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Christine Delire, Jonathan A. Foley, and Starley Thompson

Abstract

A fully coupled atmosphere–biosphere model, version 3 of the NCAR Community Climate Model (CCM3) and the Integrated Biosphere Simulator (IBIS), is used to illustrate how vegetation dynamics may be capable of producing long-term variability in the climate system, particularly through the hydrologic cycle and precipitation. Two simulations of the global climate are conducted with fixed climatological sea surface temperatures: one including vegetation as a dynamic boundary condition, and the other keeping vegetation cover fixed. A comparison of the precipitation power spectra over land from these two simulations shows that dynamic interactions between the atmosphere and vegetation enhance precipitation variability at time scales from a decade to a century, while damping variability at shorter time scales.

In these simulations, the two-way coupling between the atmosphere and the dynamic vegetation cover introduces persistent precipitation anomalies in several ecological transition zones: between forest and grasslands in the North American midwest, in southern Africa, and at the southern limit of the tropical forest in the Amazon basin, and between savanna and desert in the Sahel, Australia, and portions of the Arabian Peninsula. These regions contribute most to the long-term variability of the atmosphere–vegetation system.

Slow changes in the vegetation cover, resulting from a “red noise” integration of high-frequency atmospheric variability, are responsible for generating this long-term variability. Lead and lag correlation between precipitation and vegetation leaf area index (LAI) shows that LAI influences precipitation in the following years, and vice versa. A mechanism involving changes in LAI resulting in albedo, roughness, and evapotranspiration changes is proposed.

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Christine Delire, Nathalie de Noblet-Ducoudré, Adriana Sima, and Isabelle Gouirand

Abstract

Two different coupled climate–vegetation models, the Community Climate Model version 3 coupled to the Integrated Biosphere Simulator (CCM3–IBIS) and the Laboratoire de Météorologie Dynamique’s climate model coupled to the Organizing Carbon and Hydrology in Dynamic Ecosystems model (LMDz–ORCHIDEE), are used to study the effects of vegetation dynamics on climate variability. Two sets of simulations of the preindustrial climate are performed using fixed climatological sea surface temperatures: one set taking into account vegetation cover dynamics and the other keeping the vegetation cover fixed. Spectral analysis of the simulated precipitation and temperature over land shows that for both models the interactions between vegetation dynamics and the atmosphere enhance the low-frequency variability of the biosphere–atmosphere system at time scales ranging from a few years to a century. Despite differences in the magnitude of the signal between the two models, this confirms that vegetation dynamics introduces a long-term memory into the climate system by slowly modifying the physical characteristics of the land surface (albedo, roughness evapotranspiration).

Unrealistic modeled feedbacks between the vegetation and the atmosphere would cast doubts on this result. The simulated feedback processes in the models used in this work are compared to the observed using a recently developed statistical approach. The models simulate feedbacks of the right sign and order of magnitude over large regions of the globe: positive temperature feedback in the mid- to high latitudes, negative feedback in semiarid regions, and positive precipitation feedback in semiarid regions. The models disagree in the tropics, where there is no statistical significance in the observations. The realistic modeled vegetation–atmosphere feedback gives us confidence that the vegetation dynamics enhancement of the long-term climate variability is not a model artifact.

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Jean-Christophe Calvet, Regina Santos-Alvalá, Geneviève Jaubert, Christine Delire, Carlos Nobre, Ivan Wright, and Joël Noilhan

Surface parameter digital maps of vegetation, soil, and relief are obtained over Rondonia, Brazil, covering the 5° ×5° region 8°–13°S, 65°–60°W. Numerical maps of the natural landscape structure have been achieved by digitizing existing 1:1 000 000 paper maps. Satellite data give information about the most recent modifications of the surface due to human activities. This mapping work is the first step of a mesoscale meteorological modeling program.

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Aaron Boone, Patricia de Rosnay, Gianpaolo Balsamo, Anton Beljaars, Franck Chopin, Bertrand Decharme, Christine Delire, Agnes Ducharne, Simon Gascoin, Manuela Grippa, Françoise Guichard, Yeugeniy Gusev, Phil Harris, Lionel Jarlan, Laurent Kergoat, Eric Mougin, Olga Nasonova, Anette Norgaard, Tristan Orgeval, Catherine Ottlé, Isabelle Poccard-Leclercq, Jan Polcher, Inge Sandholt, Stephane Saux-Picart, Christopher Taylor, and Yongkang Xue

The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land-atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region.

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