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Chu, H., Luo, X., Ouyang, Z., Chan, W. 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. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., 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., Zona, D.
Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements …
Journal: Agricultural And Forest Meteorology, Volume 301-302: 108350 (2021), ISBN . DOI: 10.1016/j.agrformet.2021.108350 Sites: CA-ARB, CA-ARF, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-DBB, CA-ER1, CA-Gro, CA-Let, CA-Man, CA-MR3, CA-MR5, CA-Na1, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Oas, CA-Obs, CA-Ojp, CA-Qc2, CA-Qcu, CA-Qfo, CA-SCC, CA-SF1, CA-SF2, CA-SF3, CA-SJ2, CA-SJ3, CA-TP1, CA-TP3, CA-TP4, CA-TPD, CA-WP1, US-A03, US-A10, US-A32, US-A74, US-ADR, US-AR1, US-AR2, US-ARb, US-ARc, US-ARM, US-Aud, US-Bar, US-Bi1, US-Bi2, US-Bkg, US-Blk, US-Blo, US-Bn1, US-Bn2, US-Bn3, US-Bo1, US-Bo2, US-Br3, US-CaV, US-Ced, US-CF1, US-CF2, US-CF3, US-CF4, US-ChR, US-Cop, US-CPk, US-CRT, US-Ctn, US-Dia, US-Dix, US-Dk1, US-Dk2, US-Dk3, US-EDN, US-Elm, US-EML, US-Fmf, US-FPe, US-FR2, US-FR3, US-Fuf, US-Fwf, US-GLE, US-GMF, US-Goo, US-Ha1, US-Ha2, US-Hn2, US-Hn3, US-Ho1, US-Ho2, US-Ho3, US-IB1, US-IB2, US-Ivo, US-KFS, US-KLS, US-Kon, US-KS1, US-KS2, US-KUT, US-Lin, US-Los, US-LPH, US-LWW, US-Me1, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MOz, US-Mpj, US-MRf, US-MtB, US-Myb, US-NC1, US-NC2, US-NC3, US-NC4, US-Ne1, US-Ne2, US-Ne3, US-NGB, US-NR1, US-Oho, US-ORv, US-PHM, US-Pon, US-Prr, US-RC1, US-RC2, US-RC3, US-RC4, US-RC5, US-Rls, US-Rms, US-Ro1, US-Ro2, US-Ro5, US-Ro6, US-Rpf, US-Rws, US-SdH, US-Seg, US-Ses, US-SFP, US-Shd, US-Skr, US-Slt, US-Snd, US-Sne, US-Snf, US-SO2, US-SO3, US-SO4, US-SP1, US-SP2, US-SP3, US-SRC, US-SRG, US-SRM, US-Srr, US-Sta, US-StJ, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Tw5, US-Twt, US-Uaf, US-UMB, US-UMd, US-Var, US-Vcm, US-Vcp, US-Vcs, US-WBW, US-WCr, US-Wdn, US-Wgr, US-Whs, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi6, US-Wi7, US-Wi8, US-Wi9, US-Wjs, US-Wkg, US-Wlr, US-Wpp, US-WPT, US-Wrc, US-xBR, US-xCP, US-xDL, US-xHA, US-xKA, US-xKZ, US-xRM, US-xSR, US-xWD
Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., Martin, T. A.
Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface‐atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance …
Journal: Geophysical Research Letters, Volume 45: 9275–9287 (2018), ISBN . DOI: 10.1029/2018GL079306 Sites: BR-Sa1, BR-Sa3, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-ER1, CA-Gro, CA-Man, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-Oas, CA-Obs, CA-Ojp, CA-Qfo, CA-TP1, CA-TP3, CA-TP4, CA-TPD, US-Blo, US-Bn1, US-Bn2, US-Br1, US-Br3, US-Ced, US-CPk, US-CRT, US-Dix, US-Dk2, US-Dk3, US-Fmf, US-Fuf, US-GBT, US-GLE, US-GMF, US-Ha1, US-Ha2, US-Ho2, US-Ho3, US-IB1, US-IB2, US-KL1, US-KL2, US-KL3, US-KM1, US-KM2, US-KM3, US-KM4, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MRf, US-NC1, US-NC2, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-Oho, US-Prr, US-Ro1, US-Ro3, US-SB1, US-Shd, US-Skr, US-Slt, US-SP1, US-SP2, US-SP3, US-SRM, US-Srr, US-Syv, US-Ton, US-Tw3, US-Twt, US-UMB, US-UMd, US-Var, US-Vcm, US-WBW, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi8, US-Wi9, US-Wrc
Ueyama, M., Tahara, N., Iwata, H., Euskirchen, E. S., Ikawa, H., Kobayashi, H., Nagano, H., Nakai, T., Harazono, Y.
Understanding how high-latitude terrestrial productivity and evapotranspiration change in association with rising atmospheric CO2 concentration ([CO2]), also known as ‘CO2 fertilization’, is important for predicting future climate change. To quantify the magnitude of this fertilization effect, we have devel- oped a …
Journal: Agricultural And Forest Meteorology, Volume 222: 98-111 (2016), ISBN . DOI: 10.1016/j.agrformet.2016.03.007 Sites: US-Bn1, US-Bn2, US-Bn3, US-Prr
Liu, H., Randerson, J. T., Lindfors, J., Chapin, F. S.
Understanding links between the disturbance regime and regional climate in boreal regions requires observations of the surface energy budget from ecosystems in various stages of secondary succession. While several studies have characterized fire-induced differences in surface energy fluxes from boreal ecosystems during summer months, …
Journal: Journal Of Geophysical Research, Volume 110 (D13): n/a-n/a (2005), ISBN . DOI: 10.1029/2004jd005158 Sites: US-Bn1, US-Bn2, US-Bn3