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  • Author or Editor: Sietse O. Los x
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Sietse O. Los, G. James Collatz, Lahouari Bounoua, Piers J. Sellers, and Compton J. Tucker

Abstract

Anomalies in global vegetation greenness, SST, land surface air temperature, and precipitation exhibit linked, low-frequency interannual variations. These interannual variations were detected and analyzed for 1982–90 with a multivariate spectral method. The two most dominant signals for 1982–90 had periods of about 2.6 and 3.4 yr. Signals centered at 2.6 years per cycle corresponded to variations in the El Niño–Southern Oscillation index and explained about 28% of the variance in anomalies of SST, land surface air temperature, precipitation, and vegetation; these signals were most pronounced in 1) SST anomalies in the eastern equatorial Pacific Ocean, 2) land surface vegetation and precipitation anomalies in tropical and subtropical regions, and 3) land surface vegetation, precipitation, and temperature anomalies in North America. Signals at 3.4 years per cycle corresponded to variations in the North Atlantic oscillation index and explained 8.6% of the variance in the combined datasets; their occurrence was most pronounced in 1) Atlantic SST anomalies, 2) in land surface temperature and vegetation anomalies in Europe and eastern Asia, and 3) in precipitation and vegetation anomalies in sub-Saharan Africa, southern Africa, and eastern North America. Anomalies in vegetation were positively related to anomalies in precipitation throughout the Tropics and subtropics and in midlatitudes in the central parts of continents. Anomalies in vegetation and temperature were positively linked in coastal temperate climates such as in Europe and eastern Asia. These associations between temperature and vegetation may be explained by the sensitivity of the length of growing season to variations in temperature.

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Piers J. Sellers, Compton J. Tucker, G. James Collatz, Sietse O. Los, Christopher O. Justice, Donald A. Dazlich, and David A. Randall

Abstract

The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief description is provided of source material, mainly derived from in situ observations, that was used in addition to the NDVI data. The datasets summarized in this paper should he superior to prescriptions currently used in most land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular those related to vegetation, are obtained directly from a consistent set of global-scale observations instead of being inferred from a variety of survey-based land-cover classifications.

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