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40 results on '"Richardson A.D."'

2. Phenology across scales: an intercontinental analysis of leaf-out dates in temperate deciduous tree communities

Author

Delpierre, N., Garnier, S., Treuil-Dussouet, H., Hufkens, K., Lin, J., Beier, C., Bell, M., Berveiller, D., Cuntz, M., Curioni, G., Dahlin, K., Denham, S.O., Desai, A.R., Domec, J.-C., Hart, K.M., Ibrom, A., Joetzjer, E., King, J., Klosterhalfen, A., Koebsch, F., Mc Hale, P., Morfin, A., Munger, J.W., Noormets, A., Pilegaard, K., Pohl, Felix, Rebmann, C., Richardson, A.D., Rothstein, D., Schwartz, M.D., Wilkinson, M., Soudani, K., Delpierre, N., Garnier, S., Treuil-Dussouet, H., Hufkens, K., Lin, J., Beier, C., Bell, M., Berveiller, D., Cuntz, M., Curioni, G., Dahlin, K., Denham, S.O., Desai, A.R., Domec, J.-C., Hart, K.M., Ibrom, A., Joetzjer, E., King, J., Klosterhalfen, A., Koebsch, F., Mc Hale, P., Morfin, A., Munger, J.W., Noormets, A., Pilegaard, K., Pohl, Felix, Rebmann, C., Richardson, A.D., Rothstein, D., Schwartz, M.D., Wilkinson, M., and Soudani, K.

Abstract

Aim To quantify the intra-community variability of leaf-out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers, and deduce its ecological consequences with regard to radiation received and exposure to late frost.Location Eastern North America (ENA) and Europe (EUR).Time period 2009-2022Major taxa studied Temperate deciduous forest trees.Methods We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra-community variability of the amount of radiation received and of exposure to late frost.Results Leaf-out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf-out to temperature was identical in both regions (-3.4 days per °C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species-rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf-out resulted in higher ICVLo. ICVLo resulted in a ca. 8% difference of radiation absorption over spring among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts.Main conclusions We conducted the first intercontinental analysis of the variability of leaf-out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo (difference in terms of radiation absorption and exposure to late frost), which should be explored in the context of ongoing climate change, which affects trees differently according to their ph

Published
2024

3. Global climate - phenology of primary producers [in: State of the climate in 2023]

Author

Hemming, D.L., Anneville, O., Aono, Y., Crimmins, T., Estrella, N., Matsuzaki, S.-I., Menzel, A., Mrekaj, I., O’Keefe, J., Richardson, A.D., Rozkošný, J., Rutishauser, T., Shinohara, R., Thackeray, S.J., van Vliet, A.J.H., Garforth, J., Hemming, D.L., Anneville, O., Aono, Y., Crimmins, T., Estrella, N., Matsuzaki, S.-I., Menzel, A., Mrekaj, I., O’Keefe, J., Richardson, A.D., Rozkošný, J., Rutishauser, T., Shinohara, R., Thackeray, S.J., van Vliet, A.J.H., and Garforth, J.

Published
2024

6. Phenology of primary producers

Author

Hemming, D.L., Anneville, O., Aono, Y., Crimmins, T., Estrella, N., Menzel, A., Mrekaj, I., O'Keefe, J., Park, T., Richardson, A.D., Rozkošný, J., Rutishauser, T., Sparks, T.H., Thackeray, S.J., van Vliet, A.J.H., West, F., Hemming, D.L., Anneville, O., Aono, Y., Crimmins, T., Estrella, N., Menzel, A., Mrekaj, I., O'Keefe, J., Park, T., Richardson, A.D., Rozkošný, J., Rutishauser, T., Sparks, T.H., Thackeray, S.J., van Vliet, A.J.H., and West, F.

Published
2023

12. Phenology of primary producers

Author

Hemming, D.L., Anneville, O., Aono, Y., Garforth, J., Menzel, A., O'Keefe, J., Park, T., Richardson, A.D., Rutishauser, T., Sparks, T.H., Thackeray, S.J., van Vliet, A.J.H., Yuan, Y., Hemming, D.L., Anneville, O., Aono, Y., Garforth, J., Menzel, A., O'Keefe, J., Park, T., Richardson, A.D., Rutishauser, T., Sparks, T.H., Thackeray, S.J., van Vliet, A.J.H., and Yuan, Y.

Published
2022

14. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks

Author

Ollinger, S.V., Richardson, A.D., Martin, M.E., Hollinger, D.Y., Frolking, S.E., Reich, P.B., Plourde, L.C., Katul, G.G., Munger, J.W., Oren, R., Smith, M.-L., U., K.T. Paw, Bolstad, P.V., Cook, B.D., Day, M.C., Martin, T.A., Monson, R.K., and Schmid, H.P.

Subjects
Climatic changes -- Research, Nitrogen cycle -- Research, Carbon cycle (Biogeochemistry) -- Research, Broadband transmission -- Usage, Broadband Internet, Science and technology
Abstract

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem C[O.sub.2] uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both C[O.sub.2] uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle--climate models. nitrogen cycle | climate change | foliar nitrogen | ecosystem-climate feedback | remote sensing

Published
2008

15. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author

Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., Jackson, R.B., Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., and Jackson, R.B.

Abstract

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an impro

Published
2021
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18. Global climate - phenology of primary producers

Author

Hemming, D.L., Garforth, J., Park, T., Richardson, A.D., Rutishäuser, T., Sparks, T.H., Thackeray, S.J., Myneni, R., Hemming, D.L., Garforth, J., Park, T., Richardson, A.D., Rutishäuser, T., Sparks, T.H., Thackeray, S.J., and Myneni, R.

Published
2020

19. State of the climate in 2018

Author

Ades, M., Adler, R., Aldeco, L.S., Alejandra, G., Alfaro, E.J., Aliaga-Nestares, V., Allan, R.P., Allan, R., Alves, L.M., Amador, J.A., Andersen, J.K., Anderson, J., Arndt, D.S., Arosio, C., Arrigo, K., Azorin-Molina, C., Bardin, M.Y., Barichivich, J., Barreira, S., Baxter, S., Beck, H.E., Becker, A., Bell, G.D., Bellouin, N., Belmont, M., Benedetti, A., Benedict, I., Bernhard, G.H., Berrisford, P., Berry, D.I., Bettio, L., Bhatt, U.S., Biskaborn, B.K., Bissolli, P., Bjella, K.L., Bjerke, J.K., Blake, E.S., Blenkinsop, S., Blunden, J., Bock, O., Bosilovich, M.G., Boucher, O., Box, J.E., Boyer, T., Braathen, G., Bringas, F.G., Bromwich, D.H., Brown, A., Brown, R., Brown, T.J., Buehler, S.A., Cáceres, L., Calderón, B., Camargo, S.J., Campbell, J.D., Campos Diaz, D.A., Cappelen, J., Carrea, L., Carrier, S.B., Carter, B.R., Castro, A.Y., Cetinic, I., Chambers, D.P., Chen, L., Cheng, L., Cheng, V.Y.S., Christiansen, H.H., Christy, J.R., Chung, E.-S., Claus, F., Clem, K.R., Coelho, C.A.S., Coldewey-Egbers, M., Colwell, S., Cooper, O.R., Cosca, C., Covey, C., Coy, L., Dávila, C.P., Davis, S.M., de Eyto, E., de Jeu, R.A.M., De Laat, J., Decharme, B., Degasperi, C.L., Degenstein, D., Demircan, M., Derksen, C., Dhurmea, K.R., Di Girolamo, L., Diamond, H.J., Diaz, E., Diniz, F.A., Dlugokencky, E.J., Dohan, K., Dokulil, M.T., Dolman, A.J., Domingues, C.M., Domingues, R., Donat, M.G., Dorigo, W.A., Drozdov, D.S., Druckenmiller, M.L., Dunn, R.J.H., Durre, I., Dutton, G.S., Elkharrim, M., Elkins, J.W., Epstein, H.E., Espinoza, J.C., Famiglietti, J.S., Farrell, S.L., Fausto, R.S., Feely, R.A., Feng, Z., Fenimore, C., Fettweis, X., Fioletov, V.E., Flemming, J., Fogt, R.L., Forbes, B.C., Foster, M.J., Francis, S.D., Franz, B.A., Frey, R.A., Frith, S.M., Froidevaux, L., Ganter, C., Garforth, J., Gerland, S., Gilson, J., Gleason, K., Gobron, N., Goetz, S., Goldenberg, S.B., Goni, G., Gray, A., Groo, J.-U., Gruber, A., Gu, G., Guard, C.C.P., Gupta, S.K., Gutiérrez, D., Haas, Christian, Hagos, S., Hahn, S., Haimberger, L., Hall, B.D., Halpert, M.S., Hamlington, B.D., Hanna, E., Hanssen-Bauer, I., Harris, I., Hazeleger, W., He, Q., Heidinger, A.K., Heim, Jr., Hemming, D.L., Hendricks, Stefan, Hernández, R., Hersbach, H.E., Hidalgo, H.G., Ho, S.-P.B., Holmes, R.M., Hu, C., Huang, B., Hubbard, K., Hubert, D., Hurst, D.F., Ialongo, I., Ijampy, J.A., Inness, A., Isaac, V., Isaksen, K., Ishii, M., Jeffries, M.O., Jevrejeva, S., Jia, G., Jiménez, C., Jin, X., John, V., Johnsen, B., Johnson, G.C., Johnson, K.S., Johnson, B., Jones, P.D., Jumaux, G., Kabidi, K., Kaiser, J.W., Karaköylü, E.M., Karlsen, S.-R., Karnauskas, M., Kato, S., Kazemi, A.F., Kelble, C., Keller, L.M., Kennedy, J., Kholodov, A.L., Khoshkam, M., Kidd, R., Killick, R., Kim, H., Kim, S.-J., King, A.D., King, B.A., Kipling, Z., Klotzbach, P.J., Knaff, J.A., Korhonen, J., Korshunova, N.N., Kramarova, N.A., Kratz, D.P., Kruger, A., Kruk, M.C., Krumpen, Thomas, Labbé, L., Ladd, C., Lakatos, M., Lakkala, K., Lander, M.A., Landschützer, P., Landsea, C.W., Lareau, N.P., Lavado-Casimiro, W., Lazzara, M.A., Lee, T.C., Leuliette, E., L�heureux, M., Li, B., Li, T., Lieser, J.L., Lim, J.-Y., Lin, I.-I., Liu, H., Locarnini, R., Loeb, N.G., Long, C.S., López, L.A., Lorrey, A.M., Loyola, D., Lumpkin, R., Luo, J.-J., Luojus, K., Lyman, J.M., Malkova, G.V., Manney, G.L., Marchenko, S.S., Marengo, J.A., Marin, D., Marquardt Collow, A.B., Marra, J.J., Marszelewski, W., Martens, B., Martínez-Güingla, R., Massom, R.A., May, L., Mayer, M., Mazloff, M., McBride, C., McCabe, M., McClelland, J.W., McEvoy, D.J., McGree, S., McVicar, T.R., Mears, C.A., Meier, W., Meijers, A., Mekonnen, A., Mengistu Tsidu, G., Menzel, W.P., Merchant, C.J., Meredith, M.P., Merrifield, M.A., Miller, B., Miralles, D.G., Misevicius, N., Mitchum, G.T., Mochizuki, Y., Monselesan, D., Montzka, S.A., Mora, N., Morice, C., Mosquera-Vásquez, K., Mostafa, A.E., Mote, T., Mudryk, L., Mühle, J., Mullan, A.B., Müller, R., Myneni, R., Nash, E.R., Nauslar, N.J., Nerem, R.S., Newman, P.A., Nicolas, J.P., Nieto, J.J., Noetzli, J., Osborn, T.J., Osborne, E., Overland, J., Oyunjargal, L., Park, T., Pasch, R.J., Pascual Ramírez, R., Pastor Saavedra, M.A., Paterson, A.M., Pearce, P.R., Pelto, M.S., Perovich, D., Petropavlovskikh, I., Pezza, A.B., Phillips, C., Phillips, D., Phoenix, G., Pinty, B., Pitts, M., Po-Chedley, S., Polashenski, C., Preimesberger, W., Purkey, S.G., Quispe, N., Rajeevan, M., Rakotoarimalala, C.L., Ramos, A.M., Ramos, I., Randel, W., Raynolds, M.K., Reagan, J., Reid, P., Reimer, C., Rémy, S., Revadekar, J.V., Richardson, A.D., Richter-Menge, J., Ricker, Robert, Ripaldi, A., Robinson, D.A., Rodell, M., Rodriguez Camino, E., Romanovsky, V.E., Ronchail, J., Rosenlof, K.H., Rösner, B., Roth, C., Rozanov, A., Rusak, J.A., Rustemeier, E., Rutishäuser, T., Sallée, J.-B., Sánchez-Lugo, A., Santee, M.L., Sawaengphokhai, P., Sayouri, A., Scambos, T.A., Scanlon, T., Scardilli, A.S., Schenzinger, V., Schladow, S.G., Schmid, C., Schmid, M., Schoeneich, P., Schreck, III, Selkirk, H.B., Sensoy, S., Shi, L., Shiklomanov, A.I., Shiklomanov, N.I., Shimpo, A., Shuman, C.A., Siegel, D.A., Sima, F., Simmons, A.J., Smeets, C.J.P.P., Smith, A., Smith, S.L., Soden, B., Sofieva, V., Sparks, T.H., Spence, J., Spencer, R.G.M., Spillane, S., Srivastava, A.K., Stabeno, P.J., Stackhouse, Jr., Stammerjohn, S., Stanitski, D.M., Steinbrecht, W., Stella, J.L., Stengel, M., Stephenson, T.S., Strahan, S.E., Streeter, C., Streletskiy, D.A., Sun-Mack, S., Suslova, A., Sutton, A.J., Swart, S., Sweet, W., Takahashi, K.S., Tank, S.E., Taylor, M.A., Tedesco, M., Thackeray, S.J., Thompson, P.R., Timbal, B., Timmermans, M.-L., Tobin, S., Tømmervik, H., Tourpali, K., Trachte, K., Tretiakov, M., Trewin, B.C., Triñanes, J.A., Trotman, A.R., Tschudi, M., Tye, M.R., van As, D., van de Wal, R.S.W., van der A, R.J., van der Schalie, R., van der Schrier, G., van der Werf, G.R., van Heerwaarden, C., Van Meerbeeck, C.J., Verburg, P., Vieira, G., Vincent, L.A., Vömel, H., Vose, R.S., Walker, D.A., Walsh, J.E., Wang, B., Wang, H., Wang, L., Wang, M., Wang, R., Wang, S.-H., Wanninkhof, R., Watanabe, S., Weber, M., Webster, M., Weerts, A., Weller, R.A., Westberry, T.K., Weyhenmeyer, G.A., Widlansky, M.J., Wijffels, S.E., Wilber, A.C., Wild, J.D., Willett, K.M., Wong, T., Wood, E.F., Woolway, R.I., Xue, Y., Yin, X., Yu, L., Zambrano, E., Zeyaeyan, S., Zhang, H.-M., Zhang, P., Zhao, G., Zhao, L., Zhou, X., Zhu, Z., Ziemke, J.R., Ziese, M., Andersen, A., Griffin, J., Hammer, G., Love-Brotak, S.E., Misch, D.J., Riddle, D.B., Veasey, S.W., Ades, M., Adler, R., Aldeco, L.S., Alejandra, G., Alfaro, E.J., Aliaga-Nestares, V., Allan, R.P., Allan, R., Alves, L.M., Amador, J.A., Andersen, J.K., Anderson, J., Arndt, D.S., Arosio, C., Arrigo, K., Azorin-Molina, C., Bardin, M.Y., Barichivich, J., Barreira, S., Baxter, S., Beck, H.E., Becker, A., Bell, G.D., Bellouin, N., Belmont, M., Benedetti, A., Benedict, I., Bernhard, G.H., Berrisford, P., Berry, D.I., Bettio, L., Bhatt, U.S., Biskaborn, B.K., Bissolli, P., Bjella, K.L., Bjerke, J.K., Blake, E.S., Blenkinsop, S., Blunden, J., Bock, O., Bosilovich, M.G., Boucher, O., Box, J.E., Boyer, T., Braathen, G., Bringas, F.G., Bromwich, D.H., Brown, A., Brown, R., Brown, T.J., Buehler, S.A., Cáceres, L., Calderón, B., Camargo, S.J., Campbell, J.D., Campos Diaz, D.A., Cappelen, J., Carrea, L., Carrier, S.B., Carter, B.R., Castro, A.Y., Cetinic, I., Chambers, D.P., Chen, L., Cheng, L., Cheng, V.Y.S., Christiansen, H.H., Christy, J.R., Chung, E.-S., Claus, F., Clem, K.R., Coelho, C.A.S., Coldewey-Egbers, M., Colwell, S., Cooper, O.R., Cosca, C., Covey, C., Coy, L., Dávila, C.P., Davis, S.M., de Eyto, E., de Jeu, R.A.M., De Laat, J., Decharme, B., Degasperi, C.L., Degenstein, D., Demircan, M., Derksen, C., Dhurmea, K.R., Di Girolamo, L., Diamond, H.J., Diaz, E., Diniz, F.A., Dlugokencky, E.J., Dohan, K., Dokulil, M.T., Dolman, A.J., Domingues, C.M., Domingues, R., Donat, M.G., Dorigo, W.A., Drozdov, D.S., Druckenmiller, M.L., Dunn, R.J.H., Durre, I., Dutton, G.S., Elkharrim, M., Elkins, J.W., Epstein, H.E., Espinoza, J.C., Famiglietti, J.S., Farrell, S.L., Fausto, R.S., Feely, R.A., Feng, Z., Fenimore, C., Fettweis, X., Fioletov, V.E., Flemming, J., Fogt, R.L., Forbes, B.C., Foster, M.J., Francis, S.D., Franz, B.A., Frey, R.A., Frith, S.M., Froidevaux, L., Ganter, C., Garforth, J., Gerland, S., Gilson, J., Gleason, K., Gobron, N., Goetz, S., Goldenberg, S.B., Goni, G., Gray, A., Groo, J.-U., Gruber, A., Gu, G., Guard, C.C.P., Gupta, S.K., Gutiérrez, D., Haas, Christian, Hagos, S., Hahn, S., Haimberger, L., Hall, B.D., Halpert, M.S., Hamlington, B.D., Hanna, E., Hanssen-Bauer, I., Harris, I., Hazeleger, W., He, Q., Heidinger, A.K., Heim, Jr., Hemming, D.L., Hendricks, Stefan, Hernández, R., Hersbach, H.E., Hidalgo, H.G., Ho, S.-P.B., Holmes, R.M., Hu, C., Huang, B., Hubbard, K., Hubert, D., Hurst, D.F., Ialongo, I., Ijampy, J.A., Inness, A., Isaac, V., Isaksen, K., Ishii, M., Jeffries, M.O., Jevrejeva, S., Jia, G., Jiménez, C., Jin, X., John, V., Johnsen, B., Johnson, G.C., Johnson, K.S., Johnson, B., Jones, P.D., Jumaux, G., Kabidi, K., Kaiser, J.W., Karaköylü, E.M., Karlsen, S.-R., Karnauskas, M., Kato, S., Kazemi, A.F., Kelble, C., Keller, L.M., Kennedy, J., Kholodov, A.L., Khoshkam, M., Kidd, R., Killick, R., Kim, H., Kim, S.-J., King, A.D., King, B.A., Kipling, Z., Klotzbach, P.J., Knaff, J.A., Korhonen, J., Korshunova, N.N., Kramarova, N.A., Kratz, D.P., Kruger, A., Kruk, M.C., Krumpen, Thomas, Labbé, L., Ladd, C., Lakatos, M., Lakkala, K., Lander, M.A., Landschützer, P., Landsea, C.W., Lareau, N.P., Lavado-Casimiro, W., Lazzara, M.A., Lee, T.C., Leuliette, E., L�heureux, M., Li, B., Li, T., Lieser, J.L., Lim, J.-Y., Lin, I.-I., Liu, H., Locarnini, R., Loeb, N.G., Long, C.S., López, L.A., Lorrey, A.M., Loyola, D., Lumpkin, R., Luo, J.-J., Luojus, K., Lyman, J.M., Malkova, G.V., Manney, G.L., Marchenko, S.S., Marengo, J.A., Marin, D., Marquardt Collow, A.B., Marra, J.J., Marszelewski, W., Martens, B., Martínez-Güingla, R., Massom, R.A., May, L., Mayer, M., Mazloff, M., McBride, C., McCabe, M., McClelland, J.W., McEvoy, D.J., McGree, S., McVicar, T.R., Mears, C.A., Meier, W., Meijers, A., Mekonnen, A., Mengistu Tsidu, G., Menzel, W.P., Merchant, C.J., Meredith, M.P., Merrifield, M.A., Miller, B., Miralles, D.G., Misevicius, N., Mitchum, G.T., Mochizuki, Y., Monselesan, D., Montzka, S.A., Mora, N., Morice, C., Mosquera-Vásquez, K., Mostafa, A.E., Mote, T., Mudryk, L., Mühle, J., Mullan, A.B., Müller, R., Myneni, R., Nash, E.R., Nauslar, N.J., Nerem, R.S., Newman, P.A., Nicolas, J.P., Nieto, J.J., Noetzli, J., Osborn, T.J., Osborne, E., Overland, J., Oyunjargal, L., Park, T., Pasch, R.J., Pascual Ramírez, R., Pastor Saavedra, M.A., Paterson, A.M., Pearce, P.R., Pelto, M.S., Perovich, D., Petropavlovskikh, I., Pezza, A.B., Phillips, C., Phillips, D., Phoenix, G., Pinty, B., Pitts, M., Po-Chedley, S., Polashenski, C., Preimesberger, W., Purkey, S.G., Quispe, N., Rajeevan, M., Rakotoarimalala, C.L., Ramos, A.M., Ramos, I., Randel, W., Raynolds, M.K., Reagan, J., Reid, P., Reimer, C., Rémy, S., Revadekar, J.V., Richardson, A.D., Richter-Menge, J., Ricker, Robert, Ripaldi, A., Robinson, D.A., Rodell, M., Rodriguez Camino, E., Romanovsky, V.E., Ronchail, J., Rosenlof, K.H., Rösner, B., Roth, C., Rozanov, A., Rusak, J.A., Rustemeier, E., Rutishäuser, T., Sallée, J.-B., Sánchez-Lugo, A., Santee, M.L., Sawaengphokhai, P., Sayouri, A., Scambos, T.A., Scanlon, T., Scardilli, A.S., Schenzinger, V., Schladow, S.G., Schmid, C., Schmid, M., Schoeneich, P., Schreck, III, Selkirk, H.B., Sensoy, S., Shi, L., Shiklomanov, A.I., Shiklomanov, N.I., Shimpo, A., Shuman, C.A., Siegel, D.A., Sima, F., Simmons, A.J., Smeets, C.J.P.P., Smith, A., Smith, S.L., Soden, B., Sofieva, V., Sparks, T.H., Spence, J., Spencer, R.G.M., Spillane, S., Srivastava, A.K., Stabeno, P.J., Stackhouse, Jr., Stammerjohn, S., Stanitski, D.M., Steinbrecht, W., Stella, J.L., Stengel, M., Stephenson, T.S., Strahan, S.E., Streeter, C., Streletskiy, D.A., Sun-Mack, S., Suslova, A., Sutton, A.J., Swart, S., Sweet, W., Takahashi, K.S., Tank, S.E., Taylor, M.A., Tedesco, M., Thackeray, S.J., Thompson, P.R., Timbal, B., Timmermans, M.-L., Tobin, S., Tømmervik, H., Tourpali, K., Trachte, K., Tretiakov, M., Trewin, B.C., Triñanes, J.A., Trotman, A.R., Tschudi, M., Tye, M.R., van As, D., van de Wal, R.S.W., van der A, R.J., van der Schalie, R., van der Schrier, G., van der Werf, G.R., van Heerwaarden, C., Van Meerbeeck, C.J., Verburg, P., Vieira, G., Vincent, L.A., Vömel, H., Vose, R.S., Walker, D.A., Walsh, J.E., Wang, B., Wang, H., Wang, L., Wang, M., Wang, R., Wang, S.-H., Wanninkhof, R., Watanabe, S., Weber, M., Webster, M., Weerts, A., Weller, R.A., Westberry, T.K., Weyhenmeyer, G.A., Widlansky, M.J., Wijffels, S.E., Wilber, A.C., Wild, J.D., Willett, K.M., Wong, T., Wood, E.F., Woolway, R.I., Xue, Y., Yin, X., Yu, L., Zambrano, E., Zeyaeyan, S., Zhang, H.-M., Zhang, P., Zhao, G., Zhao, L., Zhou, X., Zhu, Z., Ziemke, J.R., Ziese, M., Andersen, A., Griffin, J., Hammer, G., Love-Brotak, S.E., Misch, D.J., Riddle, D.B., and Veasey, S.W.

Published
2019

23. A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Author

Anderson, R., Poulter, B., Matamala, R., Lokipitiya, E., Chen, J.M., Verbeeck, H., Davis, K.J., Weng, E., Curtis, P.S., Tonitto, C., Munger, J.W., Ricciuto, D., Chen, J., Gu, L., Humphreys, E., Desai, A.R., Price, D.T., Raczka, B.M., Zhou, X., Peng, C., Torn, M., Hollinger, D.Y., Riley, W.J., Roulet, N., Black, A., Bolstad, P., Baker, I., Thornton, P., Monson, R., Jain, A., Law, B., Gough, C., Margolis, H.A., Dimitrov, D., Grant, R.F., Liu, S., McCaughey, J.H., Hilton, T.W., Sahoo, A., Dietze, M., Schaefer, K., Williams, C., Dragoni, D., Tian, H., Vargas, R., Schwalm, C.R., Richardson, A.D., Oechel, W., Kucharik, C., Barr, A., and Altaf Arain, M.

Published
2012
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24. Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis

Author

Black, T.A., Izaurralde, R.C., Lokupitiya, E., Munger, J.W., Schaefer, K., Weng, E., Richardson, A.D., Altaf Arain, M., Luo, Y., Ciais, P., Ricciuto, D.M., Stoy, P.C., Dietze, M.C., Poulter, B., Barr, A.G., Liu, S., Hollinger, D., Tian, H., Suyker, A.E., Verbeeck, H., Price, D.T., Grant, R.F., Peng, C., Baker, I.T., Vargas, R., Anderson, R.S., Tonitto, C., Sahoo, A.K., Chen, J.M., Flanagan, L.B., Riley, W.J., Wang, W., Lafleur, P., Gough, C.M., Verma, S.B., and Kucharik, C.J.

Published
2011
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25. Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

Author

Migliavacca, M., Reichstein, M., Richardson, A.D., Colombo, R., Sutton, M.A., Lasslop, G., Tomelleri, E., Wohlfahrt, G., Carvalhais, N., and van der Molen, M.K.

Subjects
Meteorologie en Luchtkwaliteit, carbon-dioxide exchange, WIMEK, Meteorology and Air Quality, european forests, terrestrial ecosystems, deciduous forest, heterotrophic components, litter decomposition, nitrogen deposition, rhizosphere respiration, water-vapor exchange, forest soil respiration
Abstract

In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref515 1C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r250.52, Po0.001, n5104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining 470% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands

Published
2011

26. Redefinition and global estimation of basal ecosystem respiration rate

Author

Yuan, W., Luo, Y., Li, X., Liu, S., Yu, G., Zhou, T., Bahn, M., Black, A., Richardson, A.D., Desai, A.R., Cescatti, A., Marcolla, B., Jacobs, C., Chen, J., Aurela, M., Bernhofer, C., Gielen, B., Bohrer, G., Cook, D.R., Dragoni, D., Dunn, A.L., Gianelle, D., Grünwald, T., Ibrom, A., Leclerc, M.Y., Lindroth, A., Liu, H., Marchesini, L.B., Montagnani, L., Rodeghiero, M., Rodrigues, A., Starr, G., and Stoy, P.C.

Subjects
Ecosystem respiration, Gross primary production, Basal ecosystem respiration rate, Settore BIO/07 - ECOLOGIA, EC-LUE model, Eddy covariance
Published
2011

27. Semi-empirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

Author

Migliavacca, M., Reichstein, M., Richardson, A.D., Colombo, R., Sutton, M.A., Lasslop, E., Tomelleri, E., Wohlfahrt, G., Carvalhais, N., Cescatti, A., Mahecha, D., Montagnani, L., Papale, D., Zaehle, S., Arain, A., Arneth, A., Black, T.A., Carrara, A., Dore, S., Gianelle, D., Helfter, C., Hollinger, D., Kutsch, W.L., Lafleur, P.M., Nouvellon, Y., Rebmann, C., Da Rocha, H.R., Rodeghiero, M., Roupsard, O., Sebastià, M.-T., Seufert, G., Soussana, J.-F., van der Molen, M.K., Remote Sensing of Environmental Dynamics, University of Milano-Bicocca, Department of Biogeochemical Integration [Jena], Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Department of Organismic and Evolutionary Biology [Cambridge] (OEB), Harvard University [Cambridge], Edinburgh Research Station, Centre for Ecology and Hydrology, Institute of Ecology, University of Innsbruck, Faculdade de Ciências e Tecnologia (FCT NOVA), Universidade Nova de Lisboa (NOVA), JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Department of Environmental Sciences, Swiss Federal Institute of Technology, Servizi Forestali, Provincia Autonoma di Bolzano, Agenzia per l'Ambiente, DISAFRI, University of Tuscia, Biogeochemical Systems Department [Jena], School of Geography & Earth Sciences, McMaster University [Hamilton, Ontario], Department of Physical Geography and Ecosystem Science [Lund], Lund University [Lund], Faculty of Land and Food Systems, University of British Columbia (UBC), School of Forestry, Northern Arizona University [Flagstaff], Centro di Ecologia Alpina, Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), NE Research Station, USDA Forest Service, Institut für Agrarrelevante Klimaforschung, Johann Heinrich von Thünen Institut, College of Forestry [Corvallis], Oregon State University (OSU), 20- Department of Geography, Trent University, Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Department Biogeochemical Processes [Jena], Micrometeorology Group [Bayreuth], Universität Bayreuth, Department of Atmospheric Sciences [São Paulo], University of São Paulo (USP), CATIE, Centro Agronómico Tropical de Investigación y Enseñanza, Agronomical Engineering School, Universitat de Lleida, Laboratory of Plant Ecology and Botany, Forest Technology Centre of Catalonia, Institut National de la Recherche Agronomique (INRA), Department of Hydrology and Geo-Environmental Sciences [Amsterdam], Vrije Universiteit Amsterdam [Amsterdam] (VU), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Centre de Ciència i Tecnologia Forestal de Catalunya (CTFC), CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, US Department of Energy, Model-Data Integration Group of the Max-Planck Institute for Biogeochemistry, Migliavacca, M, Reichstein, M, Richardson, A, Colombo, R, Sutton, M, Lasslop, G, Tomelleri, E, Wohlfahrt, G, Carvalhais, N, Cescatti, A, Mahecha, M, Montagnani, L, Papale, D, Zaehle, S, Arain, A, Arneth, A, Black, T, Carrara, A, Dore, S, Gianelle, D, Helfter, C, Hollinger, D, Kutsch, W, Lafleur, P, Nouvellon, Y, Rebmann, C, Humberto, R, Rodeghiero, M, Roupsard, O, Sebastià, M, Seufert, G, Soussana, J, Michiel, K, and Hydrology and Geo-environmental sciences

Subjects
Ecosystem respiration, [SDV]Life Sciences [q-bio], FLUXNET, Eddy covariance, Facteur climatique, Prairie, Productivité, Savane, Productivity, U10 - Informatique, mathématiques et statistiques, Respiration, Indice de surface foliaire, 000 - Autres thèmes, Life Sciences, Leaf area index, Forêt, Écosystème, Zone tropicale, P33 - Chimie et physique du sol, Carbone, F40 - Écologie végétale, Matière organique du sol, Zone tempérée, Zone froide, Settore BIO/07 - ECOLOGIA, SDG 14 - Life Below Water, Modélisation environnementale, Changement climatique, technology, industry, and agriculture, Zone méditerranéenne, Inverse modeling, GEO/10 - GEOFISICA DELLA TERRA SOLIDA
Abstract

In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15°C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, p70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands., JRC.H.2-Air and Climate

Published
2010
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28. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006

Author

White, M.A., de Beurs, K.M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., Magnuson, J., O'Keefe, J., Zhang, G., Nemani, R.R., van Leeuwen, W.J.D., Brown, J.F., de Wit, A.J.W., Schaepman, M.E., Lin, X., Dettinger, M., Bailey, A., Kimball, J., Schwartz, M.D., Baldocchi, D.D., Lee, J.T., Lauenroth, W.K., University of Zurich, and White, M A

Subjects
trends, Advanced very-high-resolution radiometer, Alterra - Centrum Geo-informatie, united-states, 2306 Global and Planetary Change, Climate change, plant phenology, carbon-dioxide, 2300 General Environmental Science, ndvi time-series, Laboratory of Geo-information Science and Remote Sensing, Streamflow, medicine, Environmental Chemistry, satellite sensor data, Laboratorium voor Geo-informatiekunde en Remote Sensing, Wageningen Environmental Research, 910 Geography & travel, General Environmental Science, Remote sensing, Global and Planetary Change, Ecology, Phenology, variability, Global change, Vegetation, deciduous forest, Seasonality, Centre Geo-information, medicine.disease, Snow, 10122 Institute of Geography, fourier-analysis, 2304 Environmental Chemistry, Climatology, climate-change, Environmental science, 2303 Ecology
Abstract

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ±60 days and in standard deviation by ±20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.

Published
2009

29. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Author

Verma, M., Friedl, M.A., Richardson, A.D., Kiely, G., Cescatti, A., Law, B.E., Wohlfahrt, G., Gielen, G., Roupsard, O., Moors, E.J., Verma, M., Friedl, M.A., Richardson, A.D., Kiely, G., Cescatti, A., Law, B.E., Wohlfahrt, G., Gielen, G., Roupsard, O., and Moors, E.J.

Abstract

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux to

Published
2014

30. Influence of spring and autumn phenological transitions on forest ecosystem productivity

Author

Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N., Hollinger, D.Y., Kutsch, W.L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Munger, J.W., Moors, E., Piao, S., Rebmann, Corinna, Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R., Varlagin, A., Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N., Hollinger, D.Y., Kutsch, W.L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Munger, J.W., Moors, E., Piao, S., Rebmann, Corinna, Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R., and Varlagin, A.

Abstract

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Published
2010

36. Vinyl dichlorosilane and vinyl dibromosilane (H2CCH–SiHX2,X=Cl,Br):conformational structure and vibrational properties determined by gas-phase electron diffraction, ab initio molecular orbital calculations, and variable-temperature Raman spectroscopy

Author

Johansen, T.H., Hagen, K., Hassler, K., Richardson, A.D., Pätzold, U., and Stølevik, R.

Abstract

The molecular structures, conformations, vibrational spectra, and torsional potentials of vinyl dichlorosilane (VDC) H2CCH–SiHCl2, and vinyl dibromosilane (VDB) H2CCH–SiHBr2, have been studied using gas-phase electron diffraction (GED) data at 23–25°C and variable-temperature Raman spectroscopy, together with ab initio molecular orbital calculations. The GED data were handled by a dynamic theoretical model using a cosine Fourier potential function in describing the torsional coordinate. According to the GED refinements, these molecules exist in the gas phase at room temperature as a mixture of two minimum energy conformers, syn(torsional angle φ(CCSiH)=0°) and gauche(torsional angle φ(CCSiH)≈120°). Relevant structural parameters for syn-VDC are as follows: Bond lengths (rg): r(Si–C)=1.847(5)Å,r(Si–Cl)=2.042(2)Å,r(CC)=1.357(7)Å.Bond angles (∠α): ∠CSiCl=110.3(6)°, ∠CCSi=121.8° (calc.). Relevant structural parameters for syn-VDB are as follows: bond lengths (rg): r(Si–C)=1.827(9)Å,r(Si–Br)=2.206(2)Å,r(CC)=1.366(10)Å.Bond angles (∠α): ∠CSiBr=110.1(8)°, ∠CCSi=121.7° (calc.). Uncertainties are given as 2σ(σincludes estimates of uncertainties in voltage/height measurements and correlation in the experimental data). From the variable-temperature Raman investigation in the liquid phase, the energy differences are: VDC, ΔE°S−G=+0.11±0.06kcalmol−1;VDB, ΔE°S−G=+0.23±0.07kcalmol−1.The Raman energies are average values obtained from two separate line doublets for each molecule, and they have been used in the GED least-squares refinements as valuable constraints.

Published
2000
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37. The Residence of Kit Carson; Village of Pueblo Indians

Author

Richardson, A.D. and Richardson, A.D.

Abstract

Two colored images set in a newspaper clipping. The first, the residence of Kit Carson in Taos, N.M.; the second, a village of the pueblo Indians, near Taos, N.M.

40. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency

Author

Thomas Kolb, Jingfeng Xiao, Rosvel Bracho-Garrillo, Rossella Guerrieri, Kimberly A. Novick, Heidi Asbjornsen, Andrew D. Richardson, Benjamin D. Stocker, Mary E. Martin, Kenneth L. Clark, Katie A. Jennings, J. William Munger, Scott V. Ollinger, Soumaya Belmecheri, Sabina Dore, David Y. Hollinger, Guerrieri R., Belmecheri S., Ollinger S.V., Asbjornsen H., Jennings K., Xiao J., Stocker B.D., Martin M., Hollinger D.Y., Bracho-Garrillo R., Clark K., Dore S., Kolb T., William Munger J., Novick K., and Richardson A.D.

Subjects
0106 biological sciences, Water-use efficiency, Stomatal conductance, 010504 meteorology & atmospheric sciences, stable isotopes, AmeriFlux, Photosynthesis, Atmospheric sciences, 01 natural sciences, Basal area, chemistry.chemical_compound, water-use efficiency, CO2 fertilization, 0105 earth and related environmental sciences, Stable isotopes, Multidisciplinary, Moisture, Stable isotope ratio, Tree rings, Biological Sciences, 15. Life on land, Stable isotope, tree rings, chemistry, fertilization, 13. Climate action, Carbon dioxide, Environmental science, CO2, Tree ring, Temperate rainforest, Environmental Sciences, 010606 plant biology & botany
Abstract

Significance Forests remove about 30% of anthropogenic CO2 emissions through photosynthesis and return almost 40% of incident precipitation back to the atmosphere via transpiration. The trade-off between photosynthesis and transpiration through stomata, the water-use efficiency (WUE), is an important driver of plant evolution and ecosystem functioning, and has profound effects on climate. Using stable carbon and oxygen isotope ratios in tree rings, we found that WUE has increased by a magnitude consistent with estimates from atmospheric measurements and model predictions. Enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to moisture-limited forests. This result points to smaller reductions in transpiration in response to increasing atmospheric CO2, with important implications for forest–climate interactions, which remain to be explored., Multiple lines of evidence suggest that plant water-use efficiency (WUE)—the ratio of carbon assimilation to water loss—has increased in recent decades. Although rising atmospheric CO2 has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimating WUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystem-scale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO2-induced reductions in stomatal conductance.

Published
2021

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