Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-5007-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-5007-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Sep 2025
Data description paper |
| 29 Sep 2025
| 29 Sep 2025
Over three decades, and counting, of near-surface turbulent flux measurements from the Atmospheric Radiation Measurement (ARM) user facility
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
David P. Billesbach
Department of Biological Systems Engineering, School of Natural Resources, University of Nebraska, Lincoln, NE 68583, USA
retired
Sebastien Biraud
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Stephen Chan
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Richard Hart
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
retired
deceased
Evan Keeler
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Jenni Kyrouac
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Sujan Pal
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Mikhail Pekour
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Sara L. Sullivan
independent researcher
Adam Theisen
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Matt Tuftedal
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
David R. Cook
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
retired
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long-range transport of aerosols from the continents was also identified.
Sébastien Roche, Kimberly Strong, Debra Wunch, Joseph Mendonca, Colm Sweeney, Bianca Baier, Sébastien C. Biraud, Joshua L. Laughner, Geoffrey C. Toon, and Brian J. Connor
Atmos. Meas. Tech., 14, 3087–3118, https://doi.org/10.5194/amt-14-3087-2021, https://doi.org/10.5194/amt-14-3087-2021, 2021
Short summary
Short summary
We evaluate CO2 profile retrievals from ground-based near-infrared solar absorption spectra after implementing several improvements to the GFIT2 retrieval algorithm. Realistic errors in the a priori temperature profile (~ 2 °C in the lower troposphere) are found to be the leading source of differences between the retrieved and true CO2 profiles, differences that are larger than typical CO2 variability. A temperature retrieval or correction is critical to improve CO2 profile retrieval results.
Cited articles
Andreas, E. L.: The effects of volume averaging on spectra measured with a Lyman-alpha hygrometer, J. Appl. Meteorol., 20, 467–475, 1981.
Bagley, J. E., Kueppers, L. M., Billesbach, D. P., Williams, I. N., Biraud, S. C., and Torn, M. S.: The influence of land cover on surface energy partitioning and evaporative fraction regimes in the US Southern Great Plains, J. Geophys. Res.-Atmos., 122, 5793–5807, 2017.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem–scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.
Baldocchi, D., Novick, K., Keenan, T., and Torn, M.: AmeriFlux: Its Impact on our understanding of the “breathing of the biosphere”, after 25 years, Agr. Forest Meteorol., 348, 109929, https://doi.org/10.1016/j.agrformet.2024.109929, 2024.
Bao, T., Xu, X., Jia, G., Billesbach, D. P., and Sullivan, R. C.: Much stronger tundra methane emissions during autumn freeze than spring thaw, Glob. Change Biol., 27, 376–387, https://doi.org/10.1111/gcb.15421, 2021.
Barr, A. G., King, K. M., Gillespie, T. J., Den Hartog, G., and Neumann, H. H.: A comparison of Bowen ratio and eddy correlation sensible and latent heat flux measurements above deciduous forest, Bounda.-Lay. Meteorol., 71, 21–41, 1994.
Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh, H. A., Cleverly, J., Resco de Dios, V., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M. J., Macfarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R. L., Prober, S. M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Yee, M. S., Tapper, N. J., van Gorsel, E., Vote, C., Walker, J., and Wardlaw, T.: An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, 2016.
Billesbach, D. P.: Estimating uncertainties in individual eddy covariance flux measurements: A comparison of methods and a proposed new method, Agr. Forest Meteorol., 151, 394–405, 2011.
Billesbach, D. P.: AmeriFlux and Methane (AMCMETHANE) VAP, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1508268, 2012.
Billesbach, D. P. and Sullivan, R. C.: FLUXNET-CH4 US-A03 ARM-AMF3-Oliktok, FLUXNET [data set], https://doi.org/10.18140/FLX/1669661, 2020a.
Billesbach, D. P. and Sullivan, R. C.: FLUXNET-CH4 US-A10 ARM-NSA-Barrow, FLUXNET [data set], https://doi.org/10.18140/FLX/1669662, 2020b.
Billesbach, D. P., Fischer, M. L., Torn, M. S., and Berry, J. A.: A portable eddy covariance system for the measurement of ecosystem–atmosphere exchange of CO2, water vapor, and energy, J. Atmos. Ocean. Tech., 21, 639–650, 2004.
Billesbach, D. P., Chan, S. W. , Cook, D. R., Papale, D. Bracho-Garrillo, R., Verfallie, J., Vargas, R., and Biraud, S. C.: Effects of the Gill-Solent WindMaster-Pro “w-boost” firmware bug on eddy covariance fluxes and some simple recovery strategies, Agr. Forest Meteorol., 265, 145–151, 2019.
Biraud, S. and Chan, S.: 30co2flx4m (b1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1989774, 2002a.
Biraud, S. and Chan, S.: 30co2flx25m (b1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1989776, 2002b.
Biraud, S. and Chan, S.: 30co2flx60m (b1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1992202, 2002c.
Biraud, S. and Chan, S.: 30co2flx4mmet (b1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1989773, 2002d.
Biraud, S., Fischer, M., Chan, S., and Torn, M.: AmeriFlux FLUXNET-1F US-ARM ARM Southern Great Plains site- Lamont, Ver. 3-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1854366, 2022.
Biraud, S., Fischer, M., Chan, S., and Torn, M.: AmeriFlux BASE US-ARM ARM Southern Great Plains site- Lamont, Ver. 13-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1246027, 2024.
Burba, G. and Anderson, D.: A brief practical guide to eddy covariance flux measurements: principles and workflow examples for scientific and industrial applications, Li-Cor Biosciences, https://books.google.com/books?hl=en&lr=&id=mCsI1_8GdrIC&oi=fnd&pg=PA6&dq=A+brief+practical+guide+to+eddy+covariance+flux+measurements:+principles+and+workflow+examples+for+30+scientific+and+industrial+applications,&ots=TMQm22PodY&sig=vrItwwiPc4ZMKFW69gEd7YZ5xPo#v=onepage&q=A%20brief%20practical%20guide%20to%20eddy%20covariance%20flux%20measurements%3A%20principles%20and%20workflow%20examples%20for%2030%20scientific%20and%20industrial%20applications%2C&f=false, 2010.
Butterworth, B. J., Desai, A. R., Durden, D., Kadum, H., LaLuzerne, D., Mauder, M., Metzger, S., Paleri, S., and Wanner, L.: Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance, Front. Earth Sci., 11, 1251138, https://doi.org/10.3389/feart.2023.1251138, 2024.
Chan, S. W. and Biraud, S. C.: Carbon Dioxide Flux Measurement System (CO2FLX) instrument handbook, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1020279, 2022.
Chu, H., Baldocchi, D. D., John, R., Wolf, S., and Reichstein, M.: Fluxes all of the time? A primer on the temporal representativeness of FLUXNET, J. Geophys. Res.-Biogeo., 122, 289–307, 2017.
Chu, H., Luo, X., Ouyang, Z., Ouyang, Z., Chan, S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger. S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N.A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles. J. F., Knox, S., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agr. Forest Meteorol., 301–302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021.
Cook, D. R.: Soil Temperature And Moisture Profile (STAMP) system handbook, DOE/SC-ARM-TR-186, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1332724, 2016a.
Cook, D. R.: Soil Water And Temperature System (SWATS) instrument handbook, DOE/SC-ARM-TR-063, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1251383, 2016b.
Cook, D. R. and Sullivan, R. C.: Energy Balance Bowen Ratio (EBBR) handbook, DOE/SC-ARM-TR-037, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1020562, 2025a.
Cook, D. R. and Sullivan, R. C.: Eddy Correlation flux measurement system (ECOR) instrument handbook, DOE/SC-ARM/TR-052, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1467448, 2025b.
Cook, D. R. and Sullivan, R. C.: Surface Energy Balance System (SEBS) instrument handbook, DOE/SC-ARM-TR-092, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1004944, 2025c.
Daub, B. J. and Lareau, N. P.: Observed covariations in boundary layer and cumulus cloud-layer processes, J. Appl. Meteorol. Clim., 61, 1497–1508, 2022.
Ershadi, A., McCabe, M. F., Evans, J. P., Chaney, N. W., and Wood, E. F.: Multi-site evaluation of terrestrial evaporation models using FLUXNET data, Agr. Forest Meteorol., 187, 46–61, 2014.
Feldman, D. R., Aiken, A. C., Boos, W. R., Carroll, R. W. H, Chandrasekar, V., Collis, S., Creamean, J. M., de Boer, G., Deems, J., DeMott, P. J., Fan, J., Flores, A. N., Gochis, D., Grover, M., Hill, T. C. J., Hodshire, A., Hulm, E., Hume, C. C., Jackson, R., Junyent, F., Kennedy, A., Kumjian, M., Levin, E. J. T., Lundquist, J. D., O’Brien, J., Raleigh, M. S., Reithel, J., Rhoades, A., Rittger, K., Rudisill, W., Sherman, Z., Siirila-Woodburn, E., Skiles, S. M., Smith, J. N., Sullivan, R. C., Theisen, A., Tuftedal, M., Varble, A. C., Wiedlea, A., Wielandt, S., Williams, K., and Xu, Z.: The Surface Atmosphere Integrated field Laboratory (SAIL) campaign, B. Am. Meteorol. Soc., 104, E2192–E2222, https://doi.org/10.1175/bams-d-22-0049.1, 2023.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K., Miralles, D. D., Perret, J., Lagouarde, J-P., Waliser, D., Purdy, A. J., French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.: The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, Water Resour. Res., 53, 2618–2626, 2017.
Foken, T., Leuning, R., Oncley, S. R., Mauder, M., and Aubinet, M.: Corrections and data quality control. Eddy Covariance: A Practical Guide to Measurement and Data Analysis, https://doi.org/10.1007/978-94-007-2351-1_4, 85–131, 2012.
Franssen, H. J. H., Stöckli, R., Lehner, I., Rotenberg, E., and Seneviratne, S. I.: Energy balance closure of eddy-covariance data: A multisite analysis for European FLUXNET stations, Agr. Forest Meteorol., 150, 1553–1567, 2010.
Gaustad, K.: 30qcecor, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1097546, 2003.
Gaustad, K. and Xie, S.: 30baebbr, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1027268, 1993.
Helbig, M., Gerken, T., Beamesderfer, E., Baldocchi, D. D., Banerjee, T., Biraud, S. C., Brown, W. O. J., Brunsell, N. A., Burakowski, W. A., Burns, S. P., Butterworth. B. J., Chan, W. S., Davis, K. J., Desai, A. R., Fuentes, J. D., Hollinger, D. Y., Kljun, N., Mauder, M., Novick, K. A., Perkins, J. M., Rahn, D. A., Rey-Sanchez, C., Santanello, J. A., Scott, R. L., Seyednasrollahm, B., Stoy, P. C., Sullivan, R. C., Vilà-Guerau de Arellano, J., Wharton, S., Yi, C., and Richardson, A. D.: Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions, Agr. Forest Meteorol., 307, 108509, https://doi.org/10.1016/j.agrformet.2021.108509, 2021.
Hickmon, N.: Field campaign guidelines, DOE/SC-ARM-14-032, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1236496, 2023.
Hojstrup, J.: A statistical data screening procedure, Meas. Sci. Technol., 4, 153–157, https://doi.org/10.1088/0957-0233/4/2/003, 1993.
Hukseflux Thermal Sensors B.V.: HFP01 & HFP03 heat flux plate heat flux sensor user manual, Hukseflux Thermal Sensors B.V., v2326, https://www.hukseflux.com/uploads/product-documents/HFP01_HFP03_manual_v2326.pdf (last access: 8 September 2025), 2023.
Kaimal, J. C.: The effect of vertical line averaging on the spectra of temperature and heat-flux, Q. J. Roy. Meteor. Soc., 94, 149–155, 1968.
Keeler, E., Burk, K., and Kyrouac, J.: Balloon-borne sounding system (BBSS), WNPN output data, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1595321, 2022.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLX4M), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1287574, 2015a.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLX25M), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1287575, 2015b.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLX60M), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1287576, 2015c.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLXSOIL), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1313010, 2015d.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLXRAD4M), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1313017, 2016a.
Koontz, A., Biraud, S., and Chan, S.: Carbon Dioxide Flux Measurement Systems (CO2FLXSOILAUX), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1313016, 2016b.
Kristensen, L. and Fitzjarrald, D. R.: The effect of line averaging on scalar flux measurements with a sonic anemometer near the surface, J. Atmos. Ocean. Tech., 1, 138–146, 1984.
Kyrouac, J., Ermold, B., and Cook, D.: ARM: Soil Water And Temperature Profiling System (SWATS): soil temp and water profiles, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1150274, 1996
Kyrouac, J., Cook, D., Ermold, B., Pal, S., Sullivan, R., and Keeler, E.: Soil Temperature and Moisture Profiles, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1238260, 2016.
Kyrouac, J., Shi, Y., and Tuftedal, M.: met.b1, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1786358, 2021.
Lee, X. and Massman, W. J.: A perspective on thirty years of the Webb, Pearman and Leuning density corrections, Bound.-Lay. Meteorol., 139, 37–59, 2011.
LI-COR Biosciences: EddyPro Software Instruction Manual, Version 7.0. Lincoln, https://www.licor.com/support/EddyPro/manuals.html (last access: 8 September 2025), 2021.
Liu, Z., Ichii, K., Yamamoto, Y., Ueyama, M., Kobayashi, H., Hiyama, T., Kotani, A., Maximov, T., Sullivan, R. C., and Biraud, S.: Can sub-daily LST be constructed in high-latitude regions using polar orbiting satellites?, IEEE Geosci. Remote S., 22, 1–5, https://doi.org/10.1109/LGRS.2025.3546797, 2025.
Massman, W. and Clement, R.: Uncertainty in eddy covariance flux estimates resulting from spectral attenuation, in: Handbook of micrometeorology: A guide for surface flux measurement and analysis, 67–99, Springer, https://doi.org/10.1007/1-4020-2265-4_4, 2004.
Massman, W. J.: A simple method for estimating frequency response corrections for eddy covariance systems, Agr. Forest Meteorol., 104, 185–198, 2000.
Massman, W. J.: Reply to comment by Rannik on “A simple method for estimating frequency response corrections for eddy covariance systems”, Agr. Forest Meteorol., 107, 247–251, 2001.
Matamala, R.: AmeriFlux BASE US-IB2 Fermi National Accelerator Laboratory-Batavia (Prairie site), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1246066, 2019.
Mauder, M. and Foken, T.: Documentation and instruction manual of the eddy-covariance software package TK3 (update), Eigenverlag, ISSN 1614-8916, 2015.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.: Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. N.Y. Acad. Sci., 1436, 19–35, 2019.
Moncrieff, J. B., Massheder, J. M., de Bruin, H., Ebers, J., Friborg, T., Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., and Verhoef, A.: A system to measure surface fluxes of momentum, sensible heat, water vapor and carbon dioxide, J. Hydrol., 188–189, 589–611, 1997.
NSF Unidata: Network Common Data Form (NetCDF), UCAR/NSF Unidata Program Center, Boulder, CO [software], https://doi.org/10.5065/D6H70CW6, 2025.
Oehri, J., Schaepman-Strub, G., Kim, J.-S., Grysko, R., Kropp, H., Grünberg, I., Zemlianskii, V., Sonnentag, O., Euskirchen, E. S., Chacko, M. R., Muscari, G., Blanken, P. D., Dean, J. F., di Sarra, A., Harding, R. J., Sobota, I., Kutzbach, L., Plekhanov, E., Riihelä, A., Boike, J., Miller, N. B., Beringer, J., Lápez-Blanco, E., Stoy, P. C., Sullivan, R. C., Kejna, M., Parmentier, F.-J. W., Gamon, J. A., Mastepanov, M., Wille, C., Jackowicz-Korczynski, M., Karger, D. N., Quinton, W. L., Putkonen, J., van As, D., Christensen, T. R., Hakuba, M .Z., Stone, R. S., Metzger, S., Vandecrux, B., Frost, G. V., Wild, M., Hansen, B., Meloni, D., Domine, F., te Beest, M., Sachs, T., Kalhori, A., Rocha, A. V., Williamson, S. N., Morris, S., Atchley, A. L., Essery, R., Runkle, B. R. K., Holl, D., Riihimaki, L. D., Iwata, H., Schuur, E. A. G., Cox, C., Grachev, A. A., McFadden, J. P., Fausto, R. S., Goeckede, M., Ueyama, M., Pirk, N., de Boer, J., Bret-Harte, M. S., Leppäranta, M., Steffen, K., Friborg, T., Ohmura, A., Edgar, C. W., Olofsson, J., Chambers, S. D.: Vegetation type is an important predictor of the arctic summer land surface energy budget, Nat. Commun., 13, 1–12, https://doi.org/10.1038/s41467-022-34049-3, 2022.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., and Humphrey, M.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Pekour, M. S.: New Eddy Correlation System for ARM SGP Site, in: Fourteenth ARM Science Team Meeting Proceedings, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://www.arm.gov/publications/proceedings/conf14/extended_abs/pekour-ms.pdf (last access: 8 September 2025), 2004.
Phillips, T. J., Klein, S. A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I. N., Santanello, J. A., Cook, D. R., and Torn, M. S.: Using ARM observations to evaluate climate model simulations of land-atmosphere coupling on the US Southern Great Plains, J. Geophys. Res.-Atmos., 122, 11524–11548, 2017.
Qin, H., Klein, S. A., Ma, H.-Y., Van Weverberg, K., Feng, Z., Chen, X., Best, M., Hu, H., Leung, L. R., Morcrette, C. J., Rumbold, H., and Webster, S.: Summertime near-surface temperature biases over the central United States in convection-permitting simulations, J. Geophys. Res.-Atmos., 128, e2023JD038624, https://doi.org/10.1029/2023JD038624, 2023.
Raz-Yaseef, N., Billebach, D. P., Fischer, M. L., Biraud, S. C., Gunter, S. A., Bradford, J. A., and Torn, M. S.: Vulnerability of crops and native grasses to summer drying in the US Southern Great Plains, Agr. Ecosyst. Environ., 213, 209–218, 2015.
Reichl, K., Shi, Y., Howie, J., Biraud, S., Reichl, K., Moyes, A., and Curtis, J.: amc.b1 datastream, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1406260, 2012.
Schmidt, A., Hanson, C., Chan, W. S., and Law, B. E.: Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network, J. Geophys. Res.-Biogeo., 117, G04014, https://doi.org/10.1029/2012JG002100, 2012.
Seneviratne, S.I., Corti, T., Davin, E.L., Hirschi, M., Jaeger, E.B., Lehner, I., Orlowsky, B. and Teuling, A.J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, 2010.
Stokes, G. M. and Schwartz, S. E.: The Atmospheric Radiation Measurement (ARM) Program: Programmatic background and design of the cloud and radiation test bed, B. Am. Meteorol. Soc., 75, 1201–1222, 1994.
Sullivan, R. C., Ermold, B., Pal, S., and Keeler, E.: 30ebbr, Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1023895, 1993.
Sullivan, R. C., Billesbach, D., Keeler, E., Ermold, B., and Pal, S.: 30ecor (a1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1879993, 1997.
Sullivan, R. C., Keeler, E., Pal, S., and Kyrouac, J.: sebs (b1), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1984921, 2010.
Sullivan, R. C., Cook, D., Shi, Y., Keeler, E., and Pal, S.: ARM Instrument: Eddy Correlation flux measurement smart flux system (ECORSF), Atmospheric Radiation Measurement (ARM) [data set], https://doi.org/10.5439/1494128, 2019a.
Sullivan, R. C., Cook, D. R., Ghate, V. P., Kotamarthi, V. R., and Feng, Y.: Improved spatiotemporal representativeness and bias reduction of satellite-based evapotranspiration retrievals via use of in situ meteorology and constrained canopy surface resistance, J. Geophys. Res.-Biogeo., 124, 342–352, https://doi.org/10.1029/2018JG004744, 2019b.
Sullivan, R. C., Kotamarthi, V. R., and Feng, Y.: Recovering evapotranspiration trends from biased CMIP5 simulations and sensitivity to changing climate over North America, J. Hydrometeorol., 20, 1619–1633, https://doi.org/10.1175/JHM-D-18-0259.1, 2019c.
Sullivan, R., Billesbach, D., and Cook, D.: Intercomparison data for ARM near-surface turbulent fluxes at Fermilab and SGP, Zenodo [data set], https://doi.org/10.5281/zenodo.14261417, 2024.
Sullivan, R., Billesbach, D., Cook, D., and Biraud, B.: AmeriFlux BASE US-A03 ARM-AMF3-Oliktok, Ver. 5-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1498752, 2025a.
Sullivan, R., Billesbach, D., Cook, D., and Biraud, B.: AmeriFlux BASE US-A10 ARM-NSA-Barrow, Ver. 4-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1498753, 2025b.
Tang, S., Xie, S., Zhang, M., Tang, Q., Zhang, Y., Klein, S. A., Cook, D. R., and Sullivan, R. C.: Differences in eddy-correlation and energy-balance surface turbulent heat flux measurements and their impacts on the large-scale forcing fields at the ARM SGP site, J. Geophys. Res.-Atmos., 124, 3301–3318, https://doi.org/10.1029/2018JD029689, 2019a.
Tang, S., Xie, S., Zhang, Y., and Cook, D. R.: The QCECOR Value-Added Product: Quality-Controlled Eddy CORrelation flux measurements, DOE/SC-ARM-TR-223, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1557426, 2019b.
Tao, C., Xie, S., Sullivan, R. C., Tang, S., Zhang, Y., Cook, D. R., and Gaustad, K. L.: The QCECOR Value-Added Product: Quality-Controlled Eddy Correlation flux measurements, DOE/SC-ARM-TR-223, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1557426, 2024.
Theisen, A., Kehoe, K., Sherman, Z., Jackson, B., Grover, M., Sockol, A. J., Godine, C., Hemedinger, J., O'Brien, J., Kyrouac, J., Levin, M., and Hackel, D.: ARM-DOE/ACT: ACT Release Version 2.1.4 (v2.1.4), Zenodo [software], https://doi.org/10.5281/zenodo.13685523, 2024.
Tian, J., Zhang, Y., Klein, S. A., Öktem, R., and Wang, L.: How does land cover and its heterogeneity length scales affect the formation of summertime shallow cumulus clouds in observations from the US Southern Great Plains?, Geophys. Res. Lett., 49, e2021GL097070, https://doi.org/10.1029/2021GL097070, 2022.
Turner, D. D. and Ellingson, R. G.: Introduction (to The Atmospheric Radiation Measurement (ARM) Program: The First 20 Years), Meteorol. Monogr., 57, v–x, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0001.1, 2016.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R., Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting eddy-covariance flux underestimates over a grassland, Agr. Forest Meteorol., 103, 279–300, 2000.
U.S. Department of Energy: Atmospheric Radiation Measurement (ARM) User Facility, https://arm.gov, last access: 8 September 2025a.
U.S. Department of Energy: Atmospheric Radiation Measurement (ARM) Data Discovery, https://adc.arm.gov/discovery, last access: 8 September 2025b.
Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P.: A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sens. Environ., 139, 35–49, 2013.
Wakefield, R. A., Turner, D. D., Rosenberger, T., Heus, T., Wagner, T. J., Santanello, J., and Basara, J.: A methodology for estimating the energy and moisture budget of the convective boundary layer using continuous ground-based infrared spectrometer observations, J. Appl. Meteorol. Clim., 62, 901–914, 2023.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water vapour transfer, Q. J. Roy. Meteor. Soc., 106, 85–100, 1980.
Wesely, M. L., Cook, D. R., and Coulter, R. L.: Surface heat flux data from energy balance Bowen ratio systems, in: Ninth Symposium on Meteorological Observations and Instrumentation, Argonne National Lab.(ANL), Argonne, IL, United States, OSTI ID 69120, https://www.osti.gov/biblio/69120 1995.
Williams, I. N. and Torn, M. S.: Vegetation controls on surface heat flux partitioning, and land-atmosphere coupling, Geophys. Res. Lett., 42, 9416–9424, 2015.
Yamamoto, S., Saigusa, N., Gamo, M., Fujinuma, Y., Inoue, G., and Hirano, T.: Findings through the AsiaFlux network and a view toward the future, J. Geogr. Sci., 15, 142–148, 2005.
Yang, Y., Roderick, M. L., Guo, H., Miralles, D. G., Zhang, L., Fatichi, S., Luo, X., Zhang, Y., McVicar, T. R., and Tu, Z.: Evapotranspiration on a greening Earth, Nat. Rev. Earth Environ., 4, 626–641, 2023.
Zolkos, S., Tank, S. E., Kokelj, S. V., Striegl, R. G., Shakil, S., Voigt, C., Sonnentag, O., Quinton, W. L., Schuur, E. A. G., Zona, D., Lafleur, P. M., Sullivan, R. C., Ueyama, M., Billesbach, D., Cook, D., Humphreys, E. R., and Marsh, P.: Permafrost landscape history shapes fluvial chemistry, ecosystem carbon balance, and potential trajectories of future change, Global Biogeochem. Cy., 36, e2022GB007403, https://doi.org/10.1029/2022gb007403, 2022.
Short summary
Turbulent fluxes quantify the exchange of energy, water, or trace gases into and out of the atmosphere. The U.S. Department of Energy Atmospheric Radiation Measurement user facility has been making atmospheric measurements since the early 1990s, including measurements of turbulent fluxes using two well-established methods: the energy balance Bowen ratio and eddy covariance. This paper documents key aspects of these datasets, including their history, changes through time, and best use practices.
Turbulent fluxes quantify the exchange of energy, water, or trace gases into and out of the...
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