Supplementary Material

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1 Supplementary Material Model physical parameterizations: The study uses the single-layer urban canopy model (SLUCM: Kusaka et al. 2001; Kusaka and Kimura 2004; Liu et al. 2006; Chen and Dudhia 2001; Chen et al. 2011) for urban parameterization as it was less computationally intensive and performed well in comparison to other. For SLUCM, we added a diurnal profile of anthropogenic heat (AH) to the sensible heat flux based on the values estimated by Sailor and Lu [2004] for Chicago. SLUCM uses a two-dimensional street canyon to explicitly parameterize canyon radiative transfer, turbulence, momentum and heat fluxes (Oke and Cleugh 1987). It accounts for urban surfaces, roofs, walls, and roads and includes shadowing and reflections. It considers subgrid-scale inhomogeneous surface fluxes using a tile approach (Avissar and Pielke 1989; Chen et al. 2004; Lee et al. 2011). The effects of fluxes calculated by SLUCM were introduced to the lowest model level of WRF. The SLUCM also has the capabilities of studying the impact of anthropogenic heat (AH) fluxes from air conditioning, transportation, and human metabolism. The four-layer Noah land surface model (LSM) (Chen et al. 2011) was utilized to update soil temperature and moisture for the non-urban part of domain. Subgrid-scale cumulus convective parameterization was turned on only for first two outermost domains at 27 and 9 km, invoking the Kain-Frisch scheme (Kain 2004). We selected WRF single-moment 3-class simple ice scheme (WSM3; (Hong et al. 2004)) for microphysics, considering that complex precipitation structures did not occur on the summer clear-sky days. We incorporated the Dudhia scheme (Dudhia 1989) for shortwave and the Rapid Radiative Transfer Model (RRTM) for longwave radiation parameterizations (Mlawer et al. 1997). The Monin-Obukhov similarity theory (MOST) was used for the surface-layer, and Mellor Yamada Janjic (MYJ) scheme (Janjic 1994) was used for the planetary boundary layer. We used 2006 National Land Cover Dataset (NLCD 2006; Fry et al. 2011) at 30-m spatial resolution and interpolated data to respective grid resolution of each domain for the surface land-use and land-cover. This study also utilized a high-resolution land data assimilation system (HRLDAS; Chen et al. 2007; Sharma et al. 2015, 2016) to initialize state variables of the land surface model (LSM) for our high-resolution experiments. References for Supplementary Material: Avissar R and Pielke R A 1989 A parameterization of heterogeneous Q6 land surfaces for atmospheric numerical models and its impact on regional meteorology Mon. Wea. Rev Chen F and Dudhia J 2001 Coupling an advanced land surface- hydrology model with the Penn State- NCAR MM5 modeling system: I. Model implementation and sensitivity Mon. Wea. Rev Chen F, Kusaka H, Tewari M, Bao J W and Hirakuchi H 2004 Utilizing the coupled WRF/LSM/Urban modeling system with detailed urban classification to simulate the urban heat island phenomena over the Greater Houston area 5 th Symp. on the Urban Environment 9 11 Chen F, Kusaka H, Bornstein R, Ching J, Grimmond C S B, Grossman-Clarke S and Zhang C 2011 The integrated WRF/ urban modelling system: development, evaluation, and applications to urban environmental problems Int. J. Climatol Chen F, Manning K W, LeMone M A, Trier S B, Alfieri J G, Roberts R and Blanken P D 2007 Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system J. Appl. Meteor. Climatol Dudhia J 1989 Numerical study of convection observed during the winter monsoon experiment using a mesoscale two- dimensional model J. Atmos. Sci Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N and Wickham J 2011 Completion of the 2006 national land cover database for the conterminous United States PE&RS Hong S Y, Dudhia J and Chen S H 2004 A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation Mon. Wea. Rev

2 Janjic Z I 1994 The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes Mon. Wea. Rev Kain J S 2004 The Kain-Fritsch convective parameterization: an update J. Appl. Meteor. Climatol Kusaka H and Kimura F 2004 Coupling a single-layer urban canopy model with a simple atmospheric model: impact of urban heat island simulation for an idealized case J. Meteor. Soc. Japan Kusaka H, Kondo H, Kikegawa Y and Kimura F 2001 A simple single-layer urban canopy model for atmospheric models: comparison with multi-layer and slab models Bound.-Layer Meteor Lee S H, Kim S W, Angevine W M, Bianco L, McKeen S A, Senff C J and Zamora R J 2011 Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign Atmos. Chem. Phys Liu Y, Chen F, Warner T and Basara J 2006 Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the Joint Urban 2003 field project J. Appl. Meteor. Climatol Mlawer E J, Taubman S J, Brown P D, Iacono M J and Clough S A 1997 Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave J. Geophys. Res.: Atms Oke T R and Cleugh H A 1987 Urban heat storage derived as energy balance residuals Bound.-Layer Meteor Sailor D J and Lu L 2004 A top down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas Atmos. Environ Sharma, A 2015 Sensitivity of WRF model to landuse, with applications to Chicago metropolitan Urban Heat Island and lake breeze. In 2015 December AGU Fall Meeting. Agu. Sharma, A, Fernando, H J S, Hamlet, A F, Hellmann, J J, Barlage, M, and Chen, F 2016 Urban meteorological modeling using WRF: A sensitivity study Int. J. Climatol., (in revisions) 2

3 Table TABLE S1: Partitioning of different urban landuse category fractions used in the study. Urban landuse Impervious/built fraction (FRACurb) Pervious fraction (FRACp=1-FRACurb) Vegetated land (0.9*FRACp) Bare land (0.1*FRACp) High-intensity Medium-intensity Low-intensity Discussion for Figures Discussion for Figure S1 and S2: The aerial view of CMA in figure S1 demonstrates how parts of the city responded differently with reductions in surface temperature and near-surface winds (10-m). Figure S1 shows the difference plot of daytime roof surface temperatures with variable green roof fractions relative to conventional roof for innermost (1-km resolution) domain. For the calculations in figure S1 (as well as figures S2) the daytime was taken as average conditions from LST for August 2013, wherein the peak daily temperature was observed. As can be seen from figure S1a, the 100% green roof scenario provided maximum reductions in roof surface temperatures of 6-7 o C over highly urbanized areas and 2-3 o C in suburban areas. As the relative percentage of urban green roofs decreased, the impact of greening on surface temperatures reduced. Green roofs also reduced near-surface (10-m) wind speed (figure S2a-d). Owing to the reduction in UHI effects, the offshore horizontal pressure gradients causing the lake breeze were also reduced. Discussion for Figure S3: The major alterations of surface fluxes across CMA by green/cool roofs resulted in changed boundary layer structure. To demonstrate the impact of green/cool roofs on boundary layer, figure S3 displays the uwrf potential temperature profiles output for conventional, 100% green and 100% cool roof fractions at different times on August 2013 above the Chicago O Hare airport, for the lower 2.5 km of the atmosphere. The convective boundary layer developed fastest in lower atmosphere with conventional roofs. By 16 August 1000 LST (figure S3b), the boundary layer was well-mixed, as demonstrated by near-zero slope on potential temperature profile, due to convection and lake-breeze. However, potential temperatures were higher for conventional roofs in comparison with green/cool roofs (figures S3c-d), and during the growth period the boundary layer was deeper for conventional roofs. At night, however, due to rapid cooling, the cool roofs showed lower temperatures, with stronger potential temperature gradients and hence enhanced stability (figures S3e-f). 3

4 Figure S1: Reduction in peak daytime roof surface temperature achieved by green roofs relative to baseline conventional roofs. Aerial view of innermost domain is shown with grid resolution of 1-km. The different panels show impacts of different green roof fractions: (a) 100% green roof coverage, (b) 75% green roof coverage, (c) 50% green roof coverage, and (d) 25% green roof coverage. The temperature difference between green and conventional roofs was calculated using model output roof surface temperatures averaged from 1400 to 1700 Local Standard Time for 3-day period of August

5 Figure S2: Same as figure S1, but for near-surface (10-m) wind speed. 5

6 Figure S3: Evolution of vertical profiles of potential temperature from the WRF grid point containing the O Hare Airport (lat-lon: N, W) for 100% green, 100% cool, and conventional roof cases. Panels show different times during August 2013 period: (a) 0600 LST, (b) 1000 LST, (c) 1400 LST, (d) 1800 LST, (e) 2200 LST, and (f) 0200 LST (17 August). Convective boundary layer develops most quickly for conventional roof case. 6