GLOBAL OBSERVING SYSTEMS Global Climate Observing System GCOS

GLOBAL OBSERVING SYSTEMS Global Climate Observing System GCOS Global Ocean Observing System GOOS Global Terrestrial Observing System GTOS

GLOBAL TERRESTRIAL OBSERVING SYSTEM Created to provide policy makers, resource managers and researchers with access to the data needed to detect, quantify, locate, understand warn of changes (especially reductions) in the capacity of terrestrial ecosystems to support sustainable development.

GT-NET Demonstration Project ¨ It will demonstrate the benefits of linking existing networks by undertaking projects which generate products which address global change issues. ¨ Demonstration projects will serve as test beds for building collaboration and sharing experience among networks and sites, including data sharing and exchange.

GT-NET key activities · Define a clear policy on data and information access. · Share and exchange environmental data, and harmonize measurement methods. · Develop standards for metadata as well as local/regional/global in situ data sets. · Undertake demonstration projects, the initial one to estimate global net primary productivity of terrestrial ecosystems.

GT-NET: Terms of reference Develop a GT-NET documentation and information center. It will serve as repository and dissemination point for data, policies on data management, and dissemination and documentation of methods among GT-Net members. ¨ Maintain a global meta-database (TEMS) of networks and sites participating in GT- NET and make it accessible on the web. ¨ Develop a personnel database and managed e-mail server maintained via the web. ¨ Use accepted GTOS policies, for the release and exchange of meta-data. Implement a GT-NET demonstration project. This will provide global products from satellite sensors that have been validated by GT-NET sites. Users will be Climate Change scientific and technical advisory body, GT-Net sites and the MODIS team of NASA. ¨ Products include : Landcover, snowcover, leaf area index (LAI), and net primary productivity (NPP) - in a format suitable for participating sites. ¨ Similar validation data or basic climatological information, from the participating sites to the GT-Net documentation and information centre.

GT-NET: participating networks • Arab Centre for the Studies of Arid Zones and Dry Lands (ACSAD) · Arctic Monitoring and Assessment Programme (AMAP) · Chinese Ecosystem Research Network (CERN) · Consultative Group on International Agricultural Research (CGIAR) · Fluxnet (Euro. Flux, Ameriflux etc. ) · International Cooperative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystem (ICP IM) · Organismo Autonomo Parques Nationales · Réseau d’Observatoires de Surveillance Ecologique à Long Terme (ROSELT) · UK Environmental Change Network (ECN) · US Long-term Ecological Research Networks (LTER) · Worldwide Network of Biosphere Reserves (MAB-BR)

An example of global Landcover (LC), Leaf Area Index (LAI) and Net Primary Production (NPP) terrestrial variables that will be produced from the Earth Observing System (EOS) every 8 days at 1 km.

An example of global Landcover that will be produced from the Earth Observing System (EOS) every 8 days at 1 km. These data will be valuable for ecological research and for land management analysis, but first need field validation. (see Running et al. , 1994, and Justice et al. , 1998 for details).

An example of global Leaf Area Index (LAI) that will be produced from the Earth Observing System (EOS) every 8 days at 1 km. These data will be valuable for ecological research and for land management analysis, but first need field validation. (see Running et al. , 1994, and Justice et al. , 1998 for details).

An example of global Net Primary Production (NPP) that will be produced from the Earth Observing System (EOS) every 8 days at 1 km. These data will be valuable for ecological research and for land management analysis, but first need field validation. (see Running et al. , 1994, and Justice et al. , 1998 for details).

Sites contributing to multiple programs have the highest synergy and efficiency. The programs depicted are: a. GPPDI = Global Primary Production Data Initiative, b. FLUXNET = global network of eddy covariance flux towers, c. Atm FLASK = global network of atmospheric flask sampling, d. GTOS-NPP = special project to measure Net Primary Productivity in field sites worldwide, e. BIGFOOT = study to establish scaling principles for sampling vegetation over large areas, f. EOS-MODIS = Moderate Resolution Imaging Spectroradiometer on the Earth Observing System, the primary terrestrial observation sensor, g. VEMAP = Vegetation/ Ecosystem Modeling and Analysis Project, h. GAIM-NPP = IGBP project in Global Analysis Integration and Modeling study of global NPP.

Diagram of the distribution of 1 degree global vegetation cells related to precipitation and temperature, illustrating the climatic distribution of sites for complete biome sampling (from Churkina and Running, 1998). The FLUXNET “A Global Terrestrial Monitoring Network Scaling Tower Fluxes with Ecosystem Modeling and EOS Satellite Data” - S. W. Running, D. Baldocchi, W. Cohen, S. T. Gower, D. Turner, P. Bakwin, K. Hibbard sites are superimposed.

A generalized FLUXNET tower configuration diagram, showing key carbon and water fluxes measured. Atmospheric optical measurements, automated surface spectral measurements, flask sampling and stable isotope sampling can be accommodated in this framework.

Multi-year trends in monthly atmospheric CO 2 measurements for two tall towers in contrasting climates (NOAA/CMDL flask monitoring network). Note the differential activity of CO 2 within the forest canopies at 30 -50 m height dominated by biological dynamics compared to the mid-planetary boundary layer at 400 -500 m where atmospheric transport dominates. When coupled with atmospheric transport models, these data can be used to estimate CO 2 fluxes at regional scales (Bakwin et al. , 1998).

Illustration of the three spatial scales that must be considered for ecological scaling and validation. (1) Measures of vegetation parameters in the atmospheric footprint of the FLUXNET towers are required for Soil-Vegetation-Atmosphere-Transfer models to simulate the NEP measured by the towers. (2) A larger area of minimum 3 x 3 km must be sampled to provide ground truth of MODIS LAI and NPP vegetation products. (3) The representativeness of the FLUXNET tower and MODIS sampling site to the larger biome/climate complex must be evaluated by cross biome sampling. (4)After these measurement scales are co-validated, synthesis of ground data, ecosystem models and satellite data can be accomplished.

A general evaluation of the varying time scales and mechanistic complexity inherent in various current Soil-Vegetation-Atmosphere-Transfer (SVAT) models. The MODIS global NPP estimate is represented by the e, a model of minimum process complexity. Models of higher process detail are required to validate and interpret the e models, but cannot be run globally because of lack of data and computing limitations (from Landsberg and Gower, 1997).

Top figure is an example of FLUXNET carbon balance data, weekly net ecosystem exchange (NEE = NEP) measured by an eddy covariance fluxtower for a temperate deciduous forest. Bottom figure is a comparison of SVAT model simulation of NEE to observed NEE in the top figure (Baldocchi, unpublished).

An example of ecosystem fluxes at regional scales. Daily GPP and transpiration for a 1000 km 2 mountain region in the USA. These ecosystem flux products were generated by scaling stand parameters with satellite data, topography, soils, and microclimate data, integrated with an ecosystem model (Running). Model extrapolation of fluxes into complex topography is essential because fluxtowers are theoretically limited to flat terrain (from White et al. , 1998).

Critical vegetation variables of LC, LAI and NPP are measured at local and regional scales, and used to validate the global satellite based estimates. NEP measurements provide a separate validation and translation of the carbon budget based NPP to estimate commodity yields (with local weather data if available).