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Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land-atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues. [ABSTRACT FROM AUTHOR]

Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and does not match the progress in modeling the rest of the physical and biogeochemical processes. Therefore, ORC-VD is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid-to-high latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different datasets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded datasets of soil organic carbon content. The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational datasets. This multi-dataset evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid-to-high latitudes and of the new parameterization are evaluated against the above-described datasets. Significant improvements were found in the modeling of the distribution of tree functional types north of 40° N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO2 concentration since 1850, and the return frequency of cold climate extremes causing tree mortality during the spin-up phase of the model. [ABSTRACT FROM AUTHOR]

High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts onbiogeochemistry and biophysics. [ABSTRACT FROM AUTHOR]

Abstract: Present-day Sahelian vegetation in a highly anthropized semi-arid region is assessed from local to regional scales, through the joint analysis of MODIS LAI (1km2 and 8-day resolutions), daily rainfall, morphopedological and land cover datasets covering the period 2000–2008. The study area is located in northwest Senegal and consists of the “Niayes” and the northwestern “Peanut Basin” eco-regions, characterized by market gardening and rain-fed cultivated crops, respectively. The objectives are i) to analyse at pixel scale LAI time series and their relation to vegetation and soil types, ii) the estimation of phenological metrics (start of season SOS, end of season EOS, growing season length GSL) and their inter-annual variability, iii) to recognize the vegetation responses to rainfall trends (mean annual precipitation, MAP; frequency of rainy events, K; combination of MAP and K, called F). Pixel-scale analyses show that LAI time series 1) describe the actual phenology (agreeing with ground-truth AGHRYMET data), and thus can be used as a proxy for Sahelian vegetation dynamics, 2) are strongly dependent on soil types. Median maps of SOS and EOS suggest an increase of the GSL from Saint-Louis to Dakar, in agreement with both the North-South rainfall gradient and the intensification of agricultural practices around Dakar. Significant correlations (R: 0.64) between annual variation coefficient of LAI and MAP for both herbaceous crops and natural vegetation are highlighted; this correlation is reinforced (R: 0.7) using the rainfall distribution factors K and F. Rainfall thresholds allowing the SOS can be defined for each type of vegetation. These thresholds are estimated at 0–5mm, 20mm and 40mm for natural herbs, herbaceous crops and shrublands, respectively. If previous works revealed the close link between the MAP and the SOS, our results highlight that LAI dynamics are also controlled by rainfall distribution during the Monsoon season. In this study, climatic indicators are proposed for estimating vegetation dynamics and monitoring SOS. Coupling Earth Observation data, such as MODIS LAI, with rainfall data, vegetation and soil information is found to be a reliable method for vegetation monitoring and for assessing the impact of human pressure on vegetation degradation. [Copyright &y& Elsevier]