S.O. Los

A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data

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 d...

A global 1° by 1° NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI

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Comparison of Radiative and Physiological Effects of Doubled Atmospheric CO2 on Climate

The physiological response of terrestrial vegetation when directly exposed to an increase in atmospheric carbon dioxide (CO2) concentration could result in warming over the continents in addition to that due to the conventional CO2 “greenhouse effect.” Results from a coupled biosphere-atmosphere model (SiB2-GCM) indicate that, for doubled CO2 conditions, evapotranspiration will drop and air temperature will increase over the tropical continents, amplifying the changes resulting from atmospheric radiative effects. The range of responses in surface air temperature and terrestrial carbon uptake due to increased CO2 are projected to be inversely related in the tropics year-round and inversely related during the growing season elsewhere.

A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data

Abstract Global, monthly, 1° by 1° biophysical land surface datasets for 1982–90 were derived from data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7, -9, and -11 satellites. The AVHRR data are adjusted for sensor degradation, volcanic aerosol effects, cloud contamination, short-term atmospheric effects (e.g., water vapor and aerosol effects ⩽2 months), solar zenith angle variations, and missing data. Interannual variation in the data is more realistic as a result. The following biophysical parameters are estimated: fraction of photosynthetically active radiation absorbed by vegetation, vegetation cover fraction, leaf area index, and fraction of green leaves. Biophysical retrieval algorithms are tested and updated with data from intensive remote sensing experiments. The multiyear vegetation datasets are consistent spatially and temporally and are useful for studying spatial, seasonal, and interannual variability in the biosphere related to the hydrological cycle, th...

A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data

Abstract A nine-year (1982–1990) global normalized difference vegetation index (NDVI) data set with a spatial resolution of 1° by 1° and a temporal resolution of one month was compiled for use in climate studies. This data set was derived from higher resolution (5–8 km) monthly continental NDVI data sets that have been processed and archived by the Global Inventory Monitoring and Modelling Studies (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data sets were calculated from Global Area Coverage (GAC) data collected at daily intervals by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-7, -9 and -11 satellites The global 1° by 1° NDVI data set was produced to calculate land surface parameters for use within general circulation model J of the atmosphere (GCM). In view of this quantitative application, an evaluation is given of the representation by the NDVI data of the spectral properties of vegetation at the landsurface. Errors are defined as deviations f...

Sensitivity of climate to changes in NDVI

Abstract The sensitivity of global and regional climate to changes in vegetation density is investigated using a coupled biosphere–atmosphere model. The magnitude of the vegetation changes and their spatial distribution are based on natural decadal variability of the normalized difference vegetation index (NDVI). Different scenarios using maximum and minimum vegetation cover were derived from satellite records spanning the period 1982–90. Albedo decreased in the northern latitudes and increased in the Tropics with increased NDVI. The increase in vegetation density revealed that the vegetation’s physiological response was constrained by the limits of the available water resources. The difference between the maximum and minimum vegetation scenarios resulted in a 46% increase in absorbed visible solar radiation and a similar increase in gross photosynthetic CO2 uptake on a global annual basis. This increase caused the canopy transpiration and interception fluxes to increase and reduced those from the soil. T...

Mapping the land surface for global atmosphere-biosphere models: Toward continuous distributions of vegetation's functional properties

Global land surface characteristics are important boundary conditions for global models that describe exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere. Existing data sets of global land cover are based on classification schemes that characterize each grid cell as a discrete vegetation type. Consequently, parameter fields derived from these data sets are dependent on the particular scheme and the number of vegetation types it includes. The functional controls on exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere are now well enough understood that it is increasingly feasible to model these exchanges using a small number of vegetation characteristics that either are related to or closely related to the functional controls. Ideally, these characteristics would be mapped as continuous distributions to capture mixtures and gradients in vegetation within the cell size of the model. While such an approach makes it more difficult to build models from detailed observations at a small number of sites, it increases the potential for capturing functionally important variation within, as well as between, vegetation types. Globally, the vegetation characteristics that appear to be most important in controlling fluxes of water, energy, and carbon dioxide include (1) growth form (tree, shrub, herb), (2) seasonality of woody vegetation (deciduous, evergreen), (3) leaf type (broadleaf, coniferous), (4) photosynthetic pathway of nonwoody vegetation (C3, C4), (5) longevity (annual, perennial), and (6) type and intensity of disturbance (e.g., cultivation, fire history). Many of these characteristics can be obtained through remote sensing, though some require ground-based information. The minimum number and the identity of the required land surface characteristics almost certainly vary with the intended objective, but the philosophy of driving models with continuous distributions of a small number of land surface characteristics is likely to be applicable to a broad range of problems.

Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989

Images are presented that show the mean and coefficient of variation of nine years (1981-1989) of NOAA AVHRR normalized difference vegetation index (NDVI) data for the growing season (July-October) in Africa, north of the equator. The variation in the growing season NDVI is represented by the coefficient of variation image that shows the large variation in the Sahelian growing season between years. It is concluded that these images illustrate some aspects of the perspective being brought to regional and continental scale processes by coarse resolution satellite sensors and the potential of these sensors to provide consistent, long-term datasets.

Interannual variation in global‐scale net primary production: Testing model estimates

Testing estimates of year-to-year variation in global net primary production (NPP) poses some challenges. Large-scale, multiyear records of production are not readily available for natural systems but are for agricultural systems. We use records of agricultural yields at selected sites to test NPP estimates produced by CASA, a global-scale production model driven by both meteorological data and the satellite-derived normalized difference vegetation index (NDVI). We also test estimates produced by the Miami model, which has underlain several analyses of biosphere response to interannual changes in climate. In addition, we test estimates against tree ring data for one boreal site for which data from both coniferous and deciduous species were available. The agricultural tests demonstrate that CASA can reasonably estimate interannual variation in production. The Miami model estimates variation more poorly. However, differences in NDVI-processing algorithms substantially affect CASAs estimates of interannual variation. Of the four versions tested, the FASIR NDVI most closely reproduced yield data and showed the least correlation with changes in equatorial crossing time of the National Oceanic and Atmospheric Administration satellites. One issue raised is the source of the positive trends evident in CASAs NDVI-based estimates of global NPP. The existence of these trends is consistent with potential stimulation of terrestrial production by factors such as CO2 enrichment, N fertilization, or temperature warming, but the magnitude of the global trends seen is significantly greater than suggested by constraints imposed by atmospheric fluxes.

Satellite-based identification of linked vegetation index and sea surface temperature Anomaly areas from 1982–1990 for Africa, Australia and South America

Meteorological satellite data from 1982 to 1990 were used to identify areas of significant association between tropical Pacific sea surface temperature (SST) and remotely sensed normalized difference vegetation index (NDVI) ano- malies, here taken as a surrogate for rainfall anomalies. Dur- ing this period, large areas of arid and semi-arid Africa, Australia and South America experienced NDVI anomalies directly correlated to tropical Pacific SST anomalies. The results are limited by the relatively short time period of analysis. However, they confirm the disruptive effects of large-scale tropical Pacific SST variations on arid and semi- arid continental rainfall patterns in Africa, Australia, and South America, as reported previously.