A comparison of projected future precipitation in Wisconsin using global and downscaled climate model simulations: implications for public health

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (2014) Published online 23 December 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3897 A comparison of projected future precipitation in Wisconsin using global and downscaled climate model simulations: implications for public health Stephen J. Vavrus a * and Ruben J. Behnke b a Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, WI, USA b Numerical Terradynamic Simulation Group, College of Forestry and Conservation, University of Montana-Missoula, MT, USA ABSTRACT: Motivated by the documented linkage between water-borne disease outbreaks and heavy rainfall, we compare simulations of precipitation in Wisconsin from two different downscaling procedures (statistical and dynamical) and global climate models (GCMs) for the late 20th and middle 21st centuries (SRES A2 greenhouse emissions scenario). In the intermodel mean, all the three methods produce reasonably accurate simulations of seasonal and annual precipitation amounts during the historical period of (yearly biases <5%), but the GCMs severely underestimate extreme precipitation compared with downscaled output and observations. The modelling methodologies agree that Wisconsin should experience a modestly wetter future climate (annual increase <10%) by the middle 21st century ( ), comprised of more precipitation during winter, spring, and autumn but an equivocal summertime signal. The future simulations also exhibit robust increases in the frequency and intensity of extreme daily precipitation, consisting of larger relative changes in the return periods of heavy events (up to 50%) than in the accumulations (<30%). Although the modelling procedures vary substantially in their projected absolute differences in future precipitation, they agree surprisingly well on the simulated relative differences (percent change) of both mean and extreme precipitation. Unfortunately, there is little consistency in the simulated spatial patterns of future precipitation change across Wisconsin, complicating societal adaptation measures to enhanced extremes. Nevertheless, the composite projections presented here suggest that impending hydrological changes in Wisconsin represent a public health threat, by virtue of increasingly extreme precipitation promoting water-borne disease outbreaks. KEY WORDS climate change; precipitation; Wisconsin; extreme; NARCCAP Received 5 October 2012; Revised 16 October 2013; Accepted 19 November Introduction Anthropogenic climate change is becoming increasingly apparent, as evidenced by rising global temperatures, shrinking ice cover, elevated sea level, and increasing atmospheric water vapour (Solomon et al., 2007). Global climate models (GCMs) are consistent in simulating that greenhouse warming will cause a strengthened hydrologic cycle, such that globally averaged precipitation rates are expected to increase by the end of this century, ranging in a middle-of-the-road greenhouse gas emissions scenario from less than 2% to as much as 7% (5% ensemble average) (Meehl et al., 2007). GCMs also generally agree on the global pattern of future precipitation changes: wetter in the deep tropics, drier in the subtropics, and wetter in higher latitudes (Meehl et al., 2007). On the regional scale of North America this pattern takes the form of a dipole and displays a strong seasonal variation, such that the transition zone dividing precipitation increases * Correspondence to: S. J. Vavrus, Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, 1225 W. Dayton Street, Madison, WI 53706, USA. sjvavrus@wisc.edu from decreases in middle latitudes shifts northward during warmer months and southward during colder months (Christensen et al., 2007). The resulting pattern over the upper Midwest, including Wisconsin, leads to expectations of a wetter climate annually and during winter, spring, and fall, but possibly drier conditions during summer (Lorenz et al., 2009). Observed trends in zonally averaged precipitation over the Northern Hemisphere during the 20th century have been found to be consistent with those expected from anthropogenic forcing, in terms of the climate becoming wetter in high latitudes and drier in low latitudes (Zhang et al., 2007). Coincident with these time-averaged changes is the strong likelihood that heavy precipitation events will become more frequent and intense under greenhouse warming, due to an increased moisture holding capacity in a warmer atmosphere (Trenberth, 1999). This type of projected climate change is so common in climate models that they often simulate increased precipitation intensity even in regions where they also project reduced total precipitation (Meehl et al., 2007). Recent observed trends suggest that heavier precipitation events have already become more frequent and intense during the 2013 Royal Meteorological Society

2 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3107 past several decades globally, including over the United States (Groisman et al., 2004, Alexander et al., 2006) and Wisconsin (Kucharik et al., 2010). These trends are consistent with paleoclimatic proxy records of precipitation from nearby caverns in southeastern Minnesota, which indicate more flooding during the latter 20th century than any other period in the 3000-year record (Dasgupta et al., 2010). Studies of future climate focused on the greater Wisconsin area also indicate a likely shift towards increased frequency of heavy rainfalls, whether utilizing the output from GCMs (Lorenz et al., 2009; Holman and Vavrus, 2012) or downscaled output (Vavrus and Van Dorn, 2010; Schuster et al., 2012). Changes in hydrology can have an especially strong impact on many societal sectors, due to the widespread influence of precipitation on agriculture, water resources, stormwater management, etc. Less obvious is the influence of precipitation on human health, yet a number of studies have documented causal relationships between heavy rainfalls and gastrointestinal illness. Currerio et al. (2001) found that more than half of the documented water-borne disease outbreaks in the United States between 1948 and 1994 were preceded by heavy precipitation events (above the 90th percentile). These included the 1993 Cryptosporidium outbreak in Milwaukee, which sickened more than people and was preceded by record flow in the Milwaukee River following heavy rainfall. Likewise, Drayna et al. (2010) discovered that paediatric admissions in Milwaukee to the emergency department for gastrointestinal illness increase significantly in the days following heavy rainfalls. Concentrations of the Escherichia coli bacteria in water discharging into Lake Michigan from the Milwaukee estuary rise significantly following rainfall, and they rise much more sharply when heavy rainfall leads to a combined sewer overflow (CSO) (McLellan et al., 2007). Bacteria concentrations in Lake Geneva (in southeast Wisconsin) vary significantly with mean summertime rainfall (Patz et al., 2008). Beach closures in Chicago depend on the amount of recent rainfall (within the previous 24 h), in addition to lake temperature and stage (Olyphant and Whitman, 2004). On a broader scale, Thomas et al. (2006) documented a large increase in the probability of a water-borne disease outbreak following a very heavy rainfall (>93rd percentile) across locations throughout southern Canada. Given this demonstrated linkage between precipitation variations and water-borne disease, this study assesses future hydrologic conditions in Wisconsin by analysing spatially downscaled climate model projections and interprets these changes in terms of likely health impacts. We believe that our approach is novel, as we will be making a clean comparison of a set of GCMs and statistically and dynamically downscaled climate model simulations by focusing on the same region (Wisconsin), using the same greenhouse gas forcing (SRES A2 scenario; Nakicenovic et al., 2000), and targeting the same time period (middle 21st century). Most other related downscaling studies have utilized either statistically downscaled or dynamically downscaled [regional climate model (RCM) simulations] model output in their analysis of climate change (Gutowski et al., 2007, Hayhoe et al., 2008, Bukovsky and Karoly, 2011, Notaro et al., 2011), while several others have compared the accuracy of the downscaling methods under recent climatic conditions (Kidson and Thompson, 1998; Murphy, 1999; Wilby et al., 2000). Our research primarily addresses the following questions: (1) What are the most likely changes in mean and extreme precipitation in Wisconsin by the middle of this century (compared with the late 20th century)?, (2) How well do projections from the two downscaling methods and raw GCMs agree with one another?, (3) What are the public health implications of the projected precipitation changes in terms of water-borne disease?, and (4) What is the value added from either downscaling method compared with the corresponding projections from raw GCM output? In the following section, we describe the datasets and the statistical techniques applied to the data. The results of our analysis are presented in Section 3, where we compare the late 20th century model simulations and show projected changes in mean and extreme precipitation across Wisconsin during the middle 21st century. The final section provides a summary and assessment of our findings, with respect to health implications, the level of confidence in the model projections, and the differences between downscaled and raw GCM simulations. 2. Methods In this study, we investigate the projected response of precipitation in Wisconsin during the middle 21st century in climate models driven with the SRES A2 greenhouse gas emissions scenario, one of the highestpollution future scenarios among models participating in the Third Coupled Model Intercomparison Project (CMIP3), in which atmospheric CO 2 concentration rises to 830 ppm by the year 2100 (Nakicenovic et al., 2000). This is the only greenhouse forcing scenario used to drive the RCMs and is close to the recent trajectory in global emissions since 1990 (Manning et al., 2010). The climate model output presented here is for the middle 21st century, which is a more relevant time period than late century from a near-term adaptation perspective and is the only interval available for the dynamically downscaled data. We compare these future simulations with late 20th century runs that use observed greenhouse forcing during the last decades of the 1900s, as detailed below. The dynamically downscaled data come from a collection of 11 RCM simulations produced by the North American Regional Climate Change Assessment Program (NARCCAP), which is described by Mearns et al. (2009) and has generated numerous scientific publications (Gutowski et al., 2010, Takle et al., 2010, Bukovsky, 2012). A combination of four GCMs were used to drive six RCMs (all using 50-km horizontal grid spacing) at their lateral boundaries over a geographic

3 3108 S. J. VAVRUS AND R. J. BEHNKE Figure 1. Geographic domain of the six RCMs used in NARCCAP, labelled by the colour codes on the map (see Table 1 for model information). The centre of Wisconsin is denoted by an asterisk. Figure obtained from the NARCCAP website: domain extending meridionally from northern Canada to Mexico and zonally from the eastern Pacific to western Atlantic Oceans (Figure 1). The state of Wisconsin is located near the centre of the models domain, thereby avoiding lateral boundary effects that can afflict RCM simulations. The six regional models provide a range of model physics and/or have been evaluated in climate change simulations over North America, while the climate sensitivity of the four driving GCMs are fairly close to the centre of the expected range reported by the Intergovernmental Panel on Climate Change Fourth Assessment Report (Meehl et al., 2007; Mearns et al., 2009). A subset of 12 GCM-driven RCM simulations (half of the possible combinations) are being run and processed by NARCCAP, in addition to two time slice simulations that use a high-resolution (50 km grid spacing) version of a GCM forced by prescribed sea surface temperatures from either the model s fully coupled late-20th century run or its fully coupled, middle-21st century A2 simulation. Of these 14 eventual datasets, we use the 11 that were available during our study (Table 1). The time interval for all the RCM simulations is 30-years long for both the late 20th century (years ) and the middle 21st century ( ). The statistically downscaled data consist of a set of 11 GCM simulations (Table 1) produced under the Wisconsin Initiative on Climate Change Impacts (WICCI) that represents the greater Wisconsin region at a horizontal resolution of (approximately 10 km 10 km). The downscaling is applied to GCM output from CMIP3 models, using the methodology of Wood et al. (2002, 2004) and Maurer et al. (2007), as described in detail by Notaro et al. (2011). The statistical relationship between large-scale meteorological conditions and point observations at the surface is obtained using atmospheric data from the National Center for Environmental Prediction (NCEP) Reanalysis (Kalnay et al., 1996) and surface measurements from the National Weather Service s Cooperative Observer Program (COOP) stations. The climate model output of the late 20th century was first debiased, based on the cumulative distribution function mapping algorithm of Wood et al. (2004). To improve the characteristics of the downscaled variability and extremes, the large-scale atmospheric conditions were translated to a probability density function (pdf) of surface temperature and precipitation at a point, rather than assigning a specific value. The pdfs were then randomly sampled for each day in the record to create a specific downscaled time series of temperature and precipitation. The statistically downscaled time intervals cover the late 20th-century simulation (years ) and a 20-year long projection of the middle 21st century ( ) using the same A2 emissions scenario employed by NARCCAP. Thus, both downscaled datasets utilize virtually the same greenhouse forcing and time periods; the major structural difference is that the RCM simulations are not debiased. From both downscaled datasets, we analyze the simulated precipitation averages (seasonal and annual) and extremes (intensity, frequency, and distribution) across Wisconsin. To characterize extreme precipitation, we choose a range of daily total thresholds 5.1 cm (2 inches), 7.6 cm (3 inches), and 10.2 cm (4 inches) that are commonly used to define heavy rainfall in this region

4 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3109 Table 1. The global and regional climate model combinations used in this analysis, along with their horizontal grid spacing (km). Global climate models Model name Grid spacing Canadian Centre for Climate Modelling and Analysis CGCM Météo-France/Centre National de Recherches Météorologiques CNRM-CM3 260 CSIRO Atmospheric Research (Australia) CSIRO-Mk CSIRO Atmospheric Research (Australia) CSIRO-Mk NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM Instituto Nazionale di Geofisica e Vulcanologia (Italy) INGV 105 Institut Pierre Simon Laplace (France) IPSL-CM4 285 Center for Climate System Research (Japan) MIROC Meteorological Institute of University of Bonn (Germany) ECHO-G 360 Max Planck Institute for Meteorologie (Germany) ECHAM5/MPI 175 Meteorological Research Institute (Japan) MRI-CGCM Regional Climate Model (RCM) Simulations Canadian Regional Climate Model (CRCM) Community CRCM-CCSM3 50 Climate System Model version 3 (CCSM3) PSU/NCAR Mesoscale Model version 5 (MM5I) Community MM5I-CCSM3 50 Climate System Model version 3 (CCSM3) Weather Research and Forecasting Model (WRF) Community WRF-CCSM3 50 Climate System Model version 3 (CCSM3) Regional Climate Model version 3 (RCM3) Coupled Global RCM3-CGCM3 50 Climate Model version 3 (CGCM3) Weather Research and Forecasting Model (WRF) Coupled WRF-CGCM3 50 Global Climate Model version 3 (CGCM3) Canadian Regional Climate Model (CRCM) Coupled Global CRCM-CGCM3 50 Climate Model version 3 (CGCM3) Regional Climate Model version 3 (RCM3) Geophysical Fluid RCM3-GFDL 50 Dynamics Laboratory GCM (GFDL) Hadley Regional Model version 3 (HRM3) Geophysical Fluid HRM3-GFDL 50 Dynamics Laboratory GCM (GFDL) Experimental Climate Prediction Center (ECPC) Geophysical ECPC-GFDL 50 Fluid Dynamics Laboratory GCM (GFDL) Hadley Regional Model version 3 (HRM3) Hadley Centre HRM3-HADCM3 50 Coupled Model version 3 (HADCM3) Geophysical Fluid Dynamics Laboratory GCM (GFDL) timeslice GFDL_TS 50 (WICCI, 2011; Saunders et al., 2012). A 5-cm threshold has been used in previous studies to represent an extreme precipitation event over the United States (Karl et al., 1996; Karl and Knight, 1998), and regional observations indicate that daily rainfall exceeding 5 6 cm can lead to storm water discharge of contaminants into Lake Michigan (McLellan et al., 2007). We further describe heavy precipitation events in terms of the amount of daily rainfall that occurs with average return periods of 10, 50, and 100 years to differentiate frequency from intensity (precipitation amount). We use the annual maximum time series (AMS) of daily total precipitation for both past and future time periods as the basis for our extreme-value analysis, as is commonly done in studies of extreme events in climatology and hydrology (Kharin and Zwiers, 2000; Wehner, 2010; Villarini et al., 2011). In this method, only the highest daily precipitation amount occurring in each year is used, even though other heavy events in a year may exceed the heaviest event in another year. An alternative approach is a partial duration series (PDS), which is constructed by including all events above a defined threshold (e.g. exceedances of 5.1 cm (2 inch) daily rainfall), regardless of the year in which it occurred. For the extreme cases we examine here, there are generally small differences between the two methods. For example, the empirical conversion formula of Chow et al. (1988) indicates only a 5% or smaller difference in AMS- and PDS-derived estimates of daily precipitation for return periods of 10 years or longer. To calculate statistics on extreme events, we apply Generalized Extreme Value (GEV) Theory (Coles, 2001), which has been widely used to describe the behaviour of extremes in both observations and models (Kharin and Zwiers, 2005; Wehner, 2005; Kharin et al., 2007). We use the L-moments method (Hosking and Wallis, 1997) to estimate the three parameters of the GEV distribution (location, scale, and shape), because this technique is considered more efficient in estimating the parameters in small sample sizes of the kind used here (Hosking et al., 1985; Wehner, 2010). Of particular interest for this study is the shape parameter, k, which describes the tail of the GEV distribution (i.e. the heaviest daily precipitation events). The larger the shape parameter, the longer the tail of the distribution is and thus the higher the magnitude of the most extreme events.

5 3110 S. J. VAVRUS AND R. J. BEHNKE For both the statistically and dynamically downscaled data, we first calculate k at each model grid point in Wisconsin and then use the resulting state-average k value to compute the GEV distribution at all grid points. In doing so, we are assuming that the short time series of model output at any single location is too limited to provide an accurate measure of the shape parameter, but by pooling the data over a wide area we can obtain a more realistic estimate. This same reasoning has been invoked in the widely used Regional Frequency Analysis method (Hosking and Wallis, 1997; Fowler and Kilsby, 2003; Bernardara et al., 2011), which pools the annual maximum data over roughly homogeneous regions before fitting a GEV distribution. Our approach is supported by a recent two-part study by Koutsoyiannis (2004a, 2004b), who used very long rainfall records (>100 years) worldwide to invalidate the prevailing assumption that extreme rainfalls follow a Gumbel distribution (k = 0) and instead demonstrated that they adhere to a nearly uniform k value of 0.15 over a variety of climatic zones in the United States and Europe. Koutsoyiannis argues that the observed variability in k among individual stations is almost entirely explained by sampling variability, rather than actual hydrological differences by location. Furthermore, Wehner (2010) finds that the uncertainty in fitting GEV distributions is affected much more by differences among climate model solutions than by internal climate variability and the fit of the statistical model. We therefore focus in our study on the means and spread simulated among climate models in each downscaling dataset. 3. Results 3.1. Late 20th century simulations We first compare the seasonal and annual precipitation amounts simulated in the dynamical downscaling (NAR- CCAP) and statistical downscaling (WICCI) with the high-resolution dataset of interpolated observations produced by the widely used Maurer et al. (2002) dataset, which is taken here to represent observed values. The multi-model means and biases are illustrated in Figure 2 and summarized in Table 2. Both downscaling methods produce annual-mean totals close to observed when averaged over Wisconsin, but NARCCAP is slightly wet (+3%) and WICCI is slightly dry ( 3%). Most of NAR- CCAP s wet bias stems from excessive precipitation in the eastern half of the state, while WICCI s dry bias is more widespread but mainly occurs in the southern two-thirds of Wisconsin. On a seasonal basis, the RCMs slightly enhanced annual precipitation state-wide is attributable to wetter than observed conditions throughout Wisconsin during winter (+3.25 cm, +37%) and spring (+2.72 cm, +13%), whereas the regional models are a bit too dry in summer ( 1.24 cm, 4%) and autumn ( 2.36 cm, 11%). Because of the debiasing applied to the WICCI data, its corresponding deviations from observed are generally smaller on a seasonal basis. The largest departures occur in summer ( 1.37 cm, 4%) and autumn ( 0.89 cm, 4%). The most noticeable anomaly between WICCI and observed is the enhanced precipitation over the upper peninsula of Michigan, a difference that has little bearing on the simulated precipitation pattern over Wisconsin. The downscaled simulations of extreme precipitation show generally less frequent and less intense occurrences than observations (Figure 3, Table 2). At least some of this bias is expected, however, due to the spatial averaging over an entire grid box, compared with measurements from individual COOP stations (the Maurer dataset underestimates intense rainfall, so we instead evaluated extreme events against point observations). Both downscaled simulations produce heavy events that are generally more frequent and intense in southern Wisconsin, with maximum (minimum) values in the southwest (northeast) part of the state. The spatial biases show no coherent pattern in either dataset, although the overall weaker extremes in NARCCAP are apparent. When averaged over Wisconsin (Table 2), the modelled biases in frequency and intensity are very small in the statistically downscaled data (WICCI), indicative of the debiasing procedure applied; simulated return periods are within 1 year (6%) of observed for each of the threshold daily precipitation amounts and within 0.6 cm (4%) of observations for each of the threshold return period categories (10-, 50-, and 100-year). These biases are larger in the RCMs, which systematically undersimulate extreme precipitation: return periods exceed observations by less than 1 year (42%) for 5.1-cm events but by up to 10 years (60%) for the 10.2-cm rainfalls. Likewise, the intensity of heavy daily precipitation is also underestimated in NAR- CCAP, ranging from 1.3 cm (14%) for 10-year events to 3.3 cm (21%) for 100-year events. These underestimates are not surprising, given that in reality such heavy rainfalls are often highly localized but are being spatially averaged in the RCMs over 50-km 50-km grid cells. One of the anticipated benefits of using downscaled climate model output over raw GCM data is a better representation of extreme weather, including heavy precipitation events. This expectation is borne out by the (under)simulated frequency and intensity of these events in the global models. Although the areally averaged GCM simulations of mean seasonal and annual precipitation resemble those of the RCMs and statistically downscaled output (Figure 2), extreme precipitation is dramatically underestimated by the global models (Figure 3, Table 2). For example, the maximum precipitation intensity of the 100-year event anywhere in Wisconsin in the global models (7.3 cm) is less than the minimum intensity state-wide of the 10-year event in the statistically downscaled models (8.0 cm) (Figure 3(a)). The differences are even more dramatic in comparing frequencies, as the return period in GCMs for 10.2-cm rainfalls is more than 200 years at all locations, compared with less than 50 years everywhere in both the downscaled output (Figure 3(b)). The state-wide average return periods of heavy rainfalls in the GCMs are four times too high for the 5.1-cm events, 20 times higher

6 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3111 Figure 2. Late 20th century simulations of annual and seasonal precipitation in the (left) NARCCAP regional climate models, (middle) WICCI statistically downscaled models, and (right) GCMs. Contours denote the precipitation amounts in cm, and the shading indicates the percentage deviation from Maurer et al. (2002) observational data. for the 7.6-cm events, and 15 times higher for the 10.2 cm events (Table 2). Put another way, 7.6-cm daily rainfalls, which actually occur about twice per decade averaged over the state, only occur about once per century in the GCMs. Indicative of this underrepresentation of extreme precipitation is the GEV distribution s shape parameter (k) in the global models (Section 2), which indicates the length of the tail in the distribution of heaviest events. While large-scale observations yield a typical value of 0.15 (Koutsoyiannis, 2004b), the GCMs do not even simulate the correct sign of k (model-mean = 0.05), whereas the NARCCAP and WICCI models do (0.04 and 0.09, respectively). A likely reason for this inadequate simulation of contemporary heavy precipitation in

7 3112 S. J. VAVRUS AND R. J. BEHNKE Table 2. Simulated precipitation in the dynamically downscaled models (NARCCAP), statistically downscaled models (WICCI) and global models (GCM). Return periods (years) NARCCAP WICCI GCM Precipitation (cm) Observed Historical Scenario Change % Change Historical Scenario Change % Change Historical Scenario Change % Change Precipitation intensity (cm) NARCCAP WICCI GCM Return Period (years) Observed Historical Scenario Change % Change Historical Scenario Change % Change Historical Scenario Change % Change Total precipitation (cm) NARCCAP WICCI GCM Season Observed Historical Scenario Change % Change Historical Scenario Change % Change Historical Scenario Change % Change Annual Winter Spring Summer Fall All values represent areal averages across Wisconsin, and the return periods and intensity refer to daily accumulations. Observations are from a state-wide network of COOP stations (Kucharik et al., 2010) for extreme precipitation, while seasonal/annual values are taken from the Maurer et al. (2002) dataset. The Historical period refers to , and Scenario is the future interval. The table shows the multi-model averages.

8 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3113 global models is their much coarser horizontal resolution, which masks intense but small-scale events by averaging them over an entire grid box (Chen and Knutson, 2008). Averaged among all the GCMs, the grid boxes are around 200 km 275 km and therefore cover an area more than 20 times larger than the RCMs used in NARCCAP Middle 21st century projections Wisconsin is expected to experience a generally wetter climate by mid-century. Both downscaled datasets project higher total precipitation throughout the state (Figure 4), although the magnitude of change is rather modest: increases under 10% almost everywhere and state-wide average annual gains of 8% (NARCCAP) and 4% (WICCI). These projected increases are considerably smaller at most locations in Wisconsin than the observed trends since the middle 20th century (Kucharik et al., 2010). The pattern of annual precipitation change in NARCCAP displays a meridional gradient, with the largest increase in northern Wisconsin adjacent to the upper peninsula of Michigan (up to 9 cm) and the smallest gains in far southern regions (under 6 cm). The annualmean pattern is dictated primarily by the summertime precipitation changes, which depict a nearly symmetric split between slightly wetter conditions in the north and slightly drier to the south. The RCMs simulate a wetter climate over all of Wisconsin in every other season, however, with percentage increases during winter, spring and autumn of comparable magnitude ( 13%). The seasonal precipitation changes simulated by the WICCI models are generally smaller and even consist of slightly drier conditions throughout Wisconsin during summer ( 0.9 cm, 3% state-wide average). Otherwise, the statistically downscaled data agree with the RCMs (a) Figure 3. (a) Simulated extreme precipitation intensity (daily accumulation) in the NARCCAP, WICCI, and GCM multi-model averages for return periods of (top) 10 years, (middle) 50 years, and (bottom) 100 years, during the period. Contours indicate the modelled precipitation amount (cm) and shading denotes the models bias (cm) relative to the Kucharik et al. (2010) observational data. (b) As in (a) but for the simulated return periods of daily precipitation events exceeding (top) 5.1 cm, (middle) 7.6 cm, and (bottom) 10.2 cm. Contours indicate the modelled return period (years) and shading denotes the models bias (years) in relation to the Kucharik et al. (2010) observational data.

9 3114 S. J. VAVRUS AND R. J. BEHNKE (b) Figure 3. Continued. in projecting a wetter climate over the entire state in all other seasons and annually, although unlike NAR- CCAP, the WICCI data exhibit a zonal gradient with smallest (largest) increases in western (eastern) Wisconsin (Figure 4). Another difference is that the wintertime response using statistical downscaling (14% state-wide) is noticeably larger than the increases during spring and autumn (8 and 5%, respectively). These projections in the downscaled climate models are similar to the corresponding changes in precipitation simulated by the GCMs. For this comparison, we use the same 11 parent GCMs that were statistically downscaled for WICCI and focus on the same time period ( ) with the same SRES A2 greenhouse forcing. As expected, the global model simulations are very consistent with the WICCI results in producing a wetter climate annually in Wisconsin and precipitation increases during winter, spring, and autumn, but decreases during summer (Figure 4). The spatial patterns of change are also similar between WICCI and the GCMs, both of which simulate the largest precipitation gains in eastern and northeastern Wisconsin, although the global models generate more of a southwest-northeast gradient. The areally averaged magnitude of the seasonal and annual changes in the GCMs is also similar to the statistically downscaled output (Table 2). The only season in which there is a discrepancy of more than 1% between the two datasets is winter, when the GCMs project a 9% precipitation increase and the RCMs a 14% rise. The behaviour of extreme precipitation events in the future is summarized by the change in 10-, 50-, and 100-year return periods and the change in occurrence of 5.1-cm (2-inch), 7.6-cm (3-inch), and 10.2-cm (4-inch) annual maximum daily rainfalls. Both downscaled methods indicate elevated intensity of heavy rainfall everywhere in Wisconsin by mid-century (Figure 5). The state-wide average increase in the magnitude of these events varies from 10 to 20% between the downscaled methods, with a consistently stronger response in NAR- CCAP (Table 2). Unlike the spatial coherence exhibited by the seasonal precipitation projections (Figure 4), the patterns of change in these heavy rainfalls show little

10 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3115 Figure 4. Simulated seasonal and annual precipitation changes in the NARCCAP, WICCI, and GCM multi-model averages between the late 20th century and the middle 21st century ( ). The magnitude of the change (cm) is shown in contours, and the corresponding percentage change is shaded. structure across Wisconsin. There is, however, a strong resemblance among the patterns of change in the intensities of NARCCAP s 10-, 50-, and 100-year return periods. Maximum increases in intensity exceed 30% in the RCMs in far northern Wisconsin and around the Door County peninsula and locally over 30% in the statistically downscaled data. Notably, there is almost no agreement between the two downscaling procedures about which regions should experience the largest and smallest increases across the state, indicative of the stochastic nature of very heavy precipitation events among the models within our relatively short sampling interval. The frequency of heavy precipitation is also expected to increase in the future across the entire state, as

11 3116 S. J. VAVRUS AND R. J. BEHNKE Figure 5. As in Figure 3 but for the simulated changes in daily precipitation intensity (cm) for events with return periods of (top) 10 years, (middle) 50 years, and (bottom) 100 years. illustrated by the reduction in return period length (Figure 6). In both downscaled datasets the percentage change in the return periods of 5.1-cm (2-inch), 7.6-cm (3-inch), and 10.2-cm (4-inch) rainfalls is larger in most places than the increased intensity of the 10-, 50-, and 100-year events shown in Figure 5. The downscaled projections suggest that daily precipitation events of at least 5.1 cm will recur 12 21% sooner state-wide and that the recurrence change in very heavy 10.2-cm events will be even greater (37 47%). The downscaling methods do not exhibit a clear spatial coherence in their projected changes across Wisconsin over the three displayed precipitation thresholds. The projected magnitude of frequency changes over the state is generally larger in the RCMs, whose areally averaged return periods drop by about 10% more than those in WICCI (Table 2). Our results of changes in the frequency and intensity of heavy annual-maximum precipitation events can be compared with those of Schuster et al. (2012), who calculated mid-century changes using the same WICCI dataset and a similar greenhouse emissions scenario (SRES A1B) but considered all precipitation occurrences above a threshold (rather than only the annual maximum) at four cities across Wisconsin (Madison, Milwaukee, Green Bay and Eau Claire) over 24-h periods. Their four-station average increase in the intensity of 10-year events (8%) and 100-year events (10%) between the late 20th and middle 21st centuries is very similar to our WICCI-derived values over the entire state (10 and 14%, respectively). Likewise, their reported average decrease in the return period of 7.6-cm rainfall events (27%) is nearly identical to the 26% reduction found in our study. Given that Schuster et al. (2012) computed the intensities and frequencies directly from the statistically downscaled pdfs, rather than from the GEV distribution used here, the close agreement between these two statistical

12 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3117 Figure 6. As in Figure 4 but for the simulated changes in return periods (years) of daily precipitation events exceeding (top) 5.1 cm, (middle) 7.6 cm, and (bottom) 10.2 cm. methods suggests that the projected precipitation changes presented in this article are robust. The global models also simulate a future increase in extreme precipitation over Wisconsin, consistent with GCM projections on a global scale (Meehl et al., 2007). The GCM changes in the intensity of heavy rainfalls are smaller in absolute terms than the downscaled models but quite comparable in terms of percentage changes (Figure 5, Table 2). The global models show a fairly consistent pattern from the 10-year events through the 100- year events, such that the largest increases in intensity occur in southern Wisconsin and the smallest in northern regions (except for the extreme northeast). Despite the weaker absolute changes in the GCMs, the global models simulate similar relative increases in the intensity of heavy precipitation. The state-wide average rise in the GCMs for 10-year events is only somewhat less than in either downscaling method, falls between the downscaled values for 50-year events, and is a bit higher than either WICCI or NARCCAP for 100-year rainfalls (Table 2). Because of the much longer return periods in the GCMs in the late 20th-century climate (Figure 3(b)), their simulated absolute changes are a lot larger than the downscaled values, but their relative changes are much more comparable (Figure 6). The pattern of increased frequency (shorter return periods) of extreme precipitation in the GCMs is somewhat different than the corresponding pattern of increased intensity. For the heaviest categories (7.6 and 10.2 cm), the southeastern quadrant of Wisconsin is projected to experience the most pronounced shift

13 3118 S. J. VAVRUS AND R. J. BEHNKE towards more frequent events (about twice as common), although much of this regional accentuation appears to be a consequence of the very rare occurrences of such cases over this area in the late 20th-century reference climate (Figure 3(b)). Averaged over Wisconsin, the GCMs simulate a considerably larger relative reduction in return periods for the least extreme 5.1-cm events than either downscaled method, only a slightly larger decline than either for 7.6-cm rainfalls, and a very comparable drop for the most extreme 10.6-cm occurrences (Table 2). The GCMs magnitude of increased frequency and intensity of the most extreme rainfalls (50- to 100-year events, 7.6 cm and heavier) is more similar in percentage terms to the RCMs than to the weaker WICCI response (Table 2). Over all of Wisconsin, the sign of heavy precipitation change and the relative sensitivity among the datasets agree with the behavior of the GEV shape parameter, k, which increases uniformly between the late 20th and middle 21st centuries by a larger and very similar amount in the GCMs (+0.035) and RCMs (+0.031) compared with the statistically downscaled models (+0.016) Inter-model spread of projections We can obtain a measure of uncertainty in our results by examining the inter-model spread among the model projections. The box-and-whiskers graphs in Figure 7 illustrate these variations in terms of simulated mean and extreme precipitation. The top plot shows that the projected seasonal and annual changes described in Section 3.2 are consistent across most models and that there is very little difference in the magnitude of the intermodel spread between the two downscaled methods. In the RCMs the simulated future increase in precipitation annually and during winter and autumn occurs in every model, and a wetter springtime is generated in all but one model. The statistically downscaled models vary more on the sign of seasonal changes, although all simulate a wetter winter climate. The interquartile range in WICCI encompasses entirely positive values during autumn and spring and nearly so in the annual average. By contrast, the weaker signal of model-mean precipitation change during summer (Figure 4) is reflected in the box-andwhiskers plot, which shows the summertime interquartile range in both downscaled outputs straddling the zero line, as well as the largest interquartile ranges during that season. We find similar inter-model variability among the global models, whose interquartile range spans precipitation increases during winter, spring, autumn, and annually. Like the downscaled projections, the GCMs disagree with each other on the sign of future rainfall changes during summer, although the global models strongly suggest that the magnitude of the percentage change will be relatively small in this season. The inter-model consistency of increased extreme precipitation in the future is even more pronounced than the projected sign of the seasonal and annual changes. All the models from both downscaling methods simulate increases in heavy precipitation state-wide in every event category (Figure 7). As with the seasonal and annual output, the inter-model spread in projected heavy rainfall is similar between NARCCAP and WICCI. There is a general tendency for the range to expand from the more common to less common events. The spread among GCMs is larger and even includes a couple models that simulate longer return periods (less frequent extreme rainfall) in the future, but the vast majority of the global models also project more frequent heavy rainfall in all the event categories Distribution of precipitation Climate change is also expected to bring about a shift in the distribution of heavy precipitation. Both downscaled models realistically simulate that heavy precipitation occurs most often during summer in the late 20thcentury climate, unlike the GCMs bimodal peak during spring especially and autumn secondarily, as described by Holman and Vavrus (2012) and displayed in Figure 8. The months of June to August account for half of the heaviest 1% of all precipitation events in both NARCCAP and WICCI during the period, whereas the global models produce a pronounced dip in frequency during July and August and thus simulate less than a third of all such events during summer. Summer remains the most active season in the future in the downscaled models, but to a smaller degree, as the relative contribution of heavy rainfalls during June, July, and August declines. These reductions are compensated by a larger proportion occurring during spring and autumn, in agreement with the projected seasonal changes in total precipitation (Figure 4). Likewise, the GCMs simulate an even smaller percentage of future heavy events during summer that is also balanced by a larger share of extreme precipitation in spring and autumn. This broadening of the annual cycle of heavy precipitation is consistent with an expanded seasonal duration of high atmospheric moisture content in a warmer climate, which allows strong events to spread from the most humid summer months in the contemporary climate into the shoulder seasons of spring and autumn in the future (Holman and Vavrus, 2012). Besides these changes in the distribution of heavy precipitation seasonally, climate models also project a shift in the distribution of daily precipitation amounts. Not only are heavy events in Wisconsin expected to become more common, but light to moderate precipitation amounts (less than 1 cm) should become less frequent (Figure 9). In addition, the projected increases in heavy precipitation grow as a function of event size, such that the largest percentage gains are simulated for the most extreme rainfalls within each dataset. This result is similar to the outcome from statistically downscaled climate models for Chicago (Vavrus and Van Dorn, 2010) and from GCMs over all of North America (Gutowski et al., 2008), and it indicates a change towards a more summerlike precipitation pattern of more dry days interspersed with relatively heavy rainfalls. Such a shift might result

14 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3119 Figure 7. Inter-model variability of the simulated mid-21st century changes in (top) annual and seasonal precipitation; (middle) daily precipitation intensity of 10-, 50-, and 100-year events; and (bottom) return period of daily precipitation amounts exceeding 5.1, 7.6, and 10.2 cm. All values are state-wide averages. The boxes encompass the interquartile range (25th to 75th percentiles of models in each dataset), the crosses are the multi-model means, and the horizontal line within a box represents the inter-model median value. The whiskers denote values within 1.5 interquartile range of the upper and lower quartiles. Outliers beyond these ranges are plotted as either large outliers in blue dots or very large outliers in asterisks. in an adverse health impact, based on the positive, significant correlation between bacteria concentrations in a nearby inland lake in Wisconsin (Lake Geneva) and the duration between rainfall events (Patz et al., 2008). 4. Discussion and conclusions The results from multiple climate models and downscaling methods are in broad agreement that Wisconsin will experience a wetter future climate with more frequent and

15 3120 S. J. VAVRUS AND R. J. BEHNKE Figure 8. Annual cycle of the state-wide average frequency of extreme daily precipitation (top 1% of all events) in the historical period ( ) and future scenario ( ). The percentages indicate the monthly contribution to the annual total. Figure 9. Simulated change in the frequency of daily precipitation amounts from the late 20th to the mid-21st century averaged over Wisconsin. Event categories (cm) range from essentially no precipitation (<0.25 cm) to more than 10.2 cm. GCM values are truncated at 6.35 cm, beyond which most of the models did not simulate any events. intense heavy rainfalls. The three modelling approaches investigated here dynamical downscaling, statistical downscaling, and raw GCMs are consistent in projecting that winter, spring, and autumn will be the seasons with the largest increases in precipitation, whereas rainfall during summer will more likely decline slightly or remain nearly unchanged. The trend towards more extreme precipitation is robust in models and in recent observations, but there is little agreement among the simulations in terms of which regions in Wisconsin are most susceptible. In particular, although the areally averaged signal strengthens with the rarity of the event in all three datasets (i.e. larger changes expected state-wide for longer return periods and higher precipitation amounts), there is little agreement among the modelling approaches in the simulated spatial distribution of this signal (Figures 5 and 6). Such a divergence illustrates both the highly stochastic nature of extreme rainfall within a region and the stronger potential for heavy rainfall with increasing atmospheric moisture content across a region. Although our study domain is limited to Wisconsin, we believe that our general conclusions apply to a broader region (at least the upper Midwest of the United States), given the geographic coherence of trends in mean and extreme precipitation observed during the past several decades (Groisman et al., 2004) and projected over North America throughout this century (Meehl et al., 2007). Another interesting feature in our analysis is that the projected increase in the frequency of heavy precipitation, as defined by percentage changes in return periods, is generally larger than the simulated increase in intensity (Figure 10). These graphs illustrate the consistency

16 FUTURE PRECIPITATION IN WISCONSIN FROM GLOBAL AND DOWNSCALED MODELS 3121 Figure 10. Comparison of projected state-wide percentage changes in (top) daily precipitation intensity as a function of return periods between 10 and 100 years, and (bottom) return periods as a function of daily precipitation intensity between 1.9 and 15.2 cm events. All values are obtained using generalized extreme value (GEV) distributions, which allow for extrapolating beyond the 30 years of available model output. among modelling approaches in simulating both stronger and more frequent heavy precipitation events for all event categories, but more importantly they show agreement that the change in the frequency of extremes (as defined by the return period) exceeds the change in intensity for all events beyond a 30-year return period (about 5 cm accumulation) and for all events beyond a 25-year return period in both downscaled approaches. This distinction has implications for adaptation strategies, because certain societal impacts are likely whenever precipitation exceeds a certain threshold (therefore sensitive to frequency changes), whereas others depend more strongly on the total amount of rainfall and are thus more responsive to intensity changes. The projected hydrological changes presented here can be contextualized by comparing them to recent observed trends. Annual precipitation totals around the state are expected to rise by less than 10% almost everywhere by mid-century (Wisconsin average 4 8%), a smaller amount than the observed increase since 1950 ( 10% averaged state-wide) (Kucharik et al., 2010). Future increases by season are expected to be somewhat larger (up to 15% areally averaged), but this figure is also less than the observed trend during autumn (around 20%). Thus, the magnitude of seasonal and annual precipitation changes anticipated during the first half of the 21st century is not excessive in relation to observed trends over the last half of the 20th century. Like most of the country, Wisconsin has also experienced a positive trend in heavy precipitation during the past several decades (Gutowski et al., 2008), based on various measures used to define an extreme event. On the basis of the 1-year return period of daily precipitation, Madsen and Figdor (2007) reported that extreme events have become significantly more frequent (30%) state-wide between 1948 and On the basis of the six primary weather stations across Wisconsin used by Kucharik et al. (2010), the frequency of daily rainfalls exceeding 2.5, 5.1, and 7.6 cm has increased between 20 and 50% during this interval. These trends are very comparable to the upcoming changes in heavy rainfall projected through the middle of the current century. A likely societal consequence of the future precipitation changes described here is an increase in waterborne disease outbreaks in the absence of adaptation measures, based on previous studies linking precipitation to human health. Drayna et al. (2010) documented a statistically significant increase (11%) in paediatric emergency department visits in Milwaukee four days after any rainfall, regardless of the amount. Empirical evidence indicates that CSOs of contaminated stormwater discharge into Lake Michigan typically occur when daily rainfalls exceed 5 6 cm (2 2.5 inches) (McLellan et al., 2007, Hayhoe et al., 2008). Statistically downscaled results from two GCMs suggest that Chicago will see a % increase in the frequency of daily rainfalls 6 cm or greater by mid-century (compared with the late 20th century). Our findings also support the expectation that such events will increase significantly in Wisconsin, as the frequency of rainfalls exceeding cm along the Lake Michigan coastline is projected to climb by 20 30% in the downscaled climate models. Another expression of climate change that could affect surface runoff and therefore contaminant discharge into waterways is the shift in seasonal timing of heavy rainfalls, such that they become relatively more common during winter and spring (Figure 8), when infiltration through frozen or saturated soils may be impeded. This type of shift would favour more surface runoff and therefore enhanced contaminant discharge, consistent with winter and spring currently being the most sensitive seasons to CSOs in Milwaukee (McLellan et al., 2011), but how changes in soil hydrology and the prevalence of surface-based contaminants seasonally would affect this conclusion is uncertain. Although we believe that our study is the first to systematically compare climate change projections using multiple GCMs and two different types of downscaled