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  • Author or Editor: Ramakrishna R. Nemani x
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Ramakrishna R. Nemani and Steven W. Running

Abstract

Infrared surface temperatures from satellite sensors have been used to infer evaporation and soil moisture distribution over large areas. However, surface energy partitioning to latent versus sensible heat changes with surface vegetation cover and water availability. We tested a hypothesis that the relationship between surface temperature and canopy density is sensitive to seasonal changes in canopy resistance of conifer forests. Surface temperature (Ts) and canopy density were computed for a 20 × 25 km forested region in Montana, from the NOAA/AVHRR for 8 days during the summer of 1985. A forest ecosystem model, FOREST-BGC, simulated canopy resistance (Rc) for the same period.

For all eight days. surface temperatures had high association with canopy density, measured as Normalized Difference Vegetation Index (NDVI) (R 2 = 0.73 − 0.91), implying that latent heat exchange is the major cause of spatial variations in surface radiant temperatures. The slope of Ts and NDVI, σ, was sensitive to changes in canopy resistance on two contrasting days of canopy activity. The trajectory of σ followed seasonal changes in canopy resistance simulated by the model. The relationship found between σ and Rc (R 2 = 0.92), was nonlinear, expected because Rc values beyond 20 s cm−1 do not influence energy partitioning significantly. The slope of Ts and NDVI, σ, could provide a useful parameterization of surface resistance in regional evapotranspiration research.

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Michael A. White, Peter E. Thornton, Steven W. Running, and Ramakrishna R. Nemani

Abstract

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed.

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Arindam Samanta, Sangram Ganguly, Eric Vermote, Ramakrishna R. Nemani, and Ranga B. Myneni

Abstract

The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October–March), cloud contamination of greenness data is pervasive; nearly the entire region lacks uncorrupted observations. Even in the dry season (July–September), nearly 60%–66% of greenness data are corrupted, mainly because of biomass burning aerosol contamination. Under these conditions, spectrally varying residual atmospheric effects in surface reflectance data introduce artifacts into greenness indices; NDVI is known to artificially decrease, whereas EVI, given its formulation and use of blue channel surface reflectance data, shows artificial enhancement, which manifests as large patches of enhanced greenness. These issues render remote sensing of Amazon forest greenness a challenging task.

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Arindam Samanta, Bruce T. Anderson, Sangram Ganguly, Yuri Knyazikhin, Ramakrishna R. Nemani, and Ranga B. Myneni

Abstract

Recent research indicates that the warming of the climate system resulting from increased greenhouse gas (GHG) emissions over the next century will persist for many centuries after the cessation of these emissions, principally because of the persistence of elevated atmospheric carbon dioxide (CO2) concentrations and their attendant radiative forcing. However, it is unknown whether the responses of other components of the climate system—including those related to Greenland and Antarctic ice cover, the Atlantic thermohaline circulation, the West African monsoon, and ecosystem and human welfare—would be reversed even if atmospheric CO2 concentrations were to recover to 1990 levels. Here, using a simple set of experiments employing a current-generation numerical climate model, the authors examine the response of the physical climate system to decreasing CO2 concentrations following an initial increase. Results indicate that many characteristics of the climate system, including global temperatures, precipitation, soil moisture, and sea ice, recover as CO2 concentrations decrease. However, other components of the Earth system may still exhibit nonlinear hysteresis. In these experiments, for instance, increases in stratospheric water vapor, which initially result from increased CO2 concentrations, remain present even as CO2 concentrations recover. These results suggest that identification of additional threshold behaviors in response to human-induced global climate change should focus on subcomponents of the full Earth system, including cryosphere, biosphere, and chemistry.

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