Supplementary Information for. Seasonal hydroclimatic impacts of Sun Corridor expansion

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1 Supplementary Information for Seasonal hydroclimatic impacts of Sun Corridor expansion M. Georgescu a,b*, A. Mahalov b, and M. Moustaoui b a School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ , USA b School of Mathematical and Statistical Sciences, Global Institute of Sustainability, Arizona State University, Tempe, AZ , USA Accepted for publication in: Environmental Research Letters This file includes: 1. WRF Model Evaluation 2. Figures S1, S2, S3 3. References * Corresponding Author: Matei Georgescu School of Geographical Sciences and Urban Planning Arizona State University P.O. Box Tempe, AZ

2 1. WRF Model Evaluation We evaluate the WRF Control simulation (after averaging all four model realizations to produce a corresponding mean) against the University of Delaware Global Air Temperature dataset, a gridded product available courtesy of the Earth Systems Research Laboratory ( Figure S1 illustrates the well captured model-simulated seasonal transition of near-surface temperature for Broad features related to differences in elevation are reasonably reproduced (e.g. for all seasons, the northeastern portion of AZ is cooler relative to the southwestern semi-desert), although a warm bias is apparent over the higher terrain of the Mogollon Rim. These differences, however, may be due to the relatively coarser resolution of the gridded temperature product (0.5 ) compared to the higher resolution simulations (20km) which better resolve topographic variability. Overall, the model provides confidence in its ability to accurately reproduce the area s diverse and seasonally varying thermal behavior during the simulated time period. Model-simulated total precipitation is presented in Figure S2, using the University of Delaware Global Precipitation dataset. The model captures the seasonal transition in precipitation from spring (relatively dry across the state) to summer (the onset of the monsoon is responsible for state-wide precipitation enhancement), to fall (decreased precipitation accumulation) and winter (greater precipitation along higher elevations). Topographic-induced precipitation enhancement as well as magnitude of precipitation is reasonably reproduced, although a wet bias (exceeding 1 mm day -1 ) is evident for both summer and fall seasons relative to this gridded product. Recent work has highlighted significant disagreement among varying precipitation datasets (over both oceans and land) layering additional uncertainties when comparing model simulations to observationally-based, gridded, products (1). Anomalous regional variability among the

3 different datasets is similar, but considerable disagreement exists in the absolute magnitude of precipitation. We therefore repeat the analysis presented in Figure S2, but instead make use of the CPC US Unified Precipitation dataset provided by NOAA/OAR/ESRL PSD from their Web site at This precipitation dataset has coverage over the Continental United States at a horizontal resolution of 0.25 and a daily temporal frequency. Model-simulated precipitation, a difficult to simulate parameter owing to incomplete physical understanding of convective processes (2-4), was compared to the UNIFIED Precipitation dataset (Figure S3). A considerable reduction in summer season wet precipitation bias is noted, with excellent agreement in the magnitude of total precipitation across the Mogollon Rim and southeast Arizona, highlighting important differences between the pair of gridded products. The wet precipitation bias noted against the University of Delaware Global Precipitation dataset during the fall season, however, does persist and future work aims to diagnose the origin of this bias. Overall, the WRF Control experiment compares favorably with observationally-based gridded temperature and precipitation data, providing confidence in the model s ability to reproduce Arizona s seasonally-varying climate during the simulated time period. Lastly, it is important to note that the performance of this version of the model has also been thoroughly evaluated over urbanizing regions of the semi-arid Southwest (5).

4 References 1. Shin, D.-B., J.-H. Kim, and H.-J. Park 2011 Agreement between monthly precipitation estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite analysis, J. Geophys. Res., 116, D16105, doi: /2010jd Liang XZ, Xu M, Kunkel KE, Grell GA, Kain JS 2007 Regional climate model simulation of U.S.-Mexico summer precipitation using the optimal ensemble of two cumulus parameterizations. J Clim Sylla MB, Giorgi F, Stordal F 2012 Large-scale origins of rainfall and temperature bias in high-resolution simulations over southern Africa. Clim. Res., Bukovsky, M. S., and Karoly, D. J Precipitation Simulations Using WRF as a Nested Regional Climate Model. J. App. Meteorol. Clim., 48(10), doi: /2009jamc Georgescu, M., M. Moustaoui, A. Mahalov, and J. Dudhia 2011 An alternative explanation of the semiarid urban area oasis effect, J. Geophys. Res., 116, D24113, doi: /2011jd

5 Figure S1. Simulated (a-d; top panels) and observed (e-h; bottom panels) seasonal mean 2m air temperature (K), for (a, e) spring, (b, f) summer, (c, g) fall, and (d, h) winter. Time period of simulation is Observational dataset used is the University of Delaware Global Temperature dataset.

6 Figure S2. Simulated (a-d; top panels) and observed (e-h; bottom panels) seasonal total precipitation (mm day -1 ), for (a, e) spring, (b, f) summer, (c, g) fall, and (d, h) winter. Time period of simulation is Observational dataset used is the University of Delaware Global Air Precipitation dataset.

7 Figure S3. As Figure S2; Observational dataset used is the Daily U.S. Unified Precipitation dataset.

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