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ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2018

Documentation Revision Date: 2020-03-02

Dataset Version: 1

Summary

This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2018 on a 463 m resolution grid for the conterminous United States (CONUS) and on a 5 km resolution grid for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.

There are 708 files in NetCDF v4 format with this data set. This includes 420 files containing ensemble members of each carbon flux and 288 files that are the mean and standard deviation across ensemble members.

Figure 1. Mean and standard deviation of CASA L2 ensembles for three carbon fluxes (GPP, RECO, and NEE) and at 463-m resolution for the conterminous US (CONUS) and at 5-km resolution for North America (NA) in July of 2016.

Citation

Zhou, Yu, C.A. Williams, T. Lauvaux, S. Feng, I.T. Baker, Y. Wei, A.S. Denning, K. Keller, and K.J. Davis. 2019. ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1675

Table of Contents

  1. Dataset Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References

Dataset Overview

This dataset contains the second-level (L2 and L2B) ensemble member estimates of surface biogenic CO2 exchanges between land and atmosphere across portions of North America, including three carbon fluxes: gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) (Zhou et al., 2020). Carbon flux ensembles were derived from Carnegie Ames Stanford Approach (CASA) biogeochemical model (Potter et al. 1993; Randerson et al. 1996) with 27 perturbed parameter sets. This product contains carbon fluxes for two spatial domains, the conterminous United States and North America and at two temporal scales, monthly and 3-hourly.

Project: Atmospheric Carbon and Transport (ACT-America)

The ACT-America, or Atmospheric Carbon and Transport - America, project is a NASA Earth Venture Suborbital-2 mission to study the transport and fluxes of atmospheric carbon dioxide and methane across three regions in the eastern United States. Each flight campaign measured how weather systems transport these greenhouse gases. Ground-based measurements of greenhouse gases were also collected. Better estimates of greenhouse gas sources and sinks are needed for climate management and for prediction of future climate.

Related Publication:

Zhou, Y., C. A. Williams, T. Lauvaux, K. J. Davis, S. Feng, I. Baker, S. Denning, & Y. Wei. 2020. A multiyear gridded data ensemble of surface biogenic carbon fluxes for North America: Evaluation and analysis of results. Journal of Geophysical Research: Biogeosciences, 125(2), e2019JG005314. https://doi.org/10.1029/2019JG005314

Acknowledgements:

This work was funded by the NASA ACT-America Project under awards NNX16AN17G and NNX15AG76G.

Data Characteristics

Spatial Coverage: Conterminous United States (CONUS); North America (NA)

Spatial Resolution: 463 m (CONUS); 5 km and half-degree (NA)

Temporal Coverage: 2003-01-01 to 2018-12-31

Temporal Resolution: Monthly and 3-hourly (3-hourly data is available for North America domain in 2016 and 2018; other temporal and spatial spans can be generated by the provided R script)

Site boundaries: (All latitudes and longitudes are given in decimal degrees)

Site Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
CONUS -130.1748 -60.5999 55.3236 20.0276
NA -175.5350 -24.7704 70.3800 0.7843
NA_HalfDeg -176 -24.5 70.5 0.5

Data Description

There are 708 files in netCDF (*.nc) format: 420 files (204 monthly and 216 3-hourly files) containing ensemble members of each carbon flux and 288 files are the mean and standard deviation across ensemble members.

File Naming Convention

CASA_LEVEL_Ensemble_TIMESCALE_Biogenic_CARBONFLUX_SPATIALDOMAIN_YEARMONTH.nc4
CASA_LEVEL_Ensemble_STATISTIC_TIMESCALE_Biogenic_CARBONFLUX_SPATIALDOMAIN_YEARMONTH.nc4

where

CASALEVEL is the level of data product, we currently provide Level-2 (L2) and Level-2B (L2B).
TIMESCALE is either monthly or 3-hourly.
STATISTIC is the mean (Mean) or standard deviation (STD) across ensemble members.
CARBONFLUX is GPP, RECO or NEE.
SPATIALDOMAIN is either CONUS or NA.
YEAR is the year of simulation.
MONTH is simulated month used for 3-hourly files only.

Data File Details

CONUS files are at 463 m2 spatial resolution, NA files are at 5 km2 resolution, and NA_HalfDeg (upscaled NA) files are at half-degree resolution. The time dimension is defined as the middle time point of each time period (e.g., 15th day of Marches for monthly files; 1.5 hours of the first three-hour for 3-hourly files).

Fill value and missing values are -9999 for all files.

CONUS
projection: Lambert Conformal Conic
projection units: meters
datum (spheroid): GRS_1980
semi major Axis: 6378137.0
semi minor Axis: 6356752.314140356
inverse Flattening: 298.257222101
1st standard parallel: 50 deg N
2nd standard parallel: 50 deg N
central meridian: -107 deg W
latitude of origin: 50 deg N
false easting: 0
false northing: 0

NA
projection: Lambert Conformal Conic 2SP
projection units: meters
datum (spheroid): GCS_unnamed_ellipse (from NARR data)
semi major Axis: 6371200.0
semi minor Axis: 6371200.0
inverse Flattening: 0.0
1st standard parallel: 50 deg N
2nd standard parallel: 50 deg N
central meridian: -107 deg W
latitude of origin: 50 deg N
false easting: 0
false northing: 0

NA_HalfDeg
projection: WGS 1984
angular Unit: Degree (0.0174532925199433)
prime Meridian: Greenwich (0.0)
datum: D_WGS_1984
semi major Axis: 6378137.0
semi minor Axis: 6356752.314245179
inverse Flattening: 298.257223563

Companion Files

TemporalDownscaling.zip

These files are ancillary data from the 3-hourly NARR dataset (https://rda.ucar.edu/datasets/ds608.0/index.html#!description) to use with the script TemporalDownscaling.R.

The file naming convention for yearly directories is NARR_YEARMONTH_3h_FACTOR.tif

where

YEAR is the year for temporal downscaling.
MONTH is selected month, which only used for 3-hourly data
FACTOR is either dwsw (downward shortwave radiation) or airt (air temperature at 2-m height).

RandomEcoregionalSampling.zip

These files are gridded data to use with the script L2B_SampleEcoregions.R.

The file naming convention is SPATIALDOMAIN_Eco_CASALEVEL_CASAGrid.nc4

where

SPATIALDOMAIN is either CONUS or NA.
CASALEVEL is the level of data product, Level-1 to Level-4.

Application and Derivation

This dataset is at a finer spatial resolutions and a relatively longer time span than similar products. It could be used to access surface biogenic carbon fluxes across multiple spatial (hundred meters to continental) and temporal (hourly to annual) scales can give an indication of carbon cycle processes under different weather patterns and feedbacks to climate change. The ensemble data provide not only carbon flux estimates but also uncertainty range, and could serve as prior surface biogenic carbon fluxes for atmospheric inversion studies.

Quality Assessment

To test and confirm the accuracy of our monthly ensemble, the assessment was evaluated by a set of ground-truth data of measured carbon fluxes from the AmeriFlux database (sites are listed in Table 1) and other carbon flux products including 3-hourly MsTMIP modeled ensemble (Huntzinger et al. 2013; Fisher et al. 2016; Huntzinger et al. 2016), CarbonTracker 2017 (CT2017, Peters et al. 2007), SiB3 (Baker et al. 2008; Baker et al. 2013) from 2006 to 2010. See Zhou et al. (2020) for additional information.

Table 1. List of AmeriFlux tower sites used in the quality assessment.

Site ID Start Year End Year Lat Lon IGBP Reference
US-AR1 2009 2012 36.4 -99.4 GRA Billesbach et al. 2016a
US-AR2 2009 2012 36.6 -99.6 GRA Billesbach et al. 2016b
US-ARb 2005 2006 35.5 -98.0 GRA Torn 2006a
US-ARc 2005 2006 35.5 -98.0 GRA Torn 2006b
US-ARM 2003 2012 36.6 -97.5 CRO Fischer et al. 2007
US-Blo 1997 2007 38.9 -120.6 ENF Goldstein et al. 2000
US-Cop 2001 2007 38.1 -109.4 GRA Bowling 2007
US-EML 2008   63.9 -149.3 OSH Belshe et al. 2012
US-GBT 1991 2006 41.4 -106.2 ENF Massman 2006
US-GLE 2004 2014 41.4 -106.2 ENF Frank et al. 2014
US-Goo 2002 2006 34.3 -89.9 GRA Wilson and Meyers 2007
US-Ha1 1991 2012 42.5 -72.2 DBF Urbanski et al. 2007
US-Ho2 1999   45.2 -68.7 ENF Hollinger et al. 1999
US-Ho3 2000   45.2 -68.7 ENF Hollinger et al. 1999
US-IB2 2004 2011 41.8 -88.2 GRA Matamala 2018
US-KFS 2007   39.1 -95.2 GRA Brunsell 2018a
US-Kon 2006   39.1 -96.6 GRA Brunsell 2018b
US-KS2 2003 2006 28.6 -80.7 CSH Powell et al. 2006
US-Lin 2009 2010 36.4 -119.8 CRO Fares 2010
US-LPH 2002   42.5 -72.2 DBF Hadley 2018
US-Me2 2002 2014 44.5 -121.6 ENF Thomas et al. 2009
US-Me3 2004 2009 44.3 -121.6 ENF Vickers et al. 2009
US-Me6 2010   44.3 -121.6 ENF Ruehr et al. 2012
US-MMS 1999   39.3 -86.4 DBF Schmid et al. 2000
US-Mpj 2007   34.4 -106.2 OSH Litvak 2018a
US-MRf 2005   44.6 -123.6 ENF Law 2018
US-Ne1 2001   41.2 -96.5 CRO Verma et al. 2005
US-Ne2 2001   41.2 -96.5 CRO Verma et al. 2005
US-Ne3 2001   41.2 -96.4 CRO Verma et al. 2005
US-NR1 1998   40.0 -105.5 ENF Monson et al. 2002
US-Oho 2004 2013 41.6 -83.8 DBF Noormets et al. 2008
US-PFa 1995   45.9 -90.3 MF Desai et al. 2015
US-Prr 2010 2014 65.1 -147.5 ENF Nakai et al. 2013
US-Ro2 2003 2017 44.7 -93.1 CRO Baker and Griffis 2017
US-SRC 2008 2014 31.9 -110.8 OSH Kurc 2018
US-SRG 2008 2014 31.8 -110.8 GRA Scott et al. 2015
US-SRM 2004 2014 31.8 -110.9 WSA Scott et al. 2009
US-Sta 2005 2009 41.4 -106.8 OSH Ewers and Pendall 2009
US-Syv 2001   46.2 -89.3 MF Desai et al. 2005
US-Ton 2001   38.4 -121.0 WSA Fischer et al. 2007
US-Twt 2009 2017 38.1 -121.7 CRO Hatala et al. 2012
US-UMB 2000   45.6 -84.7 DBF Gough et al. 2008
US-UMd 2007   45.6 -84.7 DBF Gough et al. 2018
US-Var 2000   38.4 -121.0 GRA Fischer et al. 2007
US-WCr 1999   45.8 -90.1 DBF Cook et al. 2004
US-Whs 2007   31.7 -110.1 OSH Scott et al. 2015
US-Wi1 2003 2003 46.7 -91.2 DBF Chen 2003a
US-Wi2 2003 2003 46.7 -91.2 ENF Chen 2003b
US-Wi3 2002 2004 46.6 -91.1 DBF Chen 2005a
US-Wi5 2004 2004 46.7 -91.1 ENF Chen 2004
US-Wi6 2002 2003 46.6 -91.3 OSH Chen 2003c
US-Wi7 2005 2005 46.6 -91.1 OSH Chen 2005a
US-Wi9 2004 2005 46.6 -91.1 ENF Chen 2005b
US-Wjs 2007   34.4 -105.9 OSH Litvak 2018b
US-Wkg 2004 2014 31.7 -109.9 GRA Scott et al. 2010

Data Acquisition, Materials, and Methods

CASA Description

The modeling approach is based on the CASA biogeochemical model (Potter et al. 1993; Randerson et al. 1996). In CASA, NPP is calculated with a light use efficiency model driven by the absorbed fraction of photosynthetically active radiation (fPAR) and scaled by maximum light use efficiency (Emax), temperature scalar (TNPP) and moisture stresses (WNPP) at spatial location (x, y) and time (t) (Eq. 1). WNPP was derived based on a ratio of estimated evapotranspiration to potential evapotranspiration, varying from 0.5 (arid ecosystem) to 1 (very wet ecosystem). TNPP is defined as T1×T2low×T2high. T1 reflects the empirical observation that plants in very cold habitats typically have low maximum growth rate (Eq. 2). T2 reflects the concept that the efficiency of light utilization should be depressed when plants are growing at temperatures displaces from their optimum (Eq. 3 and 4). T2 has an asymmetric bell shape that falls off more quickly at high than at low temperatures. Topt is defined as the air temperature in the month when the NDVI or LAI reaches its maximum for the year.

equations 1-4

On a monthly time step, NPP is allocated to leaves, roots and wood (Eq. 5), with a default allocation ratio of 1:1:1. Each of these pools has a turnover time that specifies the rate at which carbon moves to litter pools (surface fine litter, soil fine litter, coarse woody debris). Carbon in the litterfall pool is either transferred to the microbial and soil organic matter pools or decomposed during the process. Decomposition of dead pool (e.g. litter and soil organic pools) releases carbon, i.e. heterotrophic respiration (Rh), as Eq. 6:

equations 5 and 6

where p is the number of pools, Ci is the carbon content of pool i, ki is the pool-specific decay rate constant, Wresp and Tresp are the effect of soil moisture and temperature on decomposition, and Dε is microbial carbon decomposition efficiency. The effect of temperature on soil carbon fluxes (Tresp) is treated uniformly as an exponential (Q10) response:

equation 7

where Q10 is the multiplicative increase in soil biological activity for a 10 ºC rise in soil temperature and T(x, t) is monthly averaged air temperature.

equation 8

A carbon use efficiency of 0.5 was assumed such that gross primary productivity (GPP) is 2×NPP. Correspondingly, total ecosystem respiration (RECO) would become the sum of NPP and Rh, and net ecosystem exchange (NEE) is equal to RECO – GPP. The data used as input to the model are listed in Section 4.

For 3-hourly simulation, the North American Regional Reanlaysis (NARR) 3-hourly (UTC) air temperature (Tair) and downward shortwave radiation (DWSW) was used to further downscale monthly carbon fluxes. Monthly estimates were distributed to 3-hourly temporal scale with a simple assumption of dependence on light for GPP and temperature for RECO (Olsen and Randerson 2004; Fisher et al. 2016).

Pruned Parameter Sets for Generating L1 Data

Table 2. Perturbed parameter sets used to generate CASA ensemble L1 product.

#Para 1 2 3 4 5 6 7 8 9 10 11 12
DTopt 0 -2 2 0 -2 2 0 -2 2 0 -2 2
Emax 0.25 0.25 0.25 0.5 0.5 0.5 0.75 0.75 0.75 1 1 1
Q10 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4
#Para 13 14 15 16 17 18 19 20 21 22 23 24
DTopt 0 -2 2 0 -2 2 0 -2 2 0 -2 2
Emax 0.25 0.25 0.25 0.5 0.5 0.5 0.75 0.75 0.75 1 1 1
Q10 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2
#Para 25 26 27 28 29 30 31 32 33 34 35 36
DTopt 0 -2 2 0 -2 2 0 -2 2 0 -2 2
Emax 0.25 0.25 0.25 0.5 0.5 0.5 0.75 0.75 0.75 1 1 1
Q10 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6
#Para 37 38 39 40 41 42 43 44 45 (Para 37 – 45 for cropland only)
DTopt 0 -2 2 0 -2 2 0 -2 2
Emax 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25
Q10 1.4 1.4 1.4 1.2 1.2 1.2 1.6 1.6 1.6
DTopt is the adjustment of optimal temperature. 

Pruned Parameter Sets for Generating L2 Data

To further constrain Emax for each biome type, carbon flux measurements during the growing seasons from AmeriFlux and FLUXNET datasets were used to infer the appropriate biome-specific range of Emax according to the light use efficiency model in CASA (Eq. 12). As flux sites are broadly distributed across space, we defined the growing season as months when the NPP is higher than averaged NPP within each year.

NPPobs_in is the inferred NPP value from flux measurement, fPAR is derived from MOD15A2H at each flux site, and PARobs is the ground-measured at each site (for sites lacking PAR observation, we used NLDAS-2 instead). NPP scalars (TNPP and WNPP) are computed using ground-measured precipitation and air temperature (for sites lacking these observations, we used data sampled from PRISM at corresponding flux tower locations).

Table 3. Statistics of Emax inferred from flux tower data for each biome type to generate L2 data.

Biome type WSA CRO DBF ENF MF GRA CSH OSH
Grow Seas Avg 0.51 1.01 0.69 0.64 0.51 0.69 0.47 0.4
Grow Seas STD 0.04 0.37 0.15 0.23 0.29 0.29 0.29 0.15
Emax Samples for full Uncert. [E1, E2, E3] [0.25, 0.50, 0.50] [0.75, 1.00, 1.25] [0.50, 0.75, 0.75] [0.50, 0.75, 0.75] [0.25, 0.50, 0.75] [0.50, 0.75, 1.00] [0.25, 0.50, 0.75] [0.25, 0.50, 0.50]

Table 4. Perturbed parameter sets with constrained PFT-specific Emax used to generate CASA ensemble L2 product.

#Para 1 2 3 4 5 6 7 8 9
DTopt 0 -2 2 0 -2 2 0 -2 2
Emax E1 E1 E1 E1 E1 E1 E1 E1 E1
Q10 1.4 1.4 1.4 1.2 1.2 1.2 1.6 1.6 1.6
#Para 10 11 12 13 14 15 16 17 18
DTopt 0 -2 2 0 -2 2 0 -2 2
Emax E2 E2 E2 E2 E2 E2 E2 E2 E2
Q10 1.4 1.4 1.4 1.2 1.2 1.2 1.6 1.6 1.6
#Para 19 20 21 22 23 24 25 26 27
DTopt 0 -2 2 0 -2 2 0 -2 2
Emax E3 E3 E3 E3 E3 E3 E3 E3 E3
Q10 1.4 1.4 1.4 1.2 1.2 1.2 1.6 1.6 1.6
DTopt is the adjustment of optimal temperature.

Ecoregional Sampling of L2 Ensemble for Generating L2B Data

In addition to the L2 ensemble product, L2B data were included which is the random sampling of L2 ensemble (27 members) based on the ecoregion maps. The L2B file, entitled with “CASA_L2B_Ensemble**”, has 10 members that randomly sampled L2 ensemble member (i.e., parameter set) for each Level-3 ecoregion for both North America and CONUS. Considering the data volume, only GPP and NEE were included for the L2B data. More information about ecoregion maps can be found at https://www.epa.gov/eco-research/ecoregions. L1-3 ecoregion maps are available for NA; L1-4 ecoregion maps are available for CONUS. The supplement contains an R script and converted ecoregion files (*.nc format) in order for users to generate the random sample for the ecoregion maps at other levels or change the number of samples.

Diver Data

Model input Dataset Spatial resolution Time resolution Reference
Conterminous US
fPAR MCD15A2H 463.31 m 8-day Myneni et al. (2015)
Tree and herb covers MOD44B 250 m Yearly Dimiceli et al. (2015)
Precipitation and Tair PRISM 30 ″ Monthly PRISM Climate Group (2016)
DWSW and DWLW1 NDLAS-2 Forcing 0.125 ° Monthly LDAS (2016)
DWSW1 and Tair NARR 32 km 3-hourly NCEP (2005)
Biome type National Forest Type 250 m NA Ruefenacht et al. (2008)
NAFD 30 m NA Goward et al. (2012)
MOD12Q1 IGBP 463.31 m Yearly Friedl et al. (2010)
Clay, silt, sand Fractions CONUS-Soil 1000 m NA Miller and White (1998)
North America
fPAR MCD15A2 1000 m 8-day Myneni et al. (2002)
Tree and herb covers MOD44B 250 m Yearly Dimiceli et al. (2015)
Precipitation, Tair, DWSW, and DWLW1 NARR 32 km Monthly NCEP (2005)
DWSW and Tair NARR 32 km 3-hourly NCEP (2005)
Biome type National Forest Type 250 m NA Ruefenacht et al. (2008)
NAFD 30 m NA Goward et al. (2012)
MOD12Q1 IGBP 463.31 m Yearly Friedl et al. (2010)
Clay, Silt, Sand Fractions NACP MsTMIP Soil Map 0.25 ° NA Liu et al. (2014)
1 DWSW and DWLW are downward shortwave and longwave radiation, respectively.

Guide to Using the R script for Temporal Downscaling

An R script and associated data are provided to generate the L2 ensemble at users’ end for two spatial domains, conterminous United States and North America. The R script uses three packages, including ncdf4, rgdal, and raster. One the users’ end,

  1. Users determine which ecoregional level to work with by defining "EcoregionLevel"; L1-3 data are available for North America; L1-4 data are available for the conterminous US; shapefiles of different levels from the United States Environmental Protection Agency (https://www.epa.gov/eco-research/ecoregions) are included that can be called by the script;
  2. Users define the spatial domain of the random ecoregional sampling: CONUS or NA;
  3. Users set the path of ecoregion files (e.g., if users are working with L3 ecoregions for CONUS, the ecoregion file is CONUS_Eco_Level3_CASAgrid.nc4);
  4. Users define the number of L2B sampling by change "L2BMembers";
  5. Users set the path of L2 files by change "L2Path";
  6. Users select the year(s) ("SampleYear") for sampling;
  7. Users select the carbon flux(es) ("CFluxes") to be sampled;
  8. If users would like to use the previous random samples for another sampling of the same spatial domain, change "Saved_EcoregionRandSamp" to 1 and move the file "EcoregionRandSamp_**.txt" to the output path. This file should be found in the output path when "Saved_EcoregionRandSamp" is set to 0.
  9. Users set the output path ("outputPath").

Questions on using this script can be sent to yuzhou2@clarku.edu or cwilliams@clarku.edu.

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2018

Contact for Data Center Access Information:

References

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Baker, I., Prihodko, L., Denning, A., Goulden, M., Miller, S., & Da Rocha, H. 2008. Seasonal drought stress in the Amazon: Reconciling models and observations. Journal of Geophysical Research: Biogeosciences, 113

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Desai, A.R., Bolstad, P.V., Cook, B.D., Davis, K.J., & Carey, E.V. 2005. Comparing net ecosystem exchange of carbon dioxide between an old-growth and mature forest in the upper Midwest, USA. Agricultural and Forest Meteorology, 128, 33-55

Desai, A.R., Xu, K., Tian, H., Weishampel, P., Thom, J., Baumann, D., Andrews, A.E., Cook, B.D., King, J.Y., & Kolka, R. 2015. Landscape-level terrestrial methane flux observed from a very tall tower. Agricultural and Forest Meteorology, 201, 61-75

Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D.H., Kelly, M., & Townshend, J.R.G. 2015. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Available online: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod44b_v006 (accessed on 26 July 2016)

Ewers, B., & Pendall, E. 2009. AmeriFlux US-Sta Saratoga, doi:10.17190/AMF/1246831

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Fischer, M.L., Billesbach, D.P., Berry, J.A., Riley, W.J., & Torn, M.S. 2007. Spatiotemporal variations in growing season exchanges of CO2, H2O, and sensible heat in agricultural fields of the Southern Great Plains. Earth interactions, 11, 1-21

Fisher, J.B., Sikka, M., Huntzinger, D.N., Schwalm, C., & Liu, J. 2016. 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange. Biogeosciences, 13, 4271-4277

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