Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part I: Statistical Analysis

Weile Wang Department of Geography and Environment, Boston University, Boston, Massachusetts

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Bruce T. Anderson Department of Geography and Environment, Boston University, Boston, Massachusetts

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Nathan Phillips Department of Geography and Environment, Boston University, Boston, Massachusetts

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Robert K. Kaufmann Department of Geography and Environment, Boston University, Boston, Massachusetts

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Christopher Potter Ecosystem Science and Technology Branch, NASA Ames Research Center, Moffett Field, California

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Ranga B. Myneni Department of Geography and Environment, Boston University, Boston, Massachusetts

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Abstract

Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.

* Corresponding author address: Weile Wang, Department of Geography and Environment, Boston University, 675 Commonwealth Ave., Boston, MA 02215. wlwang@bu.edu

Abstract

Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.

* Corresponding author address: Weile Wang, Department of Geography and Environment, Boston University, 675 Commonwealth Ave., Boston, MA 02215. wlwang@bu.edu

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  • Bonan, G. B. 2002. Ecological Climatology, Concepts and Applications. Cambridge University Press, 678 pp.

  • Bonan, G. B. and L. M. Stillwell-Soller. 1998. Soil water and the persistence of floods and droughts in the Mississippi River Basin. Water Resour. Res. 34:26932701.

    • Search Google Scholar
    • Export Citation
  • Bounoua, L., G. J. Collatz, S. O. Los, P. J. Sellers, D. A. Dazlich, C. J. Tucker, and D. A. Randall. 2000. Sensitivity of climate to changes in NDVI. J. Climate 13:22772292.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., C. Smith, and J. M. Wallace. 1992. An intercomparison of methods for finding coupled patterns in climate data. J. Climate 5:541560.

    • Search Google Scholar
    • Export Citation
  • Churkina, G. and S. W. Running. 1998. Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Ecosystems 1:206215.

    • Search Google Scholar
    • Export Citation
  • Czaja, A. and C. Frankignoul. 1999. Influence of the North Atlantic SST on the atmospheric circulation. Geophys. Res. Lett. 26:29692972.

    • Search Google Scholar
    • Export Citation
  • Czaja, A. and C. Frankignoul. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. J. Climate 15:606623.

    • Search Google Scholar
    • Export Citation
  • Delire, C., J. A. Foley, and S. Thompson. 2004. Long-term variability in a coupled atmosphere–biosphere model. J. Climate 17:39473959.

    • Search Google Scholar
    • Export Citation
  • Diebold, F. X. and R. S. Mariano. 1995. Comparing predictive accuracy. J. Bus. Econ. Stat. 13:253263.

  • Enders, W. 1995. Applied Econometric Time Series. Wiley & Sons, 448 pp.

  • Feteke, B. M., C. J. Vorosmarty, and W. Grabs. 2000. Global composite runoff fields based on observed river discharge and simulated water balances. UNH-GRDC Composite Runoff Fields v1.0, Complex Systems Research Center, University of New Hampshire, 120 pp.

  • Fraedrich, K., A. Kleidon, and F. Lunkeit. 1999. A green planet versus a desert world: Estimating the effect of vegetation extremes on the atmosphere. J. Climate 12:31563163.

    • Search Google Scholar
    • Export Citation
  • Friedl, M. A. Coauthors 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 83:287302.

  • Gerten, D., S. Schaphoff, U. Haberlandt, W. Lucht, and S. Sitch. 2004. Terrestrial vegetation and water balance: Hydrological evaluation of a dynamic global vegetation model. J. Hydrol. 286:249270.

    • Search Google Scholar
    • Export Citation
  • Glendinning, P. 1994. Stability, Instability, and Chaos: An Introduction to the Theory of Nonlinear Differential Equations. Cambridge University Press, 388 pp.

    • Search Google Scholar
    • Export Citation
  • Granger, C. W. J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424438.

  • Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. J. Econ. Dyn. Control 2:329352.

  • Granger, C. W. J. and L. Huang. 1997. Evaluation of Panel Data Models: Some suggestions from time series. Department of Economics, University of California, San Diego, 29 pp.

  • Hansen, J., R. Ruedy, J. Glascoe, and M. Sato. 1999. GISS analysis of surface temperature change. J. Geophys. Res. 104:3099731022.

  • Heck, P., D. Luthi, and C. Schar. 1999. The influence of vegetation on the summertime evolution of European soil moisture. Phys. Chem. Earth 24:609614.

    • Search Google Scholar
    • Export Citation
  • Heck, P., D. Luthi, H. Wernli, and C. Schar. 2001. Climate impacts of European-scale anthropogenic vegetation changes: A sensitivity study using a regional climate model. J. Geophys. Res. 106:78177835.

    • Search Google Scholar
    • Export Citation
  • Jenkins, G. M. and D. G. Watts. 1968. Spectral Analysis and Its Applications. Holden-Day, 525 pp.

  • Kaufmann, R. K. and D. I. Stern. 1997. Evidence for human influence on climate from hemispheric temperature relations. Nature 388:3944.

    • Search Google Scholar
    • Export Citation
  • Kaufmann, R. K., L. Zhou, Y. Knyazikhin, N. V. Shabanov, R. B. Myneni, and C. J. Tucker. 2000. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens. 38:25842597.

    • Search Google Scholar
    • Export Citation
  • Kaufmann, R. K., L. Zhou, R. B. Myneni, C. J. Tucker, D. Slayback, N. B. Shabanov, and J. Pinzon. 2003. The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Geophys. Res. Lett. 30.2147, doi:10.1029/2003GL018251.

    • Search Google Scholar
    • Export Citation
  • Kaufmann, R. K., R. D. d’Arrigo, C. Laskowski, R. B. Myneni, L. Zhou, and N. J. Devi. 2004. The effect of growing season and summer greenness on northern forests. Geophys. Res. Lett. 31.L09205, doi:10.1029/2004GL019608.

    • Search Google Scholar
    • Export Citation
  • Kleidon, A., K. Fraedrich, and M. Heimann. 2000. A green planet versus a desert world: Estimating the maximum effect of vegetation on the land surface climate. Climate Change 44:471493.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D. Coauthors 2004. Regions of strong coupling between soil moisture and precipitation. Science 305:11381140.

  • Kutzbach, J. E. 1967. Empirical eigenvectors of sea-level pressure, surface temperature and precipitation complexes over North America. J. Appl. Meteor. 6:791802.

    • Search Google Scholar
    • Export Citation
  • Los, S. O. 1998. Estimation of the ratio of sensor degradation between NOAA AVHRR channels 1 and 2 from monthly NDVI composites. IEEE Trans. Geosci. Remote Sens. 36:206213.

    • Search Google Scholar
    • Export Citation
  • Lotsch, A., M. A. Friedl, B. T. Anderson, and C. J. Tucker. 2003. Coupled vegetation-precipitation variability observed from satellite and climate records. Geophys. Res. Lett. 30.1774, doi:10.1029/2003GL017506.

    • Search Google Scholar
    • Export Citation
  • Montaldo, N., R. Rondena, J. D. Albertson, and M. Mancini. 2005. Parsimonious modeling of vegetation dynamics for ecohydrologic studies of water-limited ecosystems. Water Resour. Res. 41.W10416, doi:10.1029/2005WR004094.

    • Search Google Scholar
    • Export Citation
  • Myneni, R. B., C. J. Tucker, G. Asrar, and C. D. Keeling. 1998. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res. 103:D6. 61456160.

    • Search Google Scholar
    • Export Citation
  • Pal, J. S. and E. A. B. Eltahir. 2001. Pathways relating soil moisture conditions to future summer rainfall within a model of the land–atmosphere system. J. Climate 14:12271242.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., R. Avissar, M. Raupach, A. J. Dolman, X. Zeng, and A. S. Denning. 1998. Interactions between the atmosphere and terrestrial ecosystems: Influence on weather and climate. Global Change Biol. 4:461475.

    • Search Google Scholar
    • Export Citation
  • Pinzon, J. E., J. F. Pierce, and C. J. Tucker. 2001. Analysis of remote sensing data using Hilbert-Huang transform. Proc. SCI 2001, Orlando, FL, International Institute of Informatics and Systematics, 78–83.

  • Richman, M. B. 1986. Rotation of principal components. J. Climatol. 6:293335.

  • Salvucci, G. D., J. A. Saleem, and R. K. Kaufmann. 2002. Investigating soil moisture feedbacks on precipitation with tests of Granger causality. Adv. Water Resour. 25:13051312.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J. Coauthors 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275:502509.

    • Search Google Scholar
    • Export Citation
  • Shukla, J. and Y. Mintz. 1982. Influence of land-surface evapotranspiration on the Earth’s climate. Science 215:14981501.

  • Trenberth, K. E. and D. J. Shea. 2005. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32.L14703, doi:10.1029/2005GL022760.

    • Search Google Scholar
    • Export Citation
  • Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E. Vermote, and N. El Saleous. 2005. An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26:44854498.

    • Search Google Scholar
    • Export Citation
  • Vermote, E. and Y. J. Kaufman. 1995. Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views. Int. J. Remote Sens. 16:23172340.

    • Search Google Scholar
    • Export Citation
  • Wang, W., B. T. Anderson, R. K. Kaufmann, and R. B. Myneni. 2004. The relation between the North Atlantic Oscillation and SSTs in the North Atlantic basin. J. Climate 17:47524759.

    • Search Google Scholar
    • Export Citation
  • Wang, W., B. T. Anderson, D. Entekhabi, D. Huang, R. K. Kaufmann, C. Potter, and R. B. Myneni. 2006. Feedbacks of vegetation on summertime climate variability over the North America Grasslands. Part II: A coupled stochastic model. Earth Interactions in press.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. E. 1954. North American Prairie. Johnsen Publishing Company, 348 pp.

  • Wever, L. A., L. B. Flanagan, and P. J. Carlson. 2002. Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland. Agric. For. Meteor. 112:3149.

    • Search Google Scholar
    • Export Citation
  • Woodward, F. I. 1987. Climate and Plant Distribution. Cambridge University Press, 174 pp.

  • Wu, W., M. A. Geller, and R. E. Dickinson. 2002. The response of soil moisture to long-term variability of precipitation. J. Hydrometeor. 3:604613.

    • Search Google Scholar
    • Export Citation
  • Xie, P. and P. A. Arkin. 1997. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc. 78:25392558.

    • Search Google Scholar
    • Export Citation
  • Zeng, N., H. Qian, C. Roedenbeck, and M. Heimann. 2005. Impact of 1998–2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle. Geophys. Res. Lett. 32.L22709, doi:10.1029/2005GL024607.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., C. J. Tucker, R. K. Kaufmann, D. Slayback, N. V. Shabanov, and R. B. Myneni. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. 106:2006920083.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., R. K. Kaufamann, Y. Tian, R. B. Myneni, and C. J. Tucker. 2003. Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999. J. Geophys. Res. 108.4004, doi:10.1029/2002JD002510.

    • Search Google Scholar
    • Export Citation
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