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

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

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

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)

1. Two Land Surface Temperatures in Climate Change

Surface Temperatures

Skin Temperature 2-m Air Temperature(T2m)(Tsfc)

(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

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

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

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

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

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

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

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

BAMS

Jin and Shepherd 2005

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

CAM/CESM

WRF

CLM Urban model

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

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

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.)

City coreIndustry/commercial

suburban

CLM4

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

Satellite provides new information of urban for a model

Land CoverVegetation indexVegetation fractionAlbedoEmissivitySkin temperatureSnow coverageSoil moisture (derived)Building fraction….

TRMM11/27/97

Terra12/18/9

9ICESat10/02

Landsat 7

4/15/99

NASA Earth Science Spacecraft in Orbit

Snow Coverage

Video available at http://www.met.sjsu.edu/~jin/PersonalLib.html Global Snow movie

Central ValleyFresno

Sacramento

The Sierra NevadaThe Sierra Nevada

Sacramento

Fresno

Central Valley

The Sierra Nevada

Snow Cover in the Sierra Nevada

NDVI –Normalized Difference Vegetation Index

• NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies.

NIR-RED

NIR+REDNDVI =

The United States

The Sierra Nevada

Average January Skin Temperature for the Sierra Nevada

Land Skin Temperature vs Land Albedo

Region-Local Scale – Urbanization

U.S. Defense Meteorological Satellites Program (DMSP)

Urbanization is an extreme case of human activity-induced land cover change.

urban heat island effect (UHI)

urban aerosol-cloud-rainfall interactions

3 Km5/9/2011, 8 PM

MODIS land cover

WRF-urban

WRF 1km 5/5/20115 PM

6 PM, 5/5/2011

7 PM, 5/5/2011

5 PM, 5/6/2011

7 PM, 5/6/2011

9 PM, 5/6/2011

11 PM, 5/6/2011

1 AM, 5/7/2011

3 AM, 5/7/2011

5 AM, 5/7/2011

8 AM, 5/7/2011

10 AM, 5/7/2011

Evaluation of WRF-urban, MODIS

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.

MODIS Observation

Beijing

Urbanization changes surface albedo (MODIS)

(Jin, Dickinson, and Zhang 2005, J. of Climate)

Urbanization changes surface emissivity (MODIS)

3.3 Urban Aerosol Effects

Indirect Effect: serve as CCN

Cloud dropRain dropIce crystalIce precipitation

Aerosol Direct Effect: Scattering

0oC

surface

Aerosol reduce surface insolation

Aerosol Distributions over Land and Ocean have evident differences

July 2005

Satellite observations

3.3 Result: Diurnal Cycle of Urban Aerosols

(Jin et al, 2005, JGR)

3.3

(Jin and Shepherd 2008, JGR)

3.3 Aerosol effect on UHI

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

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

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

Domains: D1=18km; D2=6km

D1

D2

Domain 2: 6km Grid spacing

Beijing City

Soil moisture at the first soil layer (10cm)

Green Vegetation Fraction: Beijing

City Domain 2

Finer Domain

Case: 00Z July 26, 2008; 48-h integration

Plots: from 00Z July 27 to 00Z July 28

Cloud Water (Qc) and Water Vapor (Qv) at 850 hPa & 700 hPa

700 hPa

850 hPa

Tsfc / T2m Diurnal change: Surface insolation reduced by 100 Wm-2

Tsfc T2m

Tsfc decreases about 2-3 degrees

Control Run Sensitivity 00 UTC

SensitivityControl Run 06 UTC

12 UTCControl Run Sensitivity

18 UTC SensitivityControl Run

Surface flux / PBL change: Surface insolation reduced by 100 Wm-2

Winds at 950 hPa and 850 hPa

850 hPa

950 hPa

Control Run Sensitivity

Beijing City

WRF-urban simulated urban aerosol effects10-day simulation

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

Albedo Distribution:

Albedo in Beijing city decreases from 0.15 to 0.10

06 Z, 2008-07-26

Difference of Tsfc because Albedo change

Tsfc increases about 1 degree at mid-day

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.

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.

Thank you.