SUBSET Data Product

The SUBSET Data Product for the FLUXNET2015 Release includes fewer data variables, the ones most commonly used plus minimal data quality and uncertainty information. This significantly reduces the number of variables in this data product compared to the comprehensive FULLSET Data Product.

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 SUBSET Data Product

[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 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)
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 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 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)
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_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_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_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
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
NET ECOSYSTEM EXCHANGE
NIGHT Flag indicating nighttime interval based on SW_IN_POT
HH adimensional 0 = daytime, 1 = nighttime
DD-YY not produced
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_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_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_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_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)
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_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
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_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
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_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
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_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
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