BOREAS TF-05 SSA-OJP Tower Flux Data. Summary The BOREAS TF-05 team collected tower flux data at the BOREAS Southern Study Area Old Jack Pine (SSA-OJP) site through the growing season of 1994. The data are available in tabular ASCII files. Table of Contents * 1 Data Set Overview * 2 Investigator(s) * 3 Theory of Measurements * 4 Equipment * 5 Data Acquisition Methods * 6 Observations * 7 Data Description * 8 Data Organization * 9 Data Manipulations * 10 Errors * 11 Notes * 12 Application of the Data Set * 13 Future Modifications and Plans * 14 Software * 15 Data Access * 16 Output Products and Availability * 17 References * 18 Glossary of Terms * 19 List of Acronyms * 20 Document Information 1. Data Set Overview 1.1 Data Set Identification BOREAS TF-05 SSA-OJP Tower Flux Data. 1.2 Data Set Introduction Eddy correlation flux measurements of sensible heat, latent heat, and CO2 fluxes were made above and under the canopy of the southern study area old jack pine site. 1.3 Objective/Purpose Our objective was to measure and model air-surface exchange rates of water vapor, sensible heat, and CO2 over and under a boreal forest and to study the abiotic and biotic factors that control the fluxes of scalars in this landscape. Scalar flux densities were measured with tower-mounted measurement systems. Tower- mounted flux measurement systems were installed above and below an old jack pine forest canopy. This configuration allowed us to investigate the relative roles of vegetation and the forest floor on the net canopy exchange of mass and energy. We also used the tower-mounted flux measurement system to study temporal patterns (diurnal and seasonal) of mass and energy exchange at a point in the landscape. 1.4 Summary of Parameters Key measured flux variables were net radiation, Photosynthetic Photon Flux Density (PPFD), latent heat, sensible heat, soil heat, and CO2 flux densities above and below the canopy. Key measured meteorological variables included wind speed, wind direction, air temperature, relative humidity, soil temperature, CO2 concentration, and ozone concentration. 1.5 Discussion We measured eddy flux densities of CO2, water vapor, and sensible heat and turbulence statistics above and below the old jack pine (Pinus banksiana) near Nipawin (53.92N, 104.69W), Saskatchewan, Canada. The site was relatively level and the forest stand was horizontally homogeneous throughout the area deemed as the flux footprint, a region extending over 1 km upwind. One eddy flux measurement system was mounted at 20 m above the ground. This system was about 5 to 10 m above the canopy and was mounted on the double scaffold tower provided by the BOREAS project. The sensors on a boom extended 3 m upwind of the tower, to minimize flow distortion. The azimuth angle of the boom was altered to place the instrument array into the wind. The sub-canopy flux system was mounted 2 m above the ground on a portable telescoping tower, supplied by National Oceanographic and Atmospheric Administration/Atmospheric Turbulence and Diffusion Division (NOAA/ATDD). This lower tower was placed 30 to 50 m away from the base of the main tower to avoid interference from local foot traffic. This dual flux measurement method has been successfully developed and used in a deciduous forest (Verma et al. 1986; Baldocchi et al., 1987; Baldocchi and Meyers, 1991). By analogy we feel that the dual measurement approach can be used with confidence above and below the jack pine stand. The eddy flux densities are determined by calculating the covariance between vertical velocity and scalar fluctuations (see Baldocchi et al., 1988). Wind velocity and virtual temperature fluctuations were measured with identical three- dimensional sonic anemometers. Our experience has also taught us that it is prudent to employ three-dimensional sonic anemometers in forest meteorology applications. When deploying an anemometer over a forest, it is nearly impossible to physically align the vertical velocity sensor normal to the mean wind streamlines; sensor orientation problems typically arise due to sloping terrain and to the practice of extending a long boom upwind from a tower. By deploying a three-dimensional anemometer, we are able to make numerical coordinate rotations to align the vertical velocity measurement normal to the mean wind streamlines. CO2 and water vapor fluctuations was measured with an open-path, infrared absorption gas analyzer, developed at NOAA/ATDD (Auble and Meyers, 1992). Fast response meteorology data were digitized, processed and stored using a microcomputer-controlled system and in-house software. Digitization of sensor signals was performed with hardware on the sonic anemometer. Sensor data were output at 10 Hz. Spectra and co-spectra computations showed that these sampling rates were adequate for measuring fluxes above and below forest canopies (Anderson et al., 1986; Baldocchi and Meyers, 1991; Amiro, 1990a). Scalar fluctuations were computed, real-time, using a running mean removal method (McMillen, 1988). Analytical and numerical tests showed the recursive filter time constant of 400 s yielded fluxes similar to those computed with the conventional Reynolds averaging approach. Mass and energy flux covariances were stored at half-hour intervals on high capacity Bernoulli removable disk media. Instantaneous data were recorded periodically. Proper interpretation of experimental results and model evaluation requires detailed ancillary measurements of many environmental variables. Energy balance components that were measured include the net radiation balance, soil heat flux, and canopy heat storage. 1.6 Related Data Sets Tower flux measurements made at other sites: BOREAS TF-04 SSA-YJP Tower Flux Data BOREAS TF-09 SSA-OBS Tower Flux Data BOREAS TF-11 SSA-Fen Tower Flux Data BOREAS TF-08 NSA-OJP Tower Flux Data Other measurements made at the SSA-OJP site: BOREAS AFM-07 SRC Surface Meteorological and Radiation Data BOREAS TE-06 Forest Biophysical Measurements BOREAS TGB-10 Oxidant Concentration Data over the SSA BOREAS TGB-10 Oxidant Flux Data over the SSA 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. Dennis Baldocchi and Dr. Christoph Vogel Atmospheric Turbulence and Diffusion Division (ATDD), National Oceanographic and Atmospheric Administration (NOAA) 2.2 Title of Investigation Experimental and Modeling Studies of Water Vapor, Sensible Heat, and CO2 Exchange Over and Under a Boreal Forest. 2.3 Contact Information Contact 1 --------- Dennis Baldocchi NOAA/ATDD Oak Ridge, TN (423) 576-1243 (423) 576-1327 (fax) baldocchi@atdd.noaa.gov Contact 2 --------- Christoph Vogel NOAA/ATDD Oak Ridge, TN (423) 576-1243 (423) 576-1327 (fax) Contact 3 --------- Karl F. Huemmrich University of Maryland NASA Goddard Space Flight Center Greenbelt, MD (301) 286-4862 (301) 286-0239 (fax) Karl.Huemmrich@gsfc.nasa.gov 3. Theory of Measurements Micrometeorological Measurement Theory. Micrometeorological methods allow one to measure short-term flux densities (moles per unit area and time) of scalar compounds to and from forest ecosystems. The equation describing the conservation of mass provides the basic framework for applying micrometeorological methods to measure the vertical flux density of S (F) between the surface and the atmosphere. The conservation equation describes the factors that control the time rate of change of a scalar mixing ratio in a controlled volume. To grasp an understanding of this relationship, let's consider the factors controlling the water level in a bath tub. The water level will remain the same if the amount of water flowing into the tub equals that removed through the drain. In the atmosphere, for example, the concentration of a sulfur compound will remain unchanged if the mean and turbulent fluxes entering a controlled volume equal those leaving (the flux divergence is zero). On the contrary, concentrations will vary with time if the flux of sulfur entering the system differs from that leaving, as when plume impaction occurs. How can we apply the conservation equation to measure fluxes? In the field, we measure fluxes at a given height above the surface, but we want to know the rate CO2 is taken up by the surface below. The vertical flux density of S will remain unchanged with height if the underlying surface is: 1) homogeneous and extends upwind for a considerable distance (this requirement ensures the development of a surface boundary layer); 2) if scalar concentrations are steady with time; and 3) if no chemical reactions are occurring between the surface and the measurement height. Condition 1 can be met easily through proper site selection. As a rule of thumb, the site should be flat and horizontally homogeneous for a distance between 75 and 100 times the measurement height (Monteith and Unsworth, 1990). Condition 2 is met often for many scalars. Non-steady conditions are most apt to occur during abrupt transitions between unstable and stable atmospheric thermal stratification, during the passage of a front, or from the impaction of a plume from nearby power plants. Eddy Correlation Technique. The eddy correlation method is a direct method for measuring flux densities of scalar compounds. The vertical flux density is proportional to the covariance between vertical wind velocity (w) and scalar concentration fluctuations (c). A wide range of turbulent eddies contribute to the turbulent transfer of material. Proper implementation requires that we sample across this spectrum of eddies. In frequency domain, eddies contributing to turbulent transfer having periods between 0.5 and 2000 seconds typically contribute to mass and energy exchange (Wesely et al. 1989). Hence, wind and chemical instrumentation must be capable of responding to high frequency fluctuations. Computer-controlled data acquisition systems must sample the instrumentation frequently to avoid aliasing, and average the signals over a sufficiently long period to capture all the contributions to the transfer. On applying the covariance relation, it is assumed implicitly that the mean vertical flux density is perpendicular to the streamlines of the mean horizontal wind flow. Consequently, the mean vertical velocity, perpendicular to the streamlines of the mean wind flow, equals zero. In practice, non-zero vertical velocities occur due to instrument mis-alignment, sloping terrain, and density fluctuations. These effects must be removed when processing the data, otherwise mean mass flow can introduced a bias error (see Businger, 1986; Baldocchi et al., 1988). Evaluating the accuracy of the eddy correlation method is complicated. Factors contributing to instrument errors include time response of the sensor, signal to noise ratio, sensor separation distance, height of the measurement, and signal attenuation due to path averaging and sampling through a tube. Natural variability is due to non-steady conditions and surface inhomogeneities. Under ideal conditions, natural variability exceeds about +/-10%, so it is desirable to design a system with an error approaching this metric. Moore (1986) discusses transfer functions for sensor response time and separation distance. We performed preliminary calculations of transfer function integrals. Corrections due to sensor time constants and separation are less than a few percent. Hence, we decided not to make transfer function to our flux measurements; our experimental design minimized the need for such corrections since we used an open path infrared gas analyzer and a sonic anemometer. Furthermore, these instruments were placed over a tall rough forest, so small distances in physical displacement have little impact on the measurement of scalar flux densities. 4. Equipment 4.1 Sensor/Instrument Description 4.1.1 Collection Environment Measurements were collected continuously through the growing season of 1994. The tower extended above the canopy and was exposed to direct sunlight and weather. As the site only operated during the growing season, the temperature conditions were mild, and freezing conditions were not encountered. 4.1.2 Source/Platform Above canopy measurements were made from a 26 meter double scaffold walk up tower. The sub-canopy flux system was mounted 2 m above the ground on a portable telescoping tower. This lower tower was placed 30 to 50 m away from the base of the main tower to avoid interference from local foot traffic. Eddy correlation flux measurements were made using a triple-axis Applied Technology sonic anemometer and an infrared absorption spectrometer. The sonic anemometer measured vertical (w) and horizontal (u,v) wind velocity and air temperature (T). This anemometer model provides digital output at a rate of 10 Hz. The infrared absorption spectrometer measured water vapor and CO2 density fluctuations. The sensor responds to frequencies up to 15 Hz, has low noise and high sensitivity (20 mg m^-3 volt^-1). The sensor is rugged and experiences little drift over several weeks of continuous operation. Soil heat flux density was measured by averaging the output of three soil heat flux plates (Radiation Energy Balance Systems (REBS) model HFT-3, Seattle, WA). They were buried 0.01 m below the surface and were randomly placed within a few meters of the flux system. Soil temperatures were measured with two multi-level thermocouple probes. Sensors were spaced logarithmically at 0.02, 0.04, 0.08, 0.16 and 0.32 m below the surface. Three thermocouples were used to measure bole temperatures. Sensors were placed about 1 cm into the bole and were azimuthally spaced across a tree at breast height. Canopy heat storage was calculated by measuring the time rate of change in bole temperature in the tree trunks. Photosynthetically active photon flux density and the net radiation balance were measured above the forest with a quantum sensor (LiCor model LI-190S) and a net radiometer (Swissteco Model S-1 or REBS model 6), respectively. A more detailed experimental design was implemented at the forest floor because the solar radiation field below a forest canopy is highly variable (Baldocchi and Collineau, 1994). To account for this variability, measurement of solar radiation components were made using an instrument package that traversed slowly across a 14.5 m long track. Air temperature and relative humidity were measured with appropriate sensors (Campbell model 207 and Vaisala, model HMP-35A). Wind speed and direction were measured with a propeller wind speed/direction monitor (RM Young model 05701). Infrared canopy temperature was measured with an Everest radiation thermometer (model 112C). The sensor was pointed south and oriented at 45 degrees. Ancillary data were acquired and logged on a Campbell CR-21x data logger. Half- hour averages were stored on a computer, to coincide with the flux measurements. CO2 concentration profiles were measured with a LiCor 6262 infrared gas analyzer. Samples were drawn at 22, 17, 12, 6 and 2 m. During the first two intensive field campaigns we sampled the 22 and 17 m levels exclusively between 0600 and 1800 hours, and the full profile at night. During the third intensive field campaign the whole profile was sampled continuously. Solenoid valves were switched every 30 s. Data from the third IFC are most reliable because we modified the system and measured cell pressure and temperature in addition to CO2 concentration. The eddy correlation flux systems were digitized on the tower using the analog to digital converter of the ATI sonic anemometer. The A/D board was a 12 bit system. Digital signals for the three orthogonal wind velocity components, temperature, humidity, CO2, and ozone were transmitted to a 386 computer in the field lab. In house software (FLUX.EXE) displayed the data real-time on screen for scrutiny, computed fluctuations from means and 30 minute flux covariances. Campbell 21-X data loggers were used to sample environmental variables. These data loggers were connected to another 386 computer via digital line and were interrogated every 30 minutes using Campbell Scientific software (TELCOM.EXE). 4.1.3 Source/Platform Mission Objectives To measure fluxes of sensible and latent heat and CO2 using the Eddy correlation technique, the radiation balance, and ground heat flux . Sampling, recording, and near real-time processing of the data were done with computer based data loggers. 4.1.4 Key Variables Carbon dioxide, solar, sensible and latent heat flux densities above the forest and ground surface, soil heat flux at the soil surface and canopy heat storage. 4.1.5 Principles of Operation Sonic Anemometer: Three-dimensional orthogonal wind velocities (u,v and w) and virtual temperature (Tv) were measured with a sonic anemometer (Applied Technology, model SWS-211/3K, Boulder, CO). The pathlength between transducers was 0.15 m. The sensor software corrected for transducer shadowing effects (see Kaimal et al. 1990). Virtual temperature heat flux was converted to sensible heat flux using algorithms described by Kaimal and Gaynor (1991) and Schotanus et al. (1983). Infrared Absorption Spectrometer: Water vapor and CO2 concentrations were measured with an open-path infrared absorption spectrometer. Details and performance characteristics of the spectrometer are discussed by Auble and Meyers (1992). In brief, the IR beam was reflected three times between mirrors separated by 0.20 m, making an 0.80 m absorption path. The response time of the sensor was less than 0.1 s, sensor noise was less than 300 µg m-3 and its calibration was steady (it varied +/- 3% during the course of the experiment). The sensor was calibrated periodically with three standard CO2 gases mixed in air, whose accuracy was +/- 1%. Soil Heat Flux Transducer: An encapsulated thermopile yields a voltage output proportional to the temperature difference across the top and bottom surfaces. The device has been calibrated in terms of heat flux through transducer corresponding to the observed temperature difference. 4.1.6 Sensor/Instrument Measurement Geometry One eddy flux measurement system was placed at 20 m above the ground, on a double scaffold tower provided by the BOREAS project. The instruments were mounted on a boom that extended 3 m upwind of the tower, to minimize flow distortion. The boom was 7 m above the mean tree height and was positioned in the constant flux layer. The azimuth angle of the boom was altered periodically to place the instrument array into the predominant wind direction. Another eddy flux system was positioned near the floor of the canopy. The instruments were 1.8 m above the ground. This location was in the stem space of the canopy and virtually no foliage was present between the canopy floor and the measurement height. 4.1.7 Manufacturer of Sensor/Instrument Sonic anemometer: Applied Technologies 6395 Gunpark Dr. Unit E Boulder, CO 80301 Soil heat transducer: Radiation Energy Balance Systems (REBS) PO Box 15512 Seattle, WA 98115-0512 Elmer N.J. 08318 Radiation Thermometer: Everest Interscience Inc. P.O. Box 3640 Fullerton CA 92634 Net Radiometer: REBS PO Box 15512 Seattle, WA 98115-0512 Elmer N.J. 08318 Data logging system: Campbell Scientific P. O. Box 551, Logan, UT 84321 4.2 Calibration 4.2.1 Specifications The net radiometer, quantum sensors, and soil heat flux plates were calibrated by the manufacturer. A net radiometer and quantum sensor were new and were used as a transfer standard. The instruments were sent to the manufacturer after Boreas IFC-3 for re-calibration. The net radiometer calibrations did not change over the course of the experiment. Flux and mean concentration CO2 analyzers were calibrated against standard calibration gases. The gases were referenced to the Atmospheric Environment Service (AES), World Meteorological Organization (WMO) standards, and the BOREAS standard gases. The flux sensors were calibrated 2 to 3 times a week. Three reference gases were used to double check linearity. The concentrations of the standard were about 322, 350 and 400 ppm. The zero and span of the LiCor infrared gas analyzers were measured nearly every day. The water vapor sensor was calibrated against mixed air samples and referenced to data from a chilled mirror dew point hygrometer. Stability of the water vapor calibration was checked in the field by comparing the instrument sensitivity to the output of a Vaisala relative humidity sensor. The relative humidity sensor was new and calibrated by the manufacturer. We also compared the output of the Vaisala relative humidity sensor against a redundant dew point hygrometer. Both sensors yielded identical humidity measurements. Sonic anemometer: supplied by manufacturer. 1.0 m s-1/V with sonic pathlength 0.15 m. Carbon dioxide: about 30 mg m-3 volt-1. Water vapor density fluctuations: varies with vapor density. 2.0 g m-3 volt-1 at 6 C and 3 g m-3 volt-1 at 14 C. Soil heat transducer: about 40 W m-2 mv-1 net radiation: 12 W m-2 mv-1 quantum flux density: 180 *mol m-2 s-1 mv-1 ozone: 28 ppb/volt 4.2.1.1 Tolerance Solar and net radiation: 1 W m-2. Air temperature fluctuations: 0.1 K. Vertical wind velocity fluctuations: 0.01 m s-1. Surface radiative temperature; 0.1 K. 4.2.2 Frequency of Calibration The net radiometers were calibrated before and after the 1994 field campaign. The calibration coefficient did not change. The flux sensors were calibrated 2 to 3 times a week. The zero and span of the LiCor infrared gas analyzers were measured nearly every day. 4.2.3 Other Calibration Information None. 5. Data Acquisition Methods The eddy correlation flux systems were digitized on the tower using the analog to digital converter of the ATI sonic anemometer. The A/D board was a 12 bit system. Digital signals for the three orthogonal wind velocity components, temperature, humidity, CO2, and ozone were transmitted to a 386 computer in the field lab. In-house software (FLUX.EXE) displayed the data real-time on screen for scrutiny, computed fluctuations from means and 30 minute flux covariances. Campbell 21-X data loggers were used to sample environmental variables. These data loggers were connected to another 386 computer via a digital line and were interrogated every 30 minutes using Campbell Scientific software (TELCOM.EXE). 6. Observations 6.1 Data Notes None Available. 6.2 Field Notes None Available. 7. Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage All data were collected at the BOREAS SSA-OJP site. The site is located at latitude 53.91634° N, longitude 104.69203° W, and elevation of 579 m. 7.1.2 Spatial Coverage Map Not Applicable. 7.1.3 Spatial Resolution The data represent point source measurements taken at the given location. The site was relatively level and the forest stand was horizontally homogeneous throughout the area deemed as the flux footprint, a region extending over 1 km upwind. 7.1.4 Projection Not Applicable. 7.1.5 Grid Description Not Applicable. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage Nearly continuous flux measurements from 23-May to 16-Sep-1994. 7.2.2 Temporal Coverage Map All data were collected at the SSA-OJP site. 7.2.3 Temporal Resolution Data are reported at a 30 minute average. 7.3 Data Characteristics Data characteristics are defined in the companion data definition file (tf5flxd.def). 7.4 Sample Data Record Sample data format shown in the companion data definition file (tf5flxd.def). 8. Data Organization 8.1 Data Granularity All of the SSA-OJP Tower Flux Data are contained in one dataset. 8.2 Data Format The data files contain numerical and character fields of varying length separated by commas. The character fields are enclosed with single apostrophe marks. There are no spaces between the fields. Sample data records are shown in the companion data definition file (tf5flxd.def). 9. Data Manipulations 9.1 Formulae 9.1.1 Derivation Techniques and Algorithms Sample computer code displaying processing methods: Sensible heat flux 1) Test if flux covariances are significantly different from zero for 18000 samples 'Is wT non-zero? IF TT > 0 THEN SIGT = TT ^ .5 RWT = WT / (SIGW * SIGT) ELSE RWT = 0 END IF IF ABS(RWT) < .0146 THEN WT = 0 'correct virtual temperature heat flux from sonic 'to actual heat flux. We apply the formula of Kaimal and Gaynor 1991. Bound. Layer Met. 'The Schotanus et al. 1983 BLM correction is not needed for the 'ATI sonic (-2 T u w'u'/c^2) SPECIFIC_HUMIDITY = ABSOLUTE_HUMIDITY / (AIR_DENSITY * 1000) 'Heat capacity of air weight by the moist and dry air contributions CP_AIR = 1010 * AIR_DENSITY + 4182 * ABSOLUTE_HUMIDITY SIG = ABSOLUTE_HUMIDITY/(1000 * AIR_DENSITY) SIGTOT = ABSOLUTE_HUMIDITY/1000 / (ABSOLUTE_HUMIDITY/1000 + AIR_DENSITY) 2) Correct the sonic derived sensible heat fluxes to the actual sensible heat flux. 'Note: WTCORR should be in the WE calculation, hence ' looping is needed between sensible and latent heat flux computations WTGUESS = WT_SONIC CCC = 0 NEWWE: WTCORR = (WT_SONIC - .51 * TK * WQQ) 'K m s-1 IF ABS(WTCORR - WTGUESS) > .00001 THEN CCC = CCC + 1 IF CCC > 10 THEN GOTO OUTWE WTGUESS = WTCORR GOTO NEWWE END IF OUTWE: SENSIBLE_HEAT_FLUX = WTCORR * CP_AIR 'W m-2 Water vapor and latent heat flux densities 1) 'Is wq covariance non-zero? IF WW > 0 THEN SIGW = WW ^ .5 ELSE SIGW = 9999 END IF IF QQ > 0 THEN SIGQ = QQ ^ .5 RWQ = WQ / (SIGW * SIGQ) ELSE RWQ = 0 END IF IF ABS(RWQ) < .0146 THEN WQ = 0 WQX = WQ_COVARIANCE * H2OCAL 'g m-2 s-1 2) Compute latent heat flux by considering temperature variations in the latent heat of vaporization. LFUSION = 334000 LATENT_HEAT = 3149000 - 2370 * TAIR_K IF TK < 273 THEN LATENT_HEAT = LAMBDA + LFUSION LAMBDA = LATENT_HEAT / 1000 3) Apply Webb et al density correction for E' Units are Ecorr (g m-2 s-1) ECORR = (1 + SIG * 1.6077) * (WQX + ABSOLUTE_HUMIDITY * WTGUESS / TK) 'factor of 1000 is needed to change ECORR from g m-3 to' kg m-3, so units cancel when divided by rhoa WQQ = ECORR * (1 - SPECIFIC_HUMIDITY) / (1000 * (AIR_DENSITY + ABSOLUTE_HUMIDITY/1000)) ' m s-1 LATENT_HEAT_FLUX= LAMBDA * WQX (W m-2) CO2 flux densities WC = WC_COVARIANCE * CO2CAL 'mg m-2 s-1 1) Apply Webb et al. corrections for CO2 and latent heat fluxes ' CO2CONC = CO2_DENSITY * 8.314 * TK / (PRESS_KPA * 44.01) ' NU = CO2CONC* 44.01 / (28.96 * 1000000) SIG = ABSOLUTE_HUMIDITY / (AIR_DENSITY * 1000) SIGTOT = (ABSOLUTE_HUMIDITY / 1000) / (AIR_DENSITY + ABSOLUTE_HUMIDITY / 1000) TERM1 = 1.6077 * NU * LATENT_HEAT_FLUX / LAMBDA H=SENSIBLE_HEAT_FLUX TERM2 = (1 + SIG * 1.6077) * RHOC * H / (TK * AIR_DENSITY * 1005) FC_CORR = WC + TERM1 + TERM2 Canopy heat storage 1) Define canopy biomass and representative specific heat by weighting the water and cellulose contributions. a) define volume of vegetation as the product of canopy height and its basal area. JACK PINE: height= 13.5 m; basal area=22 m2/ha ' b) heat transfer coefficient is volume x Cp/time for 30 minutes CP=4.175 MJ M^-3 C^-1 for water. CP=2.500 MJ ,-3 C-1 for cellulose c) Computed canopy heat capacity using Gower's (TE-06) biomass data i) bole contribution PLANT_COEF = 19.9 'W m-2 C-1 s-1 CG1 = DELTA_BOLE_TEMPERATURE * PLANT_COEF TBOLOLD = TBOL ii) heat storage in the air layer DELTA_AIR_TEMPERATURE = TAVG - TOLD iii) BRANCH AND NEEDLE HEAT STORAGE CG1A = 3.9 * DELTAIR CG2 = DELTAIR * 12.8 '20x1.15x1005/1800s TOLD = TAVG iv) latent heat storage in air layer of canopy DELTA_RHOV = RHOVAVG - RHOVOLD CG3 = 27.1 * DELRHOV '20x2442/1800 RHOVOLD = RHOVAVG CANOPY_HEAT_STORAGE = CG1 + CG1A + CG2 + CG3 9.2 Data Processing Sequence 9.2.1 Processing Steps Flux covariances are computed in the field by the data acquisition program. Back at home, calibrations are double and triple checked by comparing old and new calibrations and by comparing the mean response of the scalar flux sensors against independent meteorological instruments. Tests are made for energy balance closure to ensure that the data are of reliable quality. Programs are then run to delete periods when the sensors were off line, off range, being maintained, or un-reliable due to rain or instrument malfunction. BORIS processed these data by: 1) Reviewing the initial data files and loading them on-line for BOREAS team access, 2) Designing relational data base tables to inventory and store the data, 3) Loading the data into the relational data base tables, 4) Working with the team to document the data set, and 5) Extracting the data into logical files. 9.2.2 Processing Changes None 9.3 Calculations 9.3.1 Special Corrections/Adjustments Eddy fluctuations: CO2_DENSITY = CO2PPM * AIR_DENSITY * 44 / 29 'mg m-3 9.3.2 Calculated Variables None 9.4 Graphs and Plots None 10. Errors 10.1 Sources of Error Factors contributing to instrument errors include time response of the sensor, signal to noise ratio, sensor separation distance, height of the measurement, and signal attenuation due to path averaging and sampling through a tube. Natural variability is due to non-steady conditions and surface inhomogeneities. Under ideal conditions natural variability exceeds about +/-10%, 10.2 Quality Assessment 10.2.1 Data Validation by Source Surface energy balance was tested by comparing measurements of available energy against the sum of latent and sensible heat flux. Tests over the jack pine stand showed that our eddy flux system closes the surface energy balance within 12% and that this degree of closure is comparable with the state-of-art demonstrated in the literature (Verma et al., 1990). Several independent checks have been made on components of this data set. The Vaisala humidity sensor was compared against a dew point hygrometer and the comparison was excellent. The output of the net radiometer was compared against measurements made on Tim Crawford's airplane (AFM-01). There was no bias between the measurements, suggesting that the impact of the net radiometer seeing the tower structure was small. 10.2.2 Confidence Level/Accuracy Judgment The following are the best estimates of accuracy for a single flux estimate: Net radiation +/- 4 to 7% Soil heat flux +/- 10% Latent heat flux +/- 15 to 20 % or +/-30 W m^2, which ever is larger Sensible heat flux +/- 15 to 20 % or +/-30 W m^2, which ever is larger None of these estimates addresses the variability of flux estimates from site to site. Detection limit of CO2 flux system: 0.01 mg m^-2 s^-1 The intermittency of turbulence limits the sampling error of turbulent fluxes to 10 to 20%. On top of this we have to deal with measurement errors. Fortunately, lots of statistically averaging reveals stable fluxes and small bias errors (< 12%) on the surface energy fluxes. 10.2.3 Measurement Error for Parameters None given. 10.2.4 Additional Quality Assessments None given. 10.2.5 Data Verification by Data Center Data were examined to check for spikes, values that are four standard deviations from the mean, long periods of constant values, and missing data. 11. Notes 11.1 Limitations of the Data Flux data were collected during the growing season of 1994, no wintertime data were acquired. 11.2 Known Problems with the Data Data have been rejected when: 1) the voltages of the CO2 and water vapor sensor were nearly or off scale; 2) when the log book denoted problems with specific sensors; 3) when the gas analyzer was calibrated or when the instrument boom was moved; 4) when flux runs were less than 15 minutes; 5) when the mean vertical velocity of the sonic anemometer exceeded 0.3 m/s (an indication of water on the transducer or continued spiking; 6) when friction velocity exceeded 1.5 m/s. Values during these periods have been assign values of -999 (or -6999). The third order screening of the data deleted data that were several standard deviations greater than the population means and were true spikes and outliers. We deleted: 1) LE fluxes greater than 350 W m-2 and less than -50 W m-2; 2) CO2 fluxes greater than 0.50 mg m-2 s-1 and less than -0.75 mg m-2 s-1. Be careful when using data during WET periods. These periods have not been screen and are retained to complete the record. Problems may occur because the domes on the radiometers were wet and water films may be covering optics of the infrared gas analysis and the transducer of the sonic anemometer. When using these data to test models we recommend that data from wet periods not be included! Be careful about using data when wind was blowing through the tower. We have not deleted these data from the record. Closure of the surface energy balance was diminished during these periods and an upwind bias on the vertical wind velocity, due to aerodynamic distortion from the tower, was noted. Wind directions to be cautious about are in the zone between 350 and 60 degrees. When using these data to test models we recommend that data from non-ideal wind directions be omitted. Mean CO2 concentrations were assigned values of 350 when the sensor was out of range. Also be cautious about using the mean CO2 concentrations. We had some zero and span drifting problems with the IRGA. Typical CO2 concentration midday should be on the order of 330 and 350 ppm. Data exceeding 400 ppm would have by-passed data filters and would be spurious. We have double checked the mean CO2 concentration calibrations and have attempted to compensate for drift from calibration to calibration. Sometimes the drift was small. Other times it was between -10 and +10 ppm over a few days. At this point the relative change in CO2 from hour to hour is OK. Use the absolute values with much skepticism and caution. I would not use these data to assess the seasonal change in CO2 over the boreal region. Periods when calibrating or moving boom, given as day number and ending time of the half hour observation (Central Standard Time) 143 1300 145 1130 150 1100 150 1300 151 0930 153 1030 154 0930 154 1630 155 1100 156 1130 161 0930 163 1030 164 0930 164 1030 164 0930 164 1000 179 1530 188 1300 189 1300 189 1330 from 200 1400 through 200 1500 201 1130 204 1230 210 1000 211 0930 215 1130 242 1500 242 1300 245 1400 from 247 1400 through 248 1000 253 1100 Periods when net radiometer and incident PAR sensor where shaded by the tower, given as day number and ending time of the half hour observation (Central Standard Time): from 145 0800 through 145 0930 from 146 0800 through 146 0930 from 147 0800 through 147 0930 from 148 0800 through 148 0930 from 149 0800 through 149 0930 from 150 0800 through 150 0930 from 151 0800 through 151 0930 CO2 concentration data I am soft on the absolute quality of the LiCor CO2 data. The instrument I was using was new to me and seemed to be unstable. I calibrated almost everyday, but the zero and span seemed to drift. Some first order corrections are being made to put the values in line with reality, but more work needs to be done. I am especially skeptical because I am now using a brand new sensor in Oak Ridge and automatically zeroing and spanning the sensor every day and have not seen the zero or span change for 3 months!! Now spurious CO2 values may be real if smoke from forest fires were a problem. Hence my consternation about accepted elevated CO2 values. 11.3 Usage Guidance CAUTION should be exercised when using flux data for several hours surrounding dawn and dusk, since these are periods of unsteady conditions. In addition, nighttime data should be closely scrutinized. 11.4 Other Relevant Information None. 12. Application of the Data Set These data are useful for the study of water, energy, and carbon exchange in a mature jack pine forest. 13. Future Modifications and Plans None 14. Software 14.1 Software Description Some samples of code used in the analysis are shown in section 9.1.1. 14.2 Software Access None given. 15. Data Access 15.1 Contact Information Ms. Beth Nelson BOREAS Data Manager NASA GSFC Greenbelt, MD (301) 286-4005 (301) 286-0239 (fax) Elizabeth.Nelson@gsfc.nasa.gov 15.2 Data Center Identification See Section 15.1. 15.3 Procedures for Obtaining Data Users may place requests by telephone, electronic mail, or fax. 15.4 Data Center Status/Plans The TF-05 tower flux data are available from the EOSDIS ORNL DAAC (Earth Observing System Data and Information System) (Oak Ridge National Laboratory) (Distributed Active Archive Center). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory (865) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products None 16.2 Film Products None 16.3 Other Products The data are available as tabular ASCII text files. 17. References 17.1 Platform/Sensor/Instrument/Data Processing Documentation None 17.2 Journal Articles and Study Reports Auble, D. L. and T. P. Meyers. 1992. An open path, fast response infrared- absorption gas analyzer for H2O and CO2. Boundary Layer Meteorology. 59:243-256. Baldocchi, D.D., B.B. Hicks, and T. P. Meyers. 1988. Measuring biosphere- atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69:1331-1340. Baldocchi, D.D. and C.A. Vogel. 1995. Energy and CO2 flux densities above and below a temperate broad-leaved forest and boreal pine forest, Tree Physiology, Volume 16, March 1995, pp. 5-16. Baldocchi, D.D., C.A. Vogel and B. Hall. 1997. Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest, Agricultural and Forest Meteorology 83: 147-170. Baldocchi, D.D., C.A. Vogel, and B. Hall. 1997. Seasonal variation of energy and water vapor exchange rates above and below a boreal jack pine forest canopy, Journal of Geophysical Research, BOREAS Special Issue, 102(D24), Dec. 1997, pp. 28939-28952. Businger, J.A. 1986. Evaluation of the accuracy with which dry deposition can be measured with current micrometeorological techniques. J. Clim. and Appl. Meteorol. 25:1100-1124. Kaimal, J. C., and J. E. Gaynor. 1991. Another look at sonic thermometry, Boundary Layer Meteorology. 56:401-410. Kaimal, J. C., J. E. Gaynor, H. A. Zimmerman, and G. A. Zimmerman. 1990. Minimizing flow distortion errors in a sonic anemometer, Boundary Layer Meteorology. 53:103-115. Monteith, J. L., and M. H. Unsworth. 1990. Principles of Environmental Physics. Edward Arnold, London. Moore, C. J. 1986. Frequency response corrections for eddy correlation measurements, Boundary Layer Meteorology. 37:17-35. Schotanus, P., F.T.M. Nieuwstadt, and H.A.R. De Bruin. 1983. Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorology 26: 81-93. Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0, NASA BOREAS Report (EXPLAN 94). Sellers, P., F. Hall, and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society. 76(9):1549-1577. Wesely, M. L., D. H. Lenschow, and O. T. Denmead. 1989. Flux measurement techniques. In: Global Tropospheric Chemistry - Chemical Fluxes in the Global Atmosphere. D. H. Lenschow and B. B. Hicks (eds.). Report of the Workshop on the Measurements of Surface Exchange and Flux Divergence of Chemical Species in the Global Atmosphere. pp. 31-46. Prepared by the National Center for Atmospheric Research, Boulder, CO. 107 pp. 17.3 Archive/DBMS Usage Documentation None 18. Glossary of Terms None 19. List of Acronyms AES - Atmospheric Environment Service AFM - Aircraft Flux and Meteorology ATDD - Atmospheric Turbulence and Diffusion Division BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System DAAC - Distributed Active Archive Center EOS - Earth Observing System EOSDIS - EOS Data and Information System GSFC - Goddard Space Flight Center IFC - Intensive Field Campaign IRGA - Infrared Gas Analyzer NASA - National Aeronautics and Space Administration NOAA - National Oceanographic and Atmospheric Administration OJP - Old Jack Pine ORNL - Oak Ridge National Laboratory PPFD - Photosynthetic Photon Flux Density REBS - Radiation Energy Balance Systems SSA - Southern Study Area URL - Uniform Resource Locator WMO - World Meteorological Organization 20. Document Information 20.1 Document Revision Date Written: 05-MAR-1996 Revised: 02-JUL-1998 20.2 Document Review Date(s) BORIS Review: 20-MAY-1998 Science Review: 20.3 Document ID None. 20.4 Citation Scalar and energy flux data (e.g. CO2, water vapor, sensible heat and solar energy): Co-authorship if there is extensive use of the data. Acknowledgment if a few data are used to make a supporting point. Meteorological data: Acknowledgment: Field data obtained and prepared by Dennis Baldocchi and Christoph Vogel. Atmospheric Turbulence and Diffusion Division, NOAA PO Box 2456, Oak Ridge, TN 37831 20.5 Document Curator 20.6 Document URL Keywords JACK PINE TOWER FLUX METEOROLOGY SENSIBLE HEAT FLUX LATENT HEAT FLUX CARBON DIOXIDE FLUX CARBON DIOXIDE CONCENTRATION PHOTOSYNTHETIC PHOTON FLUX DENSITY PHOTOSYNTHETICALLY ACTIVE RADIATION PPFD PAR NET RADIATION AIR TEMPERATURE SOIL TEMPERATURE VAPOR PRESSURE WIND SPEED RAINFALL TF05_Flux.doc 07/07/98