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There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies. Limitations of DGVM and coupling are shown and the challenges of these methods are highlighted. During the last decade, DGVMs underwent major changes in the representation of physical and biogeochemical mechanisms such as photosynthesis and respiration processes as well as in the representation of regional properties of vegetation. However, several limitations such as carbon and nitrogen cycles, competition, land-use and land-use changes, and disturbances have been identified. In addition, recent advances in model coupling techniques allow the simulation of the vegetation–atmosphere interactions in GCMs with the help of DGVMs. Though DGVMs represent a good alternative to investigate vegetation–atmosphere interactions at a large scale, some weaknesses in evaluation methodology and model design need to be further investigated to improve the results.
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Abstract The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run‐off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run‐off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run‐off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run‐off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run‐off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run‐off, and the Zhemin hydrological region showed a significant increasing trend. Copyright © 2009 John Wiley & Sons, Ltd.
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Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.