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Satellite Remote Sensing and Applications for Hydrometeorology

Satellite Remote Sensing and Applications for Hydrometeorology. Liming Zhou Georgia Institute of Technology 2006 年 8 月 4 日于北京师范大学. /73. Motivation?. /73. Changes in Climate.

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Satellite Remote Sensing and Applications for Hydrometeorology

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  1. Satellite Remote Sensing and Applications for Hydrometeorology Liming Zhou Georgia Institute of Technology 2006年8月4日于北京师范大学 /73

  2. Motivation? /73

  3. Changes in Climate • Global mean surface temperature has risen by about 0.6C over the 20th century, with the largest increase in the past two decades (IPCC, 2001). Global mean surface temperature anomaly relative to 1951-1990 • Global land surface precipitation has increased significantly (by about 2%) over the 20th century (IPCC, 2001). /73

  4. Land and Climate Interactions • Land and climate are closely coupled. biogeophysical processes (water, energy, heat, momentum, carbon exchanges) climate land biogeochemical processes (photosynthesis and respiration) • Understanding land-climate interactions is crucial to evaluate the future state of climate. /73

  5. Presentation Overview • Response of Vegetation to Climate Changes • Impacts of Urbanization on Climate in China • Modeling Land-Climate Interactions /73

  6. Topic I: Response of Vegetation to Climate Changes climate land Evidence for warming-enhanced plan growth in the north since 1980s (Zhou et al., JGR, 2001; 2003a) /73

  7. Have Climate Changes Promoted Northern Vegetation Growth? Changes in Climate Changes in Vegetation • Pronounced warming in • northern high latitudes • Earlier disappearance of • snow in spring • Increased precipitation in • northern high latitudes • Increased concentration • of atmospheric CO2 • Increased productivity • through: • enhanced photosynthesis • enhanced nutrient availability ? /73

  8. Satellite Remote Sensing Data AVHRR MODIS Orbit type:Sun synchronous Orbit altitude: 833 km Data:1981-present Spectral bands: 7 Resolution: ~1.1 km Orbit type: Sun synchronous Orbit altitude: 705 km Data: 2000-present Spectral bands: 36 Resolution: 0.25-1.0 km /73

  9. Satellite Remote Sensing of Vegetation REDNIR Greenness Index: Normalized Difference Vegetation Index (NDVI) NDVI=(NIR-RED)/(NIR+RED) /73

  10. Data • GIMMS 15-day composite 8 km NDVI and solar zenith angle (SZA) datasets (1981-1999) • Monthly stratospheric aerosol optical depth(AOD) (1981-1999) • Observed monthly land surface climate data (1981-1999) • NOAA precipitation: 2.5x2.5 • GISS temperature: 2x2 • A land cover map at 8 km resolution /73

  11. Non-vegetation Effects • Factors that may contaminate long-term satellite measurements • calibration uncertainties (satellite drift and changeover) • atmospheric effects (aerosol, vapor, etc) • soil background effects Changes in aerosol optical depth Changes in solar zenith angle El Chichon Mt. Pinatubo • Methods that help reduce some non-vegetation effects • maximum NDVI compositing • spatial and temporal aggregations • empirical techniques /73

  12. Study Region • Vegetated pixels (defined by NDVI) between April to October • minimize the SZA effect • reduce the soil background contribution (snow, barren and sparsely vegetated areas) • study the same pixels in the entire analysis. Map of vegetated pixels at 8 km resolution /73

  13. delayed fall earlier spring Jan Jul Aug Dec NDVI Jan Jul Aug Dec Changes in Vegetation Activity • Changes in vegetation photosynthetic activity can be characterized by • changes in growing season duration • changes in NDVI magnitude Longer growing season Increases in NDVI magnitude Increase NDVI /73

  14. Lengthening in Growing Season (Increased by 12 Days) 11.9 days/18 yrs (p<0.05) (Increased by 18 Days) 17.5 days/18 yrs (p<0.05) /73

  15. Increase in NDVI Magnitude (Increased by 8%) 8.4/18 yrs (p<0.05) (Increased by 12%) 12.4/18 yrs (p<0.05) /73

  16. Spatial Patterns of NDVI Changes /73

  17. cooling warming Continental Differences in Warming • The greatest warming occurred during winter and spring. • Eurasia has an overall warming while North America has • smaller warming or cooling trends. /73

  18. Consistency Between NDVI and Temperature /73

  19. Statistical Results at Continental Scale Note: T – Temperature; EA – Eurasia; NA – North America /73

  20. Model NDVI at Regional Scale NDVIsummer=11Twinter+12T2winter+21Tspring+22T2spring+31Tsummer +32T2summer+41Pwinter+42P2winter+51Pspring+52P2spring +61Psummer +62P2summer +7SZAsummer+8AODsummer +summer+ • Data aggregated into 2x 2 boxes by seasons and vegetation types: 1430 boxes • T and P represented by a quadratic specification (a physiological optimum) with effects for earlier seasons. • SZA and AOD used to separate non-vegetation effects. • s estimated using statistical techniques from econometrics. /73

  21. Statistical Results at Regional Scale Note: T - Temperature; P - Precipitation; AOD - Stratospheric aerosol optical depth; SZA - Solar zenith angle /73

  22. Conclusions • Eurasia is photosynthetically more vigorous than North America during the past two decades: • Eurasia has a higher percentage of vegetated pixels (61% vs. 30%) showing a larger increase in the NDVI magnitude (12% vs. 8%) and a longer active growing season (18 vs. 12 days) than North America. • There is a statistically meaningful relationship between changes in satellite measured NDVI and those in observed surface air temperature at continental and regional scales. • These results suggest warming-enhanced plant growth in the north since 1980s, with important global-economic implications: warmer planet, greener north. /73

  23. Topic II: Impacts of Urbanization on Climate in China climate land Evidence for a significant urbanization effect on climate in China (Zhou et al., PNAS, 2004) /73

  24. Rapid Urbanization in China • China has experienced rapid urbanization and dramatic economic growth since late 1978: • Its GDP increased at 9.5%, compared with 2.5% for developed countries and 5% for developing countries; • The number of small towns soared from 2,176 to 20,312, nearly double of the world average; • The number of cities increased from 190 to 663; • The percent of urban population rose from 18% to 39%. • This could be a good case study to quantify the urbanization effect on China’s regional climate. /73

  25. Rapid Urbanization - Beijing 1978 2000 (http://www.unep.org/geo/yearbook/yb2004/034.htm) /73

  26. Rapid Urbanization - Shenzhen 1988 1996 (NASA Goddard Space Flight Center) /73

  27. Diurnal Cycle of Surface Air Temperature • Maximum/Minimum Temperature, Diurnal Temperature Range (DTR)  Tmax DTR=Tmax-Tmin Tmean=(Tmax+Tmin)/2 DTR Temperature Tmin 0 Local Time 24 /73

  28. Urban Heat Island (UHI) Effect • Urban areas are often warmer than their surrounding rural areas, especially during the night • UHI warms Tmin more than Tmax and thus reduces DTR   (Oke, Boundary layer climates, 1978) /73

  29. Factors Contributed to UHI • Urban materials (e.g., concrete, glass, asphalt) have higher heat storage capacities and thus store heat during daytime and release it at night.  • Urban areas have more “waterproofing” surfaces and less vegetation and thus have less evaporative cooling due to increased surface runoff and reduced moisture retention. • Increased surface roughness and reduced wind speed over urban areas trap more sunlight during daytime and keep more thermal emission during nighttime. • Increased anthropogenic heating releases from buildings and automobile and increased urban pollutants and aerosols trap thermal emission.  /73

  30. How to Estimate UHI?Observed-Reanalysis Temperature • NCEP/NCARreanalysis temperatures are estimated from atmospheric values instead of surface observations and thus are insensitive to changes in land surface. observed reanalysis US average monthly temperature anomalies in 1990s (Kalnay and Cai, Nature, 2003) /73

  31. Choose Study Region and Season:Southeast China in Winter • Use an improved new reanalysis dataset and focus only on southeast China winter to ensure the best quality in both observational and reanalysis data by considering R-2 performance and China’s complex topography and climate. Study Region: 13 provinces/municipalities 194 stations most of urbanization occurred /73

  32. Data • Observed monthly mean daily maximum and minimum land surface air temperatures in China from 1979 to 1998. • NCEP/DOE AMIP-II Reanalysis (R-2): improved quality, especially for soil moisture and clouds; covering the period 1979-present when satellite data are available. • Interpolation of R-2 temperatures for each station based on its location (longitude and latitude) • Calculations of monthly anomalies of maximum and minimum temperatures and DTR for each station for both R-2 and observed data. /73

  33. Temperature Trends Due to Urbanization • Observed-reanalysis DTR trends explain 68% of the observed DTR trend Winter temperature trends (C/10yrs) from 1979 to 1998 /73

  34. Consistency with Urbanization Index • If urbanization is responsible for the estimated temperature trends, changes in DTR should be correlated with those in urbanization index such as the percent of urban population. R=-0.77 (p < 0.01) at provincial level /73

  35. Consistency with Satellite Measured Vegetation Loss • If urbanization is responsible for the estimated temperature trends, changes in Tmin should be correlated with changes in satellite measured greenness. Summer NDVI trends (1982-1998) Correlation at provincial level /73

  36. Conclusions • The estimated warming of mean surface temperature of 0.05C per decade in China is much larger than previous estimates for other periods and locations. • (<0.006°C globally; <0.027°C in US) • The spatial pattern and magnitude of my estimatesare consistent with those of urbanization characterized by changes in the percent of urban population and in satellite-measured greenness. • These results provide further evidence of the human impact on climate, suggesting that urbanization and other human-induced changes in land use may have changed regional climate significantly. /73

  37. Topic III: ModelingLand-Climate Interactions climate land Application of remote sensing data to improve land surface processes in climate models (Zhou et al., GRL, 2003; 2005; Zhou et al., JGR, 2003b; 2003c; Tian et al., GRL, 2004;Tian et al., JGR,2004a; 2004b) /73

  38. Climate Driving Force albedo emissivity Earth’s radiation and energy budget (IPCC, 2001) Land plays a significant role in determining the net radiation absorbed by the land surface and partitioning of sensible and latent heat fluxes released back to the atmosphere. /73

  39. Land-Climate Interactions in Climate Models temperature, precipitation, downward solar radiation, downward longwave radiation, etc atmosphere reflected solar radiation sensible heat latent heat upward longwave radiation Land Surface Parameterizations: albedo, emissivity, roughness, evapotranspiration, etc input land Land Surface Parameters: vegetation type/amount, soil properties, etc land cover/use change /73

  40. water (10%) bare soil (28%) trees (35%) grass (15%) crop (12%) Land Surface Parameters Global land cover map A model grid Vegetation Type: tree/grass/crop Vegetation Amount: LAI (leaf area index) fractional vegetation cover ect /73 A tree

  41. emissivity Land Surface Parameterizations Land surface parameters: LAI, vegetation fraction/type, ect Land surface parameterizations  = f (, LAI,g…) =f (LAI, g …) more more albedo describing exchanges of momentum, energy, and mass between atmosphere and land /73

  42. Major Objectives • To find major deficiencies in climate models and to best improve them • more accurate land surface parameters • more realistic land surface parameterizations • Here MODIS products were used to improve the NCAR Community Land Model (CLM). /73

  43. Satellite Remote Sensing Data AVHRR MODIS Orbit type:Sun synchronous Orbit altitude: 833 km Data:1981-present Spectral bands: 7 Resolution: ~1.1 km Orbit type: Sun synchronous Orbit altitude: 705 km Data: 2000-present Spectral bands: 36 Resolution: 0.25-1.0 km /73

  44. Best Way to Improve Climate Models • An optimal connection between models and observations is to ensure that climate models are able to reproduce MODIS observations. LAI, vegetation type/fraction, etc satellite measured radiation land surface parameterizations MODIS algorithms albedo, emissivity albedo, emissivity, LAI, vegetation type/fraction, etc model radiation MODIS framework Modeling framework /73

  45. Topic III: Modeling Land-Climate Interactions Question 1: What are major deficiencies in climate model land surface parameters/parameterizations? Question 2: How much could the climate simulations be improved if MODIS-derived data are used? Question 3:What’s the essential problem and how to best improve climate model albedo and emissivity schemes? /73

  46. Inaccurate Land Surface Parameters:Leaf Area Index • CLM2 underestimates LAI by 0.5-1.5 globally over most areas. • CLM2 overestimates LAI by 0.5-1.5 over extra-tropical South America (winter), and eastern US and middle latitude Eurasia (summer). LAI differences (MODIS-CLM2) /73

  47. Inaccurate Land Surface Parameters: Fractional Vegetation Cover • CLM2 overestimates grass/crop fraction by 20~40% globally over most areas. • CLM2 underestimates the fractions of tree, shrub, and soil over most areas. Percent cover differences (MODIS-CLM2) /73

  48. Inaccurate Land Surface Parameterizations:Albedo • Significant model albedo biases occur over northern snow-covered vegetated surfaces and over arid/semiarid regions. • Model albedoes are consistent with the MODIS data for dense forests over snow-free regions. Albedo differences (CLM-MODIS) /73

  49. Inaccurate Land Surface Parameterizations:Emissivity • CLM2 uses a constant soil emissivity. • Satellite observed soil emissivity varies spatially over North Africa, ranging from 0.84 to 0.96. Soil emissivity = 0.96 CLM2 emissivity ASTER/MODIS emissivity /73

  50. Conclusions • CLM2 underestimatesLAI and percent cover of tree, shrub and soil, and overestimates percent cover of grass/crop globally over most areas compared to the MODIS data. • CLM2 has the largest albedo bias over snow-covered and sparely vegetated surfaces and the biggest emissivity bias over arid/semiarid regions. /73

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