Projections of heavy rainfall over the central United States based on CMIP5 models

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 14: (2013) Published online 21 June 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /asl2.440 Projections of heavy rainfall over the central United States based on CMIP5 models Gabriele Villarini, 1 * Enrico Scoccimarro 2,3 and Silvio Gualdi 2,3 1 IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA, USA 2 Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy 3 Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy *Correspondence to: G. Villarini, IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA, USA. gabriele-villarini@ uiowa.edu Received: 18 January 2013 Revised: 7 May 2013 Accepted: 8 May 2013 Abstract The central United States is a region for which observational studies have indicated an increase in heavy rainfall. This study uses projections of daily rainfall from 20 state-of-theart global climate models and one scenario [representative concentration pathway (RCP) 8.5] to examine projected changes in extreme rainfall. Analyses are performed focusing on trends in the 90th and 99th percentiles of the daily rainfall distributions for two periods ( and ). These results indicate a large increase in extreme rainfall mostly over the northern part of the region, with a much less clear signal over the Great Plains and states along the Gulf of Mexico. Keywords: extreme rainfall; central united states; CMIP5; trends 1. Introduction The central United States is a vital area for the US agriculture, producing more than half of the total harvested area of principal crops in the US (US Department of Agriculture, 2011). It is also highly susceptible to catastrophic flooding, with the 2008, 2010 and 2011 being the latest events of a long list, causing billions of dollars in economic damage and numerous fatalities ( Because of these large societal and economic impacts, it is of paramount importance to assess potential changes in the upper tail of the rainfall distribution over this area. Several studies based on observational records found increasing trends over the central United States(Karl and Knight, 1998, Kunkel et al., 1999, Groisman et al., 2004, 2012, Villarini et al., 2011a, 2013). Recently, Villarini et al. (2013) found a large increase in the number of rainfall days exceeding the 95th percentile of the rainfall distribution over the Upper Mississippi River Basin, and a much weaker signal in the Lower Mississippi River Basin. They also discussed these results in light of trends in temperature and increasing irrigation over the Great Plains. Over the central United States, Groisman et al. (2012) found a redistribution in the intense rain, with decreasing trends in the rain days within the mm day 1 and an increase in the number of events above 76.2 mm day 1. They also examined different possible mechanisms that could explain the observed increasing trends in intense rainfall. These results were based on analyses of the observational records. Are these trends bound to continue in the future? Here we address this question by using daily rainfall projections from 20 state-of-the-art global climate models (GCMs) produced under the Fifth Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012; Table I). Results will be based on the representative concentration pathways (RCPs) reflecting an 8.5 W m 2 radiative forcing change at the end of the 21st century (RCP 8.5) due to high greenhouse gas concentration level. 2. Data and methodology We use daily rainfall data from 20 CMIP5 GCMs (Table I) and focus on the 90th and 99th percentiles of the rainfall distribution to examine projected changes in the upper tail of the rainfall distribution. These models have different resolutions, ranging from 0.8 to 3.8 latitude degrees. All the analyses are performed at the 1.0-latitude degree and regridding is performed according to a bicubic interpolation. The study area is the central United States, defined as the region between 26 N and 50 N, and 83 W and 104 W. We focus on the ensemble-mean trend for the 90th and 99th percentiles of the rainfall distribution over two periods ( and ). More specifically, at each grid point we compute the values of these two percentiles for each year. We then compute the slope of the trend line for each of these times series and for each of the 20 models. Finally, we take the average of these 20 slopes as our representative slope values. As discussed in Tebaldi et al. (2006) and Orlowski and Seneviratne (2012), there is a generally weak agreement among models when working with extreme precipitation, with large inter-model variability. Resorting to the ensemble 2013 Royal Meteorological Society

2 Heavy rainfall projections over the central united states based on CMIP5 models 201 Table I. Summary of the 20 global climate models used in this study. Bold values in the second column indicate horizontal resolution finer than 1.5. Model name Latitude longitude (Number of Institute grid points) (Institute ID) BNU-ESM College of Global Change and Earth System Science, Beijing Normal University (GCESS) CCSM National Center for Atmospheric Research (NCAR) CMCC-CESM Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) CMCC-CMS Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) CMCC-CM Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) CNRM-CM Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique (CNRM-CERFACS) CSIRO-Mk Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence (CSIRO-QCCCE) CanESM Canadian Centre for Climate Modelling and Analysis (CCCMA) FGOALS-s LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG-IAP) GFDL-CM NOAA Geophysical Fluid Dynamics Laboratory (NOAA GFDL) GFDL-ESM2G NOAA Geophysical Fluid Dynamics Laboratory (NOAA GFDL) GFDL-ESM2M NOAA Geophysical Fluid Dynamics Laboratory (NOAA GFDL) HadGEM2-CC Met Office Hadley Centre (MOHC) HadGEM2-ES Met Office Hadley Centre (MOHC) INM-CM Institute for Numerical Mathematics (INM) IPSL-CM5A-MR IPSL-CM5A-LR Institut Pierre-Simon Laplace (IPSL) MIROC Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology (MIROC) MPI-ESM-MR Max Planck Institute for Meteorology (MPI-M) MRI-CGCM Meteorological Research Institute (MRI) NorESM1-M Norwegian Climate Centre (NCC) mean allows averaging out some of the noise in the models, isolating the climate signal. 3. Results Before examining projected changes in rainfall, we need to assess how well these models could reproduce the historical records. Sillmann et al. (2013) performed an extensive evaluation of the CMIP5 models in reproducing daily temperature and precipitation extremes. They compared these climate extreme indices with respect to a dataset of gridded observational indices. They found an improvement of the CMIP5 ensembles with respect to the ensemble means from the CMIP3 as far as precipitation indices are concerned. Here we compare the 90th and 99th ensemble mean percentiles with respect to the corresponding values from the Global Precipitation Climatology Project (GPCP; Huffman et al., 2001) data (Figure 1), which has the same resolution as the regridded climate models (1-latitude degree). Overall, the ensemble mean can reproduce the spatial patterns exhibited by the GPCP data, with larger rainfall values for the coastal states, and decreasing values moving radially from the area where the largest values are located (Figure 1). The ensemble means, however, are clearly smaller than GPCP, with values that are about half of what GPCP reports. Over the study region, the largest value for the 90th percentile is 32.2 mm day 1 for GPCP and 13.6 mm day 1 for the ensemble mean. Similar results for the 99th percentile, with 80 mm day 1 for GPCP and 37.6 mm day 1 for the models. Some of the possible explanations for this behavior are associated with the resolution of the models which may be too coarse, and with the known difficulties of these models in reproducing rainfall statistics, in particular for the upper tail of the distribution. Moreover, it is worth mentioning that some of the models show similar patterns but values that are much similar to GPCP. By taking the ensemble average we increase the signal but we unavoidably smooth out the larger values. With these limitations in mind, we examine the projected changes in the 90th and 99th percentiles of the rainfall distribution over this area. Figure 2 highlights some interesting features when comparing between the two percentiles as well as the two different periods. The average trend for the 90th percentile (Figure 2, top panels) indicates a tendency towards increasing trends over most of the domain. There are also few areas in the southwestern corner with negative or no trends, in particular over Texas, Louisiana and the Gulf of Mexico. This is potentially due to a poleward expansion of the Hadley Cell (Kang and Lu, 2012), resulting in drier conditions over this area. The largest trends are in the central and northern part of the domain. These patterns are consistent with the results in Villarini et al. (2013), who found that the largest increasing trends in the frequency of rainfall events

3 202 G. Villarini, E. Scoccimarro and S. Gualdi Figure 1. Maps showing the values of the 90th (top panels) and 99th (bottom panels) percentiles averaged over the period, normalized by the maximum value within the spatial domain. The maps on the left side are based on GPCP, while those on the right on the ensemble mean from 20 CMIP5 models used in this study. Maximum values used to normalize GPCP data (left panels) are 32.2 mm day 1 and 80.0 mm day 1 for 90th and 99th percentiles, respectively. Maximum values used to normalize CMIP5 data (right panels) are 13.6 mm day 1 and 37.6 mm day 1 for 90th and 99th percentiles, respectively. exceeding the 95th percentile were over the Upper Mississippi River Basin (see also Bonnin et al. (2011) for increasing trends in rainfall over the Ohio River Basin and Hirsch and Ryberg (2012) for increasing trends in flooding over the Upper Mississippi River Basin). The differences in trends between the first and second half of the 21st century are generally small, with a tendency towards increasing trends more limited to the northern half of the study region in the period (Figure 2, top panels). The changes in the 99th percentile are much stronger than those for the 90th percentile (Figure 2, bottom panels). During the period, there are large areas of the central United States with increasing trends, with values exceeding 0.6 mm day 1 decade 1 (Figure 2, bottom-left panel). The trend results for the are similar, even though there is a sharper difference between eastern and western parts of the study region. Different from the trend results for the 90th percentile, there are limited areas with decreasing trends, generally limited to the US Great Planes. The generally larger slope values for the 99th percentile indicate that there is a tendency towards more extreme events. These results are in agreement with Groisman et al. (2012) who found a redistribution towards more intense rainfall over the recent past. Analyses performed for winter and summer indicate that the largest changes occur during the winter months (figures not shown). We have examined how robust these trend results are by counting the number of GCMs with a trend of the same sign as the average trend (Figure 3). We expect that the larger the number of models with the same trend sign, the more robust the mean trend results are. The largest degree of agreement is for the northern part of the study region (from about 41 N) with more than 75% of the models agreeing on the trend sign. This is also the area with the largest slope values (Figure 2). In the southern part, on the other hand, there is a much weaker agreement, with a smaller number of models agreeing on the trend sign. This is also generally the area with decreasing or no trend. Overall, the results for the 90th and 99th percentiles are similar. 4. Discussion and conclusions In this study we have investigated the projected changes in upper tail of the daily rainfall distribution over the central United States using projections from 20 GCMs produced under the CMIP5 and the RCP 8.5.

4 Heavy rainfall projections over the central united states based on CMIP5 models 203 Figure 2. Maps showing the average trend (in mm/day/decade) in the 90th (top panels) and 99th (bottom panels) percentiles. Results are for the trend estimates over the period (left panels) and (right panels). Our results indicate that the northern part of our study region is projected to experience increasing trends in the 90th and 99th percentiles of the daily rainfall distributions. The magnitude of these trends is rather large: over a 50-year period, there are areas in the northern part of the domain in which the projected increase is 20% over the average value of the period. Moreover, the comparison between the 90th and 99th percentiles points to stronger trend in the latter. This indicates that extreme rainfall is projected to become more extreme, continuing the redistribution of rainfall intensity observed over the recent past (Groisman et al., 2012). These conclusions are valid for the RCP 8.5, which is close to a worst-case scenario. Also, caution should be exercised in translating these results into increasing flooding over this area. Rainfall is certainly a key ingredient for flood hydrology. There are, however, other factors that could potentially play an even greater role than rainfall, such as changes in land use/land cover and agricultural practice ( Changnon and Demissie, 1996, Schilling et al,. 2008, Villarini et al., 2011b). Future studies should examine both projected changes in rainfall and in land use/land cover together to get a better understanding of how these results would translate in projected changes in flooding over this area. One element that we need to keep in mind is that these results are valid as long as these models are able to provide a realistic representation of the physical processes at play. We found that the ensemble mean is lower than the GPCP data over the period The overall spatial patterns, however, are similar. Moreover, Sillmann et al. (2013) found that these models agree with observational records better than the CMIP3 models when dealing with precipitation extremes. Because we worked on trends, rather than absolute values, these results are valid as long as the models are able to realistically respond to the forcings. As recently shown by Gao et al. (2012), high resolution simulations performed using limited area atmospheric models with the GCMs to provide boundary conditions could represent an attractive way of improving the agreement between models and observations, in particular at the upper tail. Acknowledgements Gabriele Villarini acknowledges financial support from the Iowa Flood Center, IIHR-Hydroscience and Engineering, and the USACE Institute for Water Resources. Enrico Scoccimarro and Silvio Gualdi gratefully acknowledge the support of the Italian Ministry of Education, University and Research and the Ministry for Environment, Land and Sea through the project

5 204 G. Villarini, E. Scoccimarro and S. Gualdi Figure 3. Maps showing the number of GCMs (out of 20) with the sign of the trend which is the same as the mean trend for the 90th (top panels) and 99th (bottom panels) percentiles. Results are for the period (left panels) and (right panels). GEMINA. We acknowledge the World Climate Research Programme s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table I) for producing and making available their model output. For CMIP the US Department of Energy s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. References Bonnin GM, Maitaria K, Yekta M Trends in rainfall exceedances in the observed record in selected areas of the United States. Journal of the American Water Resources Association 47(6): Changnon SA, Demissie M Detection of changes in streamflow and floods resulting from climate fluctuations and land use-drainage changes. Climatic Change 32: Gao Y, Fu JS, Drake JB, Liu Y, Lamarque JF Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system. Environmental Research Letters 7: Groisman Ya P, Knight RW, Karl TR, Easterling DR, Sun B, Lawrimore JH Contemporary changes of the hydrological cycle over the contiguous United States: trends derived from in situ observations. Journal of Hydrometeorology 5: Groisman Ya P, Knight RW, Karl TR Changes in intense precipitation over the central United States. Journal of Hydrometeorology 18: Hirsch RM, Ryberg KR Has the magnitude of floods across the USA changed with global CO 2 levels? Hydrological Sciences Journal 57(1): 1 9. Huffman GJ, Adler RF, Morrissey M, Bolvin DT, Curtis S, Joyce R, McGavock B, Susskind J Global precipitation at onedegree daily resolution from multi-satellite observation. Journal of Hydrometeorology 2: Kang SM, Lu J Expansion of the Hadley cell under global warming: winter versus summer. Journal of Climate 25: Karl TR, Knight RW Secular trends of precipitation amount, frequency, and intensity in the United States. Bulletin of the American Meteorological Society 79: Kunkel KE, Andsager K, Easterling DR Long-term trends in extreme precipitation events over the conterminous United States and Canada. Journal of Climate 12: Orlowsky B, Seneviratne SI Global changes in extreme events: regional and seasonal dimension. Climatic Change 110: Schilling KE, Jha MK, Zhang YK, Gassman PW, Wolter CF Impact of land use and land cover change on the water balance of a

6 Heavy rainfall projections over the central united states based on CMIP5 models 205 large agricultural watershed: historical effects and future directions. Water Resources Research 44: W00A09. Sillmann JVV, Kharin X, Zhang FWZ, Bronaugh D Climate extreme indices in the CMIP5 multi-model ensemble. Part 1: model evaluation in the present climate. Journal of Geophysical Research 118(4): Taylor KE, Stouffer RJ, Meehl GA An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93: Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA Going to the extremes An intercomparison of modl-simulated historical and future changes in extreme events. Climatic Change 79: U.S. Department of Agriculture, Acreage, report, 41 pp., Washington, D. C. [Available at viewdocumentinfo.do?documentid=1000.], 2011 (Last retrieved 29 May 2013) Villarini G, Smith JA, Baeck ML, Vitolo R, Stephenson DB, Krajewski WF. 2011a. On the frequency of heavy rainfall for the Midwest of the United States. Journal of Hydrology 400(1 2): Villarini G, Smith JA, Baeck ML, Krajewski WF. 2011b. Examining flood frequency distributions in the Midwest U.S. Journal of the American Water Resources Association 47(3): Villarini G, Smith JA, Vecchi GA Changing frequency of heavy rainfall over the central United States. Journal of Climate 26(1):

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