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3. Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19

Author

Butterworth, Brian J., Desai, Ankur R., Metzger, Stefan, Townsend, Philip A., Schwartz, Mark D., Petty, Grant W., Mauder, Matthias, Vogelmann, Hannes, Andresen, Christian G., Augustine, Travis J., Bertram, Timothy H., Brown, William O. J., Buban, Michael, Cleary, Patricia, Durden, David J., Florian, Christopher R., Iglinski, Trevor J., Kruger, Eric L., Lantz, Kathleen, Lee, Temple R., Meyers, Tilden P., Mineau, James K., Olson, Erik R., Oncley, Steven P., Paleri, Sreenath, Pertzborn, Rosalyn A., Pettersen, Claire, Plummer, David M., Riihimaki, Laura D., Guzman, Eliceo Ruiz, Sedlar, Joseph, Smith, Elizabeth N., Speidel, Johannes, Stoy, Paul C., Sühring, Matthias, Thom, Jonathan E., Turner, David D., Vermeuel, Michael P., Wagner, Timothy J., Wang, Zhien, Wanner, Luise, White, Loren D., Wilczak, James M., Wright, Daniel B., and Zheng, Ting

Published
2021

7. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Published
2021
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8. Evaluation of soil water content and bulk electrical conductivity across the U.S. Climate Reference Network using two electromagnetic sensors.

Author

Wilson, Timothy B., Kochendorfer, John, Diamond, Howard J., Meyers, Tilden P., Hall, Mark, Lee, Temple R., Saylor, Rick D., Krishnan, Praveena, Leeper, Ronald D., and Palecki, Michael A.

Subjects
SOIL moisture, ELECTRIC conductivity of soils, ELECTRIC conductivity, WATERLOGGING (Soils), SOIL temperature measurement
Abstract

Soil bulk electrical conductivity (BEC) was evaluated alongside soil volumetric water content (VWC) and soil temperature measurements using the HydraProbe (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP) with accuracy range of BEC ≤ 0.3 S m−1, and the time domain reflectometry (TDR)‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP) suitable for BEC up to 0.6 S m−1, at 23 stations of the U.S. Climate Reference Network. Previous evaluations revealed inconsistent performance of both sensors in some clay soils using manufacturer‐recommended calibrations in converting dielectric permittivity measurements to VWC. Here, we found that hourly values of BEC reached 0.6 S m−1 in high clay content soils and exceeded 2 S m−1 in high saline soils, and these high values of BEC were associated with poor performance and failures of both HP and AP sensors. Large values of BEC occurred in predominantly saturated soils where VWC values reached about 0.5 m3 m−3 for saline soils and about 0.7 m3 m−3 for clay soils, while low magnitudes of BEC were associated with low soil water content and seldomly saturated soils. Low hourly BEC values of less than 0.1 S m−1 were observed in wide variety of soil types, where sensor performance was typically excellent. The most influential factor on BEC was high soil water content conditions. Although dielectric permittivity measurements in estimating the soil water content were sensitive to BEC as some high clay content and high salinity soils increased BEC, the impact of large BEC on dielectric permittivity measurements was smaller in the well‐drained top soil layers than in deep soil layers that remained near saturation. Soil temperature had only a small impact on BEC. With high clay content and high salinity, the specific area of clay minerals was also associated with the magnitude of BEC. Core Ideas: Estimated soil bulk electrical conductivity (BEC) and soil volumetric water content (VWC) at soil depths of 5, 10, 20, 50, and 100 cm were evaluated across 23 US Climate Reference Network stations.The estimation of BEC and VWC was based on the use of the 50 MHz HydraProbe and the Acclima 1 GHz time‐domain reflectometry (TDR)‐315L.The evaluation suggested that the clay type, specific surface area, clay content, soil salinity, and soil temperature combined to affect how soil BEC increased and decreased with soil water content. [ABSTRACT FROM AUTHOR]

Published
2024
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10. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., Cinti, Bruno De, Grandcourt, Agnes de, Ligne, Anne De, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, Tommasi, Paul di, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, Dios, Victor Resco de, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Published
2020
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11. Impact of Seasonal Snow‐Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High‐Altitude Mountain Valley

Author

Adler, Bianca, primary, Wilczak, James M., additional, Bianco, Laura, additional, Bariteau, Ludovic, additional, Cox, Christopher J., additional, de Boer, Gijs, additional, Djalalova, Irina V., additional, Gallagher, Michael R., additional, Intrieri, Janet M., additional, Meyers, Tilden P., additional, Myers, Timothy A., additional, Olson, Joseph B., additional, Pezoa, Sergio, additional, Sedlar, Joseph, additional, Smith, Elizabeth, additional, Turner, David D., additional, and White, Allen B., additional

Published
2023
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15. A new reference-quality precipitation gauge wind shield

Author

Kochendorfer, John, Meyers, Tilden P., Hall, Mark E., Landolt, Scott D., and Diamond, Howard J.

Abstract

Gauge-based precipitation measurements suffer from undercatch due to the effects of wind, with solid precipitation measurements especially susceptible to such errors. When it is snowing and windy, unshielded precipitation gauges can catch less than half of the amount of precipitation of a gauge that is protected from the wind. For this reason, the US Climate Reference Network (USCRN) developed a large, double layer, wooden wind shield called the Small Double Fence Intercomparison Reference (SDFIR). In past studies, the SDFIR has been demonstrated to be the most effective wind shield in use in any weather or climate network, reducing solid precipitation undercatch to less than 10 % in wind speeds up to 8 m s−1. However, the wooden SDFIRs are subject to decay, they are difficult to replace and maintain, and they hinder access to maintaining the gauge. For these reasons, a new precipitation gauge wind shield called the Low Porosity Double Fence (LPDF) has been developed for use in the USCRN. Tested at three separate sites chosen for prevalent windy and snowy weather, the precipitation measurements recorded within the LPDF compared well to the SDFIR. After more than two years of measurements, the total precipitation recorded by the LPDF at each individual site differed by ± 1.2 %, and the total LPDF accumulation from all sites was 0.03 % greater than the SDFIR accumulation. For the measurement of solid precipitation, the LPDF-shielded measurements were statistically indistinguishable from the SDFIR, and the time series of accumulation from precipitation gauges shielded by the SDFIR and the LPDF were almost identical. This new wind shield is much smaller and easier to install and maintain than any other reference-quality wind shield for the measurement of solid precipitation, and may be of use within other meteorological, hydrological, and climate networks. It could also serve as a secondary reference precipitation measurement for precipitation intercomparisons held in remote locations where the construction of a full-sized Double Fence Intercomparison Reference (DFIR) shield is not feasible.

Published
2023
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18. On the Efficacy of Monin–Obukhov and Bulk Richardson Surface-Layer Parameterizations over Drylands.

Author

Lee, Temple R., Pal, Sandip, Krishnan, Praveena, Hirth, Brian, Heuer, Mark, Meyers, Tilden P., Saylor, Rick D., and Schroeder, John

Subjects
PARAMETERIZATION, SURFACE of the earth, FRICTION velocity, ATMOSPHERIC models, WIND speed
Abstract

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations' applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity ( u * ), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u * and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations' performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u * ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the RibH parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth's surface covered by drylands. Significance Statement: Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST's limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models. [ABSTRACT FROM AUTHOR]

Published
2023
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19. NOAA Air Resources Laboratory Atmospheric Turbulence and Diffusion Division’s Measurements of Temperature, Humidity and Wind using Small Uncrewed Aircraft Systems to Support Short-Term Weather Forecasting Needs over Complex Terrain

Author

Lee, Temple R., Schuyler, Travis J., Buban, Michael, Dumas, Edward J., Meyers, Tilden P., and Baker, C. Bruce

Abstract

Small uncrewed aircraft systems (sUxS) are now being routinely used not only for sampling atmospheric boundary layer (ABL) processes and land-atmosphere interactions but also have significant potential to improve weather forecasting at National Weather Service (NWS) Weather Forecast Offices (WFOs). In the present study, we used observations obtained from a Meteomatics Meteodrone SSE sUxS flown on 31 days between 20 August and 10 December 2020 near Oliver Springs, Tennessee, located 35 km northwest of Knoxville, Tennessee. We flew the sUxS up to 700 m above ground level, starting around sunrise and continuing every half hour until 3.5–4.0 hours past sunrise under synoptically quiescent, fair weather conditions. These datasets were provided in real time to the local NWS WFO in Morristown, Tennessee and used by forecasters there to assist with short-term operational forecasting needs. The sUxS profiles also provided finescale details on the early-morning transition over complex terrain and how this evolution varied during the late summer to winter period, which can be used to support the initialization of numerical weather prediction models.

Published
2022
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22. Air—Surface Exchange in CHEESEHEAD19: Food for Machine Learning To Scale Energy Fluxes

Author

Butterworth, Brian J., primary, Desai, Ankur R., additional, Metzger, Stefan, additional, Townsend, Philip A., additional, Schwartz, Mark D., additional, Petty, Grant W., additional, Mauder, Matthias, additional, Vogelmann, Hannes, additional, Andresen, Christian G., additional, Augustine, Travis J., additional, Bertram, Timothy H., additional, Brown, William O.J., additional, Buban, Michael, additional, Cleary, Patricia, additional, Durden, David J., additional, Florian, Christopher R., additional, Iglinski, Trevor J., additional, Kruger, Eric L., additional, Lantz, Kathleen, additional, Lee, Temple R., additional, Meyers, Tilden P., additional, Mineau, James K., additional, Olson, Erik R., additional, Oncley, Steven P., additional, Paleri, Sreenath, additional, Pertzborn, Rosalyn A., additional, Pettersen, Claire, additional, Plummer, David M., additional, Riihimaki, Laura D., additional, Ruiz Guzman, Eliceo, additional, Sedlar, Joseph, additional, Smith, Elizabeth N., additional, Speidel, Johannes, additional, Stoy, Paul C., additional, Sühring, Matthias, additional, Thom, Jonathan E., additional, Turner, David D., additional, Vermeuel, Michael P., additional, Wagner, Timothy J., additional, Wang, Zhien, additional, Wanner, Luise, additional, White, Loren D., additional, Wilczak, James M., additional, Wright, Daniel B., additional, and Zheng, Ting, additional

Published
2021
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25. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems

Author

MacBean, Natasha, primary, Scott, Russell L, additional, Biederman, Joel A, additional, Peylin, Philippe, additional, Kolb, Thomas, additional, Litvak, Marcy E, additional, Krishnan, Praveena, additional, Meyers, Tilden P, additional, Arora, Vivek K, additional, Bastrikov, Vladislav, additional, Goll, Daniel, additional, Lombardozzi, Danica L, additional, Nabel, Julia E M S, additional, Pongratz, Julia, additional, Sitch, Stephen, additional, Walker, Anthony P, additional, Zaehle, Sönke, additional, and Moore, David J P, additional

Published
2021
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27. Connecting Land-Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD 2019

Author

Butterworth, Brian J., primary, Desai, Ankur R., additional, Metzger, Stefan, additional, Townsend, Philip A., additional, Schwartz, Mark D., additional, Petty, Grant W., additional, Mauder, Matthias, additional, Vogelmann, Hannes, additional, Andresen, Christian G., additional, Augustine, Travis J., additional, Bertram, Timothy H., additional, Brown, William O.J., additional, Buban, Michael, additional, Cleary, Patricia, additional, Durden, David J., additional, Florian, Christopher R., additional, Ruiz Guzman, Eliceo, additional, Iglinski, Trevor J., additional, Kruger, Eric L., additional, Lantz, Kathleen, additional, Lee, Temple R., additional, Meyers, Tilden P., additional, Mineau, James K., additional, Olson, Erik R., additional, Oncley, Steven P., additional, Paleri, Sreenath, additional, Pertzborn, Rosalyn A., additional, Pettersen, Claire, additional, Plummer, David M., additional, Riihimaki, Laura, additional, Sedlar, Joseph, additional, Smith, Elizabeth N., additional, Speidel, Johannes, additional, Stoy, Paul C., additional, Sühring, Matthias, additional, Thom, Jonathan E., additional, Turner, David D., additional, Vermeuel, Michael P., additional, Wagner, Timothy J., additional, Wang, Zhien, additional, Wanner, Luise, additional, White, Loren D., additional, Wilczak, James M., additional, Wright, Daniel B., additional, and Zheng, Ting, additional

Published
2020
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28. Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability.

Author

Mahmud, Kashif, Scott, Russell L., Biederman, Joel A., Litvak, Marcy E., Kolb, Thomas, Meyers, Tilden P., Krishnan, Praveena, Bastrikov, Vladislav, and MacBean, Natasha

Subjects
CARBON cycle, BIOSPHERE, ARID regions, ECOSYSTEMS, CARBON sequestration, BIOMASS
Abstract

Drylands occupy ∼40% of the land surface and are thought to dominate global carbon (C) cycle inter‐annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2 fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site‐years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or "pivot" between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long‐term carbon‐climate feedbacks. Plain Language Summary: Drylands occupy ∼40% of the land surface and are thought to dominate the inter‐annual variability and long‐term trend of the global carbon cycle. Therefore, it is imperative that global terrestrial biosphere models (TBMs) are able to accurately predict dryland vegetation and carbon cycle processes. However, models have not been widely tested or calibrated against in situ dryland ecosystem CO2 fluxes. Here, we address this gap using a data assimilation system and daily net CO2 flux data from 12 southwest US Ameriflux sites spanning forest, shrub and grass dryland ecosystems to optimize the carbon cycle related parameters of the ORCHIDEE TBM. We find that before parameter optimization, the model drastically underestimates both the mean annual magnitude and inter‐annual variability of net CO2 flux. By testing different optimization scenarios, we showed that optimizing model parameters related to phenology dramatically improves the model's ability to capture the net CO2 flux inter‐annual variability. At the high elevation forested sites, optimizing parameters related to C allocation, respiration and biomass and soil C turnover reduces the model underestimate in simulated mean annual NEE. Our study demonstrates that all global TBMs need to be calibrated specifically for dryland ecosystems before they are used to determine dryland contributions to global carbon cycle variability and long‐term carbon‐climate feedbacks. Key Points: ORCHIDEE terrestrial biosphere model radically underestimates dryland mean annual net CO2 fluxes and their inter‐annual variability (IAV)Optimizing phenology, carbon allocation, and respiration parameters are crucial for capturing net CO2 flux mean and IAVModels need to be optimized against dryland CO2 flux data to achieve accurate predictions of dryland's role in global C cycle variability [ABSTRACT FROM AUTHOR]

Published
2021
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32. CO 2 exchange and evapotranspiration across dryland ecosystems of southwestern North America

Author

Biederman, Joel A., primary, Scott, Russell L., additional, Bell, Tom W., additional, Bowling, David R., additional, Dore, Sabina, additional, Garatuza‐Payan, Jaime, additional, Kolb, Thomas E., additional, Krishnan, Praveena, additional, Krofcheck, Dan J., additional, Litvak, Marcy E., additional, Maurer, Gregory E., additional, Meyers, Tilden P., additional, Oechel, Walter C., additional, Papuga, Shirley A., additional, Ponce‐Campos, Guillermo E., additional, Rodriguez, Julio C., additional, Smith, William K., additional, Vargas, Rodrigo, additional, Watts, Christopher J., additional, Yepez, Enrico A., additional, and Goulden, Michael L., additional

Published
2017
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34. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America.

Author

Biederman, Joel A., Scott, Russell L., Bell, Tom W., Bowling, David R., Dore, Sabina, Garatuza‐Payan, Jaime, Kolb, Thomas E., Krishnan, Praveena, Krofcheck, Dan J., Litvak, Marcy E., Maurer, Gregory E., Meyers, Tilden P., Oechel, Walter C., Papuga, Shirley A., Ponce‐Campos, Guillermo E., Rodriguez, Julio C., Smith, William K., Vargas, Rodrigo, Watts, Christopher J., and Yepez, Enrico A.

Subjects
ARID regions, EVAPOTRANSPIRATION, VEGETATION & climate, PHOTOSYNTHESIS, REMOTE sensing
Abstract

Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such analyses are poorly constrained by measured CO2 exchange in drylands. Here we address this observation gap with eddy covariance data from 25 sites in the water-limited Southwest region of North America with observed ranges in annual precipitation of 100-1000 mm, annual temperatures of 2-25°C, and records of 3-10 years (150 site-years in total). Annual fluxes were integrated using site-specific ecohydrologic years to group precipitation with resulting ecosystem exchanges. We found a wide range of carbon sink/source function, with mean annual net ecosystem production ( NEP) varying from -350 to +330 gCm−2 across sites with diverse vegetation types, contrasting with the more constant sink typically measured in mesic ecosystems. In this region, only forest-dominated sites were consistent carbon sinks. Interannual variability of NEP, gross ecosystem production ( GEP), and ecosystem respiration (Reco) was larger than for mesic regions, and half the sites switched between functioning as C sinks/C sources in wet/dry years. The sites demonstrated coherent responses of GEP and NEP to anomalies in annual evapotranspiration ( ET), used here as a proxy for annually available water after hydrologic losses. Notably, GEP and Reco were negatively related to temperature, both interannually within site and spatially across sites, in contrast to positive temperature effects commonly reported for mesic ecosystems. Models based on MODIS satellite observations matched the cross-site spatial pattern in mean annual GEP but consistently underestimated mean annual ET by ~50%. Importantly, the MODIS-based models captured only 20-30% of interannual variation magnitude. These results suggest the contribution of this dryland region to variability of regional to global CO2 exchange may be up to 3-5 times larger than current estimates. [ABSTRACT FROM AUTHOR]

Published
2017
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35. Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2Flux and its Inter‐Annual Variability

Author

Mahmud, Kashif, Scott, Russell L., Biederman, Joel A., Litvak, Marcy E., Kolb, Thomas, Meyers, Tilden P., Krishnan, Praveena, Bastrikov, Vladislav, and MacBean, Natasha

Abstract

Drylands occupy ∼40% of the land surface and are thought to dominate global carbon (C) cycle inter‐annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site‐years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long‐term carbon‐climate feedbacks. Drylands occupy ∼40% of the land surface and are thought to dominate the inter‐annual variability and long‐term trend of the global carbon cycle. Therefore, it is imperative that global terrestrial biosphere models (TBMs) are able to accurately predict dryland vegetation and carbon cycle processes. However, models have not been widely tested or calibrated against in situ dryland ecosystem CO2fluxes. Here, we address this gap using a data assimilation system and daily net CO2flux data from 12 southwest US Ameriflux sites spanning forest, shrub and grass dryland ecosystems to optimize the carbon cycle related parameters of the ORCHIDEE TBM. We find that before parameter optimization, the model drastically underestimates both the mean annual magnitude and inter‐annual variability of net CO2flux. By testing different optimization scenarios, we showed that optimizing model parameters related to phenology dramatically improves the model's ability to capture the net CO2flux inter‐annual variability. At the high elevation forested sites, optimizing parameters related to C allocation, respiration and biomass and soil C turnover reduces the model underestimate in simulated mean annual NEE. Our study demonstrates that all global TBMs need to be calibrated specifically for dryland ecosystems before they are used to determine dryland contributions to global carbon cycle variability and long‐term carbon‐climate feedbacks. ORCHIDEE terrestrial biosphere model radically underestimates dryland mean annual net CO2fluxes and their inter‐annual variability (IAV)Optimizing phenology, carbon allocation, and respiration parameters are crucial for capturing net CO2flux mean and IAVModels need to be optimized against dryland CO2flux data to achieve accurate predictions of dryland's role in global C cycle variability ORCHIDEE terrestrial biosphere model radically underestimates dryland mean annual net CO2fluxes and their inter‐annual variability (IAV) Optimizing phenology, carbon allocation, and respiration parameters are crucial for capturing net CO2flux mean and IAV Models need to be optimized against dryland CO2flux data to achieve accurate predictions of dryland's role in global C cycle variability

Published
2021
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