Abstract
A recent period of increased precipitation over the Argentinian Pampas expanded the boundary of rain-fed agriculture. However, such changes may not be sustainable if they arose from transient climate regime shifts. Considerable research exists on trends and cycles in sub-daily to annual precipitation metrics including the frequency and intensity of extreme precipitation. However, efforts to identify wetter and drier phases (or regimes) in this region are scant. This article aims to bridge that gap and advance our understanding of the multi-annual behavior of regional precipitation extremes, which can have the greatest impacts. It is unlikely that all extreme events are drawn from a single probability distribution or generated by the same physical processes. Hence, hidden mixtures of Poisson distributions are fitted to several precipitation frequency metrics to explore whether the annual to decadal variations in extreme precipitation frequency are greater than anticipated from a single system, and representative of regime shifts. Statistically significant improvements in the fit over single distributions were found for statistical mixture models of the frequency of very wet days, and the frequency of wet spells. This supports the hypothesis that multiple weather regimes exist giving rise to wetter or drier epochs. Posterior probabilities of hidden states from the fitted mixture distributions were used to identify wetter and drier years for comparison with sea surface temperature anomalies. This confirmed the presence of two distinct regimes, supporting other research, into the dynamical influences of precipitation behavior in the Argentine Pampas.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Joint World Meteorological Organization (WMO) Commission for Climatology (CCl)/World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR)/Joint WMO–Intergovernmental Oceanographic Commission of the United National Educational, Scientific and Cultural Organization (UNESCO) Technical Commission for Oceanography and Marine Meteorology (JCOMM) Expert Team on Climate Change Detection and Indices (ETCCDI: http://www.clivar.org/organization/etccdi).
References
Agosta EA, Compagnucci RH (2012) Central-West Argentina summer precipitation variability and atmospheric teleconnections. J Clim. https://doi.org/10.1175/JCLI-D-11-00206.1
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723
Andreoli RV, de Oliveira SS, Kayano MT, Viegas J, de Souza RAF, Candido LA (2017) The influence of different El Niño types on the South American rainfall. Int J Climatol 37(3):1374–1390. https://doi.org/10.1002/joc.4783
Andrés Ferreyra R, Podestá GP, Messina CD, Letson D, Dardanelli J, Guevara E, Meira S (2001) A linked-modeling framework to estimate maize production risk associated with ENSO-related climate variability in Argentina. Agric For Meteorol 107:177–192. https://doi.org/10.1016/S0168-1923(00)00240-9
Anyamba A, Small JL, Britch SC, Tucker CJ, Pak EW, Reynolds CA et al (2014) Recent weather extremes and impacts on agricultural production and vector-borne disease outbreak patterns. PLoS One 9(3):e92538. https://doi.org/10.1371/journal.pone.0092538
Barreiro M, Díaz N, Renom M (2014) Role of the global oceans and land–atmosphere interaction on summertime interdecadal variability over northern Argentina. Clim Dyn 42(7–8):1733–1753. https://doi.org/10.1007/s00382-014-2088-6
Barros VR, Boninsegna JA, Camilloni IA, Chidiak M, Magrín GO, Rusticucci M (2015) Climate change in Argentina: trends, projections, impacts and adaptation. Wiley Interdiscip Rev Clim Change 6(2):151–169. https://doi.org/10.1002/wcc.316
Bellone E, Hughes JP, Guttorp P (2000) A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Clim Res 15(1):1–12
Benaglia T, Chauveau D, Hunter DR, Young D (2009) mixtools: an R package for analyzing finite mixture models. J Stat Softw 32(6):1–29. https://doi.org/10.18637/jss.v032.i06
Bettolli ML, Penalba OC (2014) Synoptic sea level pressure patterns-daily rainfall relationship over the Argentine Pampas in a multi-model simulation. Meteorol Appl 21(2):376–383. https://doi.org/10.1002/met.1349
Boers N, Rheinwalt A, Bookhagen B, Barbosa HMJ, Marwan N, Marengo J, Kurths J (2014) The South American rainfall dipole: a complex network analysis of extreme events. Geophys Res Lett 41(20):7397–7405. https://doi.org/10.1002/2014GL061829
Carey-Smith T, Sansom J, Thomson P (2014) A hidden seasonal switching model for multisite daily rainfall. Water Resour Res 50(1):257–272. https://doi.org/10.1002/2013WR014325
Carril A, Cavalcanti I, Menéndez C, Sörensson A, López-Franca N, Rivera J et al (2016) Extreme events in the La Plata basin: a retrospective analysis of what we have learned during CLARIS-LPB project. Clim Res 68(2–3):95–116. https://doi.org/10.3354/cr01374
Cavalcanti IFA (2012) Large scale and synoptic features associated with extreme precipitation over South America: a review and case studies for the first decade of the 21st century. Atmos Res 118:27–40. https://doi.org/10.1016/j.atmosres.2012.06.012
Cavalcanti IFA, Carril AF, Penalba OC, Grimm AM, Menéndez CG, Sanchez E et al (2015) Precipitation extremes over La Plata Basin—review and new results from observations and climate simulations. J Hydrol 523:211–230. https://doi.org/10.1016/j.jhydrol.2015.01.028
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, Berlin (Springer Series in Statistics)
Coles S, Pericchi L (2003) Anticipating catastrophes through extreme value modelling. J R Stat Soc Ser C (Appl Stat) 52(4):405–416. https://doi.org/10.2307/3592756
de la Casa A, Nasello O (2010) Breakpoints in annual rainfall trends in Córdoba, Argentina. Atmos Res 95(4):419–427. https://doi.org/10.1016/j.atmosres.2009.11.005
Dempster AP, Laird NM, Rubin DB (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–38
Diaz L, Vera C, Saurral R (2017) Observed and simulated summer rainfall variability in southeastern South America. CLIVAR Exchanges 71:13–16
Do CB, Batzoglou S (2008) What is the expectation maximization algorithm? Nat Biotechnol 26(8):897–899. https://doi.org/10.1038/nbt1406
Doyle ME, Saurral RI, Barros VR (2012) Trends in the distributions of aggregated monthly precipitation over the La Plata Basin. Int J Climatol 32(14):2149–2162. https://doi.org/10.1002/joc.2429
Ferro CAT, Segers J (2003) Inference for clusters of extreme values. J R Stat Soc Ser B (Methodol) 65(2):545–556. https://doi.org/10.1111/1467-9868.00401
Furrer EM, Katz RW (2008) Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resour Res. https://doi.org/10.1029/2008WR007316
Grimm AM, Barros VR, Doyle ME (2000) Climate variability in Southern South America Associated with El Niño and La Niña Events. J Clim 13(1):35–58. https://doi.org/10.1175/1520-0442(2000)013%3C0035:CVISSA%3E2.0.CO;2
Grondona MO, Podestá GP, Bidegain M, Marino M, Hordij H (2000) A stochastic precipitation generator conditioned on ENSO phase: a case study in Southeastern South America. J Clim 13(16):2973–2986. https://doi.org/10.1175/1520-0442(2000)013%3C2973:ASPGCO%3E2.0.CO;2
Haylock MR, Peterson TC, Alves LM, Ambrizzi T, Anunciação YMT, Baez J et al (2006) Trends in total and extreme South American rainfall in 1960–2000 and Links with Sea surface temperature. J Clim. https://doi.org/10.1175/JCLI3695.1
Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535. https://doi.org/10.1029/93WR02983
Hughes JP, Guttorp P, Charles SP (1999) A non-homogeneous hidden Markov model for precipitation occurrence. J R Stat Soc Ser C (Appl Stat) 48(1):15–30. https://doi.org/10.1111/1467-9876.00136
IPCC (2013) Climate Change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J et al (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781107415324
Jones PD, Lister DH, Harpham C, Rusticucci M, Penalba O (2013) Construction of a daily precipitation grid for southeastern South America for the period 1961–2000. Int J Climatol 33(11):2508–2519. https://doi.org/10.1002/joc.3605
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471
Katz RW, Brown BG (1992) Extreme events in a changing climate: Variability is more important than averages. Clim Change 21(3):289–302. https://doi.org/10.1007/BF00139728
Katz RW, Zheng X (1999) Mixture model for overdispersion of precipitation. J Clim 12(8) 2528–2537. https://doi.org/10.1175/1520-0442(1999)012%3C2528:MMFOOP%3E2.0.CO;2
Kayano MT, Andreoli RV (2007) Relations of South American summer rainfall interannual variations with the Pacific Decadal Oscillation. Int J Climatol 27(4):531–540. https://doi.org/10.1002/joc.1417
Klemeš V (1987) Hydrological and engineering relevance of flood frequency analysis BT—hydrologic frequency modeling: proceedings of the international symposium on flood frequency and risk analyses 14–17 May 1986, Louisiana State University, Baton Rouge, U.S.A. In Singh VP (ed.), (pp. 1–18). Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3953-0_1
Kundzewicz ZW, Robson AJ (2004) Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques. Hydrol Sci J 49(1):7–19. https://doi.org/10.1623/hysj.49.1.7.53993
Labraga JC, Scian B, Frumento O (2002) Anomalies in the atmospheric circulation associated with the rainfall excess or deficit in the Pampa Region in Argentina. J Geophys Res Atmos 107(D23):4666. https://doi.org/10.1029/2002JD002113
Liebmann B, Vera CS, Carvalho LMV, Camilloni IA, Hoerling MP, Allured D et al (2004) An observed trend in Central South American precipitation. J Clim 17(22):4357–4367. https://doi.org/10.1175/3205.1
Mailier PJ, Stephenson DB, Ferro CAT, Hodges KI (2006) Serial clustering of extratropical cyclones. Mon Weather Rev 134(8):2224–2240. https://doi.org/10.1175/MWR3160.1
Marani M, Zanetti S (2015) Long-term oscillations in rainfall extremes in a 268 year daily time series. Water Resour Res 51(1):639–647. https://doi.org/10.1002/2014WR015885
Martín-Gómez V, Hernández-Garcia E, Barreiro M, López C (2016) Interdecadal Variability of Southeastern South America Rainfall and Moisture Sources during the Austral Summertime. J Clim 29(18):6751–6763. https://doi.org/10.1175/JCLI-D-15-0803.1
Naveau P, Huser R, Ribereau P, Hannart A (2016) Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection. Water Resour Res 52(4):2753–2769. https://doi.org/10.1002/2015WR018552
Neukom R, Luterbacher J, Villalba R, Küttel M, Frank D, Jones PD et al (2010) Multi-centennial summer and winter precipitation variability in southern South America. Geophys Res Lett 37(14):L14708. https://doi.org/10.1029/2010GL043680
Olascoaga MJ (1950) some aspects of Argentine rainfall. Tellus 2(4):312–318. https://doi.org/10.1111/j.2153-3490.1950.tb00341.x
Penalba OC, Rivera JA (2016) Precipitation response to El Niño/La Niña events in Southern South America—emphasis in regional drought occurrences. Adv Geosci 42:1–14. https://doi.org/10.5194/adgeo-42-1-2016
Penalba OC, Vargas WM (2008) Variability of low monthly rainfall in La Plata Basin. Meteorol Appl 15(3):313–323. https://doi.org/10.1002/met.68
Penalba OC, Rivera JA, Pántano VC (2014) The CLARIS LPB database: constructing a long-term daily hydro-meteorological dataset for La Plata Basin, Southern South America. Geosci Data J 1(1):20–29. https://doi.org/10.1002/gdj3.7
Pérez S, Sierra E, Momo F, Massobrio M (2015) Changes in average annual precipitation in Argentina’s Pampa region and their possible causes. Climate 3(1):150–167. https://doi.org/10.3390/cli3010150
Peterson TC, Folland CK, Gruza G, Hogg W, Mokssit A, Plummer N (2001) Report on the activities of the working group on climate change detection and related rapporteurs 1998–2001. (WMO, Ed.) (Vol. WCDMP-47). Geneve, Switzerland
Podestá G, Bert F, Rajagopalan B, Apipattanavis S, Laciana C, Weber E et al (2009) Decadal climate variability in the Argentine Pampas: regional impacts of plausible climate scenarios on agricultural systems. Clim Res 40:199–210. https://doi.org/10.3354/cr00807
Prein AF, Holland GJ, Rasmussen RM, Clark MP, Tye MR (2016) Running dry: the U.S. Southwest’s drift into a drier climate state. Geophys Res Lett. https://doi.org/10.1002/2015GL066727
R Core Team (2017) R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.r-project.org/. Accessed 15 Mar 2018
Rivera JA, Penalba OC, Betolli ML (2013) Inter-annual and inter-decadal variability of dry days in Argentina. Int J Climatol 33(4):834–842. https://doi.org/10.1002/joc.3472
Robledo FA, Vera C, Penalba OC (2016) Influence of the large-scale climate variability on daily rainfall extremes over Argentina. Int J Climatol 36(1):412–423. https://doi.org/10.1002/joc.4359
Rosenzweig C, Iglesias A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events; implications for food production, plant diseases, and pests. Global Change Human Health 2(2):90–104
Rusticucci M (2012) Observed and simulated variability of extreme temperature events over South America. Atmos Res 106:1–17. https://doi.org/10.1016/j.atmosres.2011.11.001
Shin J-Y, Lee T, Ouarda TBMJ (2015) Heterogeneous mixture distributions for modeling multisource extreme rainfalls. J Hydrometeorol 16(6):2639–2657. https://doi.org/10.1175/JHM-D-14-0130.1
Skansi MDLM, Brunet M, Sigró J, Aguilar E, Arevalo Groening JA, Bentancur OJ et al (2013) Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Global Planet Change 100:295–307. https://doi.org/10.1016/j.gloplacha.2012.11.004
Smith JA, Villarini G, Baeck ML (2011) Mixture distributions and the hydroclimatology of extreme rainfall and flooding in the Eastern United States. J Hydrometeorol 12(2):294–309. https://doi.org/10.1175/2010JHM1242.1
Tedeschi RG, Grimm AM, Cavalcanti IFA (2014) Influence of Central and East ENSO on extreme events of precipitation in South America during austral spring and summer. Int J Climatol. https://doi.org/10.1002/joc.4106
Tye MR, Blenkinsop S, Fowler HJ, Stephenson DB, Kilsby CG (2016) Simulating multimodal seasonality in extreme daily precipitation occurrence. J Hydrol 537:117–129. https://doi.org/10.1016/j.jhydrol.2016.03.038
Vargas WM, Naumann G, Minetti JL (2011) Dry spells in the River Plata Basin: an approximation of the diagnosis of droughts using daily data. Theor Appl Climatol 104(1):159–173. https://doi.org/10.1007/s00704-010-0335-2
Verdin A, Rajagopalan B, Kleiber W, Katz RW (2014) Coupled stochastic weather generation using spatial and generalized linear models. Stoch Env Res Risk Assess 29(2):347–356. https://doi.org/10.1007/s00477-014-0911-6
Wilks DS (2011) Statistical methods in the atmospheric sciences. International geophysics series, 3rd edn. Academic Press Inc, Waltham (ISBN:9780123850225)
Xekalaki E (2004) Under- and overdispersion. In: Encyclopedia of actuarial science. Wiley, Chichester, pp 1700–1705. https://doi.org/10.1002/9780470012505.tau003
Zheng X, Katz RW (2008) Mixture model of generalized chain-dependent processes and its application to simulation of interannual variability of daily rainfall. J Hydrol 349(1–2):191–199. https://doi.org/10.1016/j.jhydrol.2007.10.061
Zhu Y, Newell RE (1998) A proposed algorithm for moisture fluxes from atmospheric rivers. Mon Weather Rev 126(3):725–735. https://doi.org/10.1175/1520-0493(1998)126%3C0725:APAFMF%3E2.0.CO;2
Zucchini W, MacDonald IL (2009) Hidden Markov models for time series: an introduction using R. CRC Press, Boca Raton
Acknowledgements
The National Center for Atmospheric Research is sponsored by the National Science Foundation (NSF). Support for this work was partially provided by NSF Grants: EaSM 1049109, CNH 0709689, and CR 1049099; and 1048829 to the NCAR Weather and Climate Assessment Program. Thanks to Guillermo Podésta for providing daily weather data for the Argentine Pampas; to Ming Ge for producing Fig. 10; to James Done for providing comments on a draft version of the manuscript; and to three anonymous reviewers who considerably improved the manuscript. All calculations were carried out in R (http://www.r-project.org/), using packages lubridate, mixtools and plyr.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tye, M.R., Katz, R.W. & Rajagopalan, B. Climate change or climate regimes? Examining multi-annual variations in the frequency of precipitation extremes over the Argentine Pampas. Clim Dyn 53, 245–260 (2019). https://doi.org/10.1007/s00382-018-4581-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-018-4581-9