Relationship between selected percentiles and return periods of extreme events

• Autorzy: Camufo, Dario , Becherini, Francesca , della Valle, Antonio
• Języki publikacji: EN

Abstrakty

EN

This paper investigates the relationship between selected percentiles, return periods and the concepts of rare and extreme events in climate and hydrological series, considering both regular and irregular datasets, and discusses the IPCC and WMO indications. IPCC (Annex II: Glossary. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, 2014) establishes that an extreme event should be rare and exceed selected upper and lower thresholds (10th and 90th percentiles); WMO (Guidelines on the defnition and monitoring of extreme weather and climate events-TT-DEWCE WMO 4/14/2016. World Meteorological Organization, Geneva, 2016) suggests thresholds near the ends of the range, but leaves them undetermined. The concept of “rare” relates the extreme events to the time domain and is typically expressed in terms of return period (RP). The key is to fnd the combination between “rare”, percentile and return period. In particular, two crucial items are analysed: (1) how the return period may vary in response to the choice of the threshold, in particular when it is expressed in terms of percentiles; (2) how the choice of producing a regular or irregular dataset may afect the yearly frequency and the related return periods. Some weather variables (e.g. temperature) are regular and recorded at fxed time intervals, while other phenomena (e.g. tornadoes) occur at times. Precipitation may be considered either regular, all-days being characterized by a precipitation amount from 0 (no precipitation) to the top of the range, or irregular (rainy-days only) considering a precipitation day over a selected instrumental or percentile threshold. These two modes of interpreting precipitation include a diferent number of events per year (365 or less) and generate diferent return periods. Every climatic information may be afected by this defnition. The 90th percentile applied to observations with daily frequency produces 10-day return period and the percentiles necessary to get 1 year, 10 years or other return periods are calculated. The general case of events with selected or variable frequencies, and selected percentiles, is also considered with an example of a precipitation series, two-century long.

Słowa kluczowe

PL

ekstremalne wydarzenia; okres zwrotu; zapis temperatury; zapis opadów; szereg czasowy; percentyl

EN

extreme events; return period; temperature record; precipitation record; time series; percentile

• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 4
• Strony: 1201--1211
• Opis fizyczny: Bibliogr. 70 poz.

Twórcy

autor

Camufo, Dario

• Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Corso Stati Uniti 4, 35127 Padua, Italy,

autor

Becherini, Francesca

• Institute of Polar Sciences (IPS), National Research Council (CNR), Via Torino 155, 3172 Venice Mestre, Italy

autor

della Valle, Antonio

• Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Corso Stati Uniti 4, 35127 Padua, Italy

Bibliografia

• 1. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization. World Meteorological Organization, WMO-TD No. 1186, WCDMP No. 53, Geneva
• 2. Allen DE, Singh AK, Powell RJ (2013) EVT and tail-risk modelling: evidence from market indices and volatility series. North Am J Econ Finan 26:355–369
• 3. AMS (2020) Meteorology glossary. American Meteorological Society, Boston. https://glossary.ametsoc.org/wiki/Main_Page
• 4. Ban N, Schmidli J, Schär C (2015) Heavy precipitation in a changing climate: does short-term summer precipitation increase faster? Geophys Res Lett 42:1165–1172. https://doi.org/10.1002/2014GL062588
• 5. Bell JE, Brown CL, Conlon K, Herring S, Kunkel KE, Lawrimore J, Luber G, Schreck C, Smith A, Ueji C (2018) Changes in extreme events and the potential impacts on human health. J Air Waste Manage 68(4):265–287
• 6. Bothe O, Wagner S, Zorita E (2018) Inconsistencies between observed, reconstructed, and simulated precipitation indices for England since the year 1650 CE. Clim Past 15:307–334
• 7. Broska LH, Poganietz WR, Vogele S (2020) Extreme events defined—a conceptual discussion applying a complex systems approach. Futures 115:102490
• 8. Brunetti M, Buffoni L, Lo Vecchio G, Maugeri M, Nanni T (2001) Tre secoli di meteorologia a Bologna. CUSL, Milan
• 9. Camuffo D, della Valle A, Bertolin C, Santorelli E (2017) Temperature observations in Bologna, Italy, from 1715 to 1815; a comparison with other contemporary series and an overview of three centuries of changing climate. Clim Chang 142(1–2):7–22
• 10. Camuffo D, Becherini F, della Valle A (2019) The Beccari Series of Precipitation in Bologna, Italy, from 1723 to 1765. Clim Chang 155:359–376
• 11. Camuffo D, della Valle A, Becherini F (2020a) A critical analysis of the definitions of climate and hydrological extreme events. Quat Int 538:5–13
• 12. Camuffo D, Becherini F, della Valle A (2020b) Three centuries of daily precipitation in Padua, Italy, 1713–2018. History, relocations, gaps, homogeneity and raw data. Clim Chang. https://doi.org/10.1007/s10584-020-02717-2
• 13. Coles S (2001) An introduction to statistical modeling of extreme values. Springer Series in Statistics, London
• 14. Coles S, Pericchi L (2003) Anticipating catastrophes through extreme value modelling. J R Stat Soc Ser C 52(4):405–416
• 15. Cooley D (2013) Return periods and return levels under climate change. In: AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds) Extremes in a changing climate: detection, analysis and uncertainty. Springer, Dordrecht, pp 97–114
• 16. Costa AC, Soares A (2009) Homogenization of climate data: review and new perspectives using geostatistics. Math Geosci 41:291–305
• 17. Craddock JM (1979) Methods of comparing annual rainfall records for climatic purposes. Weather 34(9):332–346
• 18. Domínguez-Castro F, Ramos AM, García-Herrera R, Trigo RM (2015) Iberian extreme precipitation 1855/1856: an analysis from early instrumental observations and documentary sources. Int J Climatol 35:142–153
• 19. Du T, Xiong L, Xu CY, Gippel CJ, Guo S, Liu P (2015) Return period and risk analysis of nonstationary low-flow series under climate change. J Hydrol 527:234–250
• 20. Elsner JB, Birol Kara A (1999) Hurricanes of the North Atlantic: climate and society. Oxford University Press, New York
• 21. Fréchet M (1927) Sur la loi de probabilité de l'écart maximum. Ann Soc Polon Math 6:93–116
• 22. Galton F (1879) The geometric mean in vital and social statistics. Proc R Soc Lond 29:365–367
• 23. Gilleland E, Katz RW (2011) New software to analyze how extremes change over time. Eos Trans AGU 92(2):13–14
• 24. Giorgi F, Coppola E, Raffaele F (2014) A consistent picture of the hydroclimatic response to global warming from multiple indices: models and observations. J Geophys Res Atmos 119:11695–11708
• 25. Goda Y (1988) On the methodology of selecting design wave height. In: Proc. 21st International Conference on Coastal Engineering. Malaga, pp 899–913
• 26. Goda Y (1992) Uncertainty of design parameters from viewpoint of extreme statistics. J Offshore Mech Arct Eng 114(2):76–82
• 27. Gumbel EJ (1941) The return period of flood flows. Ann Math Statist 12(2):163–190
• 28. Gumbel EJ (1958) Statistics of Extremes. Columbia University Press, New York
• 29. Huske RE (1959) Glossary of meteorology. American Meteorological Society, Boston
• 30. IPCC (2014) Climate change 2014: synthesis report. In: Mach KJ, Meyer LA, Pachauri RK, Planton S, von Stechow C (eds) Annex II: Glossary. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva
• 31. Katz RW (2010) Economic impact of extreme events: an approach based on extreme value theory. In: Chavez M, Ghil M, Urrutia-Fucugauchi J (eds) Extreme events: observations, modeling, and economics. AGU Wiley, Hoboken, pp 207–217
• 32. Kendon EJ, Roberts NM, Fowler HJ, Roberts MJ, Chan SC, Senior CA (2014) Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat Clim Chang 4:570–576
• 33. Kharin VV, Flato G, Zhang X, Gillett NP, Zwiers F, Anderson KJ (2018) Risks from climate extremes change differently from1.5 °C to 2.0 °C depending on rarity. Earths Future 6(5):704–715
• 34. Knapp AK, Hoover DL, Wilcox KR, Avolio ML, Koerner SE, Kimberly J, La Pierre KJ, Loik ME, Luo Y, Sala OE, Smith MD (2015) Characterizing differences in precipitation regimes of extreme wet and dry years: implications for climate change experiments. Global Change Biol 21:2624–2633
• 35. Lestang T, Ragone F, Bréhier CE, Herbert C, Bouchet F (2018) Computing return times or return periods with rare event algorithms. J Stat Mech 2018:043213
• 36. Maidment DR (2002) Arc hydro GIS for water resources, chapter 7 time series. ESRI Press, Redland
• 37. Miao C, Ashouri H, Hsu KL, Sorooshian S, Duan Q (2015) Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J Hydrometeorol 16:1387–1396
• 38. Moberg A, Jones PD, Lister D, Walther A, Brunet M, Jacobeit J, Alexander LV, Della-Marta PM, Luterbacher J, Yiou P, Chen D, Klein Tank AMG, Saladie O, Sigro J, Aguilar E, Alexandersson H, Almarza C, Auer I, Barriendos M, Begert M, Bergström H, Böhm R, Butler CJ, Caesar J, Drebs A, Founda D, Gerstengarbe FW, Micela G, Maugeri M, Osterle H, Pandzic K, Petrakis M, Srnec L, Tolasz R, Tuomenvirta H, Werner PC, Linderholm H, Philipp A, Wanner H, Xoplaki E (2006) Indices for daily temperature and precipitation extremes in Europe analyzed for the period 1901–2000. J Geophys Res 111(D22106):1–25
• 39. NASEM (National Academies of Sciences, Engineering, and Medicine) (2016) Attribution of Extreme Weather Events in the Context of Climate Change. The National Academic Press, Washington, DC
• 40. Neves C, Fraga-Alves MI (2008) Testing extreme value conditions—an overview and recent approaches. Revstat 6:83–100
• 41. O’Gorman PA (2014) Contrasting responses of mean and extreme snowfall to climate change. Nature 512:416–418
• 42. Osborn TJ, Hulme M, Jones PD, Basnett TA (2000) Observed trends in the daily intensity of United Kingdom precipitation. Int J Climatol 20:347–364
• 43. Pearson K (1895) Contributions to the mathematical theory of evolution. II. Skew variation in homogeneous material. Philos Trans R Soc Lond A 186:343–414
• 44. Pendergrass A (2018) What precipitation is extreme? Science 360(6393):1072–1073
• 45. Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Torok S, Auer I, Böhm R, Gullett D, Vincent L, Heino R, Tuomenvirta H, Mestre O, Szentimrey T, Salinger J, Førland EJ, Hanssen-Bauer I, Alexandersson H, Jones PE, Parker D (1998) Homogeneity adjustments of in situ atmospheric climate data: a review. Int J Climatol 18:1493–1517
• 46. Przybylak R, Vizi Z, Araźny A, Kejna M, Maszewski R, Uscka-Kowalkowska J (2007) Poland's Climate Extremes Index, 1951–2005. Geogr Pol 80:47–58
• 47. Raes D (2013) Frequency analysis of rainfall data. College on Soil Physics- 30th Anniversary (1983–2013). The Abdus Salam International Centre for Theoretical Physics, Trieste, pp 1–42
• 48. Rajczak J, Pall P, Schär C (2013) Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine region. J Geophys Res Atmos 118:3610–3626
• 49. Respighi L (1857) Notizie sul clima bolognese. Rendiconto delle sessioni della R. Accademia delle Scienze dell'Istituto di Bologna. San Tomaso d’Aquino, Bologna
• 50. Salack S, Saley IA, Bliefernicht J (2018) Observed data of extreme rainfall events over the West African Sahel. Data Brief 20:1274–1278
• 51. Salvadori G, De Michele C (2013) Multivariate extreme value methods. In: AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds) Extremes in a changing climate: detection, analysis and uncertainty. Springer, Dordrecht, pp 115–162
• 52. Schär C, Ban N, Fischer EM, Rajczak J, Schmidli J, Frei C, Giorgi F, Karl TR, Kendon EJ, Klein Tank AMG, O’Gorman PA, Sillmann J, Zhang X, Zwiers FV (2016) Percentile indices for assessing changes in heavy precipitation events. Clim Chang 137:201–216
• 53. Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 2. Future climate projections. J Geophys Res Atmos 118:1–21
• 54. Sneyers R (1990) On the statistical analysis of series of observations. Technical note WMO No 415. World Meteorological Organization, Geneva
• 55. Song JH, de Prado ML, Simon HD, Wu K (2014) Exploring irregular time series through non-uniform fast Fourier transform. In: WHPCF '14: Proceedings of the 7th Workshop on High Performance Computational Finance, IEE Computer Society, pp 37–44
• 56. Stanke C, Kerac M, Prudhomme C, Medlock J, Murray V (2013) Health effects of drought: a systematic review of the evidence. PLoS Curr. https://doi.org/10.1371/currents.dis.7a2cee9e980f91ad7697b570bcc4b004
• 57. Stedinger J, Vogel R, Foufoula-Georgiou E (1993) Frequency analysis of extreme events. In: Maidment D (ed) Handbook of hydrology. McGraw-Hill, New York, pp 181–1868
• 58. Tiago de Oliveira J (1986) Extreme values and meteorology. Theor Appl Climatol 37(4):184–193
• 59. Todd B, Macdonald N, Chiverrell RC (2015) Revision and extension of the composite Carlisle rainfall record, northwest England: 1757–2012. Int J Climatol 35:3593–3607
• 60. Tu JY, Chou C (2013) Changes in precipitation frequency and intensity in the vicinity of Taiwan: typhoon versus non-typhoon events. Environ Res Lett 8:014023
• 61. Wasko C, Lu WT, Mehotra R (2018) Relationship of extreme precipitation, dry-bulb temperature, and dew point temperature across Australia. Environ Res Lett 13:074031
• 62. Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech Trans ASME 18(3):293–297
• 63. Wijngaard J, Klein Tank AMG, Können GP (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23(6):679–692
• 64. Wilks DS (2011) Statistical methods in the atmospheric sciences. International geophysics series, 3rd edn. Academic Press, Oxford
• 65. WMO (2016) Guidelines on the Definition and Monitoring of Extreme Weather and Climate Events—TT-DEWCE WMO 4/14/2016. World Meteorological Organization, Geneva
• 66. WMO (2018) Guidance on the homogenization of climate station data. EarthArXiv World Meteorological Organization, Geneva
• 67. Wright DJ (1986) Forecasting data published at irregular time intervals using extension of Holt’s method. Manage Sci 32(4):499–510
• 68. Yevjevich V, Hatrmancioglu NB (1987) Research needs on flow characteristics. In: Singh VP (ed) Application of frequency and risk in water resources. Reidel, Dordrecht, pp 1–22
• 69. Yu T, Chawla N, Simoff S (2013) computational intelligent data analysis for sustainable development. CRC Press, Boca Raton
• 70. Zhang X, Alexander L, Hegerl GC, Jones PD, Klein Tank A, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Chang 2:851–870

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

Identyfikatory

DOI

10.1007/s11600-020-00452-x