Advertisement

Consistent scale-dependency of future increases in hourly extreme precipitation in two convection-permitting climate models

  • Samuel HelsenEmail author
  • Nicole P. M. van Lipzig
  • Matthias Demuzere
  • Sam Vanden Broucke
  • Steven Caluwaerts
  • Lesley De Cruz
  • Rozemien De Troch
  • Rafiq Hamdi
  • Piet Termonia
  • Bert Van Schaeybroeck
  • Hendrik Wouters
Article

Abstract

Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change, contrasting sensitivities with respect to resolution (CPM vs. models with parameterized convection) are found for different parts of the world. In this study, we explore to which extent this contrasting sensitivity is due to the specific characteristics of the model or due to the characteristics of the region. Therefore, we examine the results of 360 years of climate model data from two different climate models (COSMO-CLM driven by EC-EARTH and ALARO-0 driven by CNRM ARPEGE) both at convection-permitting scale (CPS, ~ 3 km resolution) and non-convection-permitting scale (non-CPS, 12.5 km resolution) over two distinct regions (flatland vs. hilly region) in Belgium. We found that both models show an overall consistent scale-dependency of the future increase in hourly extreme precipitation for day-time. More specifically, both models yield a larger discrepancy in the day-time climate change signal between CPS and non-CPS for extreme precipitation over flatland (Flanders) than for orographically induced extreme precipitation (Ardennes). This result is interesting, since both RCMs are very different (e.g., in terms of model physics and driving GCM) and use very different ways to represent deep convection processes. Despite those model differences, the scale-dependency of projected precipitation extremes is surprisingly similar in both models, suggesting that the this scale-dependency is more dependent on the characteristics of the region, than on the model used.

Keywords

CORDEX.be Convection-permitting simulations COSMO-CLM ALARO-0 Extreme hourly precipitation Climate change Parameterization 

Notes

Acknowledgements

The work presented here received funding from the Belgian federal government (Belgian Science Policy Office project BR/143/A2/CORDEX.be) and by the European Research Council (ERC), under Grant Agreement No. 715254 (DRY-2-DRY). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation –Flanders (FWO) and the Flemish Government—department EWI. The hourly observational data was provided by VMM (Flemish Environmental Agency). These datasets are available at these institutions upon request. The climate model data used in this study can be requested through the CORDEX.be project website (http://www.euro-corde.be). Finally, we would especially like to thank Erik Van Meijgaard, for providing us with the EC-EARTH GCM data, and for several constructive discussions.

Supplementary material

382_2019_5056_MOESM1_ESM.docx (1.4 mb)
Supplementary material 1 (DOCX 1436 kb)

References

  1. Baldauf M, Schulz JP (2004) Prognostic precipitation in the Lokal-Modell (LM) of DWD. COSMO Newsletter 4:77–180Google Scholar
  2. Ban N, Schmidli J, Schär C (2014) Evaluation of the new convective-resolving regional climate modeling approach in decade-long simulations. J Geophys Res Atmos 119:7889–7907CrossRefGoogle Scholar
  3. Ban N, Schmidli J, Schär C (2015) Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster? Geophys Res Lett 42(4):1165–1172CrossRefGoogle Scholar
  4. Brisson E, Demuzere M, Van LIpzig N (2015) Modelling strategies for performing convection-permitting climate simulations. Meteorol Z 25(2):149–163CrossRefGoogle Scholar
  5. Brisson E, Van Weverberg K, Demuzere M, Devis A, Saeed S, Stengel M, van Lipzig NPM (2016) How well can a convection-permitting climate model reproduce decadal statistics of precipitation, temperature and cloud characteristics? Clim Dyn 47(9–10):3043–3061CrossRefGoogle Scholar
  6. Bubnová R, Hello G, Bénard P, Geleyn J-F (1995) Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/Aladin NWP System. Mon Weather Rev 123(2):515–535.  https://doi.org/10.1175/1520-0493%281995%29123%3C0515%3AIOTFEE%3E2.0.CO%3B2[Online] CrossRefGoogle Scholar
  7. Chan SC, Kendon EJ, Fowler HJ, Blenkinsop S, Ferro CAT, Stephenson DB (2013) Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation? Clim Dyn 41(5–6):1475–1495CrossRefGoogle Scholar
  8. Chan SC, Kendon EJ, Fowler HJ, Blenkinsop S, Roberts NM (2014a) Projected increases in summer and winter UK sub-daily precipitation extremes from high-resolution regional climate models. Environ Res Lett 9(8):084019CrossRefGoogle Scholar
  9. Chan SC, Kendon EJ, Fowler HJ, Blenkinsop S, Roberts NM, Ferro CAT (2014b) The value of high-resolution Met Office regional climate models in the simulation of multihourly precipitation extremes. J Clim 27(16):6155–6174CrossRefGoogle Scholar
  10. Coppola E, Sobolovski S, Pichelli E, Rafaelle F, Ahrens B, Anders I, Ban N, Belda M, Belusic D, Caldaz-Alvares A, Cardoso RM, Davolio S, Dobler A, Fita L, Fumiere Q, Giorgi F, Goergen K, Güttler I, Halenka T, Heinzeller D, Hodnebrog Q, Jacob D, Kartsios S, Katragkou E, Kendon E, Khodayar S, Kunstmann H, Knist S, Lavín-Gullón A, Lind P, Lorenz T, Marau D, Marelle L, van Meijgaard E, Milovac J, Myhre G, Panitz H-J, Piazza M, Raffa M, Raub T, Rockel B, Schär C, Sieck K, Soares PMM, Somot S, Srnec L, Stocchi P, Tölle MH, Truhetz H, Vautard R, de Vries H, Warrach-Sagi K (2018) A first-of-its-kind multi-model convection-permitting ensemble for investigating comvective phenomena over Europe and the Mediterranean. Clim Dyn.  https://doi.org/10.1007/s00382-018-4521-8 CrossRefGoogle Scholar
  11. Cubasch U, Bréon F, Uk PF, Friedlingstein P, France PC, Uk MC, Josefino C (2013) IPCC, summary for policy makersGoogle Scholar
  12. Demuzere M, Harshan S, Järvi L, Roth M, Grimmond CSB, Masson V, Oleson KW, Velasco E, Wouters H (2017) Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city. Q J R Meteorol Soc 143(704):1581–1596CrossRefGoogle Scholar
  13. De Troch R (2016) The application of the ALARO-0 model for regional climate modeling in Belgium : extreme precipitation and unfavorable conditions for the dispersion of air pollutants under present and future climate condiitiions. https://biblio.ugent.be/publication/7247081
  14. De Troch R, Hamdi R, Van de Vyver H, Geleyn JF, Termonia P (2013) Multiscale performance of the ALARO-0 model for simulating extreme summer precipitation climatology in Belgium. J Clim 26(22):8895–8915CrossRefGoogle Scholar
  15. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, Mcnally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  16. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3–4):527–546CrossRefGoogle Scholar
  17. Doms G, Förstner J, Heise E, Herzog H-J, Mironov D, Raschendorfer M, Reinhardt T, Ritter B, Schrodin R, Schulz J-P, Vogel G (2011) Consortium for small-scale modelling a description of the nonhydrostatic regional COSMO model part II : physical parameterization. Www.Cosmo-Model.Org (September), pp 152
  18. Fosser G, Khodayar S, Berg P (2014) Benefit of convection permitting climate model simulations in the representation of convective precipitation. Clim Dyn 44(1–2):45–60Google Scholar
  19. Fosser G, Khodayar S, Berg P (2017) Climate change in the next 30 years: what can a convection-permitting model tell us that we did not already know? Clim Dyn 48(5–6):1987–2003CrossRefGoogle Scholar
  20. Gao Y, Leung LR, Zhao C, Hagos S (2017) Sensitivity of US summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J Geophy Res 122(5):2714–2733Google Scholar
  21. Geleyn JF, Catry B, Bouteloup Y, Brožková R (2008) A statistical approach for sedimentation inside a microphysical precipitation scheme. Tellus Ser A Dyn Meteorol Oceanogr 60A(4):649–662CrossRefGoogle Scholar
  22. Gerard L (2007) On the boundary‐layer structure over highly complex terrain: key findings from MAP. Q J R Meteorol Soc 133:937–948. Available from: http://onlinelibrary.wiley.com/doi/10.1002/qj.71/abstract. Accessed June 2018
  23. Gerard L, Geleyn JF (2005) Evolution of a subgrid deep convection parametrization in a limited-area model with increasing resolution. Q J R Meteorol Soc 131(610B):2293–2312CrossRefGoogle Scholar
  24. Gerard L, Piriou J-M, Brožková R, Geleyn J-F, Banciu D (2009) Cloud and precipitation parameterization in a meso-gamma-scale operational weather prediction model. Mon Weather Rev 137(11):3960–3977.  https://doi.org/10.1175/2009MWR2750.1[Online] CrossRefGoogle Scholar
  25. Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull 58(3):175Google Scholar
  26. Giot O, Termonia P, Degrauwe D, De Troch R, Caluwaerts S, Smet G, Berckmans J, Deckmyn A, De Cruz L, De Meutter P, Duerinckx A, Gerard L, Hamdi R, Van Den Bergh J, Van Ginderachter M, Van Schaeybroeck B (2016) Validation of the ALARO-0 model within the EURO-CORDEX framework. Geosci Model Dev 9(3):1143–1152CrossRefGoogle Scholar
  27. Goudenhoofdt E, Delobbe L (2013) Statistical characteristics of of convective storms in Belgium derived from volumetric weather radar observations. J Appl Meteorol Climatol 52:918–934CrossRefGoogle Scholar
  28. Hohenegger C, Brockhaus P, Schär C (2008) Towards climate simulations at cloud-resolving scales. Meteorol Zeitschrift 17(4):383–394CrossRefGoogle Scholar
  29. Hohenegger C, Brockhaus P, Bretherton CS, Schär C (2009) The soil moisture-precipitation feedback in simulations with explicit and parameterized convection. J Clim 22(19):5003–5020CrossRefGoogle Scholar
  30. Kendon EJ, Roberts NM, Senior CA, Roberts MJ (2012) Realism of rainfall in a very high-resolution regional climate model. J Clim 25(17):5791–5806CrossRefGoogle Scholar
  31. 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 Change 4(7):570–576CrossRefGoogle Scholar
  32. Kendon EJ, Ban N, Roberts NM, Fowler HJ, Roberts MJ, Chan SC, Evans JP, Fosser G, Wilkinson JM (2017) Do convection-permitting regional climate models improve projections of future precipitation change? Bull Am Meteorol Soc 98(1):79–93CrossRefGoogle Scholar
  33. Kendon EJ, Stratton RA, Tucker S, Marsham JH, Berthou S, Rowell DP, Senior CA (2019) enhanced future changes in wet and dry extremes over Africa at convection-permitting scale. Nat Commun 10:1794CrossRefGoogle Scholar
  34. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23(10):2739–2758CrossRefGoogle Scholar
  35. Langhans W, Schmidli J, Fuhrer O, Bieri S, Schar C (2013) Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloud-resolving resolutions. J Appl Meteorol Clim 52(6):1490–1510CrossRefGoogle Scholar
  36. Maraun D, Wetterhall F, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: Recent developements to bridge the gap between dynamical models and the end user. Rev Geophys 48:1–38CrossRefGoogle Scholar
  37. Mooney PA, Broderick C, Bruyère CL, Mulligan FJ, Prein AF (2017) Clustering of observed diurnal cycles of precipitation over the United States for evaluation of a WRF multiphysics regional climate ensemble. J Clim 30(22):9267–9286CrossRefGoogle Scholar
  38. Noilhan J, Planton S (1989) A simple parameterization of land surface processes for meteorological models. Mon Weather Rev 117(3):536–549.  https://doi.org/10.1175/1520-0493%281989%29117%3C0536%3AASPOLS%3E2.0.CO%3B2[Online] CrossRefGoogle Scholar
  39. Olsson J, Berg P, Kawamura A (2015) Impact of RCM Spatial Resolution on the Reproduction of Local. Subdaily Precipitation. J Hydrometeorol 16(2):534–547.  https://doi.org/10.1175/JHM-D-14-0007.1[Online] CrossRefGoogle Scholar
  40. Pendergrass AG (2018) What precipitation is extreme? Science 360(6393):1072–1073CrossRefGoogle Scholar
  41. Prein AF, Gobiet A, Suklitsch M, Truhetz H, Awan NK, Keuler K, Georgievski G (2013) Added value of convection permitting seasonal simulations. Clim Dyn 41(9–10):2655–2677CrossRefGoogle Scholar
  42. Prein AF, Langhans W, Fosser G, Ferrone A, Ban N, Goergen K, Keller M, Tölle M, Gutjahr O, Feser F, Brisson E, Kollet S, Schmidli J, Van Lipzig NPM, Leung R (2015) A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev Geophys 53(2):323–361CrossRefGoogle Scholar
  43. Prein AF, Gobiet A, Truhetz H, Keuler K, Goergen K, Teichmann C, Fox Maule C, van Meijgaard E, Déqué M, Nikulin G, Vautard R, Colette A, Kjellström E, Jacob D (2016) Precipitation in the EURO-CORDEX 011° and 044° simulations: high resolution, high benefits? Clim Dyn 46(1–2):383–412CrossRefGoogle Scholar
  44. Revadekar JV, Patwardhan SK, Rupa Kumar K (2011) Characteristic Features of prcipitation extremes over India in the warming scenarios. Adv Meteorol 2011:01–11CrossRefGoogle Scholar
  45. Riahi K, Rao S, Krey V et al (2011) Clim Change 109:33.  https://doi.org/10.1007/s10584-011-0149-y CrossRefGoogle Scholar
  46. Ritter B, Geleyn J-F (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Weather Rev 120(2):303–325.  https://doi.org/10.1175/1520-0493%281992%29120%3C0303%3AACRSFN%3E2.0.CO%3B2[Online] CrossRefGoogle Scholar
  47. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Zeitschrift 17(4):347–348CrossRefGoogle Scholar
  48. Saeed S, Brisson E, Demuzere M, Tabari H, Willems P, van Lipzig NPM (2017) Multidecadal convection permitting climate simulations over Belgium: sensitivity of future precipitation extremes. Atmos Sci Lett 18(1):29–36CrossRefGoogle Scholar
  49. Schär C, Ban N, Fischer EM, Rajczak J, Schmidli J, Frei C, Giorgi F, Karl TR, Kendon EJ, Tank AMK, O’Gorman PA (2016) Percentile indices for assessing changes in heavy precipitation events. Clim Chang 137(1–2):201–216CrossRefGoogle Scholar
  50. Shi X, Durran D (2016) Sensitivities of extreme precipitation to global warming are lower over mountains than over oceans and plains. J Clim 29:4779–4791.   https://doi.org/10.1175/JCLI-D-15-0576.1 CrossRefGoogle Scholar
  51. Stratton RA, Senior CA, Vosper SB, Folwell SS, Boutle JA, Earnshaw PD, Kendon E, Lock A, Malcolm A, Manners J, Morcrette CJ, Short C, Stirling AJ, Taylor CM, Tucker S, Webster S, Wilkinson JM (2018) A pan-african convection-permitting regional climate simulation with the met office unified model: CP4-Africa. J Clim 31:3485–3508CrossRefGoogle Scholar
  52. Tabari H, De Troch R, Giot O, Hamdi R, Termonia P, Saeed S, Brisson E, Van Lipzig N, Willems P (2016) Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations? Hydrol Earth Syst Sci 20(9):3843–3857CrossRefGoogle Scholar
  53. Taylor CM, Birch CE, Parker DJ, Dixon N, Guichard F, Nikulin G, Lister GM (2013) Modeling soil moisture-precipitation feedback in the Sahel: importance of spatial scale versus convective parameterization. Geophys Res Lett 40(23):6213–6218CrossRefGoogle Scholar
  54. Termonia P, Van Schaeybroeck B, De Cruz L, De Troch R, Caluwaerts S, Giot O, Hamdi R, Vannitsem S, Duchêne F, Willems P, Tabari H, Van Uytven E, Hosseinzadehtalaei P, Van Lipzig N, Wouters H, Vanden Broucke S, van Ypersele JP, Marbaix P, Villanueva-Birriel C, Fettweis X, Wyard C, Scholzen C, Doutreloup S, De Ridder K, Gobin A, Lauwaet D, Stavrakou T, Bauwens M, Müller JF, Luyten P, Ponsar S, Van den Eynde D, Pottiaux E (2018) The CORDEX.be initiative as a foundation for climate services in Belgium. Clim Serv 11:49–61CrossRefGoogle Scholar
  55. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Wea Rev 117(8):1779–1800CrossRefGoogle Scholar
  56. Váňa F, Bénard P, Geleyn J, Simon A, Seity Y (2008) Semi-Lagrangian advection scheme with controlled damping: an alternative to nonlinear horizontal diffusion in a numerical weather prediction model. Q J R Meteorol Soc 134:523–537.   https://doi.org/10.1002/qj.220 CrossRefGoogle Scholar
  57. Van Lipzig N, Willems P (2015) Actualisatie en verfijning klimaatscenario’s tot 2100 voor Vlaanderen: appendix 1 [Online]. https://www.milieurapport.be (Flemish source, climate report)
  58. Van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Change 109:5CrossRefGoogle Scholar
  59. Vanden Broucke S, Van Lipzig N (2017) Do convection-permitting models improve the representation of the impact of LUC? Clim Dyn 49(7):2749–2763CrossRefGoogle Scholar
  60. Vanden Broucke S, Wouters H, Demuzere M, Van Lipzig PMN (2018) The influence of convection-permitting regional climate modeling on future projections of extreme precipitation: dependency on topography and timescale. Clim Dyn.  https://doi.org/10.1007/s00382-018-4454-2 CrossRefGoogle Scholar
  61. Wang Y, Leung LR, Mcgregor JL, Lee D, Wang W, Ding Y, Kimura F (2004) Regional Clim Model: Prog Challenges Prosp 82:1599–1628Google Scholar
  62. Warrach-Sagi K, Schwitalla T, Wulfmeyer V, Bauer HS (2013) Evaluation of a climate simulation in Europe based on the WRF-NOAH model system: precipitation in Germany. Clim Dyn 41(3–4):755–774CrossRefGoogle Scholar
  63. Weisman ML, Skamarock WC, Klemp JB (1997) The resolution dependence of explicitly modeled convective systems. Mon Weather Rev 125(4):527–548.  https://doi.org/10.1175/1520-0493%281997%29125%3C0527%3ATRDOEM%3E2.0.CO%3B2[Online] CrossRefGoogle Scholar
  64. Wouters H, De Ridder K, Van Lipzig N (2012) Comprehensive parametrization of surface-layer transfer coefficients for use in atmospheric numerical models. Bound Layer Meteorol 145(3):539–550.  https://doi.org/10.1007/s10546-012-9744-3 CrossRefGoogle Scholar
  65. Wouters H, Demuzere M, De Ridder K, Van Lipzig NPM (2015) The impact of impervious water-storage parametrization on urban climate modelling. Urban Clim 11:24–50.  https://doi.org/10.1016/j.uclim.2014.11.005[Online] CrossRefGoogle Scholar
  66. Wouters H, Demuzere M, Blahak U, Fortuniak K, Maiheu B, Camps J, Tielemans D, Van Lipzig NPM (2016) The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer. Geosci Model Dev 9(9):3027–3054CrossRefGoogle Scholar
  67. Wouters H, De Ridder K, Poelmans L, Willems P, Brouwers J, Hosseinzadehtalaei P, Tabari H, Vanden Broucke S, Van Lipzig N, Demuzere M (2017) Heat stress increase under climate change twice as large in cities as in rural areas: a study for a densely populated midlatitude maritime region. Geophys Res Lett 44(17):8997–9007.  https://doi.org/10.1002/2017GL074889 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Samuel Helsen
    • 1
    Email author
  • Nicole P. M. van Lipzig
    • 1
  • Matthias Demuzere
    • 1
    • 2
    • 5
  • Sam Vanden Broucke
    • 1
  • Steven Caluwaerts
    • 4
  • Lesley De Cruz
    • 3
  • Rozemien De Troch
    • 3
  • Rafiq Hamdi
    • 3
  • Piet Termonia
    • 3
  • Bert Van Schaeybroeck
    • 3
  • Hendrik Wouters
    • 1
    • 2
    • 6
  1. 1.Division Geography and Tourism, Department Earth and Environmental SciencesKU LeuvenLouvainBelgium
  2. 2.Laboratory of Hydrology and Water ManagementGhent UniversityGhentBelgium
  3. 3.Royal Meteorological InstituteUccleBelgium
  4. 4.Department of Physics and AstronomyGhent UniversityGhentBelgium
  5. 5.Department of GeographyRuhr-University BochumBochumGermany
  6. 6.Flemish Institute for Technological Research (VITO)MolBelgium

Personalised recommendations