[en] Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Van de Vyver, Hans
Van Schaeybroeck, Bert; Royal Meteorological Institute, Brussels, Belgium
De Troch, Rozemien; Royal Meteorological Institute, Brussels, Belgium
De Cruz, Lesley; Royal Meteorological Institute, Brussels, Belgium
Hamdi, Rafiq; Royal Meteorological Institute, Brussels, Belgium
Villanueva-Birriel, Cecille; Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Marbaix, Philippe; Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Van Ypersele, Jean-Pascal; Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Wouters, Hendrick; Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
Vanden Broucke, Sam; Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
van Lipzig, Nicole P.M.; Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
Doutreloup, Sébastien ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Wyard, Coraline ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Scholtzen, Chloé; Department of Geosciences University of Oslo, Oslo, Norway
Fettweis, Xavier ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Caluwaerts, Steven; Royal Meteorological Institute, Brussels, and Department of Physics and Astronomy, Ghent University, Ghent, Belgium
Termonia, Piet; Royal Meteorological Institute, Brussels, and Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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Bibliography
Agilan, V., and N. Umamahesh, 2016: Modelling nonlinear trend for developing non-stationary rainfall intensity–duration–frequency curve. Int. J. Climatol., 37, 1265–1281, https://doi.org/10.1002/joc.4774.
Argüeso, D., J. P. Evans, L. Fita, and K. J. Bormann, 2014: Temperature response to future urbanization and climate change. Climate Dyn., 42, 2183–2199, https://doi.org/10.1007/s00382-013-1789-6.
Ban, N., J. Schmidli, and C. Schär, 2015: Heavy precipitation in a changing climate:Does short-termsummer precipitation increase faster? Geophys. Res. Lett., 42, 1165–1172, https://doi.org/10.1002/2014GL062588.
Ban, N., and Coauthors, 2021: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: Evaluation of precipitation. Climate Dyn., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w.
Beck, H. E., E. F. Wood, M. Pan, C. K. Fisher, D. Miralles, A. I. van Dijk, T. R. McVicar, and R. F. Adler, 2019:MSWEPV2 global 3-hourly 0.18 precipitation: Methodology and quantitative assessment. Bull. Amer. Meteor. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1.
Beirlant, J., Y. Goegebeur, J. Segers, and J. Teugels, 2004: Statistics of Extremes: Theory and Applications. John Wiley and Sons, 512 pp.
Berg, P., O. B. Christensen, K. Klehmet, G. Lenderink, J. Olsson, C. Teichmann, and W. Yang, 2019: Summertime precipitation extremes in a EURO-CORDEX 0.118 ensemble at an hourly resolution. Nat. Hazards Earth Syst. Sci., 19, 957–971, https://doi.org/10.5194/nhess-19-957-2019.
Brisson, E., M. Demuzere, and N. P. van Lipzig, 2016a: Modelling strategies for performing convection-permitting climate simulations. Meteor. Z., 25, 149–163, https://doi.org/10.1127/metz/2015/0598.
Brisson, E., K. Van Weverberg, M. Demuzere, A. Devis, S. Saeed, M. Stengel, and N. P. M. van Lipzig, 2016b: How well can a convection-permitting climate model reproduce decadal statistics of precipitation, temperature and cloud characteristics?Climate Dyn., 47, 3043–3061, https://doi.org/10.1007/s00382-016-3012-z.
Bubnová, R., G. Hello, P. Bénard, and J.-F.Geleyn, 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. Wea. Rev., 123, 515–535, https://doi.org/10.1175/1520-0493(1995)123,0515:IOTFEE.2.0.CO;2.
Burlando, P., and R. Rosso, 1996: Scaling and multiscaling models of depth-duration-frequency curves for storm precipitation. J. Hydrol., 187, 45–64, https://doi.org/10.1016/S0022-1694(96)03086-7.
Chandler, R. E., and E. M. Scott, 2011: Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. John Wiley and Sons, 368 pp.
Chavez-Demoulin, V., and A. C. Davison, 2012: Modelling time series extremes. REVSTAT. Stat. J., 10, 109–133.
Cheng, L., and A. AghaKouchak, 2014: Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci. Rep., 4, 7093, https://doi.org/10.1038/srep07093.
Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, 208 pp.
Coppola, E., and Coauthors, 2020: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Climate Dyn., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8.
Cortés-Hernández, V. E., F. Zheng, J. Evans, M. Lambert, A. Sharma, and S. Westra, 2016: Evaluating regional climate models for simulating sub-daily rainfall extremes. Climate Dyn., 47, 1613–1628, https://doi.org/10.1007/s00382-015-2923-4.
Davison, A. C., and M. M. Gholamrezaee, 2011: Geostatistics of extremes. Proc. Roy. Soc., 468A, 581–608, https://doi.org/10.1098/rspa.2011.0412.
Davison, A. C., S.A. Padoan, andM.Ribatet, 2012: Statisticalmodeling of spatial extremes. Stat. Sci., 27, 161–186, https://doi.org/10.1214/11-STS376.
DeRidder,K., and H. Gallée, 1998:Land surface-induced regional climate change in southern Israel. J. Appl. Meteor., 37, 1470–1485, https://doi.org/10.1175/1520-0450(1998)037,1470:LSIRCC.2.0.CO;2.
De Troch, R., R. Hamdi, H. Van de Vyver, J.-F. Geleyn, and P. Termonia, 2013: Multiscale performance of the ALARO-0 model for simulating extreme summer precipitation climatology in Belgium. J. Climate, 26, 8895–8915, https://doi.org/10.1175/JCLI-D-12-00844.1.
De Troch, R., and Coauthors, 2014: Overview of a few regional climatemodels and climate scenarios for Belgium.Royal Meteorological Institute of Belgium Doc., 32 pp., http://doi.org/10.13140/2.1.4710.5604.
EEA, 2019: Economic losses from climate-related extremes in Europe. European EnvironmentAgency, https://www.eea.europa.eu/data-andmaps/indicators/direct-losses-from-weather-disasters-4/assessment.
Fosser, G., S. Khodayar, and P. Berg, 2015: Benefit of convection permitting climate model simulations in the representation of convective precipitation. Climate Dyn., 44, 45–60, https://doi.org/10.1007/s00382-014-2242-1.
Fosser, G., S. Khodayar, and P. Berg, 2017: Climate change in the next 30 years: What can a convection-permitting model tell us that we did not already know? Climate Dyn., 48, 1987–2003, https://doi.org/10.1007/s00382-016-3186-4.
Gallée, H., and G. Schayes, 1994: Development of a threedimensional meso-g primitive equation model: Katabatic winds simulation in the area of Terra Nova Bay, Antarctica. Mon. Wea. Rev., 122, 671–685, https://doi.org/10.1175/1520-0493(1994)122,0671:DOATDM.2.0.CO;2.
Gerard, L., J.-M. Piriou, R. Brozková, J.-F. Geleyn, and D. Banciu, 2009: Cloud and precipitation parameterization in a mesogamma-scale operationalweather predictionmodel. Mon. Wea. Rev., 137, 3960–3977, https://doi.org/10.1175/2009MWR2750.1.
Giorgi, F., C. Jones, and G. R. Asrar, 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175–183.
Gupta, V. K., and E. Waymire, 1990: Multiscaling properties of spatial rainfall and river flow distributions. J. Geophys. Res., 95, 1999–2009, https://doi.org/10.1029/JD095iD03p01999.
Helsen, S., and Coauthors, 2020: Consistent scale-dependency of future increases in hourly extreme precipitation in two convectionpermitting climate models. Climate Dyn., 54, 1267–1280, https://doi.org/10.1007/s00382-019-05056-w.
Jacob, D., and Coauthors, 2014: EURO-CORDEX: New highresolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2.
Jacob, D., and Coauthors, 2020: Regional climate downscaling over Europe: Perspectives from the EURO-CORDEX community. Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9.
Kendon,E. J., N. M. Roberts,H. J. Fowler, M. J.Roberts, S.C. Chan, and C.A. Senior, 2014: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Climate Change, 4, 570–576, https://doi.org/10.1038/nclimate2258.
Kendon,E. J, and Coauthors, 2017: Do convection-permitting regional climate models improve projections of future precipitation change? Bull. Amer. Meteor. Soc., 98, 79–93, https://doi.org/10.1175/BAMS-D-15-0004.1.
Koenker, R., 2005: Quantile Regression. Cambridge University Press, 349 pp.
Koenker, R., and J. A. F. Machado, 1999: Goodness of fit and related inference processes for quantile regression. J. Amer. Stat. Assoc., 94, 1296–1310, https://doi.org/10.1080/01621459.1999.10473882.
Leadbetter, M., G. Lindgren, and H. Rootzén, 1983: Extremes and Related Properties of Random Sequences and Processes. Springer, 336 pp.
Lenderink, G., and E. van Meijgaard, 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1, 511–514, https://doi.org/10.1038/ngeo262.
Lenderink, G., and E. van Meijgaard, 2010: Linking increases in hourly precipitation extremes to atmospheric temperature and moisture changes. Environ. Res. Lett., 5, 025208, https://doi.org/10.1088/1748-9326/5/2/025208.
Lugrin, T.,A. C.Davison, and J.A. Tawn, 2016:Bayesian uncertainty management in temporal dependence of extremes. Extremes, 19, 491–515, https://doi.org/10.1007/s10687-016-0258-0.
Maraun, D., H. W. Rust, and T. J. Osborn, 2009: The annual cycle of heavy precipitation across the United Kingdom: A model based on extreme value statistics. Int. J. Climatol., 29, 1731–1744, https://doi.org/10.1002/joc.1811
Met Office, 2021: UK Climate Projections (UKCP). Met Office, accessed 14 July 2021, https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index/.
Naveau, P., A. Guillou, D. Cooley, and J. Diebolt, 2009: Modeling pairwise dependence of maxima in space. Biometrika, 96, 1–17, https://doi.org/10.1093/biomet/asp001.
Ntegeka, V., and P. Willems, 2008: Trends and multidecadal oscillations in rainfall extremes, based on a more than 100-year time series of 10 min rainfall intensities at Uccle, Belgium. Water Resour. Res., 44,W07402, https://doi.org/10.1029/2007WR006471.
Ouarda, T. B. M. J., L. A. Yousef, and C. Charron, 2018: Nonstationary intensity-duration-frequency curves integrating information concerning teleconnections and climate change. Int. J. Climatol., 39, 2306–2323, https://doi.org/10.1002/joc.5953.
Pan, L.-L., S.-H. Chen, D. Cayan, M.-Y. Lin, Q. Hart, M.-H. Zhang, Y. Liu, and J. Wang, 2011: Influences of climate change on California and Nevada regions revealed by a highresolution dynamical downscaling study. Climate Dyn., 37, 2005–2020, https://doi.org/10.1007/s00382-010-0961-5.
Prein, A. F., and Coauthors, 2015: A review on regional convectionpermitting climate modeling: Demonstrations, prospects, and challenge. Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475.
Prein, A. F., and Coauthors, 2016: Precipitation in the EURO-CORDEX 0.118 and 0.448 simulations:High resolution, high benefits? Climate Dyn., 46, 383–412, https://doi.org/10.1007/s00382-015-2589-y.
Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 48–52, https://doi.org/10.1038/nclimate3168.
Rasmussen, S., J. Christensen,M. Drews, D. Gochis, and J. Refsgaard, 2012: Spatial scale characteristics of precipitation simulated by regional climate models and the implications for hydrological modelling. J. Hydrometeor., 13, 1817–1835, https://doi.org/10.1175/JHM-D-12-07.1.
Reszler, C., M. B. Switanek, and H. Truhetz, 2018: Convectionpermitting regional climate simulations for representing floods in small- and medium-sized catchments in the Eastern Alps. Nat. Hazards Earth Syst. Sci., 18, 2653–2674, https://doi.org/10.5194/nhess-18-2653-2018.
Ribatet,M.,R. Singleton, andRCore Team, 2013: SpatialExtremes: Modelling spatial extremes, version 2.0-0 R package, https://CRAN.R-project.org/package5SpatialExtremes.
Rockel, B., A. Will, and A. Hense, 2008: The regional climate model COSMO-CLM (CCLM). Meteor. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309
Schär, C., and Coauthors, 2020: Kilometer-scale climate models: Prospects and challenges. Bull. Amer. Meteor. Soc., 101, E567– E587, https://doi.org/10.1175/BAMS-D-18-0167.1.
Schindler, A., D. Maraun, and J. Luterbacher, 2012: Validation of the present day annual cycle in heavy precipitation over the British Islands simulated by 14 RCMs. J. Geophys. Res., 117, D18107, https://doi.org/10.1029/2012JD017828.
Shikano, S., 2019:Hypothesis testing in theBayesian framework. Swiss Political Sci. Rev., 25, 288–299, https://doi.org/10.1111/spsr.12375.
Smith, R. L., and I. Weissman, 1994: Estimating the extremal index. J. Roy. Stat. Soc., 56B, 515–528, https://doi.org/10.1111/j.2517-6161.1994.tb01997.x.
Tabari, H., and Coauthors, 2016: Local impact analysis of climate change on precipitation extremes: Are high-resolution climate models needed for realistic simulations? Hydrol. Earth Syst. Sci., 20, 3843–3857, https://doi.org/10.5194/hess-20-3843-2016.
Termonia, P., and Coauthors, 2018a: The ALADIN system and its canonicalmodel configurationsAROMECY41T1 andALARO CY40T1. Geosci. Model Dev., 11, 257–281, https://doi.org/10.5194/gmd-11-257-2018.
Van de Vyver, H., 2012: Spatial regression models for extreme precipitation in Belgium. Water Resour. Res., 48, W09549, https://doi.org/10.1029/2011WR011707.
Van de Vyver, H., 2015a: Bayesian estimation of rainfall intensity-durationfrequency relationships. J. Hydrol., 529, 1451–1463, https://doi.org/10.1016/j.jhydrol.2015.08.036.
Van de Vyver, H., 2015b: On the estimation of continuous 24-h precipitation maxima. Stochastic Environ. Res. Risk Assess., 29, 653–663, https://doi.org/10.1007/s00477-014-0912-5.
Van de Vyver, H., 2018: A multiscaling-based intensity-duration-frequency model for extreme precipitation. Hydrol. Processes, 32, 1635–1647, https://doi.org/10.1002/hyp.11516.
Van de Vyver, H., 2021: R code for Bayesian estimation of rainfall intensity– duration–frequency (IDF) relationships (version V1). Zenodo, https://doi.org/10.5281/zenodo.4644184.
Van de Vyver, H., B. Van Schaeybroeck, R. De Troch, R. Hamdi, and P. Termonia, 2019: Modeling the scaling of short-duration precipitation extremes with temperature. Earth Space Sci., 6, 2031–2041, https://doi.org/10.1029/2019EA000665.
Van de Vyver, H., B. Van Schaeybroeck, R. De Troch, R. Hamdi, and P. Termonia, 2021:Rcode for modeling the scaling of short-duration precipitation extremes with temperature (version V1). Zenodo, https://doi.org/10.5281/zenodo.4644567.
Vannière, B., and Coauthors, 2019: Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. Climate Dyn., 52, 6817–6846, https://doi.org/10.1007/s00382-018-4547-y.
Vannitsem, S., and P. Naveau, 2007: Spatial dependences among precipitation maxima over Belgium. Nonlinear Processes Geophys., 14, 621–630, https://doi.org/10.5194/npg-14-621-2007.
Van Weverberg, K., E. Goudenhoofdt, U. Blahak, E. Brisson, M. Demuzere, P. Marbaix, and J.-P. van Ypersele, 2014: Comparison of one-moment and two-moment bulk microphysics for high-resolution climate simulations of intense precipitation. Atmos. Res., 147–148, 145–161, https://doi.org/10.1016/j.atmosres.2014.05.012.
Wasko, C., and A. Sharma, 2014: Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour. Res., 50, 3608–3614, https://doi.org/10.1002/2013WR015194.
Westra, S., and Coauthors, 2014: Future changes to the intensity and frequency of short-duration extreme rainfall.Rev. Geophys., 52, 522–555, https://doi.org/10.1002/2014RG000464.
Willems, P., 2000: Compound intensity/duration/frequency-relationships of extreme precipitation for two seasons and two storm types. J. Hydrol., 233, 189–205, https://doi.org/10.1016/S0022-1694(00)00233-X.
Wouters, H., M. Demuzere, U. Blahak, K. Fortuniak, B. Maiheu, J. Camps, D. Tielemans, and N. P. M. van Lipzig, 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, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016.
Wyard, C., C. Scholzen, X. Fettweis, J. Van Campenhout, and L. François, 2017: Decrease in climatic conditions favouring floods in the south-east of Belgium over 1959-2010 using the regional climate model MAR. Int. J. Climatol., 37, 2782–2796, https://doi.org/10.1002/joc.4879
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