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Carlos A. Nobre, Piers J. Sellers, and Jagadish Shukla

Abstract

Large-scale conversion of tropical forests into pastures or annual crops could lead to changes in the climate. We have used a coupled numerical model of the global atmosphere and biosphere (Center for Ocean-Land- Atmosphere GCM) to assess the effects of Amazonian deforestation on the regional and global climate. We found that when the Amazonian tropical forests were replaced by degraded grass (pasture) in the model, there was a significant increase in the mean surface temperature (about 2.5°C) and a decrease in the annual evapo-transpiration (30% reduction), precipitation (25% reduction), and runoff (20% reduction) in the region. The differences between the two simulations were greatest during the dry season. The deforested case was associated with larger diurnal fluctuations of surface temperature and vapor pressure deficit; such effects have been observed in existing deforested arms in Amazonia. The calculated reduction in precipitation was larger than the calculated decrease in evapotranspiration, indicating a reduction in the regional moisture convergence. There was also an increase in the length of the dry season in the southern half of the Amazon Basin, which could have serious implications for the reetablishment of the tropical forests following massive deforestation since rainforests only occur where the dry season is very short or nonexistent. An empirical bioclimatic scheme based on an integrated soil moisture stress index was used to derive the movement of the savanna-forest boundary in response to the simulated climate change produced by large-scale deforestation. The implications of possible climate changes in adjacent regions are discussed.

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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, W. James Shuttleworth, Jeff L. Dorman, Amnon Dalcher, and John M. Roberts

Abstract

This paper describes the operation and calibration of the simple biosphere model (SiB) of Sellers et al. using micrometeorological and hydrological measurements taken in and above tropical forest in the central Amazon basin. The paper provides:

(i) an overview of the philosophy, structure and assumptions used in the model with particular reference to the tropical forest;

(ii) a review of the experimental systems and procedures used to obtain the field data; and

(iii) a specification of the physiological parameterization required in the model to provide an adequate average description of the data.

In the course of this study, it was found that some of the existing literature on stomatal behavior for tropical tropical species is inconsistent with the observed behavior of the complete canopy in Amazonia and that the rainfall interception store of the canopy is considerably smaller than originally specified in SiB. Also the turbulent transfer model used in SiB was modified to account for the effects of height-varying foliage density. Finally, it was demonstrated that there is a distinct annual cycle in the biophysical properties of the forest canopy which influences the partitioning of energy into sensible and latent heat fluxes.

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Yongkang Xue, Heidi G. Bastable, Paul A. Dirmeyer, and Piers J. Sellers

Abstract

The simplified Simple Biosphere model (SSiB) has been validated using observed meteorological, turbulent flux, and vegetation property data from the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) over a forest clearing site. The results show that SSiB is able to simulate the observed fluxes realistically. The differences between the simulated and observed latent and sensible heat fluxes are less than 10 W m−2. Compared to previous deforestation experiments, the new vegetation dataset produces significantly different latent heat fluxes and surface temperatures in off-line and general circulation model (GCM) simulation. Using the new dataset the GCM simulated surface temperature is about 2 K higher, and the simulated latent heat flux is about 25 W m−2 lower than that generated using a previous dataset. These differences can be expected to result in substantially different responses in rainfall and atmosphere circulation. The parameters that are most significant in producing such large differences are leaf area index and soil properties. This study again demonstrates that to realistically assess the climatic impact of land surface degradation a realistic specification of the land surface conditions within GCMs is crucial.

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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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Robert E. Dickinson, Joseph A. Berry, Gordon B. Bonan, G. James Collatz, Christopher B. Field, Inez Y. Fung, Michael Goulden, William A. Hoffmann, Robert B. Jackson, Ranga Myneni, Piers J. Sellers, and Muhammad Shaikh

Abstract

Most evapotranspiration over land occurs through vegetation. The fraction of net radiation balanced by evapotranspiration depends on stomatal controls. Stomates transpire water for the leaf to assimilate carbon, depending on the canopy carbon demand, and on root uptake, if it is limiting. Canopy carbon demand in turn depends on the balancing between visible photon-driven and enzyme-driven steps in the leaf carbon physiology. The enzyme-driven component is here represented by a Rubisco-related nitrogen reservoir that interacts with plant–soil nitrogen cycling and other components of a climate model. Previous canopy carbon models included in GCMs have assumed either fixed leaf nitrogen, that is, prescribed photosynthetic capacities, or an optimization between leaf nitrogen and light levels so that in either case stomatal conductance varied only with light levels and temperature.

A nitrogen model is coupled to a previously derived but here modified carbon model and includes, besides the enzyme reservoir, additional plant stores for leaf structure and roots. It also includes organic and mineral reservoirs in the soil; the latter are generated, exchanged, and lost by biological fixation, deposition and fertilization, mineralization, nitrification, root uptake, denitrification, and leaching. The root nutrient uptake model is a novel and simple, but rigorous, treatment of soil transport and root physiological uptake. The other soil components are largely derived from previously published parameterizations and global budget constraints.

The feasibility of applying the derived biogeochemical cycling model to climate model calculations of evapotranspiration is demonstrated through its incorporation in the Biosphere–Atmosphere Transfer Scheme land model and a 17-yr Atmospheric Model Inter comparison Project II integration with the NCAR CCM3 GCM. The derived global budgets show land net primary production (NPP), fine root carbon, and various aspects of the nitrogen cycling are reasonably consistent with past studies. Time series for monthly statistics averaged over model grid points for the Amazon evergreen forest and lower Colorado basin demonstrate the coupled interannual variability of modeled precipitation, evapotranspiration, NPP, and canopy Rubisco enzymes.

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