Introduction, Land cover data, Simulations and Results.

Slides:



Advertisements
Similar presentations
Surface Heat Balance Analysis by using ASTER and Formosat-2 data
Advertisements

What’s quasi-equilibrium all about?
Land surface in climate models Parameterization of surface fluxes Bart van den Hurk (KNMI/IMAU)
Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen.
Joint GABLS-GLASS/LoCo workshop, September 2004, De Bilt, Netherlands Interactions of the land-surface with the atmospheric boundary layer: Single.
A numerical simulation of urban and regional meteorology and assessment of its impact on pollution transport A. Starchenko Tomsk State University.
Reducing Canada's vulnerability to climate change - ESS Variation of land surface albedo and its simulation Shusen Wang Andrew Davidson Canada Centre for.
Watershed Hydrology, a Hawaiian Prospective: Evapotranspiration Ali Fares, PhD Evaluation of Natural Resource Management, NREM 600 UHM-CTAHR-NREM.
Session 2, Unit 3 Atmospheric Thermodynamics
Reading: Text, (p40-42, p49-60) Foken 2006 Key questions:
Will Pendergrass NOAA/ARL/ATDD OAR Senior Research Council Meeting Oak Ridge, TN August 18-19, 2010 Boundary–Layer Dispersion Urban Meteorology 5/20/2015Air.
Some Approaches and Issues related to ISCCP-based Land Fluxes Eric F Wood Princeton University.
03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen.
Surface Exchange Processes SOEE3410 : Lecture 3 Ian Brooks.
1 AirWare : R elease R5.3 beta AERMOD/AERMET DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA
Atmospheric Analysis Lecture 3.
Climates at (Very) Small Scales ENVS
ERS 482/682 Small Watershed Hydrology
A Sensitivity Analysis on Remote Sensing ET Algorithm— Remote Evapotranspiration Calculation (RET) Junming Wang, Ted. Sammis, Luke Simmons, David Miller,
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Atmospheric temperature
Evapotranspiration - Rate and amount of ET is the core information needed to design irrigation projects, managing water quality, predicting flow yields,
MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes.
Evaporation Theory Dennis Baldocchi Department of Environmental Science, Policy and Management University of California, Berkeley Shortcourse on ADAPTIVE.
Evaporation Slides prepared by Daene C. McKinney and Venkatesh Merwade
Figure 1: Schematic representation of the VIC model. 2. Model description Hydrologic model The VIC macroscale hydrologic model [Liang et al., 1994] solves.
Distinct properties of snow
Land Processes Group, NASA Marshall Space Flight Center, Huntsville, AL Response of Atmospheric Model Predictions at Different Grid Resolutions Maudood.
Changes and Feedbacks of Land-use and Land-cover under Global Change Mingjie Shi Physical Climatology Course, 387H The University of Texas at Austin, Austin,
Evaporation What is evaporation? How is evaporation measured? How is evaporation estimated? Reading: Applied Hydrology Sections 3.5 and 3.6 With assistance.
Xin Xi. 1946: Obukhov Length, as a universal length scale for exchange processes in surface layer. 1954: Monin-Obukhov Similarity Theory, as a starting.
SENSIBLE HEAT FLUX ESTIMATION USING SURFACE ENERGY BALANCE SYSTEM (SEBS), MODIS PRODUCTS, AND NCEP REANALYSIS DATA Yuanyuan Wang a, Xiang Li a,b a, National.
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar Abhilash Vijayan Kanwar Siddharth Bhardwaj University of Toledo.
Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting A. Baklanov, A. Mahura, C. Petersen, N.W. Nielsen, B. Amstrup Danish.
Simulations of the Urban Boundary Layer in Phoenix, Arizona Susanne Grossman-Clarke Arizona State University Global Institute of Sustainability 17 January.
Land Surface Processes in Global Climate Models (1)
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar University of Toledo Introduction.
JULES on Eddie Richard Essery (with thanks to Mike Mineter and Magnus Hagdorn Contemporary Climate, 8 October 2009.
Remote Sensing Derived Land Use/Cover Data for Urban Modeling in MM5 and WRF Susanne Grossman-Clarke 1 Joseph A. Zehnder 1 William Stefanov 2 Matthias.
Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Land Surface Hydrology Research Group Civil and Environmental.
How Do Forests, Agriculture and Residential Neighborhoods Interact with Climate? Andrew Ouimette, Lucie Lepine, Mary Martin, Scott Ollinger Earth Systems.
1/26 APPLICATION OF THE URBAN VERSION OF MM5 FOR HOUSTON University Corporation for Atmospheric Research Sylvain Dupont Collaborators: Steve Burian, Jason.
Ground-based energy flux measurements for calibration of the Advanced Thermal and Land Application Sensor (ATLAS) Eric Harmsen, Associate Professor Dept.
LMWG progress towards CLM4 –Soil hydrology CLM3.5 major improvement over CLM3 (partitioning of ET into transpiration, soil evap, canopy evap; seasonal.
Vegetation Phenology and the Hydrological Cycle of Monsoons
Application of Models-3/CMAQ to Phoenix Airshed Sang-Mi Lee and Harindra J. S. Fernando Environmental Fluid Dynamics Program Arizona State University.
學生:張立農 NUMERICAL STUDY ON ADJUSTING AND CONTROLLING EFFECT OF FOREST COVER ON PM 10 AND O 3.
Investigating Land-Atmosphere CO 2 Exchange with a Coupled Biosphere-Atmosphere Model: SiB3-RAMS K.D. Corbin, A.S. Denning, I. Baker, N. Parazoo, A. Schuh,
CITES 2005, Novosibirsk Modeling and Simulation of Global Structure of Urban Boundary Layer Kurbatskiy A. F. Institute of Theoretical and Applied Mechanics.
Implementation and preliminary test of the unified Noah LSM in WRF F. Chen, M. Tewari, W. Wang, J. Dudhia, NCAR K. Mitchell, M. Ek, NCEP G. Gayno, J. Wegiel,
Urban Heat Island and Pollution
How Do Forests, Agriculture and Residential Neighborhoods Interact with Climate? Andrew Ouimette, Lucie Lepine, Mary Martin, Scott Ollinger Earth Systems.
ATM 301 Lecture #11 (sections ) E from water surface and bare soil.
Ground-based energy flux measurements for calibration of the Advanced Thermal and Land Application Sensor (ATLAS) Eric Harmsen, Associate Professor Dept.
Evapotranspiration Eric Peterson GEO Hydrology.
Results Time Study Site Measured data Alfalfa Numerical Analysis of Water and Heat Transport in Vegetated Soils Using HYDRUS-1D Masaru Sakai 1), Jirka.
OEAS 604: Introduction to Physical Oceanography Surface heat balance and flux Chapters 2,3 – Knauss Chapter 5 – Talley et al. 1.
Diagnosis of Performance of the Noah LSM Snow Model *Ben Livneh, *D.P. Lettenmaier, and K. E. Mitchell *Dept. of Civil Engineering, University of Washington.
SiSPAT-Isotope model Better estimates of E and T Jessie Cable Postdoc - IARC.
© Oxford University Press, All rights reserved. 1 Chapter 3 CHAPTER 3 THE GLOBAL ENERGY SYSTEM.
Meteorology for modeling AP Marti Blad PhD PE. Meteorology Study of Earth’s atmosphere Weather science Climatology and study of weather patterns Study.
Evaporation What is evaporation? How is evaporation measured? How is evaporation estimated? Reading for today: Applied Hydrology Sections 3.5 and 3.6 Reading.
Hydrologic Losses - Evaporation Learning Objectives Be able to calculate Evaporation from a lake or reservoir using the following methods – Energy Balance.
Hydrologic Losses - Evaporation
Community Land Model (CLM)
Remote Sensing ET Algorithm— Remote Evapotranspiration Calculation (RET) Junming Wang,
Surface Energy Budget, Part I
Hydrologic Losses - Evaporation
MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching
Colombe Siegenthaler - Le Drian
Presentation transcript:

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

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.

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

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.

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.

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 1000-3000 nm, longwave infrared 3000-15000nm Vegetation index from ratio red band/near-infrared band Additional data sets are for example water rights, city boundaries, texture, land use.

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.

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.

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

Surface Parameters Albedo Roughness length Moisture availability Emissivity Heat storage capacity

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.

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

1km x 1km Land Use: 1998 Satellite Data

2km x 2km Land Use: 1998 Satellite Data

2km x 2km Land Use: 1976 USGS Data

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

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.

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]

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

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]

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

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 2001 14:00 LST

Differences in Ground Temperatures

Simulated Latent Heat Fluxes (a) USGS and (b) 1998 Land Use Data 29 May 2001 14:00 LST

Differences in Latent Heat Fluxes

Simulated Sensible Heat Fluxes (a) USGS (b) 1998 Land Use Data 29 May 2001 14:00 LST

Differences in Sensible Heat Fluxes

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 2001 14:00 LST

Differences in 2m Air Temperatures

Simulated Boundary Layer Heights (a) USGS (b) 1998 Land Use Data 29 May 2001 14:00 LST

Differences in Boundary Layer Heights

Results

Results

Results

Results

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.  

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

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

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.

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.

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.

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.

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.

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

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

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)

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

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

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

Canopy Resistance rmin Minimum canopy resistance Rs Incoming solar radiation T Air temperature Tmin Cold limit (–5 – 0 C) Tmax Heat limit (45 - 50 C) To Optimum temperature (30 C)

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

Nitrogen Dry Deposition Modeling

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

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