Summary

The BOREAS FLX-03 follow-on team performed calculations of area-averaged fluxes using extracted flux data from BORIS for the period 1-June-1994 through 30-September-1994 for the SSA and the NSA. In 1996, data from measurements taken from 31-May through 29-September in the SSA were used. Fluxes are used from the highest tower level at each site. One flux tower was used to represent each of the major vegetation types. For the Young Jack Pine site, the Campbell sonic anemometer was selected. The data are stored in comma separated ASCII files. Figures that accompany this documentation are stored as GIF formatted files.

Data Citation

Cite this data set as follows (citation revised on October 30, 2002):

Mahrt, L., D. Vickers, J. Sun, and J. H. McCaughey. 2001. BOREAS Follow-On FLX-03 Area-Averaged Flux Data for the NSA and SSA. Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

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

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1. Data Set Overview

1.1 Data Set Identification
      BOREAS Follow-On FLX-03 Area-Averaged Flux Data for NSA and SSA Sites

1.2 Data Set Introduction
      Area-averaged fluxes were calculated using flux data extracted from the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) as part of the follow-on activities. Data for the period of 1-June-1994 through 30-September-1994 for the Southern Study Area (SSA) and the Northern Study Area (NSA) were used in 1994. In 1996, measurement made from 31-May through 29-September in the SSA were used.

1.3 Objective/Purpose
      The objective of this research were to fulfill the need for area-averaged flux data in the BOREAS region.

1.4 Summary of Parameters
Date (day)
Hour of Day (hour)
Sensible Heat Flux (W/m²)
Latent Heat Flux (W/m²)
Net Radiation (W/m²)
CO₂ Flux (mg/m²/s)

1.5 Discussion
      Data were taken from raw Canadian Twin Otter Aircraft data and tower data from all of the sites from the BOREAS information system. Fluxes are used from the highest tower level at each site. One flux tower was implemented for each of the major vegetation types. For the Young Jack Pine site, the Campbell sonic anemometer was selected.
      The area-averaged fluxes are estimated as the sum of the fluxes over each vegetation type, weighted according to the fractional coverage of that vegetation type and modified by adjustments discussed below. (See equation 1) A more detailed document is available from mahrt@oce.orst.edu.
      The area-averaged fluxes, [F_phi], are estimated by:

      [F_phi] = ^I_i=1 * alpha_i * W_i * F_phi,i                  (1)

where phi is the vertically transported variable such as potential temperature or moisture, the brackets denote spatial averaging, F_phi,i is the tower flux for the i^th surface type, alpha_i represent corrections for the tower data for the i^th surface type and W_i is the fractional coverage for the i^th surface type.
      A tower cannot be completely representative of the area-averaged flux because of spatial variations even within a given vegetation type and because towers for wet surface types are sometimes located in drier areas for practical reasons. In the latter case, the aircraft measured less sensible heat flux and greater latent heat flux compared to the tower (Desjardins et al., 1997). As a result, the Bowen ratio for the aircraft data were substantially smaller than the tower Bowen ratio for some sites. Table 1 shows the Bowen ratio for the aircraft and tower data computed from the sensible and latent heat fluxes averaged over all the periods with aircraft passes over a given tower. One could impose the average aircraft Bowen ratio on the tower data. However, this procedure would remove seasonal and synoptic changes in the Bowen ratio. Instead, we adjust the tower Bowen ratio downward by a fixed fraction (adjustment factor, Table 1). The tower latent and sensible heat fluxes for each observation are then adjusted (augmented latent heat flux and reduced sensible heat flux) to match the adjusted Bowen ratio while preserving the sum of the tower sensible and latent heat fluxes. The Bowen ratio adjustment is applied only to daytime periods when the net radiation, sensible heat flux and latent heat flux are all upward.

TABLE 1: Bowen ratios for sites with adequate aircraft data and adjustment factor

Surface Type	Aircraft	Tower	adj. factor
old aspen SSA	0.71	0.94	0.75
old black spruce SSA	1.31	1.48	0.88
old Jack Pine SSA	1.58	1.72	0.92
young jack pine NSA	1.72	4.48	0.38
old black spruce NSA	1.6	2.31	0.69
old jack pine NSA	2.23	4.53	0.49

Two sets of fractional coverages are used. The primary set is based on Hall et al. (1997). To study the sensitivity to exact geographical location, we also use a set of values from Desjardins et al. (1997) that are based on a part of the SSA where grid flights were flown. This area contained substantially more conifer and less aspen compared to the entire SSA.
      Based on 1994 Landsat TM using bands 2 (red), 4 (infrared) and 5 (mid-infrared), Hall et al. (1997) identified 13 classes of surface type. To proceed, we must translate the thirteen classes into the categories based on the five available towers plus the water category (Table 2). We assume that all deciduous, regeneration deciduous and medium age deciduous are aspen, wet conifer is black spruce, dry conifer is jack pine, mixed is half aspen and half black spruce and new generation and medium age regeneration conifer are pro-rated between black spruce and jack pine. The June 1994 Landsat image omitted 12% of the eastern part of the SSA. Fractional coverages for this part of the domain were estimated from Chen et al. (1999). As a final adjustment, the fractional coverage of the fen is thought to be substantially higher than the originally reported 8%. We have augmented the value to 13% and decreased the other non-water fractional coverages in proportion. The final values of fractional coverage are listed in Table 2.

TABLE 2: Fractional coverages

Class	Hall et al., adjusted	Desjardins et al.
Black spruce	0.51	0.58
Jack Pine	0.02	0.19
Aspen	0.27	0.16
Fen	0.13	0.07
Water	0.07	0

The area-averaged fluxes are highly correlated to the net radiation, underscoring the need for models to correctly predict the cloud cover; the noon-time relationship between area-averaged fluxes and net radiation is shown in Figure 1. The relationship between area-averaged fluxes and area-averaged net radiation is a little stronger than the relationship at individual locations.

Figure 1. Area-averaged fluxes versus area-averaged net radiation for noon local time for a) sensible and b) latent heat fluxes.

      Changing the overall fractional coverages between deciduous and conifer is much more important than distinction between conifer subclasses since virtually all of the conifer in the region are characterized by substantially larger sensible heat flux and smaller latent heat flux compared to the deciduous canopy.
      Figure 2 shows how the use of the fractional coverages from Desjardins et al. (1997) leads to significantly larger noon-time sensible heat flux, about 25% larger on average. The corresponding area-averaged latent heat fluxes were typically 5-10% smaller. This comparison underscores the fact that exact choice of the boundary of the integration area is important.

Figure 2. Area-averaged fluxes using the fractional coverages from Hall et al. (1997) versus those using Desjardins et al. (1997) for noon local time for a) sensible and b) latent heat fluxes.

      Figure 3 isolates the role of the lakes by comparing the above area-averaged estimates with a version of the area-averaged fluxes where the lake flux is specified to be zero. The comparison is shown for 0400 when the lake influence is a maximum. While the sensible heat flux is not significantly altered, the area-averaged latent heat flux is enhanced substantially by the crudely modeled lake effect. Without the effect of the lakes, the area-averaged latent heat flux ranges between about zero and 10 Wm^-2 while the lakes augment this area-averaged value by about 5 Wm^-2 (70Wm^-2 x 7%). In the daytime, the fluxes over the lake are negligible compared to those over land so that the influence of the lakes is to reduce the area-averaged fluxes by about 7%.
 
 

Figure 3. Area-averaged fluxes versus fluxes calculated assuming zero flux over the lakes for 0400 local time for a) sensible and b) latent heat fluxes.

Figure 4. Area-averaged net radiation versus net radiation calculated without applying the adjustment according to Hodges and Smith (1997) for noon local time.

Figure 5. Area-averaged fluxes versus fluxes calculated without applying the Bowen Ratio adjustment for noon local time for a) sensible and b) latent heat fluxes.

Figure 4 reveals the influence of the adjustment of the net radiation based on Hodges and Smith (1997) for observations at noon, which are small partly because of cancellation in the area average (Section 3.2). Figure 5 shows that the adjustment of the Bowen ratio increases the noon-time area-averaged latent heat flux by about 6% and decreases the area-averaged moisture flux by about 6%. Notice that the comparisons between different versions of the noon-time area-averaged fluxes (Figures 2 and 5) show much more variation along the one-to-one line compared to the deviations from the one-to-one line. This implies that predicting the cloud cover and net radiation is the single most important step in assessing area-averaged fluxes.
      Some investigators maintain that fluxes can be improved by forcing them to balance the surface energy budget. This adjustment assumes that the errors in the surface energy imbalance are due mainly to the eddy correlation estimate of the heat and moisture fluxes and that the estimates of the net radiation and soil heat flux are approximately correct. The usual procedure is to increase the sensible and latent heat fluxes proportionally to preserve the Bowen ratio. That is, the total of the sensible and latent heat fluxes is increased to balance the surface energy budget without modifying the Bowen ratio.

Figure 6. Adjustments of the flux by balancing the surface energy budget.

      In some cases, adjustment of the fluxes to balance the surface energy is not justified. For example, over fens and in black spruce forests with considerable standing water, much of the solar radiation is absorbed by the water. Since this loss is not explicitly included in the surface energy budget, adjustment of the sensible and latent heat fluxes would overestimate such fluxes. As expected, using fluxes adjusted to balance the surface energy budget increases the area-averaged fluxes. For example, area-averaged fluxes at noon increase typically by 25% (Figure 6). Because of difficulties in assessment of errors in the surface energy, we recommend the above flux adjustment as a measure of uncertainty of fluxes rather than a definite improved flux estimate.
      The NSA did not have an aspen tower and had more missing data than the SSA. Most notable is absence of flux data for the aspen forests. The aircraft tracks in the NSA were more heterogeneous compared to those of the Southern Study Area.
It was not possible to directly use data from the aspen site of the Southern Study Area because of the difference in cloud cover between the NSA and the SSA. To estimate the fluxes for the northern aspen forests, we applied the net radiation from the northern black spruce site to average evaporative fraction (evapotranspiration/net radiation) and heat flux fraction (heat flux/net radiation) computed for daytime periods for the southern aspen site. A similar procedure was used for nocturnal conditions at the aspen site except that the evaporative flux was set to zero if the net radiation was zero. As with the SSA, the fluxes are sensitive to the net radiation, although the correlation is somewhat artificial for the aspen part of the NSA.
      Data were also taken in 1996, although from a smaller set of towers. Flux estimates were not made for the Northern Study Area for 1996 because the results are thought to be unreliable. Data are available for the old aspen and old black spruce towers in the Southern Study Area. The small fraction of jack pine was incorporated into the black spruce category because of similar flux values. The fluxes over the Fen were estimated using the fluxes from the aspen site and the average ratio of the heat flux and the latent heat flux between the two sites based on 1994 data. Similarly, the Bowen ratio correction for the tower data is based on the ratios constructed for the 1994 data when the data sample was much larger.

1.6 Related Data Sets
BOREAS AFM-04 Twin Otter Aircraft Flux Data
BOREAS AFM-11 Aircraft Flux Analysis Reports
BOREAS AFM-13 Aircraft Flux Analyses
BOREAS TF-01 SSA-OA Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-02 SSA-OA Tower Flux, Meteorological, and Precipitation Data
BOREAS TF-03 Automated Chamber CO2 Flux Data From The NSA-OBS Data
BOREAS TF-03 Tower Flux & Meteorological Data
BOREAS TF-04 SSA-YJP Tower Flux, Meteorological, and Canopy Condition Data
BOREAS TF-05 Tower Flux & Meteorological Data
BOREAS TF-06 SSA-YA Surface Energy Flux and Meteorological Data
BOREAS TF-07 SSA-OBS Tower Flux and Meteorological Data
BOREAS TF-08 NSA-OJP Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-09 SSA-OBS Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-10 NSA-Fen Tower Flux And Meteorological Data
BOREAS TF-10 NSA-YJP Tower Flux, Meteorological, and Porometry Data
BOREAS TF-11 SSA-Fen Tower Flux and Meteorological Data

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2. Investigator(s)

2.1 Investigator(s) Name and Title
Dr. L. Mahrt
Dr. Dean Vickers
Dr. Jielun Sun
Dr. J. Harry McCaughey

2.2 Title of Investigation
      Spatially-averaged fluxes of CO₂, heat, and moisture

2.3 Contact Information

Contact 1:
L. Mahrt
Oregon State University
Corvallis, OR
mahrt@oce.orst.edu

Contact 2:
Dean Vickers
Oregon State University
Corvallis, OR

Contact 3:
Jielun Sun
National Center for Atmospheric Research
Boulder, CO
jsun@ucar.edu

Contact 4:
J. Harry McCaughey
Queen's University
Kingston, Ontario, Canada
mccaughe@post.queensu.ca

Contact 5:
Andrea Papagno
Raytheon ITSS
NASA GSFC
Greenbelt, MD
(301) 286-3134
(301) 286-0239 (fax)
Andrea.Papagno@gsfc.nasa.gov

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3. Theory of Measurements

A number of field programs have been motivated by a need to estimate area-averaged fluxes. Such an averaging area could correspond to a grid area in a large scale numerical model. However actual estimates of area-averaged fluxes over heterogeneous surfaces are not generally available. Such estimates require a number of pragmatic assumptions that are not very appealing to most scientists. Problems with construction of area-averaged fluxes from data include:

1. Numerous observational and analysis problems arise with tower and aircraft eddy correlation measurements (e.g., Mahrt 1997). Many of the problems are most severe with weak nocturnal wind conditions.
2. Tower measurements are sometimes biased toward warmer, drier locations due to pragmatic or logistical tendencies to avoid low-lying wet areas (Desjardins et al., 1997).
3. Over heterogeneous surfaces, measurements do not sample all types of vegetation classes. Generally, bodies of water are not sampled with towers.
4. Aircraft provide spatial coverage but usually only for mid-day periods. A single aircraft can sample only a subdomain of the area on a given day. Attempts to sample an entire area have led to inadequate sample size for each vegetation class.
5. Construction of area-averaged fluxes must substitute for missing and unreliable data or discard such periods. When area-averaged fluxes are estimated from a significant number of point measurements, data may be missing from at least one point a substantial fraction of the time.
6. Tower eddy correlation measurements must be high enough above the canopy to obtain representative measurements of the micro-region. That is, the footprint of the measurement must be large compared to the vegetation elements (bushes, trees) or clumping of vegetation elements.

Recognizing that these problems cannot be completely overcome, this study develops a general method for estimating area-averaged fluxes based on a network of eddy correlation tower sites and eddy correlation aircraft measurements from BOREAS. The aircraft data are available only for mid-day periods and only for three intensive campaigns during the four-month field program.

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4. Equipment

4.1 Sensor/Instrument Description

4.1.1 Collection Environment
      These data were extracted from the BORIS Information System using the following data sets: (with the exception of the soil temperature and precipitation data)

BOREAS AFM-04 Twin Otter Aircraft Flux Data
BOREAS AFM-11 Aircraft Flux Analysis Reports
BOREAS AFM-13 Aircraft Flux Analyses
BOREAS TF-01 SSA-OA Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-02 SSA-OA Tower Flux, Meteorological, and Precipitation Data
BOREAS TF-03 Automated Chamber CO2 Flux Data From The NSA-OBS Data
BOREAS TF-03 Tower Flux & Meteorological Data
BOREAS TF-04 SSA-YJP Tower Flux, Meteorological, and Canopy Condition Data
BOREAS TF-05 Tower Flux & Meteorological Data
BOREAS TF-06 SSA-YA Surface Energy Flux and Meteorological Data
BOREAS TF-07 SSA-OBS Tower Flux and Meteorological Data
BOREAS TF-08 NSA-OJP Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-09 SSA-OBS Tower Flux, Meteorological, and Soil Temperature Data
BOREAS TF-10 NSA-Fen Tower Flux And Meteorological Data
BOREAS TF-10 NSA-YJP Tower Flux, Meteorological, and Porometry Data
BOREAS TF-11 SSA-Fen Tower Flux and Meteorological Data

4.1.2 Source/Platform
      We used raw Canadian Twin Otter Aircraft data and used tower data from all of the sites (highest level only) from the BOREAS information system. See Section 4.1.1 for more details.

4.1.3 Source/Platform Mission Objectives
      See Section 4.1.3 in the documentation for the data sets listed in Section 4.1.1.

4.1.4 Key Variables
Sensible Heat Flux (W/m²)
Latent Heat Flux (W/m²)
Net Radiation (W/m²)
CO₂ Flux (mg/m²/s)

4.1.5 Principles of Operation
      See Section 4.1.5 in the documentation for the data sets listed in Section 4.1.1.

4.1.6 Sensor/Instrument Measurement Geometry
      See Section 4.1.6 in the documentation for the data sets listed in Section 4.1.1.

4.1.7 Manufacturer of Sensor/Instrument
      See Section 4.1.7 in the documentation for the data sets listed in Section 4.1.1.

4.2 Calibration

4.2.1 Specifications
      See Section 4.2.1 in the documentation for the data sets listed in Section 4.1.1.

4.2.1.1 Tolerance
      See Section 4.2.1.1 in the documentation for the data sets listed in Section 4.1.1.

4.2.2 Frequency of Calibration
      See Section 4.2.2 in the documentation for the data sets listed in Section 4.1.1.

4.2.3 Other Calibration Information
      See Section 4.2.3 in the documentation for the data sets listed in Section 4.1.1.

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5. Data Acquisition Methods

See Section 5 in the documentation for the data sets listed in Section 4.1.1.

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6. Observations

6.1 Data Notes
      None given.

6.2 Field Notes
      See Section 6.2 in the documentation for the data sets listed in Section 4.1.1.

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7. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage
      The measurement sites and associated North American Datum of 1983 (NAD83) coordinates are:

              Operational                         UTM   UTM         UTM
   Site        Grid ID  Latitude     Longitude    Zone  Northing    Easting
------------- --------- ----------   -----------  ----  ---------   --------
SSA-9OA-FLXTR   C3B7T   53.62889°N   106.19779°W   13   5942899.9   420790.5
SSA-YJP-FLXTR   F8L6T   53.87581°N   104.64529°W   13   5969762.5   523320.2
SSA-OJP-FLXTR   G2L3T   53.91634°N   104.69203°W   13   5974257.5   520227.7
SSA-OBS-FLXTR   G8I4T   53.98717°N   105.11779°W   13   5982100.5   492276.5
NSA-OBS-FLXTR   T3R8T   55.88007°N   98.48139°W    14   6192853.4   532444.5
NSA-OJP-FLXTR   T7Q8T   55.92842°N   98.62396°W    14   6198176.3   523496.2
NSA-YJP-FLXTR   T8S9T   55.89575°N   98.28706°W    14   6194706.9   544583.9
SSA-ASP-AUX02   B9B7A   53.59098°N   106.18693°W   13   5938447.2   421469.8
SSA-9BS-AUX01   D0H6S   53.64877°N   105.29534°W   13   5944263.4   480508.7
SSA-ASP-AUX03   D6L9A   53.66879°N   104.6388°W    13   5946733.2   523864
SSA-ASP-AUX05   D9G4A   53.74019°N   105.46929°W   13   5954718.4   469047.1
SSA-ASP-AUX06   E7C3A   53.84741°N   106.08112°W   13   5966863.1   428905.9
SSA-MIX-AUX01   F1N0M   53.80594°N   104.533°W     13   5962031.8   530753.7
SSA-9JP-AUX02   F5I6P   53.86608°N   105.11175°W   13   5968627.1   492651.3
SSA-9JP-AUX04   F7J0P   53.88336°N   105.05115°W   13   5970323.3   496667
SSA-9JP-AUX03   F7J1P   53.88211°N   105.03226°W   13   5970405.6   497879.4
SSA-9JP-AUX05   G1K9P   53.9088°N    104.74812°W   13   5973404.5   516546.7
SSA-9BS-AUX03   G2I4S   53.93021°N   105.13964°W   13   5975766.3   490831.4
SSA-9BS-AUX02   G2L7S   53.90349°N   104.63785°W   13   5972844.3   523793.6
SSA-MIX-AUX02   G4I3M   53.9375°N    105.14246°W   13   5976354.9   490677.3
SSA-9JP-AUX06   G4K8P   53.91883°N   104.76401°W   13   5974516.6   515499.1
SSA-9BS-AUX04   G6K8S   53.94446°N   104.759°W     13   5977146.9   515847.9
SSA-9JP-AUX07   G7K8P   53.95882°N   104.77148°W   13   5978963.8   514994.2
SSA-9JP-AUX08   G8L6P   53.96558°N   104.63755°W   13   5979752.7   523778
SSA-9BS-AUX05   G9I4S   53.99877°N   105.11805°W   13   5983169.1   492291.2
SSA-9JP-AUX09   G9L0P   53.97576°N   104.73779°W   13   5980856     517197.7
SSA-9BS-AUX06   H1E4S   54.04093°N   105.73581°W   13   5988326.1   451815.7
SSA-MIX-AUX03   H2D1M   54.06535°N   105.92706°W   13   5991190.3   439327.7
SSA-9BS-AUX07   H2D1S   54.06199°N   105.92545°W   13   5990814.4   439428.1
SSA-MIX-AUX04   H3D1M   54.066°N     105.92982°W   13   5991042.3   439178.4
SSA-9JP-AUX10   I2I8P   54.11181°N   105.05107°W   13   5995963.1   496661.4
NSA-ASP-AUX01   P7V1A   55.50253°N   98.07478°W    14   6151103.7   558442.1
NSA-MIX-AUX01   Q1V2M   55.54568°N   98.03769°W    14   6155937.3   560718.3
NSA-9JP-AUX01   Q3V3P   55.55712°N   98.02473°W    14   6157222.2   561517.9
NSA-ASP-AUX03   R8V8A   55.67779°N   97.8926°W     14   6170774.8   569638.4
NSA-9BS-AUX01   S8W0S   55.76824°N   97.84024°W    14   6180894.9   572761.9
NSA-ASP-AUX05   S9P3A   55.88576°N   98.87621°W    14   6193371.6   507743.3
NSA-MIX-AUX02   T0P5M   55.88911°N   98.85662°W    14   6193747.3   508967.7
NSA-9BS-AUX08   T0P7S   55.88371°N   98.82345°W    14   6193151.1   511043.9
NSA-9BS-AUX07   T0P8S   55.88351°N   98.80225°W    14   6193132     512370.1
NSA-9BS-AUX02   T0W1S   55.78239°N   97.80937°W    14   6182502     574671.7
NSA-9OA-9TETR   T2Q6A   55.88691°N   98.67479°W    14   6193540.7   520342
NSA-9BS-AUX03   T3U9S   55.83083°N   97.98339°W    14   6187719.2   563679.1
NSA-ASP-AUX04   T4U5A   55.84757°N   98.04329°W    14   6189528.2   559901.6
NSA-9BS-AUX05   T4U8S   55.83913°N   97.99325°W    14   6188633.4   563048.2
NSA-9BS-AUX04   T4U9S   55.83455°N   97.98364°W    14   6188132.8   563657.5
NSA-9BS-AUX14   T5Q7S   55.9161°N    98.64022°W    14   6196800.5   522487.2
NSA-9BS-9TETR   T6R5S   55.90802°N   98.51865°W    14   6195947     530092
NSA-9BS-AUX06   T6T6S   55.87968°N   98.18658°W    14   6192987.9   550887.9
NSA-9BS-AUX13   T7R9S   55.91506°N   98.44877°W    14   6196763.6   534454.5
NSA-9JP-AUX03   T7S9P   55.89486°N   98.30037°W    14   6194599.1   543752.4
NSA-9BS-AUX09   T7T3S   55.89358°N   98.22621°W    14   6194505.6   548391.8
NSA-9JP-AUX06   T8Q9P   55.93219°N   98.6105°W     14   6198601.4   524334.5
NSA-ASP-AUX07   T8S4A   55.91856°N   98.37041°W    14   6197194.6   539348.3
NSA-9BS-AUX15   T8S4S   55.91689°N   98.37111°W    14   6197008.6   539306.4
NSA-9JP-AUX04   T8S9P   55.90456°N   98.28385°W    14   6195688.9   544774.3
NSA-9JP-AUX05   T8T1P   55.90539°N   98.26269°W    14   6195795.3   546096.3
NSA-9JP-AUX07   T9Q8P   55.93737°N   98.59568°W    14   6199183.2   525257.1
NSA-9BS-AUX10   U5W5S   55.9061°N    97.70986°W    14   6196380.8   580655.5
NSA-9BS-AUX12   U6W5S   55.91021°N   97.70281°W    14   6196846.5   581087.8
NSA-ASP-AUX08   V5X7A   55.97396°N   97.48565°W    14   6204216.6   594506.1
NSA-ASP-AUX09   W0Y5A   56.00339°N   97.3355°W     14   6207706.6   603796.6
NSA-9JP-AUX02   99O9P   55.88173°N   99.03952°W    14   6192917.5   497527.8
NSA-ASP-AUX02   Q3V2A   55.56227°N   98.02635°W    14   6157793.5   561407.9
SSA-MIX-9TETR   D9I1M   53.7254°N    105.20643°W   13   5952989.7   486379.7
SSA-OJP-FLXTR   G2L3T   53.91634°N   104.69203°W   13   5974257.5   520227.7

7.1.2 Spatial Coverage Map
      See Section 7.1.2 in the documentation for the data sets listed in Section 4.1.1.

7.1.3 Spatial Resolution
      See Section 7.1.3 in the documentation for the data sets listed in Section 4.1.1.

7.1.4 Projection
      See Section 7.1.4 in the documentation for the data sets listed in Section 4.1.1.

7.1.5 Grid Description
      See Section 7.1.5 in the documentation for the data sets listed in Section 4.1.1.

7.2 Temporal Characteristics

7.2.1 Temporal Coverage
      Data are provided between the period of 1-June-1994 through 30-September-1994 for the SSA and the NSA. In 1996, the coverage period is from 31-May through 29-September in the SSA.

7.2.2 Temporal Coverage Map
      Not available.

7.2.3 Temporal Resolution
      None given.

7.3 Data Characteristics

7.3.1 Parameter/Variable
     The parameters contained in the data files are:

         Column Name
------------------------------
SITE_ID
DATE_OBS
TIME_OBS_LOCAL
INDEX_NUM
SENSIBLE_HEAT_FLUX
LATENT_HEAT_FLUX
NET_RADIATION
CO2_FLUX
CRTFCN_CODE
REVISION_DATE

7.3.2 Variable Description/Definition
     The descriptions of the parameters contained in the data files are:

         Column Name                             Description
------------------------------ --------------------------------------
SITE_ID                        The identifier assigned to the site by
                               BOREAS, in the format SSS-TTT-CCCCC,
                               where SSS identifies the portion of
                               the study area: NSA, SSA, REG, TRN,
                               and TTT identifies the cover type for
                               the site, 999 if unknown, and CCCCC is
                               the identifier for site, exactly what it means
                               will vary with site type.
DATE_OBS                       The date on which the data were collected.
TIME_OBS_LOCAL                 The Local Time (Local) when the data were
                               collected.
INDEX_NUM                      The index number of the observation.
SENSIBLE_HEAT_FLUX             The sensible heat flux 
LATENT_HEAT_FLUX               The latent heat flux 
NET_RADIATION                  The net radiation
CO2_FLUX                       The carbon dioxide flux
CRTFCN_CODE                    The BOREAS certification level of the data.
                               Examples are CPI (Checked by PI), CGR (Certified
                               by Group), PRE (Preliminary), and CPI-??? (CPI but
                               questionable).
REVISION_DATE                  The most recent date when the information in the
                               referenced data base table record was revised

7.3.3 Unit of Measurement
     The measurement units for the parameters contained in the data files are:

         Column Name                                Units
------------------------------ ------------------------------------------------
SITE_ID                        [none]
DATE_OBS                       [DD-MON-YY]
TIME_OBS_LOCAL                 [HHMM Local]
INDEX_NUM                      [none]
SENSIBLE_HEAT_FLUX             [Watts][meter^-2]
LATENT_HEAT_FLUX               [Watts][meter^-2]
NET_RADIATION                  [Watts][meter^-2]
CO2_FLUX                       [micromoles][meter^-2][second^-1]
CRTFCN_CODE                    [none]
REVISION_DATE                  [DD-MON-YY]

7.3.4 Data Source
     The source of the parameter values contained in the data files is:

         Column Name                             Data Source
------------------------------ ------------------------------------------------
SITE_ID                        [Assigned by BORIS]
DATE_OBS                       [Investigator]
TIME_OBS_LOCAL                 [Investigator]
INDEX_NUM                      [Investigator]
SENSIBLE_HEAT_FLUX             [Calculated]
LATENT_HEAT_FLUX               [Calculated]
NET_RADIATION                  [Calculated]
CO2_FLUX                       [Calculated]
CRTFCN_CODE                    [Assigned by BORIS]
REVISION_DATE                  [Assigned by BORIS]

7.3.5 Data Range
     Data range values were not computed. A value of 999 indicates that data were not available.

7.4 Sample Data Record
     The following are sample data records contained in the data files:

SITE_ID,DATE_OBS,TIME_OBS_LOCAL,INDEX_NUM,SENSIBLE_HEAT_FLUX,LATENT_HEAT_FLUX,
NET_RADIATION,CO2_FLUX,CRTFCN_CODE,REVISION_DATE
NSA,9-Jun-94,1630,209,54.471,85.635,187.438,0.24,CPI,29-Sep-00
NSA,9-Jun-94,1730,210,73.682,95.29,213.897,0.51,CPI,29-Sep-00
NSA,9-Jun-94,1830,211,6.382,34.779,72.857,0.01,CPI,29-Sep-00
NSA,9-Jun-94,1930,212,-1.641,6.976,-18.036,0.36,CPI,29-Sep-00

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8. Data Organization

8.1 Data Granularity
      The smallest unit of data tracked by the BOREAS Information System (BORIS) is the data collected at a given site on a given date.

8.2 Data Format(s)
      The data are stored in comma separated variable (CSV) files. There are no spaces between the fields.

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9. Data Manipulations

9.1 Formulae

9.1.1 Derivation Techniques and Algorithms
      Bodies of water can potentially contribute significantly to the area-averaged heat flux at night since the air is often unstable over the water at night due to advection of cool air from land over the warm water, and, due to the weakness of the nocturnal fluxes over land. Advection of warm daytime air over cooler water may substantially suppress fluxes during the afternoon.
      Two levels of complexity are considered. The simplest algorithm fits the diurnal variation of the latent and sensible heat fluxes based on aircraft data (Sun et al., 1997; Sun et al., 1998) and assume the form for heat flux

H = (10Wm^-2) * cos(omega*(hr-0400))                  (2)

where omega is the frequency corresponding to diurnal variations 2 pi/24 hrs. and hr is the local time. According to this formulation, the heat flux over the lake is maximum upward in the early morning (0400 local time)When the air temperature is a minimum and reaches a maximum downward in the late afternoon (1600)When the air temperature is a maximum. Even though flow over the lake is stable in the afternoon, the heat flux is comparable to the nocturnal value because of advection of strong turbulence from the heated land surface. This relationship ignores the seasonal shift of the diurnal cycle that occurs between the end of June and September.
      The latent heat flux is modeled as

LE = (70Wm^-2) + (30Wm^-2) * cos(omega*(hr-0400))                  (3)

With this formulation, the latent heat flux reaches a maximum in the early morning with a value of 100 Wm^-2 and a minimum of 40 Wm^-2 in the late afternoon. For this simple calculation of area-averaged net radiation, the net radiation over the Water is assumed equal to the area-averaged value over the land region. The carbon dioxide flux over the Water surfaces is assumed to be zero.
      Application of the bulk aerodynamic method to provide a more scientific estimate of the fluxes encounters numerous problems. The airflow and wave field would not achieve equilibrium and information on wave state would be required. Furthermore, the thermal roughness length required by similarity theory in the bulk aerodynamic approach is poorly behaved over fetches less than 5 km.

The bulk aerodynamic formula is:                  (4)

the average of (w'*theta') = CH(z) * the average of u(z) * [theta₀ - theta(z)]

where z is the observational height, theta₀ is the aerodynamic temperature, and the average of u is the speed of the vector-averaged wind. The transfer coefficient is computed as:

CH(z) = [k/(ln(z/z₀) - psi_m)] * [k/(ln(z/z_0T) - psi_h)]                  (5)

where k is the von Karman constant, z₀ and z_0T are the roughness lengths for momentum and heat, respectively, and psi_m and psi_h are the stability functions for momentum and heat.

9.2 Data Processing Sequence

9.2.1 Processing Steps
      Data were acquired from the data sources listed in Section 4.1.1. Averaged area flux data were calculated using the equations in Sections 1.5 and 9.1.1.

9.2.2 Processing Changes
      None given.

9.3 Calculations

9.3.1 Special Corrections/Adjustments
      The net radiation measurements are adjusted according to Hodges and Smith (1997). Such adjustments are posed in terms of a linear relationship with an offset. Relationships were constructed separately for daytime and nighttime cases, here defined in terms of positive or negative net radiation. These adjustments led to minor changes in the area-averaged fluxes, partly because the sign of the difference between the corrected and uncorrected values changed between different tower sites, leading to cancellation.

9.3.2 Calculated Variables
      See equations in Sections 1.5 and 9.1.1.

9.4 Graphs and Plots
      See figures in Section 1.5.

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10. Errors

10.1 Sources of Error
      The Young Jack Pine site did not include aircraft passes. The Bowen ratio adjustment (Section 3.1) for this site uses the value for the Old Jack Pine site. No adjustment of the Bowen ratio was carried out for the Fen site which also did not include aircraft measurements. To reduce difficulties associated with isolated missing data, such as a single half-hour value, fluxes were computed for each hour by searching for all flux values within a 2-hour window, centered about 0030, 0130 ..... Up to 5 flux values were then obtained. The fluxes were then averaged by weighting according to the inverse of the difference between the desired time and the reported time. This procedure circumvented problems with some irregularities in reporting time and reduced the influence of random clouds on a given flux value. The Young Jack Pine site often suffered more extensive missing data, in which case the value from the Old Jack Pine site was used. This replacement does not significantly influence the area-averaged fluxes. When data were not available for the other sites, the area integration was not performed. Missing data is mostly due to rain, when data from the sonic anemometers are unreliable.
      The original flux data for the Fen site were considered unacceptable for low wind speed conditions and were replaced with modeled data (Sashi Verma, personal communication).

10.2 Quality Assessment

10.2.1 Data Validation by Source
      See Section 10.2.1 in the documentation for the data sets listed in Section 4.1.1.

10.2.2 Confidence Level/Accuracy Judgment
      See Section 10.2.2 in the documentation for the data sets listed in Section 4.1.1.

10.2.3 Measurement Error for Parameters
      See Section 10.2.3 in the documentation for the data sets listed in Section 4.1.1.

10.2.4 Additional Quality Assessments
      See Section 10.2.4 in the documentation for the data sets listed in Section 4.1.1.

10.2.5 Data Verification by Data Center
      Data were examined for general consistency and clarity.

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11. Notes

11.1 Limitations of the Data
      See Section 11.1 in the documentation for the data sets listed in Section 4.1.1.

11.2 Known Problems with the Data
      See Section 11.2 in the documentation for the data sets listed in Section 4.1.1.

11.3 Usage Guidance
      See Section 11.3 in the documentation for the data sets listed in Section 4.1.1.

11.4 Other Relevant Information
      See Section 11.4 in the documentation for the data sets listed in Section 4.1.1.

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12. Application of the Data Set

This data set can be used to study the area-averaged fluxes of the BOREAS tower sites of the boreal forest.

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13. Future Modifications and Plans

None given.

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14. Software

14.1 Software Description
      None given.

14.2 Software Access
      None given.

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15. Data Access

15.1 Contact for Data Center/Data Access Information
      These BOREAS Follow-On data are available from the Earth Observing System Data and Information System (EOS-DIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The contact at ORNL is:

ORNL DAAC User Services
Oak Ridge National Laboratory
(865) 241-3952
ornldaac@ornl.gov
ornl@eos.nasa.gov

15.2 Procedures for Obtaining Data
      Data may be obtained through the ORNL DAAC World Wide Web site at http://daac.ornl.gov/ [Internet Link] or users may place requests for data by telephone or by electronic mail.

15.3 Output Products and Availability
      Requested data can be provided electronically on the ORNL DAAC's data pool site or on various media including, CD-ROMs, 8-MM tapes, or diskettes.

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16. Output Products and Availability

16.1 Tape Products
      None.

16.2 Film Products
      None.

16.3 Other Products
      Figures that accompany this documentation are stored in GIF format.

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17. References

17.1 Platform/Sensor/Instrument/Data Processing Documentation
      None given.
 

17.2 Journal Articles and Study Reports
Chen, J., S. Leblanc, J. Cihlar, R. Desjardins and J. MacPherson, 1999: Extending aircraft and tower-based CO₂ flux measurements to a boreal region using a Landsat TM landcover map. J. Geophys. Res., to appear.

Desjardins, R. L., MacPherson, J. I., Mahrt, L., Schuepp, P. H., Pattey, E., Neumann, H., Baldocchi, D.,Wofsy, S., Fitzjarrald, D., McCaughey, H., and D.W. Joiner, 1997: Scaling up flux measurements for the boreal forest using aircraft-tower combinations. J. Geophys. Res., 102, 29,125-29,134.

Hodges, G. and E. Smith, 1997: Intercalibration, objective analysis, intercomparison and synthesis of BOREAS surface net radiation measurements. J. Geophys. Res., 102, 28885-28900,

Hall, F., D. Knapp and K. Huemmrich, 1997: Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest. J. Geophys. Res., 102, 29,567-29,580.

Mahrt, L., 1998: Flux sampling strategy for aircraft and tower observations. J. Atm. Oc. Tech., 15, 416-429.

Sun, J., D. Lenschow, L. Mahrt, T. Crawford, K. Davis, S. Oncley, J. I. MacPherson, Q.Wang, R. Dobosy and Desjardins, R. L.: 1997, 'Lake-Induced Atmospheric Circulations During BOREAS', J. Geophys. Res., 102, 29,155 - 29,166.

Sun, J., Desjardins, R., Mahrt, L. and MacPherson, J. I.: 1998, Transport of Carbon Dioxide,Water Vapor and Ozone by Turbulence and Local Circulations', J. Geophys. Res., 103, 25873-25,885.

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 (OPSDOC 94).

Sellers, P.J., 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. Bull. Am. Meteorol. Soc. 76:1549-1577.

Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1996-2.0, NASA BOREAS Report (EXPLAN 96).

Sellers, P., F. Hall, and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPSDOC 96).

Sellers, P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry, M. Ryan, K.J. Ranson, P.M. Crill, D.P. Lettenmaier, H. Margolis, J. Cihlar, J. Newcomer, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D. Halliwell, D.Williams, B. Goodison, D.E.Wickland, and F.E. Guertin. 1997. BOREAS in 1997: Experiment overview, scientific results, and future directions. Journal of Geophysical Research, 102 (D24), 28,731-28,769.
 

17.3 Archive/DBMS Usage Documentation
      None.

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18. Glossary of Terms

None.

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19. List of Acronyms

ASCII   - American Standard Code for Information Interchange
Batoche - The study site located in the Batoche National Historic Park
BFTCS   - Boreal Forest Transect Case Study
BOREAS  - BOReal Ecosystem-Atmosphere Study
BORIS   - BOREAS Information System
CD-ROM  - Compact Disk-Read Only memory
CFS     - Canadian Forest Service
CSV     - Comma Separated Values
DAAC    - Distributed Active Archive Center
DOY     - Julian Day of Year
EOS     - Earth Observing System
EOSDIS  - EOS Data and Information System
GMT     - Greenwich Mean Time
GSFC    - Goddard Space Flight Center
HTML    - HyperText Markup Language
IFC     - Intensive Field Campaign
MIX     - Mixed wood
NAD83   - North American Datum of 1983
NASA    - National Aeronautics and Space Administration
NOAA    - National Oceanic and Atmospheric Administration
NSA     - Northern Study Area
OA      - Old Aspen
OBS     - Old Black Spruce
ORNL    - Oak Ridge National Laboratory
PANP    - Prince Albert National Park
RSS     - Remote Sensing Science
SSA     - Southern Study Area
TE      - Terrestrial Ecology
TF      - Tower Flux
URL     - Uniform Resource Locator
UTM     - Universal Transverse Mercator

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20. Document Information

20.1 Document Revision Date

Written: 20-Jan-2000
Last Updated: 21-Sep-2000 (citation revised on 30-Oct-2002)

20.2 Document Review Date(s)

BORIS Review: 21-Sep-2000
Science Review:

20.3 Document ID

FLX3_AREA_AVG

20.4 Citation

Cite this data set as follows (citation revised on October 30, 2002):

Mahrt, L., D. Vickers, J. Sun, and J. H. McCaughey. 2001. BOREAS Follow-On FLX-03 Area-Averaged Flux Data for the NSA and SSA. Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

20.5 Document Curator:

webmaster@daac.ornl.gov

20.6 Document URL:

http://daac.ornl.gov/BOREAS/FollowOn/guides/flx03_area_avg_doc.html

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Keywords
Aircraft
Area
Average
CO₂
Flux
Latent Heat
Net Radiation
Sensible Heat
Tower
Twin Otter

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