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- Author or Editor: G. James Collatz x
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Abstract
This study assesses the impact of two different remote sensing–derived leaf area index (RSLAI) datasets retrieved from the same source (i.e., Advanced Very High Resolution Radiometer measurements) on a general circulation model’s (GCM) seasonal climate simulations as well as the mechanisms that lead to the improvement in simulations over several regions. Based on the analysis of these two RSLAI datasets for 17 yr from 1982 to 1998, their spatial distribution patterns and characteristics are discussed. Despite some disagreements in the RSLAI magnitudes and the temporal variability between these two datasets over some areas, their effects on the simulation of near-surface climate and the regions with significant impact are generally similar to each other. Major disagreements in the simulated climate appear in a few limited regions.
The GCM experiment using the RSLAI and other satellite-derived land surface products showed substantial improvements in the near-surface climate in the East Asian and West African summer monsoon areas and boreal forests of North America compared to the control experiment that used LAI extrapolated from limited ground surveys. For the East Asia and northwest U.S. regions, the major role of RSLAI changes is in partitioning the net radiative energy into latent and sensible heat fluxes, which results in discernable warming and decrease of precipitation due to the smaller RSLAI values compared to the control. Meanwhile, for the West African semiarid regions, where the LAI difference between RSLAI and control experiments is negligible, the decrease in surface albedo caused by the high vegetation cover fraction in the satellite-derived dataset plays an important role in altering local circulation that produces a positive feedback in land/atmosphere interaction.
Abstract
This study assesses the impact of two different remote sensing–derived leaf area index (RSLAI) datasets retrieved from the same source (i.e., Advanced Very High Resolution Radiometer measurements) on a general circulation model’s (GCM) seasonal climate simulations as well as the mechanisms that lead to the improvement in simulations over several regions. Based on the analysis of these two RSLAI datasets for 17 yr from 1982 to 1998, their spatial distribution patterns and characteristics are discussed. Despite some disagreements in the RSLAI magnitudes and the temporal variability between these two datasets over some areas, their effects on the simulation of near-surface climate and the regions with significant impact are generally similar to each other. Major disagreements in the simulated climate appear in a few limited regions.
The GCM experiment using the RSLAI and other satellite-derived land surface products showed substantial improvements in the near-surface climate in the East Asian and West African summer monsoon areas and boreal forests of North America compared to the control experiment that used LAI extrapolated from limited ground surveys. For the East Asia and northwest U.S. regions, the major role of RSLAI changes is in partitioning the net radiative energy into latent and sensible heat fluxes, which results in discernable warming and decrease of precipitation due to the smaller RSLAI values compared to the control. Meanwhile, for the West African semiarid regions, where the LAI difference between RSLAI and control experiments is negligible, the decrease in surface albedo caused by the high vegetation cover fraction in the satellite-derived dataset plays an important role in altering local circulation that produces a positive feedback in land/atmosphere interaction.
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.
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.
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.
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.
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.
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.
