Detect and Simulate Urban Climate Change Via EOS

Detect and Simulate Urban Climate Change Via EOS

Detect and Simulate Urban Climate Change Via EOS Observations and Land Surface Model Dr. Menglin Jin, Department of Meteorology University of Maryland, College Park Dr. Christa D. Peters-Lidard NASA GSFC April 20, 2004, Acknowledgements Funded by NASA EOSIDS and NASA DDF

Outline: 1. Rationale and Objectives 2. Observed Modifications of Urban Regions Skin Temperature Surface Albedo Surface Emissivity Aerosol and Clouds 3. Description of urban model 4. Model Results 5. Summary and future direction

Dr. Menglin Jin Univ. of Maryland, College Park 1. Rationale and Objectives Current Problem: Land surface schemes in the leading GCM/regional models do not include urban landscape. For example, NCAR Community Land Model (CLM), NASA land model, NCEP land surface model, etc Objectives: Develop urban scheme in land surface model Needs: Need to know what

is urban how to simulate urban Basic idea: Optimally combine satellite data into urban model. Satellite observations can help (a) better identify urban features and (b) improve models surface parameters Dr. Menglin Jin Univ. of Maryland, College Park Question 1: Is urban region important enough for us to simulate it specifically in a land surface scheme?

(a) Is urban region big enough? (b) Are urban physical processes unique enough? Question 2: How to simulate urbanization? Dr. Menglin Jin Univ. of Maryland, College Park Human Density of 1700 (Source: Ame. Association for the Advancement of Science)

Dr. Menglin Jin Univ. of Maryland, College Park Human Density of 1800 (Source: Ame. Association for the Advancement of Science) Dr. Menglin Jin Univ. of Maryland, College Park

Human Density of 1900 (Source: Ame. Association for the Advancement of Science) Dr. Menglin Jin Univ. of Maryland, College Park Human Density of 1998 (Source: Ame. Association for the Advancement of Science)

Dr. Menglin Jin Univ. of Maryland, College Park MODIS Observed Urban and Built-up 1000 household can make Tair higher about 2C than surroundings (Oke, 1976, Torok et al. 2002) Question 1. Is Urban region important enough for us to simulate it a GCM?

As an extreme case of land cover and land use changes, urban region: 1. modifies surface and atmospheric properties, and thus changes heat, water, and momentum transports 2. Adds new surface physical processes into original existing, natural physical processes Land Surface Energy Budget: (1-)Sd +LWd-Tskin4 +SH+LE + G= 0 Dr. Menglin Jin Univ. of Maryland, College Park

2. How to Simulate Urban? Urbanization Modifies Surface Energy Budget: (1-)Sd +LWd-Tskin4 +SH+LE + G= 0 Urban adds new physical processes: Storage term, Anthropogenic flux Modified roughness length Canyon effect Dr. Menglin Jin

Univ. of Maryland, College Park Radiation attenuation Canyon effect Turbulence production Radiation trapping Canopy heating & cooling

Urban thermal properties Dr. Menglin Jin Univ. of Maryland, College Park 3.1 Urbanization changes surface temperature (T skin) MODIS Urban heat island effect Daytime

Nighttime 50km 50km 50km Jin et al. 2004, submitted to J. of Climate 3.2 MODIS Observed Global urban heat island effect Dr. Menglin Jin

Univ. of Maryland, College Park Comparison of skin temperature for urban and nearby forests MODIS Cities have higher Tskin than forests 3.3 Urbanization changes surface albedo (MODIS) Dr. Menglin Jin

Univ. of Maryland, College Park Urban region NIR Albedo VIS The decrease of urban albedo is mainly caused by the decrease of reflectance at NIR Urban albedo lower than cropland

Albedo Mean, J anuary 2001. Upper Midwest urban 0.50 0.45 0.40 Evergreen Needle Forest Evergreen Broad Forest Deciduous Needle Forest

Deciduous Broad Forest Mixed Forest Closed Shrubs Open / Shrubs Woody Savanna Savanna Grassland Wetland Cropland Urban Crop Mosaic Barren / Desert

0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0.4 0.6

0.8 1.0 1.2 1.4 1.6 1.8

2.0 2.2 Wavelength (m) Spectral Albedo, January 2001, courtesy M. King Urban Mean Albedo, year 2001, London-Paris July

0.5 0.45 0.4 J1 J 17 J 33 J 49 J 65

J 81 J 97 J 113 J 129 J 145 J 161 J 177 J 193 J 209 J 225 J 241 J 257

J 273 J 289 J 305 J 321 J 337 J 353 0.35 0.3 0.25

0.2 0.15 0.1 0.05 0 0.4

Seasonality of urban Albedo, 0.6 0.8 1 1.2 1.4 1.6

1.8 courtesy M. King Wavelength (m) 2 2.2 Evident seasonality is observed on urban albedo

January 3.4 Urbanization changes surface emissivity (MODIS) 50km 50km Zonal Averages from MODIS Urban albedo is lower than

that of cropland Urban emissivity is lower than that of cropland 3.5. Urbanization changes atmospheric conditions MODIS Aerosol Optical Depth Dr. Menglin Jin Univ. of Maryland, College Park

Interannual variation of Aerosol optical depth Diurnal variation of aerosol from EPA PM2.5 Dr. Menglin Jin Univ. of Maryland, College Park MODIS Seasonal variation of urban aerosol Houston

Jin, Shepherd, and King 2004 Houston Jin, Shepherd, and King 2004 Aerosol decreases surface insolation Total solar radiation decreased by aerosol = 20Wm-2 (Based on model of Ming-Dah Chou of NASA GSFC)

(1-)Sd +LWd-Tskin4 +SH+LE + G= 0 SH, LE, and G cannot be directly observed from satellite. Need to use model framework to examine their changes. Dr. Menglin Jin Univ. of Maryland, College Park Conceptual Urban Model

Is land cover urban n Existing CLM Dr. Menglin Jin y Urban scheme water

Urban model type: Urban Canyon Suburban Human-grass Roads Univ. of Maryland, College Park Model Design--- Two Steps: 1. Modify Physical Parameters which are changed for urban regions: 1). Albedo, 2). Emissivity

3). LAI, 4). Heat capacity 5). Thermal conductivity 6). Hydro-conductivity 2. Modify physical process: 1). Anthropogenic heat flux (follow Voogt and Grimmond 2000) 2). Storage heat flux 3). Roughness length 4). Momentum changes by buildings Dr. Menglin Jin Univ. of Maryland, College Park

Model Design ---Two Steps: 1. Modify Physical Parameters which are changed for urban regions: 1). Albedo, MODIS 2). Emissivity 3). LAI, 4). Heat capacity 5). Thermal conductivity 6). Hydro-conductivity 2. Modify physical process: 1). Anthropogenic heat flux (follow Voogt and Grimmond 2000)

2). Storage heat flux 3). Roughness length (Follow Grimmond and Oke 2002) 4). Momentum changes by buildings (not implemented yet) Dr. Menglin Jin Univ. of Maryland, College Park Use MODIS observed surface properties into model Dr. Menglin Jin Univ. of Maryland, College Park

MODIS15_A2 Leaf Area Index (LAI) over Houston regions Dr. Menglin Jin Univ. of Maryland, College Park MODIS11_L2 Emissivity_BAND 32 over Houston regions Dr. Menglin Jin Univ. of Maryland, College Park

Model Design Two steps: 2. Modified physical processes: 1). Anthropogenic heat flux (Qf) Following Voogt and Grimmond 2000 Qf(hour) = Qf0 {1-0.6*cos(*(hour-3)/12)} Qf0 = 62.5W/m2 in January Qf0 =37.5W/m2 in July 2.) Storage term (QQs) Adopted from Arnfield and Grimmond 1998 QQs = (aiQ*+biQs+ci)Ai a,b,c are experical coefficients corresponding

to surface type Ai surface areas for each urban land surface type Q* - net radiation radiation Qs absorbed surface solar radiation ew Physical Processes (cont.) 3). Roughness Length (rub), Building impact on momentum calculation Complex Surface Parameters -Some Background Literature Earliest study U Wisc. MS thesis of John Kutzback, 1961 studies effect on surface roughness of density of objects that

were bushel baskets placed on frozen Lake Mendota Mike Raupach1992,1994 formulates theoretically the issue of partitioning of stress between smooth and such roughness objects- relates roughness length and displacement height to fractional area of obstacles normal to wind direction Linroth, 1993, identifies LAI as contributing to tree effect MacDonald et al, 1998, formulates modified version for buildings (Copied from R. Dickinson Fall AGU 2003 urban session invited talk) Key Elements

Uh = wind at the top of the canopy Cd = drag coefficient ,such that the overlying atmosphere loses = Cd Uh 2 of momentum to the complex canopy where = effective frontal area density But sqrt( Cd ) = 0.4 / log(( h d)/ z0 ) from the simple relationship between logarithmic wind profile and momentum transfer, which is inverted to get z0 / (h d ) (Copied from R. Dickinson Fall AGU 2003 urban session invited talk) Resolution of Roughness Issues

McDonald scaling likely ok if modified to include reduction by leaf area factor for vegetation Raupach expression for d/h should be modified to allow for eddy sheltering (i.e. wake vortices of building only interact over the area not covered by building so that d should become h as structures become a single building - however for closed canopy, this is between 0.7 and 0.85 h depending on crown-aspect ratio (Copied from R. Dickinson Fall AGU 2003 urban session invited talk)

Table for properties modified for Case 1 run variable LAI Control run 1.5 Case1 run 0.5 Albedo-shortwave

Control run 0.25 Albedo-visible Control run 0.15 emissivity Heat capacity Soil moisture Dr. Menglin Jin

0.96 0.92 1.5*control run Set as zero at first layer Univ. of Maryland, College Park 4. CLM-urban model results Ground Temperature Urban increase ground temperature

by 1-3C, with the largest increase occurring at local daytime 4.2 CLM-urban model results Surface air temperature Urban increases land surface 2m surface air temperature, at a lower rate than its effects on ground temperature/skin temperature maximum at nighttime! 4.3 Urban Model Results Absorbed Solar Radiation

Urban absorbs more Solar radiation 4.3 CLM-Urban Model Results Urban increase of SH can be as high as 15Wm-2, with maximum at local afternoon. 4.3 CLM-Urban Model Results

Urban increase upward longwave radiation 4.3 CLM-Urban Model Results Urban reduces ground flux 4.4 Single Column NCAR SCAM-CLM-Urban Model Beijing

4.4 SCAM/CLM2/urban-scheme Results Beijing Sep. 1998 LE 4.4 Single Column NCAR CAM-CLM-Urban Model Houston 4.4 SCAM/CLM2/urban-scheme Results

LE Summary 1. Satellite observations are extremely useful for understanding and simulating urbanization in climate models. 2. Urbanization scheme is needed in GCM/RCMs land surface model, in order to accurately reflect human impacts on global land climate system. 3. We need more accurate urban land cover, building density, and population information for simulating urban in global

and regional scales. Dr. Menglin Jin Univ. of Maryland, College Park

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