------------------------------------------------------------ Readme file to accompany COBRA 2004 continuous aircraft data ------------------------------------------------------------ TERMS AND CONDITIONS (Adapted from NOAA and NACP data policy) ------------------------------------------------------------ CITATION INFORMATION Use of these data in any part implies an agreement on the part of the user that individuals and/or institutions responsible for contributing to data sets used must be specifically cited in addition to a general citation of the NACP greenhouse gas database. The COBRA 2004 continuous aircraft data set should be cited as follows: Matross, D.M., A. E. Andrews, M. Pathmathevan, C. Gerbig, J. C. Lin, S. C. Wofsy, B. C. Daube, E. W. Gottieb, V. Y. Chow, J. T. Lee, C. Zhao, P. S. Bakwin, J. W. Munger, and D. Hollinger, Estimating regional carbon exchange in New England and Quebec by combining atmospheric, ground-based, and satellite data, Tellus Ser. B-Chem.Phys. Met. 58 (5): 344-358 NOV 2006. NACP investigators will include an acknowledgement in each publication or presentation arising from participation in NACP. The wording shall be similar to the following: "This study was part of the North American Carbon Program." Data providers and funding agencies may request additional acknowledgements. Upon publication of results, investigators should send the NACP Office an electronic copy of the publication. USE OF DATA These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to a greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. We rely on the ethics and integrity of the user to assure that the source(s) receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, the source(s) should be informed at the outset of the nature of this work. If the source's data are essential to the work, or if an important result or conclusion depends on their data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the source's data should be sent to the source(s) for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. RECIPROCITY AGREEMENT Use of these data implies an agreement to reciprocate. Laboratories making similar measurements agree to make their own data available to the general public and to the scientific community in an equally complete and easily accessible form. Modelers are encouraged to make available to the community, upon request, their own tools used in the interpretation of the source data, namely well documented model code, transport fields, and additional information necessary for other scientists to repeat the work and to run modified versions. Model availability includes collaborative support for new users of the models. COBRA 2004 PROJECT PURPOSE AND DESCRIPTION ------------------------------------------ Concerns over recent and future changes in atmospheric CO2 concentrations and climate make it critically important to improve understanding of where, why, and at what rates terrestrial ecosystems remove CO2 from the atmosphere, or release CO2 to the atmosphere. The key gap is our inability to link process-level biological knowledge, typically obtained for individual plants or ecosystems over short time scales, with observations and models that characterize the large spatial domain and long time scales of regional and global concern. See the following PDF for more information about Constraining the North American Carbon Budget. Research Approach With support from the National Science Foundation -- Biocomplexity in the Environment and Atmospheric Chemistry, COBRA-ME addresses this issue by obtaining comprehensive observations and using archived data for spatial and temporal scales from local to regional, and by developing an integrated ecosystem-atmosphere model that captures both slow and fast ecosystem processes, providing the capability to assimilate biological knowledge with diverse atmospheric and ecological data. The integrated model will be optimized using constraints from atmospheric and biospheric data to quantitatively link emergent properties of the terrestrial biosphere-atmosphere system with the underlying fundamental biological and physical processes. The focus will be on Maine and adjacent areas (see Maps 1 and 2), for length scales from individual trees up to 1000 km and time scales from hours to centuries. Four types of data will be acquired: (1) Stand-level assessments of forest biomass and turnover on decadal time scales from the Forest Inventory Analysis (FIA). These data integrate temporally (up to centuries) and across the landscape, but are limited to live biomass and demographic factors. (2) Measurements of surfaceatmosphere CO2 fluxes (net ecosystem exchange, NEE) and forest ecological processes at the Howland Forest eddy flux site, spanning hours to almost a decade, covering above- and below-ground processes and full carbon accounting at one location; (3) Aircraft observations of CO2 and other gases in two seasons using the Wyoming King Air. These data integrate over the region and sense CO2 fluxes from above- and below- ground processes, and fill in the "missing scale" of measurements (regional) for times from hourly to about seasonal; (4) Long-term measurements of CO2 and CO from a new tall tower in Maine to provide continuous data with a footprint of 100s to 1000s of km, at a single receptor point. The synthesis will use the Ecosystem Demography (ED) model, coupled with a mesoscale atmospheric model (RAMS) and with a Lagrangian code ("STILT") recently developed to derive atmospheric sourcereceptor relationships from analyzed ("observed") winds. ED is a mechanistic terrestrial biosphere model that predicts above- and below-ground ecosystem structure and associated fluxes of carbon, water and nitrogen, driven by climate, soil, and ecosystem properties. It has a physiology-based model describing growth, reproduction and mortality dynamics for individual plants and the plant community, coupled to models describing below-ground fluxes and pools of carbon, water and nitrogen. ED explicitly links processes ranging from rapid physiological responses of individual plants to changes in weather (hourly), soil hydrology and phenology (weekly-seasonal), and development of the vegetation assemblage and below-ground carbon (yearly-centennial). ED can be run as a stochastic, individual-based model of plant canopy dynamics, or the mean trajectory of the ensemble of grid cells can be computed using a system of size- and age-structured partial differential equations, allowing rapid scaling up of the individual-based gap simulator to the landscape scale over long times. We will optimize parameters of the coupled model to obtain a set of biospheric parameter estimates that best agree with observed quantities characterizing the comprehensive suite of spatial and temporal scales, i.e. aircraft- and tall-tower-derived constraints for regional carbon flux, long-term eddy-flux data at one site, and decadal forest inventory growth and mortality data. This is in effect an inversion of the full suite of FIA, ecological and atmospheric data to obtain quantitative characterization of the ecosystem state variables and environmental response parameters that regulate the carbon cycle in the region. Products The products of this research will be: (1) an integrated ecosystem-atmosphere model with important new capabilities; (2) comprehensive data for atmospheric concentrations and fluxes of CO2 in northern New England and southern Canada; (3) quantitative information on the environmental dependencies of photosynthesis, plant respiration, mortality, decomposition, and other factors that are consistent with both short and long-term dynamics of terrestrial carbon fluxes at regional scales as constrained by FIA, ecological, and atmospheric data. The integrated model meets a very diverse set of observed constraints and readily assimilates large-scale meteorological, atmospheric, and ecological data; it can be used to make robust predictions of future changes in the ecosystem and in the carbon cycle in response to environmental changes (climate, pollution) or human forcing (harvesting, land use changes). VARIABLE NAMES -------------- YYYYMMDD : Year, month, and day of sampling. doy : absolute day of year flt.num : Flight number, ranges between 1 and 3 UTC : Coordinated Universal Time, in seconds from midnight latitude : Latitude, in degrees longitude : Longitude, in degrees gps.latitude : GPS latitude, in degrees gps.longitude : GPS longitude, in degrees altitude : Hypsometric altitude, in meters gps.altitude : GPS altitude, in meters press.altitude : Pressure altitude (Std Atm), in meters rad1.altitude : Radar altitude (King), in meters rad2.altitude : Radar altitude (APN159), in meters airT.C : Air temperature, in degrees celsius THETA : Potential temperature, in Kelvin THETAe : Equivalent potential temperature, in Kelvin rstb : IR radiometric temperature (Heiman) in degrees celsius static.press : Static pressure, in mbar turb : MRI Eddy dissipation rate ^1/3 u-component (corrected), in MKS ndvi : NDVI vegetation index from Exotech radiometer, dimensionless CO_VUV : Carbon monoxide mixing ratio from Harvard VUV instrument, in ppbv CO_VUV.10sec : Carbon monoxide mixing ratio, time averaged over 10 seconds (moving block average), in ppbv CO2_LICOR : Carbon dioxide mixing ratio from Harvard CO2 instrument, in ppmv H2O : H2O mixing ratio measured by U. Wy, in g/kg H2O_LICOR : H2O mixing ratio measured by U. Wy LICOR, in g/kg MISSING VALUES -------------- For files in .RData format, missing values are represented by "NA". For files in .csv format, missing values are represented by "NaN"