The response of global terrestrial ecosystems to interannual temperature variability

Science

Volumn 278, Issue 5339, 1997, Pages 870-872

  Braswell, B H ^a   Schimel, D S ^b,c   Linder, E ^d   Moore III, B ^a  

^a UNIVERSITY OF NEW HAMPSHIRE   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c Veterinary Dentistry and Oral Surgery   (United States)

^d University of New Hampshire   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON DIOXIDE;

CARBON DIOXIDE; CARBON STORAGE; CLIMATE; GLOBAL RESPONSE; TEMPERATURE; TEMPERATURE VARIATION; TERRESTRIAL ECOSYSTEM; VEGETATION INDEX;

AIR TEMPERATURE; ARTICLE; CARBON DIOXIDE MEASUREMENT; CLIMATE; ECOSYSTEM; FEEDBACK SYSTEM; PRIORITY JOURNAL; VEGETATION;

EID: 0030716515     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.278.5339.870     Document Type: Article

References (30)

1. D. S. Schimel, VEMAP Participants, B. H. Braswell, Ecol. Monogr. 67, 251 (1997).
2. G. M. Woodwell and F. T. MacKenzie, Eds., Biotic Feedbacks in the Global Climatic System: Will the Warming Feed the Warming? (Oxford Univ. Press, New York, 1995).
3. D. S. Schimel et al., in Climate Change 1994: Radiative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios, J. T. Houghton et al., Eds. (Cambridge Univ. Press, Cambridge, UK, 1995).
4. VEMAP, Global Biogeochem. Cycles 9, 407 (1995).
5. D. S. Schimel et al., "Stabilisation of Atmospheric Greenhouse Gases: Physical, Biological, and Socioeconomic Implications" (Intergovernmental Panel on Climate Change Technical Paper 3, World Meteorological Organization/United Nations Environment Programme, Geneva, 1997).
6. R. J. Francey et al., Nature 373, 326 (1995); R. F. Keeling, S. C. Piper, M. Heimann, ibid. 381, 218 (1996).
7. R. J. Francey et al., Nature 373, 326 (1995); R. F. Keeling, S. C. Piper, M. Heimann, ibid. 381, 218 (1996).
8. C. D. Keeling, T. P. Whorf, M. Wahlen, J. van der Plicht, ibid. 375, 666 (1995).
9. P. Ciais, P. P. Tans, M. Trolier, J. W. C. White, R. J. Francey, Science 269, 1098 (1995).
10. C. D. Keeling et al., in Aspects of Climate Variability in the Pacific and the Western Americas, D. H. Peterson, Ed. (American Geophysical Union, Washington, DC, 1989), vol. 55, pp. 165-236.
11. D. S. Schimel et al., Global Biogeochem. Cycles 10, 677 (1996); D. S. Schimel, B. H. Braswell, W. J. Parton, Proc. Natl. Acad. Sci. U.S.A. 94, 8280 (1997).
12. D. S. Schimel et al., Global Biogeochem. Cycles 10, 677 (1996); D. S. Schimel, B. H. Braswell, W. J. Parton, Proc. Natl. Acad. Sci. U.S.A. 94, 8280 (1997).
13. T. J. Conway et al., J. Geophys. Res. 99D, 22831 (1994).
14. R. W. Spencer, J. R. Christy, N. C. Grody, J. Clim. 3, 1111 (1990). The MSU temperatures are based on the observed thermal emission of oxygen in the lower troposphere. The monthly temperature anomaly product has the mean annual cycle subtracted from the time series.
15. P. A. Agbu and M. E. James, The NOAA/NASA Pathfinder AVHRR Land Data Set User's Manual (Goddard Distributed Active Archive Center, NASA, Goddard Space Flight Center, Greenbelt, MD, 1995). The NDVI is equal to (N - R)/(N + R), where N is the reflectance measured in the near-infrared spectral band and R is the reflectance measured in the red spectral band.
16. 2 data).
17. 2 growth rate time series by averaging the MLO and SPO curves (9).
18. The MSU temperature anomaly data are produced on a 2.5° by 2.5° grid. We used a global 1° by 1° digital elevation model to apply an adiabatic lapse rate adjustment and create a corresponding 1° by 1° temperature anomaly map. We chose 1° by 1° degree to be consistent with the global vegetation database we used to characterize biome-specific relations (Table 1). Data were available from 1979 to 1994, with a base period for the annual cycle defined as 1982 to 1991. We used the base annual cycle and a temperature climatology [R. Leemans and W. Cramer, The IIASA Database for Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Terrestrial Grid (International Institute for Applied System Analysis, report RR-91-18, 1991)] to adjust the anomalies to reflect biologically meaningful temperature variations, setting temperatures less than 0°C equal to 0°C to emphasize growing season anomalies. We constructed. NDVI anomalies by similarly truncating negative values (typically, for vegetated surfaces, 0 < NDVI < 0.7) and subtracting the mean annual cycle for the period 1982 to 1990. We limited our use of AVHRR data to this period because the aerosols released by the Mount Pinatubo eruption in 1991 caused persistent global reductions in atmospheric optical depth, noticeably corrupting the NDVI signal.
19. R. B. Myneni, S. O. Los, C. J. Tucker, Geophys. Res. Lett. 7, 729 (1996).
20. R. S. DeFries et al., J. Geophys. Res. 100, 20867 (1995).
21. Formally, Y = Xb + ε, where X is a matrix constructed from the temperature anomalies in year t, t - 1, t - 2, and so on, Y is the column vector containing the dependent variable, NDVI, b is a vector of regression coefficient, and ε is a vector of residuals.
22. Though spatial and temporal averaging of NDVI reduces errors associated with small-scale spatial and temporal variability, low-frequency contamination remains and is associated with atmospheric composition (principally, aerosols and water vapor), orbital drift, and instrument changeover. We applied a simple adjustment to correct for satellite orbital drift and the changeover from NOAA9 to NOAA11 [C. J. Tucker, W. W. Newcomb, H. E. Dregne, Int. J. Remote Sens. 15, 3547 (1994)], but this bias adjustment is on the order of year-to-year changes in NDVI, usually 0.01 to 0.06 units.
23. N. R. Draper and H. Smith, Applied Regression Analysis (Wiley, New York, 1966).
24. i in (26) taken from estimated semivariograms.
25. N. Cressie, Statistics for Spatial Data (Wiley, New York, 1991).
26. Y is the standard deviaton of the vegetation index values Y. Thus, we present coefficients p̂ (Fig. 2) that are a "commensurate measure of response" of NDVI to temperature [S. Selvin, Practical Biostatistical Methods (Duxbury, Belmont, CA, 1995)].
27. "Immediate," in this context, implies phenomena that appear in the data during the same averaging period (1 year).
28. 2 growth rate at these time scales reflect changes to global net ecosystem production (NEP, which is equal to net primary production minus heterotrophic respiration). Because temperature affects both plant and microbial physiology similarly, the instantaneous response of NEP to temperature reflects the small asymmetry in the temperature responses of autotrophs and heterotrophs.
29. J. S. Famiglietti, personal communication.
30. We acknowledge helpful suggestions from P. Tans, C. J. Tucker, R. Myneni, G. Asrar, J. L. Privette, T. R. Seastedt, F. S. Chapin, and three anonymous reviewers. We thank the participants of the Seventh Annual Symposium Centre National D'Etudes Spatiales, Physical Measurements and Signals in Remote Sensing, held in Courchevel, France, 9 to 11 April 1997, for comments on a preliminary version of this work. This research was supported in part by Oak Ridge Associated Universities' Graduate Fellowships for Global Change and NASA [Earth Observing System-Interdisciplinary Science (EOS-IDS)]. The National Center for Atmospheric Research is sponsored by NSF.