FULLSET Data Product

The FULLSET Data Product for the FLUXNET2015 Release includes all variables for the data product, including all quality and uncertainty variables, plus a few selected variables from the intermediate data processing steps in the data processing pipeline.

ERAI Data Product

Auxiliary data product containing full record (1989-2014) of downscaled micrometeorological variables (as related to the site’s measured variables) using the ERA-Interim reanalysis data product (details in the document about the data processing pipeline).

AUXMETEO Data Product

Auxiliary data product containing results from the downscaling of micrometeorological variables using the ERA-Interim reanalysis data product. Variables in this files relate to the linear regression and error/correlation estimates for each data variable used in the downscaling.

Variables (see list below): TA, PA, VPD, WS, P, SW_IN, LW_IN, LW_IN_JSB

Parameters:

  • ERA_SLOPE: slope of linear regression
  • ERA_INTERCEPT: intercept point of linear regression
  • ERA_RMSE: root mean square error between site data and downscaled data
  • ERA_CORRELATION: correlation coefficient of linear fit (R-Squared == ERA_CORRELATION * ERA_CORRELATION)

AUXNEE Data Product

Auxiliary data product with variables resulting from the processing of NEE (mainly related to USTAR filtering) and generation of RECO and GPP. Variables in this product include success/failure of execution of USTAR filtering methods, USTAR thresholds applied to different versions of variables, and percentile/threshold pairs with best model efficiency results.

Variables:

  • USTAR_MP_METHOD: Moving Point Test USTAR threshold method run
  • USTAR_CP_METHOD: Change Point Detection USTAR threshold method run
  • NEE_USTAR50_[UT]: NEE using 50-percentile ofUSTAR thresholds from bootstrapping at USTAR filtering step using method UT (CUT,  VUT)
  • NEE_[UT]_REF: Reference NEE, using model efficiency approach, using method UT (CUT,  VUT)
  • [PROD]_[ALG]_[UT]_REF: Reference product PROD (RECO or GPP), using model efficiency approach, using algorithm ALG(NT, DT) for partitioning  and method UT (CUT, VUT)

Parameters:

  • SUCCESS_RUN: 1 if run of method (USTAR_MP_METHOD or USTAR_CP_METHOD) was successful, 0 otherwise
  • USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR filtering step
  • USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCENTILE
  • [RR]_USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR filtering step at resolution RR (HH, DD, WW, MM, YY)
  • [RR]_USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCENTILE at resolution RR (HH, DD, WW, MM, YY)

 

Variables in the FULLSET Data Product (all variables)

[CSV version, PDF version]

Variable Units Description
TIMEKEEPING
TIMESTAMP YYYYMMDDHHMM ISO timestamp – short format
TIMESTAMP_START YYYYMMDDHHMM ISO timestamp start of averaging period – short format
TIMESTAMP_END YYYYMMDDHHMM ISO timestamp end of averaging period – short format
MICROMETEOROLOGICAL
TA_F_MDS Air temperature, gapfilled using MDS method
HH deg C
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_MDS_QC Quality flag for TA_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TA_F_MDS_NIGHT Average nighttime TA_F_MDS
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_MDS_NIGHT_SD Standard deviation for TA_F_MDS_NIGHT
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_F_MDS_NIGHT_QC Quality flag for TA_F_MDS_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TA_F_MDS_DAY Average daytime TA_F_MDS
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_MDS_DAY_SD Standard deviation for TA_F_MDS_DAY
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_F_MDS_DAY_QC Quality flag for TA_F_MDS_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TA_ERA Air temperature, downscaled from ERA, linearly regressed using measured only site data
HH deg C
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_ERA_NIGHT Average nighttime TA_ERA
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_ERA_NIGHT_SD Standard deviation for TA_ERA_NIGHT
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_ERA_DAY Average daytime TA_ERA
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_ERA_DAY_SD Standard deviation for TA_ERA_DAY
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_F Air temperature, consolidated from TA_F_MDS and TA_ERA
HH deg C TA_F_MDS used if TA_F_MDS_QC is 0 or 1
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_QC Quality flag for TA_F
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TA_F_NIGHT Average nighttime TA_F
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_NIGHT_SD Standard deviation for TA_F_NIGHT
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_F_NIGHT_QC Quality flag for TA_F_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TA_F_DAY Average daytime TA_F
HH not produced
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TA_F_DAY_SD Standard deviation for TA_F_DAY
HH not produced
DD deg C from half-hourly data
WW-YY deg C average SD from daily data
TA_F_DAY_QC Quality flag for TA_F_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
SW_IN_POT Shortwave radiation, incoming, potential (top of atmosphere)
HH W m-2
DD W m-2 average from half-hourly data
WW-MM W m-2 average from daily data
YY W m-2 not defined
SW_IN_F_MDS Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise)
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
SW_IN_F_MDS_QC Quality flag for SW_IN_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
SW_IN_ERA Shortwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data (negative values set to zero)
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
SW_IN_F Shortwave radiation, incoming consolidated from SW_IN_F_MDS and SW_IN_ERA (negative values set to zero)
HH W m-2 SW_IN_F_MDS used if SW_IN_F_MDS_QC is 0 or 1
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
SW_IN_F_QC Quality flag for SW_IN_F
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
LW_IN_F_MDS Longwave radiation, incoming, gapfilled using MDS
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_F_MDS_QC Quality flag for LW_IN_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
LW_IN_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_F Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA
HH W m-2 LW_IN_F_MDS used if LW_IN_F_MDS_QC is 0 or 1
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_F_QC Quality flag for LW_IN_F
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
LW_IN_JSB Longwave radiation, incoming, calculated from TA_F_MDS, SW_IN_F_MDS, VPD_F_MDS and SW_IN_POT using the JSBACH algorithm (Sonke Zaehle)
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_JSB_QC Quality flag for LW_IN_JSB
HH adimensional highest from TA_F_MDS_QC, SW_IN_F_MDS_QC, and VPD_F_MDS_QC, poorest quality prevails
DD adimensional fraction between 0-1, indicating percentage of calculated LW_IN starting from measured and good quality gapfill drivers data
WW-YY adimensional fraction between 0-1, indicating percentage ofcalculated LW_IN starting from measured and good quality gapfill drivers data (average from daily data)
LW_IN_JSB_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using site level LW_IN_JSB calculated from measured only drivers
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_JSB_F Longwave radiation, incoming, consolidated from LW_IN_JSB and LW_IN_JSB_ERA
HH W m-2 LW_IN_JSB used if LW_IN_JSB_QC is 0 or 1
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LW_IN_JSB_F_QC Quality flag for LW_IN_JSB_F
HH adimensional 0 = calculated from measured drivers; 1 = calculated from good quality gapfilled drivers; 2: downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
VPD_F_MDS Vapor Pressure Deficit, gapfilled using MDS
HH hPa
DD hPa average from half-hourly data
WW-YY hPa average from daily data
VPD_F_MDS_QC Quality flag for VPD_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
VPD_ERA Vapor Pressure Deficit, downscaled from ERA, linearly regressed using measured only site data
HH hPa
DD hPa average from half-hourly data
WW-YY hPa average from daily data
VPD_F Vapor Pressure Deficit consolidated from VPD_F_MDS and VPD_ERA
HH hPa VPD_F_MDS used if VPD_F_MDS_QC is 0 or 1
DD hPa average from half-hourly data
WW-YY hPa average from daily data
VPD_F_QC Quality flag for VPD_F
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
PA Atmospheric pressure
HH kPa
DD-YY kPa not defined
PA_ERA Atmospheric pressure, downscaled from ERA, linearly regressed using measured only site data
HH kPa
DD kPa average from half-hourly data
WW-YY kPa average from daily data
PA_F Atmospheric pressure consolidated from PA and PA_ERA
HH kPa PA used if measured
DD kPa average from half-hourly data
WW-YY kPa average from daily data
PA_F_QC Quality flag for PA_F
HH adimensional 0 = measured; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured data
WW-YY adimensional fraction between 0-1, indicating percentage of measured data (average from daily data)
P Precipitation
HH mm
DD-YY mm not defined
P_ERA Precipitation, downscaled from ERA, linearly regressed using measured only site data
HH mm
DD mm average from half-hourly data
WW-YY mm average from daily data
P_F Precipitation consolidated from P and P_ERA
HH mm P used if measured
DD mm average from half-hourly data
WW-YY mm average from daily data
P_F_QC Quality flag for P_F
HH adimensional 0 = measured; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured data
WW-YY adimensional fraction between 0-1, indicating percentage of measured data (average from daily data)
WS Wind speed
HH m s-1
DD-YY m s-1 not defined
WS_ERA Wind speed, downscaled from ERA, linearly regressed using measured only site data
HH m s-1
DD m s-1 average from half-hourly data
WW-YY m s-1 average from daily data
WS_F Wind speed, consolidated from WS and WS_ERA
HH m s-1 WS used if measured
DD m s-1 average from half-hourly data
WW-YY m s-1 average from daily data
WS_F_QC Quality flag of WS_F
HH adimensional 0 = measured; 2 = downscaled from ERA
DD adimensional fraction between 0-1, indicating percentage of measured data
WW-YY adimensional fraction between 0-1, indicating percentage of measured data (average from daily data)
WD Wind direction
HH Decimal degrees
DD-YY Decimal degrees not defined
RH Relative humidity, range 0-100
HH %
DD-YY % not defined
USTAR Friction velocity
HH m s-1
DD m s-1 average from half-hourly data (only days with more than 50% records available)
WW-YY m s-1 average from daily data (only periods with more than 50% records available)
USTAR_QC Quality flag of USTAR
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
NETRAD Net radiation
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
NETRAD_QC Quality flag of NETRAD
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
PPFD_IN Photosynthetic photon flux density, incoming
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
PPFD_IN_QC Quality flag of PPFD_IN
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
PPFD_DIF Photosynthetic photon flux density, diffuse incoming
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
PPFD_DIF_QC Quality flag of PPFD_DIF
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
PPFD_OUT Photosynthetic photon flux density, outgoing
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
PPFD_OUT_QC Quality flag of PPFD_OUT
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
SW_DIF Shortwave radiation, diffuse incoming
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
SW_DIF_QC Quality flag of SW_DIF
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
SW_OUT Shortwave radiation, outgoing
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
SW_OUT_QC Quality flag of SW_OUT
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
LW_OUT Longwave radiation, outgoing
HH W m-2
DD W m-2 average from half-hourly data (only days with more than 50% records available)
WW-YY W m-2 average from daily data (only periods with more than 50% records available)
LW_OUT_QC Quality flag of LW_OUT
HH adimensional not defined
DD adimensional fraction between 0-1, indicating percentage of data available (measured)
WW-YY adimensional fraction between 0-1, indicating percentage of data available (average from daily data)
CO2_F_MDS CO2 mole fraction, gapfilled with MDS
HH umolCO2 mol-1
DD umolCO2 mol-1 average from half-hourly data
WW-YY umolCO2 mol-1 average from daily data
CO2_F_MDS_QC Quality flag for CO2_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
TS_F_MDS_# Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
HH deg C
DD deg C average from half-hourly data
WW-YY deg C average from daily data
TS_F_MDS_#_QC Quality flag for TS_F_MDS_#
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
SWC_F_MDS_# Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
HH %
DD % average from half-hourly data
WW-YY % average from daily data
SWC_F_MDS_#_QC Quality flag for SWC_F_MDS_#
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
ENERGY PROCESSING
G_F_MDS Soil heat flux
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
G_F_MDS_QC Quality flag of G_F_MDS
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
LE_F_MDS Latent heat flux, gapfilled using MDS method
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LE_F_MDS_QC Quality flag for LE_F_MDS, LE_CORR, LE_CORR25, and LE_CORR75
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
LE_CORR Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
LE_CORR_25 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 25th percentile
HH W m-2
DD W m-2 average from half-hourly data
WW-YY not produced
LE_CORR_75 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 75th percentile
HH W m-2
DD W m-2 average from half-hourly data
WW-YY not produced
LE_RANDUNC Random uncertainty of LE, from measured only data
HH W m-2 uses only data point where LE_F_MDS_QC is 0 and two hierarchical methods (see header and LE_RANDUNC_METHOD)
DD-YY W m-2 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
LE_RANDUNC_METHOD Method used to estimate the random uncertainty of LE
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
LE_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of LE
HH adimensional
DD-YY not produced
LE_CORR_JOINTUNC Joint uncertainty estimation for LE
HH-DD W m-2 [SQRT(LE_RANDUNC^2 + ((LE_CORR75 – LE_CORR25) / 1.349)^2)]
WW-YY not produced
H_F_MDS Sensible heat flux, gapfilled using MDS method
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
H_F_MDS_QC Quality flag for H_F_MDS, H_CORR, H_CORR25, and H_CORR75
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
H_CORR Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor
HH W m-2
DD W m-2 average from half-hourly data
WW-YY W m-2 average from daily data
H_CORR_25 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 25th percentile
HH W m-2
DD W m-2 average from half-hourly data
WW-YY not produced
H_CORR_75 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 75th percentile
HH W m-2
DD W m-2 average from half-hourly data
WW-YY not produced
H_RANDUNC Random uncertainty of H, from measured only data
HH W m-2 uses only data point where H_F_MDS_QC is 0 and two hierarchical methods (see header and H_RANDUNC_METHOD)
DD-YY W m-2 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
H_RANDUNC_METHOD Method used to estimate the random uncertainty of H
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
H_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of H
HH adimensional
DD-YY not produced
H_CORR_JOINTUNC Joint uncertainty estimation for H
HH-DD W m-2 [SQRT(H_RANDUNC^2 + ((H_CORR75 – H_CORR25) / 1.349)^2)]
WW-YY not produced
EBC_CF_N Number of data points used to calculate energy closure balance correction factor. Driver data points within sliding window (ECB_CF Method 1) or number of ECB_CF data points (for ECB_CF Methods 2 and 3)
HH adimensional for ECB_CF Method 1 (minimum 5, maximum 93)
DD adimensional for ECB_CF Method 1 (minimum 5, maximum 15)
WW–YY adimensional fraction between 0-1, indicating percentages of half-hours used with respect to theoretical maximum number of half hours
EBC_CF_METHOD Method used to calculate the energy balance closure correction factor
HH-YY adimensional 1 = ECB_CF Method 1, 2 = ECB_CF Method 2, 3 = ECB_CF Method 3. See general description for details
NET ECOSYSTEM EXCHANGE
NIGHT Flag indicating nighttime interval based on SW_IN_POT
HH adimensional 0 = daytime, 1 = nighttime
DD-YY not produced
NIGHT_D Number of half hours classified as nighttime in the period, i.e., when SW_IN_POT is 0
HH not produced
DD adimensional number of half-hours
WW-MM adimensional number of halfhours (average of the daily data)
YY not produced
DAY_D Number of half hours classified as daytime in the period, i.e., when SW_IN_POT is greater than 0
HH not produced
DD adimensional number of half-hours
WW-MM adimensional number of halfhours (average of the daily data)
YY not produced
NIGHT_RANDUNC_N Number of half hours classified as nighttime and used to calculate the aggregated random uncertainty
HH not produced
DD adimensional number of half-hours
WW-YY adimensional number of halfhours (average of the daily data)
DAY_RANDUNC_N Number of half hours classified as daytime and used to calculate the aggregated random uncertainty
HH not produced
DD adimensional number of half-hours
WW-YY adimensional number of halfhours (average of the daily data)
NEE_CUT_REF Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, reference selected on the basis of the model efficiency
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
NEE_VUT_REF Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, reference selected on the basis of the model efficiency
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
NEE_CUT_REF_QC Quality flag for NEE_CUT_REF
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_REF_QC Quality flag for NEE_VUT_REF
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_REF_RANDUNC Random uncertainty for NEE_CUT_REF, from measured only data
HH umolCO2 m-2 s-1 uses only data points where NEE_CUT_REF_QC is 0 and two hierarchical methods – see header and NEE_CUT_REF_RANDUNC_METHOD
DD-MM gC m-2 d-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
YY gC m-2 y-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
NEE_VUT_REF_RANDUNC Random uncertainty for NEE_VUT_REF, from measured only data
HH umolCO2 m-2 s-1 uses only data points where NEE_VUT_REF_QC is 0 and two hierarchical methods – see header and NEE_VUT_REF_RANDUNC_METHOD
DD-MM gC m-2 d-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
YY gC m-2 y-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
NEE_CUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_REF
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
NEE_VUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_REF
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
NEE_CUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_CUT_REF
HH adimensional
DD-YY not produced
NEE_VUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_VUT_REF
HH adimensional
DD-YY not produced
NEE_CUT_REF_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF, including random uncertainty and USTAR filtering uncertainty
HH umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each half-hour
DD gC m-2 d-1 [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each day
WW gC m-2 d-1 [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each week
MM gC m-2 d-1 [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each month
YY gC m-2 y-1 [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each year
NEE_VUT_REF_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF, including random uncertainty and USTAR filtering uncertainty
HH umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each half-hour
DD gC m-2 d-1 [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each day
WW gC m-2 d-1 [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each week
MM gC m-2 d-1 [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each month
YY gC m-2 y-1 [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each year
NEE_CUT_USTAR50 Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, from 50 percentile of USTAR threshold
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
NEE_VUT_USTAR50 Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, from 50 percentile of USTAR threshold
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
NEE_CUT_USTAR50_QC Quality flag for NEE_CUT_USTAR50
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_USTAR50_QC Quality flag for NEE_VUT_USTAR50
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_USTAR50_RANDUNC Random uncertainty for NEE_CUT_USTAR50, from measured only data
HH umolCO2 m-2 s-1 uses only data points where NEE_CUT_USTAR50_QC is 0 and two hierarchical methods – see header and NEE_CUT_USTAR50_RANDUNC_METHOD
DD-MM gC m-2 d-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
YY gC m-2 y-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
NEE_VUT_USTAR50_RANDUNC Random uncertainty for NEE_VUT_USTAR50, from measured only data
HH umolCO2 m-2 s-1 uses only data points where NEE_VUT_USTAR50_QC is 0 and two hierarchical methods see header and NEE_VUT_USTAR50_RANDUNC_METHOD
DD-MM gC m-2 d-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
YY gC m-2 y-1 from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used
NEE_CUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_USTAR50
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
NEE_VUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_USTAR50
HH adimensional 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method)
DD-YY not produced
NEE_CUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_CUT_USTAR50
HH adimensional
DD-YY not produced
NEE_VUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_VUT_USTAR50
HH adimensional
DD-YY not produced
NEE_CUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50, including random uncertainty and USTAR filtering uncertainty
HH umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each half-hour
DD gC m-2 d-1 [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each day
WW gC m-2 d-1 [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each week
MM gC m-2 d-1 [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each month
YY gC m-2 y-1 [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each year
NEE_VUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50, including random uncertainty and USTAR filtering uncertainty
HH umolCO2 m-2 s-1 [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each half-hour
DD gC m-2 d-1 [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each day
WW gC m-2 d-1 [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each week
MM gC m-2 d-1 [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each month
YY gC m-2 y-1 [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each year
NEE_CUT_MEAN Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, average from 40 NEE_CUT_XX versions
HH umolCO2 m-2 s-1 average from 40 half-hourly NEE_CUT_XX
DD gC m-2 d-1 average from 40 daily NEE_CUT_XX
WW gC m-2 d-1 average from 40 weekly NEE_CUT_XX
MM gC m-2 d-1 average from 40 monthly NEE_CUT_XX
YY gC m-2 y-1 average from 40 yearly NEE_CUT_XX
NEE_VUT_MEAN Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, average from 40 NEE_VUT_XX versions
HH umolCO2 m-2 s-1 average from 40 half-hourly NEE_CUT_XX
DD gC m-2 d-1 average from 40 daily NEE_CUT_XX
WW gC m-2 d-1 average from 40 weekly NEE_CUT_XX
MM gC m-2 d-1 average from 40 monthly NEE_CUT_XX
YY gC m-2 y-1 average from 40 yearly NEE_CUT_XX
NEE_CUT_MEAN_QC Quality flag for NEE_CUT_MEAN, fraction between 0-1 indicating percentage of good quality data
HH adimensional average of percentages of good data (NEE_CUT_XX_QC is 0 or 1) from 40 NEE_CUT_XX_QC
DD-YY adimensional average of 40 NEE_CUT_XX_QC for the period
NEE_VUT_MEAN_QC Quality flag for NEE_VUT_MEAN, fraction between 0-1 indicating percentage of good quality data
HH adimensional average of percentages of good data (NEE_VUT_XX_QC is 0 or 1) from 40 NEE_VUT_XX_QC
DD-YY adimensional average of 40 NEE_VUT_XX_QC for the period
NEE_CUT_SE Standard Error for NEE_CUT, calculated as SD(NEE_CUT_XX) / SQRT(40)
HH umolCO2 m-2 s-1 SE from 40 half-hourly NEE_CUT_XX
DD gC m-2 d-1 SE from 40 daily NEE_CUT_XX
WW gC m-2 d-1 SE from 40 weekly NEE_CUT_XX
MM gC m-2 d-1 SE from 40 monthly NEE_CUT_XX
YY gC m-2 y-1 SE from 40 yearly NEE_CUT_XX
NEE_VUT_SE Standard Error for NEE_VUT, calculated as SD(NEE_VUT_XX) / SQRT(40)
HH umolCO2 m-2 s-1 SE from 40 half-hourly NEE_CUT_XX
DD gC m-2 d-1 SE from 40 daily NEE_CUT_XX
WW gC m-2 d-1 SE from 40 weekly NEE_CUT_XX
MM gC m-2 d-1 SE from 40 monthly NEE_CUT_XX
YY gC m-2 y-1 SE from 40 yearly NEE_CUT_XX
NEE_CUT_XX NEE CUT percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH umolCO2 m-2 s-1 XXth percentile from 40 half-hourly NEE_CUT_XX
DD gC m-2 d-1 XXth percentile from 40 daily NEE_CUT_XX
WW gC m-2 d-1 XXth percentile from 40 weekly NEE_CUT_XX
MM gC m-2 d-1 XXth percentile from 40 monthly NEE_CUT_XX
YY gC m-2 y-1 XXth percentile from 40 yearly NEE_CUT_XX
NEE_VUT_XX NEE VUT percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH umolCO2 m-2 s-1 XXth percentile from 40 half-hourly NEE_VUT_XX
DD gC m-2 d-1 XXth percentile from 40 daily NEE_VUT_XX
WW gC m-2 d-1 XXth percentile from 40 weekly NEE_VUT_XX
MM gC m-2 d-1 XXth percentile from 40 monthly NEE_VUT_XX
YY gC m-2 y-1 XXth percentile from 40 yearly NEE_VUT_XX
NEE_CUT_XX_QC Quality flag for NEE_CUT_XX — XX = 05, 16, 25, 50, 75, 84, 95
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_XX_QC Quality flag for NEE_VUT_XX — XX = 05, 16, 25, 50, 75, 84, 95
HH adimensional 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_REF_NIGHT Average nighttime NEE, from NEE_CUT_REF
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_VUT_REF_NIGHT Average nighttime NEE, from NEE_VUT_REF
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_CUT_REF_NIGHT_SD Standard Deviation of the nighttime NEE, from the NEE_CUT_REF
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_VUT_REF_NIGHT_SD Standard Deviation of the nighttime NEE, from the NEE_VUT_REF
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_CUT_REF_NIGHT_QC Quality flag for NEE_CUT_REF_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_REF_NIGHT_QC Quality flag for NEE_VUT_REF_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_REF_NIGHT_RANDUNC Random uncertainty of NEE_CUT_REF_NIGHT, from the random uncertainty of the single nighttime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day.
NEE_VUT_REF_NIGHT_RANDUNC Random uncertainty of NEE_VUT_REF_NIGHT, from the random uncertainty of the single nighttime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day.
NEE_CUT_REF_NIGHT_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF_NIGHT, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each year
NEE_VUT_REF_NIGHT_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF_NIGHT, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each year
NEE_CUT_REF_DAY Average daytime NEE, from NEE_CUT_REF
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_VUT_REF_DAY Average daytime NEE, from NEE_VUT_REF
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_CUT_REF_DAY_SD Standard Deviation of the daytime NEE, from the NEE_CUT_REF
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_VUT_REF_DAY_SD Standard Deviation of the daytime NEE, from the NEE_VUT_REF
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_CUT_REF_DAY_QC Quality flag for NEE_CUT_REF_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_REF_DAY_QC Quality flag for NEE_VUT_REF_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_REF_DAY_RANDUNC Random uncertainty of NEE_CUT_REF_DAY, from the random uncertainty of the single daytime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day.
NEE_VUT_REF_DAY_RANDUNC Random uncertainty of NEE_VUT_REF_DAY, from the random uncertainty of the single daytime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day.
NEE_CUT_REF_DAY_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF_DAY, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each year
NEE_VUT_REF_DAY_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF_DAY, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each year
NEE_CUT_USTAR50_NIGHT Average nighttime NEE, from NEE_CUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_VUT_USTAR50_NIGHT Average nighttime NEE, from NEE_VUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_CUT_USTAR50_NIGHT_SD Standard Deviation of the nighttime NEE, from the NEE_CUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_VUT_USTAR50_NIGHT_SD Standard Deviation of the nighttime NEE, from the NEE_VUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 1)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_CUT_USTAR50_NIGHT_QC Quality flag for NEE_CUT_USTAR50_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_USTAR50_NIGHT_QC Quality flag for NEE_VUT_USTAR50_NIGHT
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_USTAR50_NIGHT_RANDUNC Random uncertainty of NEE_CUT_USTAR50_NIGHT, from the random uncertainty of the single nighttime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day.
NEE_VUT_USTAR50_NIGHT_RANDUNC Random uncertainty of NEE_VUT_USTAR50_NIGHT, from the random uncertainty of the single nighttime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day.
NEE_CUT_USTAR50_NIGHT_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50_NIGHT, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each year
NEE_VUT_USTAR50_NIGHT_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50_NIGHT, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each year
NEE_CUT_USTAR50_DAY Average daytime NEE, from NEE_CUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_VUT_USTAR50_DAY Average daytime NEE, from NEE_VUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 average from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 average from daily data
NEE_CUT_USTAR50_DAY_SD Standard Deviation of the daytime NEE, from the NEE_CUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_VUT_USTAR50_DAY_SD Standard Deviation of the daytime NEE, from the NEE_VUT_USTAR50
HH not produced
DD umolCO2 m-2 s-1 from half-hourly data (where NIGHT is 0)
WW-YY umolCO2 m-2 s-1 from daily data
NEE_CUT_USTAR50_DAY_QC Quality flag for NEE_CUT_USTAR50_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_USTAR50_DAY_QC Quality flag for NEE_VUT_USTAR50_DAY
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_USTAR50_DAY_RANDUNC Random uncertainty of NEE_CUT_USTAR50_DAY, from the random uncertainty of the single daytime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day.
NEE_VUT_USTAR50_DAY_RANDUNC Random uncertainty of NEE_VUT_USTAR50_DAY, from the random uncertainty of the single daytime half-hours
HH not produced
DD-YY umolCO2 m-2 s-1 from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day.
NEE_CUT_USTAR50_DAY_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50_DAY, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each day
WW umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each week
MM umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each month
YY umolCO2 m-2 s-1 [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each year
NEE_VUT_USTAR50_DAY_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50_DAY, including random uncertainty and USTAR filtering uncertainty
HH not produced
DD umolCO2 m-2 s-1 SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each day
WW umolCO2 m-2 s-1 SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each week
MM umolCO2 m-2 s-1 SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each month
YY umolCO2 m-2 s-1 SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each year
NEE_CUT_XX_NIGHT NEE CUT nighttime percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD umolCO2 m-2 s-1 XXth nighttime percentile from 40 daily NEE_CUT_XX_NIGHT
WW umolCO2 m-2 s-1 XXth nighttime percentile from 40 weekly NEE_CUT_XX_NIGHT
MM umolCO2 m-2 s-1 XXth nighttime percentile from 40 monthly NEE_CUT_XX_NIGHT
YY umolCO2 m-2 s-1 XXth nighttime percentile from 40 yearly NEE_CUT_XX_NIGHT
NEE_VUT_XX_NIGHT NEE VUT nighttime percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD umolCO2 m-2 s-1 XXth nighttime percentile from 40 daily NEE_VUT_XX_NIGHT
WW umolCO2 m-2 s-1 XXth nighttime percentile from 40 weekly NEE_VUT_XX_NIGHT
MM umolCO2 m-2 s-1 XXth nighttime percentile from 40 monthly NEE_VUT_XX_NIGHT
YY umolCO2 m-2 s-1 XXth nighttime percentile from 40 yearly NEE_VUT_XX_NIGHT
NEE_CUT_XX_NIGHT_QC Quality flag for NEE_CUT_XX_NIGHT — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_XX_NIGHT_QC Quality flag for NEE_VUT_XX_NIGHT — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_CUT_XX_DAY NEE CUT daytime percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD umolCO2 m-2 s-1 XXth daytime percentile from 40 daily NEE_CUT_XX_DAY
WW umolCO2 m-2 s-1 XXth daytime percentile from 40 weekly NEE_CUT_XX_DAY
MM umolCO2 m-2 s-1 XXth daytime percentile from 40 monthly NEE_CUT_XX_DAY
YY umolCO2 m-2 s-1 XXth daytime percentile from 40 yearly NEE_CUT_XX_DAY
NEE_VUT_XX_DAY NEE VUT daytime percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates for each period — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD umolCO2 m-2 s-1 XXth daytime percentile from 40 daily NEE_VUT_XX_DAY
WW umolCO2 m-2 s-1 XXth daytime percentile from 40 weekly NEE_VUT_XX_DAY
MM umolCO2 m-2 s-1 XXth daytime percentile from 40 monthly NEE_VUT_XX_DAY
YY umolCO2 m-2 s-1 XXth daytime percentile from 40 yearly NEE_VUT_XX_DAY
NEE_CUT_XX_DAY_QC Quality flag for NEE_CUT_XX_DAY — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
NEE_VUT_XX_DAY_QC Quality flag for NEE_VUT_XX_DAY — XX = 05, 16, 25, 50, 75, 84, 95
HH not produced
DD adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data
WW-YY adimensional fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data)
PARTITIONING    
NIGHTTIME
RECO_NT_VUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_NT_VUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_VUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_NT_VUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly RECO_NT_VUT_XX
DD gC m-2 d-1 average from 40 daily RECO_NT_VUT_XX
WW gC m-2 d-1 average from 40 weekly RECO_NT_VUT_XX
MM gC m-2 d-1 average from 40 monthly RECO_NT_VUT_XX
YY gC m-2 y-1 average from 40 yearly RECO_NT_VUT_XX
RECO_NT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_VUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly RECO_NT_CUT_XX
DD gC m-2 d-1 SE from 40 daily RECO_NT_VUT_XX
WW gC m-2 d-1 SE from 40 weekly RECO_NT_VUT_XX
MM gC m-2 d-1 SE from 40 monthly RECO_NT_VUT_XX
YY gC m-2 y-1 SE from 40 yearly RECO_NT_VUT_XX
RECO_NT_VUT_XX Ecosystem Respiration, from Nighttime partitioning method, based on corresponding NEE_VUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_NT_CUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_NT_CUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_CUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_NT_CUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly RECO_NT_CUT_XX
DD gC m-2 d-1 average from 40 daily RECO_NT_CUT_XX
WW gC m-2 d-1 average from 40 weekly RECO_NT_CUT_XX
MM gC m-2 d-1 average from 40 monthly RECO_NT_CUT_XX
YY gC m-2 y-1 average from 40 yearly RECO_NT_CUT_XX
RECO_NT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_CUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly RECO_NT_CUT_XX
DD gC m-2 d-1 SE from 40 daily RECO_NT_CUT_XX
WW gC m-2 d-1 SE from 40 weekly RECO_NT_CUT_XX
MM gC m-2 d-1 SE from 40 monthly RECO_NT_CUT_XX
YY gC m-2 y-1 SE from 40 yearly RECO_NT_CUT_XX
RECO_NT_CUT_XX Ecosystem Respiration, from Nighttime partitioning method, based on corresponding NEE_CUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_VUT_REF Gross Primary Production, from Nighttime partitioning method, reference version selected from GPP versions using a model efficiency approach. Based on corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_VUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_VUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_VUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly GPP_NT_VUT_XX
DD gC m-2 d-1 average from 40 daily GPP_NT_VUT_XX
WW gC m-2 d-1 average from 40 weekly GPP_NT_VUT_XX
MM gC m-2 d-1 average from 40 monthly GPP_NT_VUT_XX
YY gC m-2 y-1 average from 40 yearly GPP_NT_VUT_XX
GPP_NT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_VUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly GPP_NT_VUT_XX
DD gC m-2 d-1 SE from 40 daily GPP_NT_VUT_XX
WW gC m-2 d-1 SE from 40 weekly GPP_NT_VUT_XX
MM gC m-2 d-1 SE from 40 monthly GPP_NT_VUT_XX
YY gC m-2 y-1 SE from 40 yearly GPP_NT_VUT_XX
GPP_NT_VUT_XX Gross Primary Production, from Nighttime partitioning method, based on corresponding NEE_VUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_CUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_CUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_CUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_NT_CUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly GPP_NT_CUT_XX
DD gC m-2 d-1 average from 40 daily GPP_NT_CUT_XX
WW gC m-2 d-1 average from 40 weekly GPP_NT_CUT_XX
MM gC m-2 d-1 average from 40 monthly GPP_NT_CUT_XX
YY gC m-2 y-1 average from 40 yearly GPP_NT_CUT_XX
GPP_NT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_CUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly GPP_NT_CUT_XX
DD gC m-2 d-1 SE from 40 daily GPP_NT_CUT_XX
WW gC m-2 d-1 SE from 40 weekly GPP_NT_CUT_XX
MM gC m-2 d-1 SE from 40 monthly GPP_NT_CUT_XX
YY gC m-2 y-1 SE from 40 yearly GPP_NT_CUT_XX
GPP_NT_CUT_XX Gross Primary Production, from Nighttime partitioning method, based on corresponding NEE_CUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
DAYTIME
RECO_DT_VUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_DT_VUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_VUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_DT_VUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly RECO_DT_VUT_XX
DD gC m-2 d-1 average from 40 daily RECO_DT_VUT_XX
WW gC m-2 d-1 average from 40 weekly RECO_DT_VUT_XX
MM gC m-2 d-1 average from 40 monthly RECO_DT_VUT_XX
YY gC m-2 y-1 average from 40 yearly RECO_DT_VUT_XX
RECO_DT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_VUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly RECO_DT_CUT_XX
DD gC m-2 d-1 SE from 40 daily RECO_DT_VUT_XX
WW gC m-2 d-1 SE from 40 weekly RECO_DT_VUT_XX
MM gC m-2 d-1 SE from 40 monthly RECO_DT_VUT_XX
YY gC m-2 y-1 SE from 40 yearly RECO_DT_VUT_XX
RECO_DT_VUT_XX Ecosystem Respiration, from Daytime partitioning method, based on corresponding NEE_VUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_DT_CUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_DT_CUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_CUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_DT_CUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly RECO_DT_CUT_XX
DD gC m-2 d-1 average from 40 daily RECO_DT_CUT_XX
WW gC m-2 d-1 average from 40 weekly RECO_DT_CUT_XX
MM gC m-2 d-1 average from 40 monthly RECO_DT_CUT_XX
YY gC m-2 y-1 average from 40 yearly RECO_DT_CUT_XX
RECO_DT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_CUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly RECO_DT_CUT_XX
DD gC m-2 d-1 SE from 40 daily RECO_DT_CUT_XX
WW gC m-2 d-1 SE from 40 weekly RECO_DT_CUT_XX
MM gC m-2 d-1 SE from 40 monthly RECO_DT_CUT_XX
YY gC m-2 y-1 SE from 40 yearly RECO_DT_CUT_XX
RECO_DT_CUT_XX Ecosystem Respiration, from Daytime partitioning method, based on corresponding NEE_CUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_VUT_REF Gross Primary Production, from Daytime partitioning method, reference version selected from GPP versions using a model efficiency approach. Based on corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_VUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_VUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_VUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly GPP_DT_VUT_XX
DD gC m-2 d-1 average from 40 daily GPP_DT_VUT_XX
WW gC m-2 d-1 average from 40 weekly GPP_DT_VUT_XX
MM gC m-2 d-1 average from 40 monthly GPP_DT_VUT_XX
YY gC m-2 y-1 average from 40 yearly GPP_DT_VUT_XX
GPP_DT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_VUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly GPP_DT_VUT_XX
DD gC m-2 d-1 SE from 40 daily GPP_DT_VUT_XX
WW gC m-2 d-1 SE from 40 weekly GPP_DT_VUT_XX
MM gC m-2 d-1 SE from 40 monthly GPP_DT_VUT_XX
YY gC m-2 y-1 SE from 40 yearly GPP_DT_VUT_XX
GPP_DT_VUT_XX Gross Primary Production, from Daytime partitioning method, based on corresponding NEE_VUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_CUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_CUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_CUT_USTAR50
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
GPP_DT_CUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version
HH umolCO2 m-2 s-1 average from 40 half-hourly GPP_DT_CUT_XX
DD gC m-2 d-1 average from 40 daily GPP_DT_CUT_XX
WW gC m-2 d-1 average from 40 weekly GPP_DT_CUT_XX
MM gC m-2 d-1 average from 40 monthly GPP_DT_CUT_XX
YY gC m-2 y-1 average from 40 yearly GPP_DT_CUT_XX
GPP_DT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_CUT_XX) / SQRT(40))
HH umolCO2 m-2 s-1 SE from 40 half-hourly GPP_DT_CUT_XX
DD gC m-2 d-1 SE from 40 daily GPP_DT_CUT_XX
WW gC m-2 d-1 SE from 40 weekly GPP_DT_CUT_XX
MM gC m-2 d-1 SE from 40 monthly GPP_DT_CUT_XX
YY gC m-2 y-1 SE from 40 yearly GPP_DT_CUT_XX
GPP_DT_CUT_XX Gross Primary Production, from Daytime partitioning method, based on corresponding NEE_CUT_XX (with XX = 05, 16, 25, 50, 75, 84, 95)
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
SUNDOWN
RECO_SR Ecosystem Respiration, from Sundown Respiration partitioning method
HH umolCO2 m-2 s-1
DD gC m-2 d-1 calculated from half-hourly data
WW-MM gC m-2 d-1 average from daily data
YY gC m-2 y-1 sum from daily data
RECO_SR_N Fraction between 0-1, indicating the percentage of data avaiable in the averaging period to parametrize the respiration model
HH not produced
DD-YY adimensional percentage of data available