Study Land Cover Change from Climate Model and Satellite Remote Sensing
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Transcript of Study Land Cover Change from Climate Model and Satellite Remote Sensing
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Study Land Cover Change from Climate Model and
Satellite Remote SensingMenglin S. Jin
Department of Meteorology and Climate Science San José State UniversityUC Davis, May 18 2011
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Outline
1. Rationale of this topic2. Hypothesis for Regional Land Climate Change3. Results
GlobalRegional – CA in 2000-2010Local Scale – Urbanization Simulation
CABeijing -WRF-urban studies
Aerosol ExperimentAlbedo Experiment
4. Future Directions
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Jin, M. and R. E. Dickinson, 2010: Land Surface Skin Temperature Climatology: Benefitting from the Strengths of Satellite Observations.
Environmental Research letters, 5 044004
Jin, M., J. M. Shepherd and W. Zheng, 2010: Aerosol Direct Effects on Surface Skin Temperature: A Study from WRF modeling and MODIS Observations.
In press by Advances in Meteorology
Jin, M. 2009: Greenland surface height and its impacts on skin temperature: A study using ICEsat observations. Advances in Meteorology. Volume 2009, Article ID 189406, doi:10.1155/2009/189406.
Jin, M., and J. M. Shepherd 2008, Aerosol relationships to warm season clouds and rainfall at monthly scales over east China: Urban land versus ocean, J. Geophys. Res., 113, D24S90, doi:10.1029/2008JD010276.
Based on 20 leading-author papers
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Funded by
-NSF Large-scale Dynamics and Climate Program (PI, co-PI: Robert Dickinson)
-NASA Precipitation Program (PI-Marshall Shepherd, co-PI Jin, Steve Burain)
- Dept of Defense Threat Reduction Agency (PI –Steve Burain)
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1. Two Land Surface Temperatures in Climate Change
Surface Temperatures
Skin Temperature 2-m Air Temperature(T2m)(Tsfc)
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(Jin and Dickinson 2002, GRL)
Global Land Surface Temperature Trend
0.43°C/decade
0.23°C/decade
Skin TemperatureTsfs
2-m Air Temperature T2m
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Regional climate have different changes
(Folland et al., 2001, IPCC 2001)
Annual temperature trends (°C/decade), 1976-2000.
In a changing climate, we need todetect, understand, and predict regional climate change
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Regional Land Climate Change
Large-scale Dynamics MechanismENSO, etc
Clouds,Rainfall
Local MechanismLand cover change
Snow coverSoil moisture
urbanization
2. Hypothesis for Regional Land Climate Change
Satellite ObservationsAVHRR,
MODIS, ICEsat
Climate Model ApproachNCAR CAM/CLM, WRFOffline CLM, single column
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Change in Water and Heat Cycles
Large-scale DynamicsMechanismNAO, etcClouds,Rainfall
Local MechanismLand cover change
AlbedoSoil Moisture
urbanization
Satellite retrieval Tskin
AVHRR, MODIS, ICEsat
Model Approach
NCAR CAM/CLM. WRF Offline CLM, single column
Jin 1999Jin et al. 2005aJin and Shepherd 2007
Jin et al. 2005bJin and Shepherd 2005Jin et al. 2007Jin and Shepherd 2008Jin et al. 2010
Jin and Dickinson 1999, 2000 2010Jin and Treadon 2003Jin 2004Jin 2007
Jin et al. 1997Jin and Liang 2006
Jin 2006
Jin and Zhang 2002
Jin et al. 2007
Jin 2009
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Downscaling
Coarse grid size
Regional climate is strongly influenced by features such as mountains, coastline, lakes, urbanization and so on, cannot accurately represented on the GCM grids
1-10 km grid size
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include the atmospheric, land-surface and chemistry components similar to those in the GCM
Fine spatial resolutionRefined temporal resolution – typical 5 minute in RCM vs. 30 minute in GCM
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The skin temperature used in calculating heat fluxes and radiation:G = f( Tskin
- Tsoil) Eq. (1)H = CDHU(Taero-Ta) Eq. (2)LE =CDEU(qTskin*-qa) Eq. (3)
(1-α)Sd +LWd-εσTskin4 -H-LE - G= 0
Rn
Surface temperature is used in H, LE, G calculations
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http://www.usgcrp.gov/usgcrp/images/ocp2003/ocpfy2003-fig3-4.htm
The past, present and future of climate models
During the last 25 years, different components are added to the climate model to better represent our climate system
Early 2000s
Urban model
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BAMS
Jin and Shepherd 2005
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History of Land Surface Model• Gen-0 (prior to 60s): lack of land-surface processes (prescribed diurnal
cycle of surface temperature)• Gen-1a (mid 60s): simple surface model with time-fixed soil moisture• Gen-1b (late 60s): Bucket Model (Manabe 1969): time- and space-varying
soil moisture• Gen-2 (70s): Big-leaf model (Deardorff 1978): explicit vegetation
treatment; a major milestone• Gen-3 (late 80s): development of more sophisticated models including hydrological, biophysical, biochemical, ecological processes (e.g.,
BATS, Dickinson 1986; SiB, Sellers 1987)• mid 90s: implementation of advanced LSMs at major operational
numerical weather prediction (NWP) centers• 2000s – community land model (Dai et al. )
Modified after F. Chen, 2007
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CAM/CESM
WRF
CLM Urban model
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Satellite ObservationsMODIS observation at 1km
land cover, albedo, emissivity, vegetation index clouds, water vapor, and aerosol observationsAster 30 mICEsat 1km surface elevation
Derived soil wetness, building densitivity
NCAR Community Land Model (CLM) – urban modelNCAR WRFNCAR CAM/CLM
MODEL
Our emphasis: Use Satellite Observations toBetter Simulate Urban Climate System in model
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WRF
• The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers.
http://www.wrf-model.org/index.php
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Land Model CLM 4.0
• CLM4.0 is the community land model developed in NCAR and community
• CLM4.0 is the latest version, (CLM0, CLM1, CLM2, CLM3.5)
CLM Technical Notes (Oleson et al.)
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City coreIndustry/commercial
suburban
CLM4
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Problem in CLM4 Urban Scheme and our Urban approach
• Currently. the urban landunit has five columns (roof, sunlit and shaded wall, and pervious and impervious canyon floor) (Oleson et al. 2010).
• Problem is: No detail information for these columns
Jin, Shepherd, and Lidard-Peters developed UMCP-GSFC Urban Scheme (Jin et al. 2007) to represent urban from satellite data
Specifically, 1. Treat urban as fractions of dense building, roads, water, grass, suburban2. Change albedo, emissivity, NDVI/LAI, Vegetation fraction, soil moisture from satellite3. Add human heat fluxes into original Surface Energy Balance
Calculate the first order urban effects on local and regional weather
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Satellite provides new information of urban for a model
Land CoverVegetation indexVegetation fractionAlbedoEmissivitySkin temperatureSnow coverageSoil moisture (derived)Building fraction….
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TRMM11/27/97
Terra12/18/9
9ICESat10/02
Landsat 7
4/15/99
NASA Earth Science Spacecraft in Orbit
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Snow Coverage
Video available at http://www.met.sjsu.edu/~jin/PersonalLib.html Global Snow movie
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Central ValleyFresno
Sacramento
The Sierra NevadaThe Sierra Nevada
Sacramento
Fresno
Central Valley
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The Sierra Nevada
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Snow Cover in the Sierra Nevada
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NDVI –Normalized Difference Vegetation Index
• NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies.
NIR-RED
NIR+REDNDVI =
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The United States
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The Sierra Nevada
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Average January Skin Temperature for the Sierra Nevada
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Land Skin Temperature vs Land Albedo
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Region-Local Scale – Urbanization
U.S. Defense Meteorological Satellites Program (DMSP)
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Urbanization is an extreme case of human activity-induced land cover change.
urban heat island effect (UHI)
urban aerosol-cloud-rainfall interactions
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3 Km5/9/2011, 8 PM
MODIS land cover
WRF-urban
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WRF 1km 5/5/20115 PM
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6 PM, 5/5/2011
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7 PM, 5/5/2011
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5 PM, 5/6/2011
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7 PM, 5/6/2011
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9 PM, 5/6/2011
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11 PM, 5/6/2011
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1 AM, 5/7/2011
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3 AM, 5/7/2011
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5 AM, 5/7/2011
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8 AM, 5/7/2011
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10 AM, 5/7/2011
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Evaluation of WRF-urban, MODIS
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Urban Heat Island Effect (UHI)
This phenomenon describes urban and suburban temperatures that are 2 to 10°F (1 to 6°C) hotter than nearby rural areas.
(1-α)Sd +LWd-εσTskin4 –SH-LE - G= 0
Because all the terms in the surface energy balance are changed in urban regions.
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MODIS Observation
Beijing
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Urbanization changes surface albedo (MODIS)
(Jin, Dickinson, and Zhang 2005, J. of Climate)
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Urbanization changes surface emissivity (MODIS)
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3.3 Urban Aerosol Effects
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Indirect Effect: serve as CCN
Cloud dropRain dropIce crystalIce precipitation
Aerosol Direct Effect: Scattering
0oC
surface
Aerosol reduce surface insolation
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Aerosol Distributions over Land and Ocean have evident differences
July 2005
Satellite observations
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3.3 Result: Diurnal Cycle of Urban Aerosols
(Jin et al, 2005, JGR)
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3.3
(Jin and Shepherd 2008, JGR)
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3.3 Aerosol effect on UHI
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45.5
105.5
29.2
52.8
0
20
40
60
80
100
120
January July
So
lar
Rad
iati
on
(W
m-2
)NYC
BJ
Aerosol reduction on Surface Insolation
Using Chou and Suarez’s radiative transfer model
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3.4 WRF-urban model to examine relative contributions of different physical processes
Aerosol Experiment 48-hours sensitivity study July 26-27-2810-day sensitivity study
Albedo ExperimentEmissivity Experiment Soil moistre experiment
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Aerosol Experiment for July 2008
WRF: Version 3
Domains: D1=18km; D2=6km
Case: 00Z July 26, 2008; 48-h integration
Domain Centre: 40.0N, 116.0E
Beijing City: 39”56’N, 116”20’
Aerosol Domain: 39.7 - 40.1 N; 116.1 - 116.7 E
SW reduced by 100 Wm-2
April 16, 2009
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Domains: D1=18km; D2=6km
D1
D2
Domain 2: 6km Grid spacing
Beijing City
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Soil moisture at the first soil layer (10cm)
Green Vegetation Fraction: Beijing
City Domain 2
Finer Domain
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Case: 00Z July 26, 2008; 48-h integration
Plots: from 00Z July 27 to 00Z July 28
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Cloud Water (Qc) and Water Vapor (Qv) at 850 hPa & 700 hPa
700 hPa
850 hPa
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Tsfc / T2m Diurnal change: Surface insolation reduced by 100 Wm-2
Tsfc T2m
Tsfc decreases about 2-3 degrees
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Control Run Sensitivity 00 UTC
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SensitivityControl Run 06 UTC
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12 UTCControl Run Sensitivity
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18 UTC SensitivityControl Run
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Surface flux / PBL change: Surface insolation reduced by 100 Wm-2
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Winds at 950 hPa and 850 hPa
850 hPa
950 hPa
Control Run Sensitivity
Beijing City
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WRF-urban simulated urban aerosol effects10-day simulation
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3.4 Albedo Experiment for July 2008
WRF: Version 3
Domains: D1=18km; D2=6km
Case: (1) 00Z July 25, 2008; 48-h integration
(2) 00Z July 26, 2008; 48-h integration
Domain Centre: 40.0N, 116.0E
Beijing City: 39”56’N, 116”20’
Urban Domain: 39.7 - 40.1 N; 116.1 - 116.7 E
Albedo: change from 0.15 to 0.10
April 26, 2009
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Albedo Distribution:
Albedo in Beijing city decreases from 0.15 to 0.10
06 Z, 2008-07-26
Difference of Tsfc because Albedo change
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Tsfc increases about 1 degree at mid-day
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4. Future Directions
• Simulate SF Urban System in WRF-CLM4-urban
• Study urban impacts on local agriculture, for example, wine
• Use WRF model to assess the relative importance of snow cover change over the Sierra Nevada and urbanization to the regional water resources.
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Summary
• Urbanization has significant impacts on natural climate system, and thus shall be accurately simulated to predict such impacts.
• Satellite remote sensing and regional climate model are extremely useful for understanding regional climate change.
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Thank you.