100436108, 2011. a, b
Namias, J.: Seasonal persistence and recurrence of European blocking during
1958–1960, Tellus, 16, 394–407, https://doi.org/10.1111/j.2153-3490.1964.tb00176.x,
1964. a
Økland, H. and Lejenäs, H.: Blocking and persistence, Tellus A, 39,
33–38, https://doi.org/10.1111/j.1600-0870.1987.tb00286.x, 1987. a, b
Ontañón, S.: An overview of distance and similarity functions for structured
data, Artif. Intell. Rev., 53, 5309–5351, https://doi.org/10.1007/s10462-020-09821-w,
2020. a
Overland, J. E. and Wang, M.: The 2020 Siberian heat wave, Int. J. Climatol.,
41, E2341–E2346, https://doi.org/10.1002/joc.6850, 2021. a
Pandolfo, L.: Observational Aspects of the Low-Frequency Intraseasonal
Variability of the Atmosphere in Middle Latitudes, Adv.
Geophys., 34, 93–174, https://doi.org/10.1016/S0065-2687(08)60435-5, 1993. a
Parzen, E.: Quantile spectral analysis and long-memory time series, J. Appl.
Probab., 23, 41–54, https://doi.org/10.2307/3214341, 1986. a
Pelletier, J. D. and Turcotte, D. L.: Long-range persistence in climatological
and hydrological time series: analysis, modeling and application to drought
hazard assessment, J. Hydrol., 203, 198–208,
https://doi.org/10.1016/S0022-1694(97)00102-9, 1997. a
Perez-Zanon, N., et al.: CSTools: Assessing Skill of Climate Forecasts
on Seasonal-to-Decadal Timescales, R package
version 4.1.1,
https://CRAN.R-project.org/package=CSTools (last access: 7 September 2023), 2022. a
Pfleiderer, P., Schleussner, C.-F., Kornhuber, K., and Coumou, D.: Summer
weather becomes more persistent in a 2 degree C world, Nat. Clim. Change, 9,
666–671, https://doi.org/10.1038/s41558-019-0555-0, 2019. a, b, c
Pinto, J. G., Bellenbaum, N., Karremann, M. K., and Della-Marta, P. M.: Serial
clustering of extratropical cyclones over the North Atlantic and Europe under
recent and future climate conditions, J. Geophys. Res.-Atmos., 118,
12476–12485, https://doi.org/10.1002/2013JD020564, 2013. a
Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., and
Caballero, R.: Large-scale dynamics associated with clustering of
extratropical cyclones affecting Western Europe, J. Geophys. Res.-Atmos., 119,
13704–13719, https://doi.org/10.1002/2014JD022305, 2014. a, b, c, d
Pinto, J. G., Ulbrich, S., Economou, T., Stephenson, D. B., Karremann, M. K.,
and Shaffrey, L. C.: Robustness of serial clustering of extratropical
cyclones to the choice of tracking method, TELLUS A, 68, 32204,
https://doi.org/10.3402/tellusa.v68.32204, 2016. a
Pires, C. A. L. and Hannachi, A.: Bispectral analysis of nonlinear
interaction, predictability and stochastic modelling with application to
ENSO, Tellus A, 73, 1–30, https://doi.org/10.1080/16000870.2020.1866393, 2021. a, b, c
Potter, K. W.: Annual precipitation in the northeast United States: Long
memory, short memory, or no memory?, Water Resour. Res., 15, 340–346,
https://doi.org/10.1029/WR015i002p00340, 1979. a
Quandt, L.-A., Keller, J. H., Martius, O., and Jones, S. C.: Forecast
Variability of the Blocking System over Russia in Summer 2010 and Its Impact
on Surface Conditions, Weather Forecast., 32, 61–82,
https://doi.org/10.1175/WAF-D-16-0065.1, 2017. a
Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S.,
Hanel, M., and Kumar, R.: The 2018-2020 Multi-Year Drought Sets a New
Benchmark in Europe, Earth's Future, 10, e2021EF002394, https://doi.org/10.1029/2021EF002394, 2022. a
Ramirez-Amaro, K. and Figueroa-Nazuno, J.: Recurrence Plot Analysis and its
Application to Teleconnection Patterns, in: 2006 15th International
Conference on Computing, 65–72, https://doi.org/10.1109/CIC.2006.59, 2006. a, b
Ray, R., Khondekar, M. H., Ghosh, K., and Bhattacharjee, A. K.: Complexity and
periodicity of daily mean temperature and dew-point across India, J. Earth
Syst. Sci., 128, 143, https://doi.org/10.1007/s12040-019-1174-x, 2019. a
Rehman, S. and Siddiqi, A.: Wavelet-based Hurst exponent and fractal
dimensional analysis of Saudi climatic dynamics, Chaos Soliton Fract., 40,
1081–1090, https://doi.org/10.1016/j.chaos.2007.08.063, 2009. a
Richardson, D., Kilsby, C. G., Fowler, H. J., and Bárdossy, A.: Weekly
to multi-month persistence in sets of daily weather patterns over Europe and
the North Atlantic Ocean, Int. J. Climatol., 39, 2041–2056,
https://doi.org/10.1002/joc.5932, 2019. a, b, c
Ripley, B. D.: Spatial Statistics, Wiley Series in Probability and
Statistics, John Wiley & Sons, Inc., Hoboken, NJ, USA,
https://doi.org/10.1002/0471725218, 1981. a
Robin, Y.: CDSK (Chaotic Dynamical System Kit), MIT [code], https://github.com/yrobink/CDSK (last access: 8 July 2023), 2021. a
Röthlisberger, M. and Martius, O.: Quantifying the Local Effect of
Northern Hemisphere Atmospheric Blocks on the Persistence of Summer Hot and
Dry Spells, Geophys. Res. Lett., 46, 10101–10111,
https://doi.org/10.1029/2019GL083745, 2019. a, b, c
Rousi, E., Fink, A. H., Andersen, L. S., Becker, F. N., Beobide-Arsuaga, G., Breil, M., Cozzi, G., Heinke, J., Jach, L., Niermann, D., Petrovic, D., Richling, A., Riebold, J., Steidl, S., Suarez-Gutierrez, L., Tradowsky, J., Coumou, D., Düsterhus, A., Ellsäßer, F., Fragkoulidis, G., Gliksman, D., Handorf, D., Haustein, K., Kornhuber, K., Kunstmann, H., Pinto, J. G., Warrach-Sagi, K., and Xoplaki, E.: The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-813, 2022a. a, b
Rousi, E., Kornhuber, K., Beobide-Arsuaga, G., Luo, F., and Coumou, D.:
Accelerated western European heatwave trends linked to more-persistent double
jets over Eurasia, Nat. Commun., 13, 3851, https://doi.org/10.1038/s41467-022-31432-y,
2022b. a, b, c, d
Santhanam, M. S. and Kantz, H.: Return interval distribution of extreme events
and long-term memory, Phys. Rev. E, 78, 051113,
https://doi.org/10.1103/PhysRevE.78.051113, 2008. a
Schwierz, C., Croci-Maspoli, M., and Davies, H. C.: Perspicacious indicators of
atmospheric blocking, Geophys. Res. Lett., 31, L06125, https://doi.org/10.1029/2003GL019341, 2004. a
Sericola, B.: Markov Chains, John Wiley & Sons, Inc., Hoboken, NJ USA,
https://doi.org/10.1002/9781118731543, 2013. a
Serinaldi, F. and Kilsby, C. G.: On the sampling distribution of Allan factor
estimator for a homogeneous Poisson process and its use to test
inhomogeneities at multiple scales, Physica A, 392, 1080–1089,
https://doi.org/10.1016/j.physa.2012.11.015, 2013. a, b, c
Sharma, T. C. and Panu, U. S.: Modeling of hydrological drought durations and
magnitudes: Experiences on Canadian streamflows, J. Hydrol.
Reg. Stud., 1, 92–106, https://doi.org/10.1016/j.ejrh.2014.06.006, 2014. a
Smith, J. A. and Karr, A. F.: Flood Frequency Analysis Using the Cox
Regression Model, Water Resour. Res., 22, 890–896,
https://doi.org/10.1029/WR022i006p00890, 1986. a
Son, R., Wang, S.-Y. S., Kim, S. H., Kim, H., Jeong, J.-H., and Yoon, J.-H.:
Recurrent pattern of extreme fire weather in California, Environ. Res. Lett.,
16, 094031, https://doi.org/10.1088/1748-9326/ac1f44, 2021. a
Steinfeld, D.: ConTrack – Contour Tracking of circulation anomalies in weather
and climate data, Zenodo, https://doi.org/10.5281/ZENODO.4765560, 2021. a
Steinfeld, D., Boettcher, M., Forbes, R., and Pfahl, S.: The sensitivity of
atmospheric blocking to upstream latent heating – numerical experiments,
Weather Clim. Dynam., 1, 405–426, https://doi.org/10.5194/wcd-1-405-2020,
2020. a
Stephenson, D. B., Hannachi, A., and O'Neill, A.: On the existence of multiple
climate regimes, Q. J. Roy. Meteor. Soc., 130, 583–605, https://doi.org/10.1256/qj.02.146,
2004. a
Strommen, K., Chantry, M., Dorrington, J., and Otter, N.: A topological
perspective on weather regimes, Clim. Dynam., 60, 1415–1445,
https://doi.org/10.1007/s00382-022-06395-x, 2022. a
Tatli, H.: Detecting persistence of meteorological drought via the Hurst
exponent, Meteorol. Appl., 22, 763–769, https://doi.org/10.1002/met.1519, 2015. a
Telesca, L.: Time-clustering of NAT HAZARDS, Nat. Hazards, 6, 593–601,
https://doi.org/10.1007/s11069-006-9023-z, 2007. a, b, c
Telesca, L., Lovallo, M., and Kanevski, M.: Power spectrum and multifractal
detrended fluctuation analysis of high-frequency wind measurements in
mountainous regions, Appl. Energ., 162, 1052–1061,
https://doi.org/10.1016/j.apenergy.2015.10.187, 2016. a
Telesca, L., Guignard, F., Laib, M., and Kanevski, M.: Analysis of temporal
properties of extremes of wind measurements from 132 stations over
Switzerland, Renew. Energ., 145, 1091–1103,
https://doi.org/10.1016/j.renene.2019.06.089, 2020. a
Thao, S.: dtheta, MIT [code], https://github.com/thaos/dtheta (last access: 8 September 2023), 2021. a
Trenberth, K. E.: Some Effects of Finite Sample Size and Persistence on
Meteorological Statistics.Part II: Potential Predictability, Mon. Weather Rev., 112, 2369–2379,
https://doi.org/10.1175/1520-0493(1984)112<2369:SEOFSS>2.0.CO;2, 1984. a
Tuel, A.: Quantifying persistence in weather data, MIT [code], https://doi.org/10.5281/ZENODO.8329531, 2023. a
Tuel, A. and Martius, O.: A climatology of sub-seasonal temporal clustering of extreme precipitation in Switzerland and its links to extreme discharge, Nat. Hazards Earth Syst. Sci., 21, 2949–2972, https://doi.org/10.5194/nhess-21-2949-2021, 2021b. a, b
Tuel, A. and Martius, O.: The influence of modes of climate variability on the
sub-seasonal temporal clustering of extreme precipitation, Science, 25,
103855, https://doi.org/10.1016/j.isci.2022.103855, 2022b. a, b
Tuel, A. and Martius, O.: On the persistence of warm and cold spells in the Northern Hemisphere extratropics: regionalisation, synoptic-scale dynamics, and temperature budget, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1478, 2023. a
Tuel, A., Schaefli, B., Zscheischler, J., and Martius, O.: On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe, Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, 2022a. a
Tuel, A., Steinfeld, D., Ali, S. M., Sprenger, M., and Martius, O.:
Large-scale drivers of persistent extreme weather during early summer 2021
in Europe, Geophys. Res. Lett., 49, e2022GL099624,
https://doi.org/10.1029/2022GL099624, 2022b. a, b, c
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F.,
Warner, J. D., Yager, N., Gouillart, E., Yu, T., and the scikit-image
contributors: scikit-image: image processing in Python, PeerJ, 2, e453,
https://doi.org/10.7717/peerj.453, 2014. a
Velásquez Valle, M. A., Medina García, G., Sánchez Cohen, I.,
Klaudia Oleschko, L., Ruiz Corral, J. A., and Korvin, G.: Spatial
Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the
State of Zacatecas, Mexico, J. Appl. Meteorol. Clim., 52, 2771–2780,
https://doi.org/10.1175/JAMC-D-13-0136.1, 2013. a
Vigaud, N., Robertson, A., and Tippett, M. K.: Predictability of Recurrent
Weather Regimes over North America during Winter from Submonthly
Reforecasts, Mon. Weather Rev., 146, 2559–2577,
https://doi.org/10.1175/MWR-D-18-0058.1, 2018. a
Villarini, G., Smith, J. A., Baeck, M. L., Vitolo, R., Stephenson, D. B., and
Krajewski, W. F.: On the frequency of heavy rainfall for the Midwest of the
United States, J. Hydrol., 400, 103–120, https://doi.org/10.1016/j.jhydrol.2011.01.027,
2011. a
Villarini, G., Smith, J. A., Vitolo, R., and Stephenson, D. B.: On the
temporal clustering of US floods and its relationship to climate
teleconnection patterns, Int. J. Climatol., 33, 629–640,
https://doi.org/10.1002/joc.3458, 2013. a, b
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H.,
Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P.,
Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P.,
Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M.,
Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F.,
Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and
Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database, B.
Am. Meteorol. Soc., 98, 163–173, https://doi.org/10.1175/BAMS-D-16-0017.1, 2017. a
Vitolo, R., Stephenson, D. B., Cook, I. M., and Mitchell-Wallace, K.: Serial
clustering of intense European storms, Meteorol. Z., 18, 411–424,
https://doi.org/10.1127/0941-2948/2009/0393, 2009. a, b
von Lindheim, J., Harikrishnan, A., Dörffel, T., Klein, R., Koltai, P.,
Mikula, N., Müller, A., Névir, P., Pacey, G., Polzin, R., and Vercauteren,
N.: Definition, detection, and tracking of persistent structures in
atmospheric flows, https://doi.org/10.48550/ARXIV.2111.13645, 2021. a
Weatherhead, E., Gearheard, S., and Barry, R.: Changes in weather persistence:
Insight from Inuit knowledge, Glob. Environ. Change, 20, 523–528,
https://doi.org/10.1016/j.gloenvcha.2010.02.002,
2010. a
Weber, J., Reyers, M., Beck, C., Timme, M., Pinto, J. G., Witthaut, D., and
Schäfer, B.: Wind Power Persistence Characterized by Superstatistics, Sci.
Rep.-UK, 9, 19971, https://doi.org/10.1038/s41598-019-56286-1, 2019. a
Weiland, R. S., van der Wiel, K., Selten, F., and Coumou, D.: Intransitive
Atmosphere Dynamics Leading to Persistent Hot-Dry or Cold-Wet European
Summers, J. Clim., 34, 6303–6317, https://doi.org/10.1175/JCLI-D-20-0943.1, 2021. a, b, c
Weiss, J. P. and Weiss, J. B.: Quantifying Persistence in ENSO, J. Atmos. Sci.,
56, 2737–2760, https://doi.org/10.1175/1520-0469(1999)056<2737:QPIE>2.0.CO;2, 1999. a, b
Wernli, H. and Schwierz, C.: Surface Cyclones in the ERA-40 Dataset
(1958–2001). Part I: Novel Identification Method and Global Climatology, J. Atmos. Sci., 63, 2486–2507, https://doi.org/10.1175/JAS3766.1, 2006. a
Wharton, E., Panetta, K., and Agaian, S.: Human visual system based similarity
metrics, in: 2008 IEEE International Conference on Systems, Man
Cybernet., 685–690, https://doi.org/10.1109/ICSMC.2008.4811357, 2008. a
Wikipedia: 2022 Eastern Australia floods,
https://en.wikipedia.org/wiki/2022_Eastern_Australia_floods (last access: 7 September 2023),
2022. a
Wilby, R. L., Noone, S., Murphy, C., Matthews, T., Harrigan, S., and Broderick,
C.: An evaluation of persistent meteorological drought using a homogeneous
Island of Ireland precipitation network, Int. J. Climatol., 36, 2854–2865,
https://doi.org/10.1002/joc.4523, 2016. a
Witt, A. and Malamud, B. D.: Quantification of Long-Range Persistence in
Geophysical Time Series: Conventional and Benchmark-Based Improvement
Techniques, Surv. Geophys., 34, 541–651, https://doi.org/10.1007/s10712-012-9217-8,
2013. a
Wolff, N. H., Wong, A., Vitolo, R., Stolberg, K., Anthony, K. R. N., and Mumby,
P. J.: Temporal clustering of tropical cyclones on the Great Barrier Reef
and its ecological importance, Coral Reefs, 35, 613–623,
https://doi.org/10.1007/s00338-016-1400-9, 2016. a
Wolters, M. A.: Better Autologistic Regression, Front. Appl.
Mathemat. Stat., 3, 24, https://doi.org/10.3389/fams.2017.00024, 2017. a
Woollings, T., Hannachi, A., and Hoskins, B.: Variability of the North
Atlantic eddy-driven jet stream, Q. J. Roy. Meteor. Soc., 136, 856–868,
https://doi.org/10.1002/qj.625, 2010.
a, b
World Meteorological Organization: Extreme weather in China highlights
climate change impacts and need for early warnings,
https://public.wmo.int/en/ (last access: 7 September 2023),
2022. a
Yang, L. and Fu, Z.: Process-dependent persistence in precipitation records,
Physica A, 527, 121459, https://doi.org/10.1016/j.physa.2019.121459, 2019. a, b
Yang, Z. and Villarini, G.: Examining the capability of reanalyses in
capturing the temporal clustering of heavy precipitation across Europe, Clim. Dynam., 53, 1845–1857, https://doi.org/10.1007/s00382-019-04742-z, 2019. a, b
Yiou, P., Cattiaux, J., Ribes, A., Vautard, R., and Vrac, M.: Recent Trends in
the Recurrence of North Atlantic Atmospheric Circulation Patterns,
Complexity, 2018, 3140915, https://doi.org/10.1155/2018/3140915, 2018. a
Yuan, N., Fu, Z., and Mao, J.: Different scaling behaviors in daily temperature
records over China, Physica A, 389, 4087–4095,
https://doi.org/10.1016/j.physa.2010.05.026, 2010. a
Zerzucha, P. and Walczak, B.: Concept of (dis)similarity in data analysis,
Trac.-Trend. Anal. Chem., 38, 116–128, https://doi.org/10.1016/j.trac.2012.05.005, 2012. a
Zolina, O., Simmer, C., Belyaev, K., Gulev, S. K., and Koltermann, P.: Changes
in the Duration of European Wet and Dry Spells during the Last 60 Years, J. Clim., 26, 2022–2047, https://doi.org/10.1175/JCLI-D-11-00498.1, 2013. a, b
Short summary
Weather persistence on sub-seasonal to seasonal timescales has been a topic of research since the early days of meteorology. Stationary or recurrent behavior are common features of weather dynamics and are strongly related to fundamental physical processes, weather predictability and surface weather impacts. In this review, we propose a typology for the broad concepts related to persistence and discuss various methods that have been used to characterize persistence in weather data.
Weather persistence on sub-seasonal to seasonal timescales has been a topic of research since...
Altmetrics
Final-revised paper
Preprint