1. Effects of Land Cover Modifications in MM5 on Surface Energetics in Phoenix
Introduction, Land cover data, Simulations and Results.
Susanne Grossman-Clarke, Joseph. A. Zehnder,
William L. Stefanov,
Harindra J.S. Fernando, Sang-Mi Lee
Environmental Fluid Dynamics Program
Arizona State University

2. Introduction Focus on Phoenix Central-Arizona Phoenix (CAP)
Long-Term Ecological Research (LTER) Project.
Mesoscale Meteorological Modeling Group
Neighborhood scale distributions of near-surface
meteorological variables.
CAP LTER – Investigates spatial and temporal interaction of ecological, socioeconomic and atmospheric processes. Generalization within 100 Cities project.
Meteorology: Use in process based ecological, social, air quality models.

3. Introduction - Applications
Urban heat island
Water use (evaporation & transpiration)
CO2 dome
Air quality
Urban design
Biogeochemical cycles
Application of MM5 to problems related to:

4. Introduction – Characteristics of Phoenix
Fastest growing city in the US.
Mostly suburban core, surrounded by irrigated agricultural land and dry sparsely vegetated desert, embedded in complex terrain.
Irrigated vegetation in suburban neighborhoods is important for urban energy balance.
Fastest growing city in the US – implications for land use changes, will be shown later
Standard release of MM5 includes only one urban type based on traditional city centers not reflecting suburban neighborhoods in fast growing urban areas.

5. Introduction - Land Surface Representation in MM5
Land use and soil data
Land use and soil classes
Physical and biological parameters
Physical approach for describing energy, momentum and matter exchange between land surfaces and the atmosphere.

6. Land Use Data Preparation
Land cover data 30 meter resolution
Based on 1998 Landsat Thematic Mapper satellite images for Phoenix (visible and shortwave infrared & vegetation index).
Postclassification using additional data sets in expert system.
Land cover data for Phoenix metropolitan region. An expert system was constructed to perform postclassification sorting of the initial land cover classification using additional spatial datasets such as texture, land use, water rights, city boundaries, and Native American reservation boundaries. Landsat Thematic Mapper (TM) reflectance data were acquired for May 24, 1998 and June 18, 1998 completely covering the Phoenix metropolitan region.
Shortwave infrared nm, longwave infrared nm
Vegetation index from ratio red band/near-infrared band
Additional data sets are for example water rights, city boundaries, texture, land use.

7. Land Use Data 1998
Pixels were reclassified into 12 classes producing the final land cover map with a 30 m spatial resolution.
North-south and east-west extension:
Light and dark purple – mesic and xeric residential
Yellow – bare soil and impervious surfaces (asphalt).
Dark green – agricultural areas.
Light green – riparian vegetation.

8. Land Use Data Preparation
Reprojecting land use data according to the grid information of USGS 30-second data in GIS.
Zonal summing of the 30 m data set within 30 second grid cells.
The land use class with the highest fraction of cover was assigned to the 30 second grid cell and mapped to the 25 category USGS land use classification.

9. Land Use Data Preparation
Three urban classes in 25-category USGS land cover classification:
Built-up urban, mesic and xeric residential.
Composition of mesic and xeric residential areas in terms of typical fractions of irrigated and total vegetation.
MM5 water availabilty factor.
Data for MM5
Ground Truth measurements

10. Surface Parameters Albedo Roughness length Moisture availability
Emissivity
Heat storage capacity

11. LU class USGS class 1 Cultivated veget. 3 - Irrigated agric. 2
Cultivated grass 3
River gravels
19 - Bare soil 4
Compacted soil 5
Vegetation
11 - Decid. forest 6
Com./Industrial
1 - Urban and built-up 7
Asphalt/concrete 8
Undisturbed desert
8 - Shrub land 9 10
Mesic residential
New 11
Xeric residential 12
Water
16 – Water
Mapping of land use classes to USGS classes.

12. Land Use Class Characteristics (LTER - 200 point survey)
Irrigated vegetation
Xeric vegetation
Bare soil
Asphalt, concrete
Mesic residential 40 - 2 58
Xeric residential 3 22 73
Build-up urban
0-18
0-3
79-100
Native desert 38 62

13. 1km x 1km Land Use: 1998 Satellite Data

14. 2km x 2km Land Use: 1998 Satellite Data

15. 2km x 2km Land Use: 1976 USGS Data

16. MM5 (a) 1976 USGS (b) 1998 Land Use Data

17. Design of Numerical Simulation
1700 LST May 28 – 1700 LST May 30, 2001
Spatial dimension
Nested Run of MM5: 54 Km  18 Km  6 Km  2 Km
32 vertical layers
Meteorological data
Initial & Boundary conditions : NCEP Eta Analysis 40 km
Elevation and land use data resolution: 30 sec.
MRF boundary layer scheme & 5 layer soil model.

18. Surface Energy Balance Equation
Tg … Ground temperature [K]
Cg … Heat capacity of the ground [J m-2 K-1]
Rn … Net radiation balance [W m-2]
H … Sensible heat flux [W m-2]
G … Soil heat flux [W m-2]
lE … Latent heat flux [W m-2]

19. Latent Heat Flux M … Moisture availability factor [-]
z0 … Roughness length [m]
Yh … Stability function [-]
qvs … Saturation specific humidity [-]
qva … Specific humidity at za[-]

20. Sensible Heat Flux Ta … Air temperature at za [K]
u* … Friction velocity [m s-1]
L … Monin Obukhov length [m]
k … von Karman constant [-]
cp … Specific heat capacity of air [J K-1 kg-1]

21. Boundary Layer Height h … Boundary layer height
Ribcr … Critical bulk Richardson number (0.5)
Qva … Virtual potential temperature at za
Qv … Virtual potential temperature at z=h
Qs … Virtual potential temperature at ground level z=0
U(h) … Wind speed at z=h

22. Simulated Ground Temperatures (a) USGS (b) 1998 Land Use Data
Mostly determine by physical characteristics of the land surface and therefore reflect spatial inhomogeneity of the land sue distribution in the modeling domain. In the urban area significant differences in the simulated ground temperatures of up to 4 K were found between the two model versions. This results from differences in the simulated latent heat fluxes of about 150 W m-2 (not shown). Sensible heat fluxes between the ground level and the prognostic levels in the atmosphere determine the Richardson number and the simulated boundary layer height in the model. The increased latent heat fluxes and resulting reduction in sensible heat fluxes with the new land use cover led to a significant drop of the simulated PBL heights of a few hundred meters over the central part of the city (Figure 3). This implies a reduced mixing volume for pollutants and eventually a significant impact on pollutant concentrations in the area.
29 May :00 LST

23. Differences in Ground Temperatures

24. Simulated Latent Heat Fluxes (a) USGS and (b) 1998 Land Use Data
29 May :00 LST

25. Differences in Latent Heat Fluxes

26. Simulated Sensible Heat Fluxes (a) USGS (b) 1998 Land Use Data
29 May :00 LST

27. Differences in Sensible Heat Fluxes

28. Simulated 2m Air Temperatures (a) USGS (b) 1998 Land Use Data
The air temperatures at 2 m height are influenced by surface heat fluxes as well as by advection with neighboring grid cells. Therefore the simulated differences in 2 m air temperatures due to the different urban land cover are less pronounced than for the ground temperatures, about 1 to 2 degrees Celsius.
29 May :00 LST

29. Differences in 2m Air Temperatures

30. Simulated Boundary Layer Heights (a) USGS (b) 1998 Land Use Data
29 May :00 LST

31. Differences in Boundary Layer Heights

32. Results

33. Results

34. Results

35. Results

36. Results
2 km x 2 km land use in the modeling domain based on 30-sec 1998 land use data.
Expanded map shows the urban fringe zone and six monitoring sites used for comparisons.

37. Results 1 - Built-up urban 2 - Mesic residential 3 - Built-up urban
4 - Bare soil
5 & 6 - Irrigated cropland

38. Results 1 - Built-up urban 2 - Mesic residential 3 - Built-up urban
4 - Bare soil
5 & 6 - Irrigated cropland

39. Summary
Urban land use is likely to have a significant impact on the simulated near surface temperatures and PBL heights in MM5.
Model validation is necessary.

40. Summary Problems Physical representation of urban surfaces in MM5.
Slope flows in complex terrain (timing, strength), eddy diffusivities.
Relatively high air temperatures near the surface under stable conditions suggest that the diffusion of warmer air from upper air layers is overestimated by the model. This is likely due to the eddy diffusion coefficients not properly reflecting conditions in the region of complex terrain in which Phoenix is embedded. Also, due to the dry air aloft typically present in the region, surface radiative cooling is extreme and the model does not adequately capture the resulting stable stratification. Differences in the simulated ground temperatures which result from modification in the surface energetics are not realized near the surface at night due to an overestimation of mixing resulting from underestimated stable stratification in the real atmosphere. There are also differences in the observed and simulated winds (not shown). The change in wind direction from upslope to downslope (south-westerly to north-easterly winds) was detected in the observations at around LST 22:00 whereas the model shows this change at around LST 2:00. The change in wind direction is accompanied by a drop in air temperatures due to cool drainage flow from the high terrain to the southeast of the region of interest. Poor performance of the model temperature during night is due also to simulated wind speeds exceeding the measured values. This also leads to an overestimation of the vertical transport under stable conditions by MM5. Sensitivity studies (not shown) with different values for the eddy diffusivities for stable conditions in MM5 confirm that the sudden drop of the observed 2m air temperatures at around LST 22:00 is accompanied by a change in wind direction which is associated with the onset of north-easterly down slope flow and transport of cooler air from the mountainous regions to the area where the monitoring stations were located. Another possible reason for this behavior of the model is the inhomogeneous land use around the stations at finer scales. If there is a large degree of inhomogeneity near the observing site, this is not captured by the model, since the model assigns a single, dominant land use type to each grid cell.

41. Nitrogen Dry Deposition Modeling
Assess indirect and direct effects of urban vegetation on nitrogen dry deposition in the CAP LTER study area, including Phoenix metropolitan area.
Is N deposition significant input to N mass balance of the area.
Changes in biogeochemical cycles.
Effects on ecosystems.

42. Nitrogen Dry Deposition Modeling
Models-3/CMAQ – Problems:
Physical approach of describing matter transport in urban roughness sub-layer.
Land use data.
Diagnostic model
Make use of long-term measured pollutant concentrations and weather variables
Investigate seasonal changes of dry nitrogen deposition.

43. Nitrogen Dry Deposition Modeling
Assess indirect and direct effects of urban vegetation on nitrogen dry deposition in the CAP LTER study area, including Phoenix metropolitan area.
Is N deposition significant input to N mass balance of the area.
Changes in biogeochemical cycles.
Effects on ecosystems.

45. Vertical Dry Deposition Flux
z0 Sink height at the surface
zr Reference height in the atmosphere
C(zr) Pollutant concentration at reference height
C(z0) Pollutant concentration at the surface
vd Deposition velocity
a Air density

46. Deposition Velocity ra Aerodynamic resistance
rb Boundary layer resistance
rs Surface resistance

47. Aerodynamic resistance
L Monin-Obukhov length
k von Karman constant
u* Friction velocity
h Similarity function for heat (Holtslag & van Ulden, 1983 and Dyer & Hicks,1970)

48. Monin-Obukhov Length H Sensible heat flux k von Karman constant
u* Friction velocity
Ta Air temperature

49. Sensible Heat Flux Rn Net radiation G Soil heat flux
A Anthropogenic heat production
a Water availability factor

50. Water Availability Factor
fi Fraction of irrigated vegetation cover (Oke, 2001)

51. Canopy Resistance rmin Minimum canopy resistance
Rs Incoming solar radiation
T Air temperature
Tmin Cold limit (–5 – 0 C)
Tmax Heat limit ( C)
To Optimum temperature (30 C)

52. Air quality monitoring station Phoenix Supersite.
NO2 dry deposition flux (FNO2 —) and measured NO2 concentrations (CNO2 --- )

53. Nitrogen Dry Deposition Modeling

54. Nitrogen Dry Deposition Modeling
Urban
Irrig. Veg.
Bare soil
Xeric
Cover [%] 59 21 8 12
FNOX[%] 31 41 6 22

57. Modeling Nitrogen Dry Deposition
Spatial distribution of total nitrogen dry deposition flux 1998