Urban Precipitation Extremes: how reliable are the regional climate models?

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1 Urban Precipitation Extremes: how reliable are the regional climate models? Vimal Mishra 1, Francina Dominguez 2 Dennis P. Lettenmaier 1 1. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, Department of Atmospheric Sciences, University of Arizona, Tucson, AZ, Abstract Increases in precipitation extremes as the climate warms may pose immense pressure to existing urban stormwater infrastructure. We evaluate the ability of regional climate models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to reproduce the timing, mean, and variability of sub-daily precipitation extremes for 20 major urban areas across the Western U.S. Here, we show that RCMs with both global climate model (GCM) and reanalysis boundary conditions behave remarkably similarly and fail to capture the seasonality (timing) of precipitation extremes in most of the urban areas. RCMs with both reanalysis and GCM boundary conditions showed a tendency to overpredict (underpredict) annual precipitation maxima at urban areas that have low (high) mean annual maximum precipitation, respectively. For urban areas located along the Pacific Coast and in the Southwest, RCMs underpredict annual precipitation maxima at durations ranging from 3 to 24 hours. 5- and 100-year return period precipitation maxima were also underpredicted for most urban areas, with the same general patterns as for the mean of the annual maxima. On the other hand, the RCMs overpredict the annual precipitation maxima at 5 and 100 years return period for urban areas that receive low observed annual precipitation maxima, located primarily in the interior of the West. RCMs also mostly underpredict coefficients of variation (CV) of annual precipitation maxima. Introduction 26 About half of the human population lives in urban areas [Martine and Marshall, 2007], in

2 contrast with only about 10 percent a century ago [Grimm et al., 2008]. Due to immense increases in population and built infrastructure, urban areas are emerging as first responders of climate change adaptation and mitigation [Rosenzweig et al., 2010]. Changes in extreme precipitation as the climate warms may pose challenges for stormwater management in urban areas, because most stormwater infrastructure was designed under the assumption of stationary climate that is dead as argued by Milly et al. [2008]. The design of urban drainage systems is mostly based on precipitation and stormwater discharge with return periods ranging from about 5 to 100 years, corresponding to events with a probability of of occurring in any year. Because urban drainage areas are relatively small and have substantial impervious or semi-pervious fractions, their response times to extreme precipitation are usually short. Therefore, precipitation durations that control urban stormwater design usually range from less than an hour to a few hours at most. Hence, we focus here on sub-daily time scales There is some evidence that the hydrologic cycle has already begun to intensify in response to a warmer atmosphere [ Huntington, 2006; Trenberth et al., 2003]. Observational evidence also suggests that precipitation extremes have increased in many parts of the world. For instance, a consistent increase in heavy and very heavy precipitation events has occurred in many regions including North America [Trenberth et al., 2003]. A 10% increase in annual precipitation across the United States since 1910 and more than 50% of the increase was attributed to trends in both intensity and frequency of precipitation in the upper tenth percentile of the daily precipitation distribution [Karl and Knight, 1998]. Statistically significant trends in the intensity of precipitation events above the 95th percentile were also noticed over the continental U.S by Pryor et al. [Pryor et al., 2009]. A statistically significant increase in the frequency of extreme

3 precipitation in the state of Washington from 1948 to 2006 was found, especially in the Seattle metropolitan area by Madsen et al. [Madsen et al., 2007], although trends were not consistent in other parts of the Pacific Northwest. For the period , a modest increase in heavy precipitation over southern and central coastal California, declines along the northern California and central Oregon coast, and a substantial increase in Washington was found by Mass et al. [Mass et al., 2011]. All the above mentioned studies were based on daily or multi-day precipitation extremes; to date changes in sub-daily extremes have received less attention Most climate models project increases in precipitation extremes as the climate warms [O Gorman and Schneider, 2009]. Moreover, model-predicted changes in precipitation extremes are somewhat independent of changes in mean precipitation as they are largely controlled by variations in the atmosphere s moisture transport capacity [Allen and Ingram, 2002]. Increased moistening of the atmosphere in a warmer climate could therefore further lead to substantial increases in extreme precipitation frequency and intensity [Groisman et al., 1999], irrespective of changes in (seasonal and annual) means. GCM simulations of future precipitation extremes generally show increases [Mearns et al., 2009], which roughly follow the Clausius-Clapyron rate of increase of atmospheric water holding capacity with temperature GCMs, which run over the entire globe and are self-consistent with respect to moisture and energy fluxes, are the primary source of information about possible changes in future precipitation. However, their spatial resolution (typically degrees latitude by longitude, equivalent to hundreds of km) is too coarse to resolve the processes that control precipitation extremes. Furthermore, they are unable to resolve topographic variations that strongly affect precipitation and precipitation extremes in the western U.S. On the other hand, regional climate models (RCMs) provide higher-resolution climate projections that at least partially resolve finer

4 scale variability associated with topography and land cover and hence are increasingly being used in studies aimed at helping society adapt to climate change (20). However, these models have not been subjected to nearly as much scrutiny as have GCMs, especially with respect to their ability to reproduce properties of observed precipitation and precipitation extremes. In this study, we compare predictions of precipitation extremes archived as part of the North American Regional Climate Change Assessment Program (NARCCAP) with observations over a set of 20 western U.S. urban areas, for precipitation accumulation intervals from sub-daily to daily Methods: 2.1 Study Region and Data Our study region consists of 20 major urban areas across the western U.S. (Figure S1a and Table S1). We obtained hourly precipitation data for precipitation stations located in the vicinity (within 150 km) of each of the selected urban areas from the National Climatic Data Center (NCDC) (Figure S1b) for the period For a few urban areas, where long-term stations were unavailable, the 150 km threshold was increased somewhat. Data quality was carefully checked and years with more than 10% missing data were removed. Moreover, we removed any station that had more than two missing years in the entire record We used RCM-simulated precipitation output from participating models in the North American Regional Climate Change Assessment Program (NARCCAP) [Mearns et al., 2009]. For most of the NARCCAP RCMs, two runs were made: the first run forced the RCMs with reanalysis data from the National Center for Environmental Prediction/Department of Energy (NCEP/DOE) at the boundaries [Kanamitsu et al., 2002]. For the second run, output from selected global climate models (GCMs) was used to provide the RCM boundary conditions. We used RCM output from

5 both sets of runs in our analyses. Spatial and temporal resolutions for the NARCCAP RCMs were 50-km, with a minimum precipitation aggregation time of 3-hours (Table S2). We used output from five RCMs that used reanalysis boundary conditions, and four RCMs that were run with GCM boundary conditions (Table S2) Analysis We extracted observed and RCM-simulated annual precipitation maxima at 3, 6, 12, and 24 hours precipitation durations from NCDC archives and NARCCAP model output, respectively. RCM-simulated precipitation maxima were estimated for the RCM grid cell that was nearest to the selected precipitation stations (Figure S1) for each urban area. We used areal reduction factors to convert point precipitation measurement to their areal equivalent that can be compared to precipitation maxima simulated by the RCMs. Detailed methodology of the estimation of precipitation maxima is presented in section S We estimated annual precipitation maxima at 2- to 100-year return periods from observed and RCM simulated precipitation. We estimated L-moments parameters and fit the Generalized Extreme Value distribution (GEV) to estimate precipitation maxima at the selected durations for each urban area. Details of the estimation approach are provided in section S We estimated annual and seasonal (winter and summer) precipitation maxima from observations and RCM simulations. The timing of precipitation maxima in the selected urban areas was estimated based on the dominant season when most of the annual precipitation maxima occurred. For instance, for a given urban area in a given time period (e.g., ), if most of the annual maxima occurred during winter then we considered winter as the dominant season for the occurrence of the precipitation maxima. More details are provided in section S1.3.

6 Results 3.1 RCM performance for individual urban areas a. Seasonality of Precipitation Extremes Figure 1 shows the timing of annual precipitation maxima for the NARCCAP models (hereafter RCMs) with reanalysis and GCM boundary conditions. The observations show that the coastal urban areas ( locations ) all receive their 3-hourly and 24-hourly annual maximum precipitation during winter (Figure 1a and 1g). On the other hand, observed precipitation maxima occur during summer at most of the locations in the interior West and Southwest. RCMs with reanalysis boundary conditions showed disparities in the timing of precipitation maxima for most of the locations. For instance, in the CRCM, MM5I, and HRM3 models the precipitation maxima are in winter at most locations regardless of duration, and fail to capture the transition in occurrence of precipitation maxima from winter to summer for locations in the coastal and interior West. Most of the models capture the timing of the 3-hourly precipitation maxima for locations in the Pacific Northwest coastal area (Seattle, Portland, and Salem) successfully but they fail to reproduce the timing at locations in coastal California, the Southwest, and the interior of the West. RCM3 with the reanalysis boundary condition reproduces the timing of the 3-hourly precipitation maxima the best, and matches the observed summer precipitation maxima for some locations in the Southwest and interior West. The timing of the 24-hourly annual precipitation maxima was reproduced well in the Pacific Northwest by most of the RCMs (Figure 1g-i). However, for other regions (e.g. coastal California, Southwest, and interior West), most of the models (except RCM3) failed to reproduce the observed timing of the 24-hour precipitation. The timing of observed 3 and 24-hour annual precipitation maxima for (the period for comparison with RCMs with GCM boundary conditions) is essentially identical to the

7 period used for the RCM-reanalysis comparisons (Figure 1m-v). As for the RCMreanalysis results, most of the models with the GCM boundary conditions successfully reproduce the timing of 3-hour precipitation maxima for locations in the coastal Pacific Northwest. However, similar to the models with reanalysis boundary conditions, most of the RCMs with GCM boundary conditions fail to capture the timing of 3-hourly precipitation maxima at locations in the Southwest, coastal California, and interior West. Moreover, the performance of most of the models with GCM and reanalysis boundary conditions with respect to their simulations of the season of 24-hourly annual precipitation maxima (Figure 1r-v) was similar. We infer from this that the timing of precipitation maxima was largely controlled by the RCM physics rather than the models boundary conditions (e.g. reanalysis or GCM). For instance, the dominance in occurrence of precipitation maxima in winter was consistent in the CRCM and MM5I models with both reanalysis and GCM boundary conditions. On the other hand, the RCM3 model was somewhat less consistent with respect to the season of precipitation maxima when run with reanalysis and GCM boundary conditions b. Annual maximum precipitation means: Ensemble mean biases in annual and seasonal precipitation mean maxima for the RCMreanalysis and RCM-GCM simulations are shown in Figure 2 (similar results for individual models are shown in Figures S2 and S3). For the majority of locations, 3 and 6-hourly precipitation mean maxima were underestimated (-6 to -57 %). However, for a few locations (mostly relatively dry locations in the interior of the Northwest), the precipitation mean maxima were overestimated (0.5 to 97%) by the models with the reanalysis boundary conditions (Figure 2a and 2b). At the longer 12 and 24-hourly durations, the maxima were overestimated for most

8 locations (Figures 2c and 2d). RCM-reanalysis overestimated precipitation mean maxima at locations in the interior-west for all durations, whereas they were mostly underestimated for locations along the coast, and in the Southwest for most durations (Figures 2a-d). Similar to RCM-reanalysis, RCM-GCMs underestimated (-3 to -56 %) 3-hourly precipitation mean maxima for most locations (Figure 2a). The number of locations with overestimates for 6, 12, and 24-hour durations were higher in RCM-GCM (Figure 2) compared to RCM-reanalysis simulations. Consistent with the RCM-reanalysis simulations, the number and magnitude of downward biases decreased with duration (Figures 2a-d). The spatial patterns of over- and under-estimation were generally similar for RCM-GCM and RCM-reanalysis simulations. c. Variability (CV) in Annual Maximum Precipitation: We estimated ensemble mean bias as well as the bias in the coefficient of variation (CV) for each model (Figures S4 and S5) relative to the CV of observations. As for ensemble mean bias, the analysis was conducted separately for reanalysis and GCM boundary conditions (Figure 3). Figure 3 shows that for the RCMs with reanalysis boundary conditions, the ensemble mean CV was underestimated at most (about 2/3) of the locations by as much as -44%. The number of locations with negative CV biases decreased with duration; however the maximum positive and negative biases did not change much with duration. Furthermore, there was no consistent spatial pattern in the biases. RCM-reanalysis consistently underestimated CV of precipitation maxima at Los Angeles, San Jose, Las Vegas, and Billings for most of the precipitation maxima durations. On the other hand, CV was consistently overestimated at Phoenix, Seattle, Tucson, and Denver.

9 Similar to RCM-reanalysis, the RCM-GCM simulations underestimated CV at most (also about 2/3) of the locations, and this fraction did not change much with duration (Figure 3a-d). The range of positive bias in CV was slightly lower in RCM-GCM than in RCM-reanalysis, and the range of negative bias was slightly higher in RCM-GCM than in RCM-reanalysis. In both simulations (RCM-reanalysis and RCM-GCM), CV was underestimated at most of the locations in the interior-west and overestimated at locations along the coast, and in the Southwest (Figure 3a-d) d. 5- and 100-year return periods: We estimated 3 and 24-hourly precipitation maxima at 5- and 100-year return periods for observed and RCM-simulated precipitation annual maxima using the GEV distribution (Figure 4). Our interest was to determine the ability of RCMs to reproduce extreme precipitation risk estimates that are relevant to urban drainage system design. As for our analysis of means and CVs, we compared estimates based on RCM-reanalysis and RCM-GCM simulations. Figure 4 shows the ensemble biases (bias for individual models are presented in Figures S6 and S7) in 3 and 24 hourly precipitation maxima for 5- and 100-year return periods in the RCM-reanalysis simulations. RCM-reanalysis underpredicted (-5 to -60%) 3-hourly precipitation maxima at most (about 2/3) of the locations at 5 and 100 year return periods (Figure 4a and 4b). At approximately 1/3 of locations with overestimates, RCM-reanalysis overestimated (1 to 98%) 3-hourly precipitation maxima at 5- and 100-year return periods. At 24-hour duration, the number of locations with over- and under-estimates was about equal, but the magnitude of both positive and negative bases was larger than for 3 hour duration. In agreement with the mean annual precipitation maxima analysis, both 3 and 24-hourly precipitation maxima were consistently overestimated at locations in the interior Northwest

10 (Spokane, Boise, Billings). On the other hand, for locations along the coast and the Southwest, both 3 and 24 hourly precipitation maxima were underestimated at 5 and 100 year return periods (Figure 4a-d ). Biases in the 3-hourly annual precipitation maxima for both 5-year and 100-year return periods are generally similar in RCM-GCM and RCM-reanalysis simulations (Figure 4a). For instance, for the majority of locations 3 hourly precipitation maxima in RCM-GCM simulations were underestimated for both 5- and 100-year return periods (Figure 4a and 4b). However, more locations showed positive bias in 3- hourly precipitation maxima in RCM-GCM than in RCMreanalysis (Figure 4a and 4b). Consistent with RCM-reanalysis, most of the locations showed positive bias in 24 hourly precipitation maxima at 5- and 100-year return periods (Figure 4c and 4d). However, the range of positive bias (0-275 %) was slightly lower in RCM-GCM simulations than in RCM-reanalysis (0-300%). For locations in the interior West (Spokane, Boise, and Casper), 3 and 24 hourly precipitation maxima were consistently overestimated in both RCM-GCM and RCM-reanalysis simulations. In general, RCMs underestimate precipitation maxima for the locations that have higher mean annual precipitation maxima (e.g. Phoenix, Seattle) and overestimate precipitation maxima for the locations that have lower mean annual precipitation maxima (e.g. Boise, Spokane, and Casper), irrespective of their boundary conditions. RCMs with both reanalysis and GCM boundary conditions underestimate inter quartile range of annual precipitation maxima in their domain average response (Figure S8 and Section S1.4). In the domain average response (taking all 20 urban areas), RCMs largely underestimate the upper part of the 3 and 24 hourly precipitation maxima at 5 and 100 years return periods in both of the boundary conditions (Figure S9, Section S1.4).

11 Discussion and conclusions Our aim was to evaluate extreme precipitation as simulated by the NARCCAP RCMs to understand how well RCMs can capture the magnitude, variability, and seasonality of precipitation maxima for urban areas in the western U.S. The ability of RCMs to reproduce precipitation extremes is an important problem, given concerns about the implications of possible changes in precipitation extremes on flooding, and the increased recognition that current methods of engineering design, which are based on risk estimation keyed to historic observations, are no longer justifiable [Milly et al., 2008]. In this respect, our results are somewhat discouraging. We found that most of the NARCCAP RCMs, regardless of whether they were run with GCM or reanalysis boundary conditions, were unable to reproduce observed variations in the seasonality of precipitation extremes across the region, with the exception of the coastal Pacific Northwest. In general, the RCMs, whether forced with reanalysis or GCM boundary conditions, over-simulated the propagation of winter-dominant extremes into the interior, which is observed to increase with duration (i.e., locations in the interior of the West are more likely to have winter 24-hour maxima than 3-hour). In contrast with observations, and irrespective of boundary conditions, most of the RCMs tend to simulate precipitation maxima predominantly in the winter season for most of the urban areas. Our results also indicate that for the shortest duration we studied (3-hourly) for most (about 2/3) of the urban areas, and especially those with large observed precipitation maxima (generally along the Pacific coast and in the Southwest), RCMs underpredict mean annual precipitation maxima (by up to -60%), regardless of whether the boundary conditions come from GCMs or reanalysis. The fraction of over- and under-simulation became progressively more

12 balanced as the duration increased from 3 to 24 hours, however the pattern of underprediction at locations with large maxima and overprediction at locations with small maxima remained. The physical mechanisms that lead to these biases are clearly different. The maxima in the Southwest are linked to summer convective forcings, leading us to conclude that there are persistent issues with the convective parameterizations in the RCMs that lead to under-estimation of the intensity of extreme convective precipitation. Along the Pacific Coast, the model deficiencies are predominantly in the winter season. Some of these biases may be resolution dependent in particular, the tendency of the RCMs to overpredict precipitation extremes at the dryer locations (many of which are valleys in topographically complex regions) may be related to resolutiondependent biases in the mean precipitation. However, resolution is not such an obvious cause of downward biases in the wetter sites. We found considerable biases in RCM-simulated variability of precipitation maxima across the urban areas, with similar results for RCM-reanalysis and RCM-GCM simulations. The interannual variability of 3-hourly precipitation maxima was largely underestimated by the RCM-reanalysis as well as RCM-GCM. Regardless of RCMs boundary conditions, interannual variability in annual precipitation maxima was underestimated at most of the locations in the interior-west while overestimated at locations along the coast, and in the Southwest. Precipitation maxima at 5 and 100 years return period for urban areas in the western interior that have low observed mean annual precipitation maxima (e.g. Spokane, Boise, Salt Lake City, and Casper) were consistently over-simulated (0-300% percentage bias) by the RCMs, regardless of boundary conditions, a pattern that followed that of the mean precipitation maxima. This pattern held for about 1/3 of the total locations for 3-hour duration, and increased to about half the locations for 24 hour duration. RCMs with reanalysis as well as GCM boundary

13 conditions underestimate inter quartile range (across all 20 urban areas) of the mean, 5-year, and 100-year precipitation maxima across all durations. Some recent work has suggested that sub-daily (e.g. hourly) precipitation extremes may increase faster than daily extremes as the climate warms [Lenderink and Van Meijgaard, 2008]. Understanding the magnitude of these changes for urban areas has important implications for urban stormwater design and management, and RCMs will be looked to as a source of information for the development of future design standards. However, based on our findings, the NARCCAP RCMs have serious shortcomings in their ability to reproduce the statistical characteristics of precipitation extremes under historical climate conditions, making confidence in their ability to project future conditions tenuous. These deficiencies may, in part, be attributable to the relatively coarse spatial resolution of the NARCCAP RCMs, but appear more likely to be due to issues in the RCM physical parameterizations of the processes that lead to extreme precipitation. These will require more attention by model developers before RCMs can be considered a reliable source of information for engineering design purposes. Acknowledgement This work reported herein was supported by U.S. Department of Energy Grant No. DE to the University of Washington, and by Grant No. DE-SC to the University of Arizona. We also thank the North American Regional Climate Change Assessment Program (NARCCAP) for providing access to the RCM output used in our work. NARCCAP is funded by the National Science Foundation (NSF), the Department of Energy (DoE), National Oceanic and Atmospheric Administration (NOAA), and the U.S. Environmental Protection Agency (EPA). References:

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16 Figure Captions Figure 1. Observed and simulated (RCMs with reanalysis and GCM boundary conditions) seasonal variability in 3-hourly and 24-hourly precipitation extremes. Blue circles represent the occurrence of precipitation maxima in summer, while red squares represent the occurrence of maxima in winter Figure 2. Ensemble mean percent bias in mean annual precipitation maxima for (a) 3-hour, (b) 6-hour, (c) 12-hour, and (d) 24-hour durations for RCMs with reanalysis (yellow bars) and GCM (green bars) boundary conditions Figure 3. Ensemble mean percent bias in CV of the annual precipitation maxima for (a) 3-hour, (b) 6-hour, (c) 12-hour, and (d) 24-hour durations for RCMs with reanalysis (yellow bars) and GCM (green bars) boundary conditions Figure 4. Ensemble mean (for all RCMs with reanalysis (yellow) and GCM (green) boundary conditions) percent bias in (a) 3-hourly 5 year return period, (b) 3-hourly 100 year return period, (c) 24-hourly 5 year return period, and (d) 24-hourly 100 year return period for GEV distribution fit to observed and model-simulated annual maximum precipitation. 358

17 Figure 1. Observed and simulated (RCMs with reanalysis and GCM boundary conditions) seasonal variability in 3-hourly and 24-hourly precipitation extremes. Blue circles represent the occurrence of precipitation maxima in summer, while red squares represent the occurrence of maxima in winter. 365

18 Figure 2. Ensemble mean percent bias in mean annual precipitation maxima for (a) 3-hour, (b) 6-hour, (c) 12-hour, and (d) 24-hour durations for RCMs with reanalysis (yellow bars) and GCM (green bars) boundary conditions

19 Figure 3. Ensemble mean percent bias in CV of the annual precipitation maxima for (a) 3-hour, (b) 6-hour, (c) 12-hour, and (d) 24-hour durations for RCMs with reanalysis (yellow bars) and GCM (green bars) boundary conditions

20 Figure 4. Ensemble mean (for all RCMs with reanalysis (yellow) and GCM (green) boundary conditions) percent bias in (a) 3-hourly 5 year return period, (b) 3-hourly 100 year return period, (c) 24-hourly 5 year return period, and (d) 24-hourly 100 year return period for GEV distribution fit to observed and model-simulated annual maximum precipitation. 389