Study Land Cover Change from Climate Model and Satellite Remote Sensing Menglin S. Jin Department of Meteorology and Climate Science San Jos State University UC Davis, May Outline 1. Rationale of this topic 2. Hypothesis for Regional Land Climate Change 3. Results Global Regional CA in Local Scale Urbanization Simulation CA Beijing -WRF-urban studies Aerosol Experiment Albedo 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, 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 , doi: /2009/ 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: /2008JD 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 (T 2m ) (T sfc ) (Jin and Dickinson 2002, GRL) Global Land Surface Temperature Trend 0.43C/decade 0.23C/decade Skin Temperature T sfs 2-m Air Temperature T 2m Regional climate have different changes ( Folland et al., 2001, IPCC 2001) Annual temperature trends (C/decade), In a changing climate, we need to detect, understand, and predict regional climate change Regional Land Climate Change Large-scale Dynamics Mechanism ENSO, etc Clouds, Rainfall Local Mechanism Land cover change Snow cover Soil moisture urbanization 2. Hypothesis for Regional Land Climate Change Satellite Observations AVHRR, MODIS, ICEsat Climate Model Approach NCAR CAM/CLM, WRF Offline CLM, single column Change in Water and Heat Cycles Large-scale Dynamics Mechanism NAO, etc Clouds, Rainfall Local Mechanism Land cover change Albedo Soil Moisture urbanization Satellite retrieval T skin AVHRR, MODIS, ICEsat Model Approach NCAR CAM/CLM. WRF Offline CLM, single column Jin 1999 Jin et al. 2005a Jin and Shepherd 2007 Jin et al. 2005b Jin and Shepherd 2005 Jin et al Jin and Shepherd 2008 Jin et al Jin and Dickinson 1999, Jin and Treadon 2003 Jin 2004 Jin 2007 Jin et al Jin and Liang 2006 Jin 2006 Jin and Zhang 2002 Jin et al 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 resolution Refined temporal resolution typical 5 minute in RCM vs. 30 minute in GCM The skin temperature used in calculating heat fluxes and radiation: G = f( T skin - T soil )Eq. (1) H = C DH U(T aero -T a )Eq. (2) LE =C DE U(q Tskin *-q a )Eq. (3) (1-)S d +LW d -T skin 4 -H-LE - G= 0 RnRn Surface temperature is used in H, LE, G calculations 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 Observations MODIS observation at 1km land cover, albedo, emissivity, vegetation index clouds, water vapor, and aerosol observations Aster 30 m ICEsat 1km surface elevation Derived soil wetness, building densitivity NCAR Community Land Model (CLM) urban model NCAR WRF NCAR CAM/CLM MODEL Our emphasis: Use Satellite Observations to Better 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. 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 core Industry/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, suburban 2. Change albedo, emissivity, NDVI/LAI, Vegetation fraction, soil moisture from satellite 3. 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 Cover Vegetation index Vegetation fraction Albedo Emissivity Skin temperature Snow coverage Soil moisture (derived) Building fraction . TRMM 11/27/97 Terra 12/18/99 ICESat 10/02 Landsat 7 4/15/99 NASA Earth Science Spacecraft in Orbit Snow Coverage Video available atGlobal Snow moviehttp://www.met.sjsu.edu/~jin/PersonalLib.html Central Valley Fresno Sacramento The 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+RED NDVI = 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 Km 5/9/2011, 8 PM MODIS land cover WRF-urban WRF 1km 5/5/ 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 10F (1 to 6C) hotter than nearby rural areas. (1-)Sd +LWd-Tskin 4 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 drop Rain drop Ice crystal Ice precipitation Aerosol Direct Effect: Scattering 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 Aerosol reduction on Surface Insolation Using Chou and Suarezs radiative transfer model 3.4 WRF-urban model to examine relative contributions of different physical processes Aerosol Experiment 48-hours sensitivity study July day sensitivity study Albedo Experiment Emissivity 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: 3956N, 11620 Aerosol Domain: N; 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 TsfcT2m Tsfc decreases about 2-3 degrees Control RunSensitivity 00 UTC SensitivityControl Run 06 UTC 12 UTC Control RunSensitivity 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 RunSensitivity Beijing City WRF-urban simulated urban aerosol effects 10-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: 3956N, 11620 Urban Domain: N; E Albedo: change from 0.15 to 0.10 April 26, 2009 Albedo Distribution: Albedo in Beijing city decreases from 0.15 to Z, 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.