1. Remote Impacts of Lowland Urbanization on Orographic Cloud Properties Brian Freitag 1 Udaysankar Nair 1 Yuling Wu 1 - 1 – University of Alabama in Huntsville 96 th Annual American Meteorological Society Meeting New Orleans, LA

2. Introduction Goal: Examine the effects of an urban environment on atmospheric flow in a complex topographical setting Previous Research: Urbanization impacts regional precipitation and cloud patterns because of flow modification Impacts of urbanization are dependent upon the size of the urban environment (> 20 km 2 ) LULC modification changes the surface energy budget thereby impacting land/atmosphere exchange Impacts of LULC modification are strongly tied to changes in Bowen ratio Topographical barriers generate lifting and enhance windward cloud development and precipitation frequency

3. Introduction Study area: San Miguel de Tucumán, Argentina (-26.83, -65.25) Areal Extent: ~130 km 2 Elevation: ~500 m Surrounding environment: cropland/grassland to the east, subtropical forests to the west Located 20 km upwind of Sierras de San Javier Mountains (1900 m elevation) and 40-50 km upwind of the Andes Mountains (peaks >4000 m elevation). Climate: Monsoonal subtropical climate. 75% of annual precipitation occurs during December-March (nearly 750 mm).

4. Introduction Previous Work in Argentina (Houze, 2012) South American LLJ responsible for moisture transport from Atlantic. Instability typically released at first opportunity (lower foothills) Mid-level westerlies propagate orographically forced convective cells eastward

5. Modeling Experiments Simulation Period: 1-8 December 2012 Model: WRF-ARW V3.7.1 Land-use Data: USGS 24- category data Grid Format: 4 Nested grids 27 km outer grid (3:1 parent/child ratio) One-way nesting 100 x 100 points Centered on San Miguel de Tucumán

6. Modeling Experiments Integration time: 24 hours at 00UTC Lateral Boundary Nudging time: 6 hours Initialization data: GFS Vertical levels: 56 Timestep: 60 s (4:1 parent/child ratio) Two land-use scenarios: Control No-Urban

7. Modeling Experiments Parameterizations Microphysics: Thompson Aerosol Aware Scheme Radiation (LW/SW): RRTMG Scheme Surface Layer: Monin-Obukhov (Janjic) Scheme Surface: Noah Land Surface Model Urban: Multi-Layer, Building Environment Parameterization PBL: Mellor-Yamada-Janjic TKE Scheme Cumulus: Kain-Fritsch Scheme

8. Model Validation - Clouds

9. GOES: Cloud mask developed using Saunders & Kriebel (1988) and Martins (2002) techniques. Model-derived LWP threshold (> 25 g m -2 ) used for identifying clouds in simulations Weekly cloud frequency (CF) obtained at one-hour resolution. Slight underestimation of CF in both simulations Control simulation found to be more consistent with observations

10. Model Validation - Clouds CloudSat: Cloud Mask product from the 2B-GEOPROF data set Only 2 overpasses occurred within any of the domains during the simulation period. Model-derived cloud mask developed using liquid water content threshold (> 30 mg m -3 ) Model propagates storm 3 hours faster than observed in CloudSat (overpass occurs in D02) Vertical extent and phase of storm structure is consistent with CloudSat observations.

11. Model Validation - Precipitation TRMM: 3-hourly accumulated precipitation product (3B42) TRMM 0.25° x 0.25° resolution Model overestimation over the topography because of small-scale convective features Model underestimation in eastern portion of D01 Boundary forcing issue in southeastern quadrant. Large-scale spatial distribution of precipitation fairly consistent between model simulations and TRMM.

12. Model Results – Atmospheric Flow Weekly averaged model- simulated daytime 10-m wind for D04 (10-23UTC) 3-4 m s -1 flow from SW to NE over the city during the simulation period. Greater than 1 m s -1 decrease in wind speed over urban environment Increased surface roughness within the urban environment

13. Model Results – Atmospheric Flow Transition from SW to S winds shifted westward with urban environment Increased friction due to the urban environment. Enhanced convergence upwind, enhanced divergence downwind Reduced upslope flow downstream Enhanced turbulent mixing over urban environment.

14. Model Results - Precipitation Weekly model-simulated accumulated precipitation for D04 Precipitation generated by orographic forcings and synoptic- scale forcings during simulation period 300-400 mm accumulated on Andes peaks for both simulations. < 100 mm over San Miguel de Tucumán

15. Model Results - Precipitation Differences between the two simulations < 10 % increase in and around San Miguel de Tucumán Complex topography enhances increase in precipitation north of the city. Precipitation generally shifted downslope with urban environment. Weaker surface winds decrease upslope flow and reduce moisture transport to peaks

16. Model Results - Precipitation Decrease > 25% on peaks higher than 1000m downstream Reduced upslope flow Decrease ~ 10-15% in the valley downstream Reduced latent heating, reduced moisture available downstream Enhanced effects on elevation > 1000 meters suggests changes in orographic forcing are an important component of the observed changes

17. Model Results – Cloud Properties Weekly averaged model- derived LWP Values ranging from 100 – 400 g m -2 for both LULC scenarios Differences between the two simulations Distinct pattern with increase in LWP south of the city and decrease north. Greatest differences (> 30%) located ~30 km SW and 10-20 km NNE of the city.

18. Model Results – Cloud Properties Persistent positive vertical velocity anomaly  increased vertical moisture flux upwind Reduced convergence at the topography interface  less vertical lifting + reduced surface moisture available Decrease of over 30% over channel just north of city where maximum increase in precipation. Circulation driven by geopotential height anomaly over city enhances convection

19. Summary & Conclusions Positive precipitation anomaly of nearly 30-40% on the lower slopes of the topography between 500-1000m elevation Potential for increased landslides particularly on steepest slopes Negative precipitation anomaly on the higher peaks further downstream by 30-40% Bimodal pattern in the cloud distribution: Increase in cloudiness south, decrease in cloudiness north of the city. Mesoscale circulations associated with the urban heat island enhance convection on the northeastern side of the city.

20. Future Work Evaluate the influence of the urban environment on the Sali River drainage basin. How does the shift in the precipitation pattern affect the flood potential in this environment? How does the influence of the urban environment impact the water availability in the region? Does the urban environment influence the river flow rates which could impact electricity generated at the hydroelectric dams downstream?

21. Questions? Email: freitagb@nsstc.uah.edu Ph: (256) 961-7599freitagb@nsstc.uah.edu