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1. A MHz-repetition-rate hard X-ray free-electron laser driven by a superconducting linear accelerator

Author

Decking, W., Abeghyan, S., Abramian, P., Abramsky, A., Aguirre, A., Albrecht, C., Alou, P., Altarelli, M., Altmann, P., Amyan, K., Anashin, V., Apostolov, E., Appel, K., Auguste, D., Ayvazyan, V., Baark, S., Babies, F., Baboi, N., Bak, P., Balandin, V., Baldinger, R., Baranasic, B., Barbanotti, S., Belikov, O., Belokurov, V., Belova, L., Belyakov, V., Berry, S., Bertucci, M., Beutner, B., Block, A., Blöcher, M., Böckmann, T., Bohm, C., Böhnert, M., Bondar, V., Bondarchuk, E., Bonezzi, M., Borowiec, P., Bösch, C., Bösenberg, U., Bosotti, A., Böspflug, R., Bousonville, M., Boyd, E., Bozhko, Y., Brand, A., Branlard, J., Briechle, S., Brinker, F., Brinker, S., Brinkmann, R., Brockhauser, S., Brovko, O., Brück, H., Brüdgam, A., Butkowski, L., Büttner, T., Calero, J., Castro-Carballo, E., Cattalanotto, G., Charrier, J., Chen, J., Cherepenko, A., Cheskidov, V., Chiodini, M., Chong, A., Choroba, S., Chorowski, M., Churanov, D., Cichalewski, W., Clausen, M., Clement, W., Cloué, C., Cobos, J. A., Coppola, N., Cunis, S., Czuba, K., Czwalinna, M., D’Almagne, B., Dammann, J., Danared, H., de Zubiaurre Wagner, A., Delfs, A., Delfs, T., Dietrich, F., Dietrich, T., Dohlus, M., Dommach, M., Donat, A., Dong, X., Doynikov, N., Dressel, M., Duda, M., Duda, P., Eckoldt, H., Ehsan, W., Eidam, J., Eints, F., Engling, C., Englisch, U., Ermakov, A., Escherich, K., Eschke, J., Saldin, E., Faesing, M., Fallou, A., Felber, M., Fenner, M., Fernandes, B., Fernández, J. M., Feuker, S., Filippakopoulos, K., Floettmann, K., Fogel, V., Fontaine, M., Francés, A., Martin, I. Freijo, Freund, W., Freyermuth, T., Friedland, M., Fröhlich, L., Fusetti, M., Fydrych, J., Gallas, A., García, O., Garcia-Tabares, L., Geloni, G., Gerasimova, N., Gerth, C., Geßler, P., Gharibyan, V., Gloor, M., Głowinkowski, J., Goessel, A., Gołębiewski, Z., Golubeva, N., Grabowski, W., Graeff, W., Grebentsov, A., Grecki, M., Grevsmuehl, T., Gross, M., Grosse-Wortmann, U., Grünert, J., Grunewald, S., Grzegory, P., Feng, G., Guler, H., Gusev, G., Gutierrez, J. L., Hagge, L., Hamberg, M., Hanneken, R., Harms, E., Hartl, I., Hauberg, A., Hauf, S., Hauschildt, J., Hauser, J., Havlicek, J., Hedqvist, A., Heidbrook, N., Hellberg, F., Henning, D., Hensler, O., Hermann, T., Hidvégi, A., Hierholzer, M., Hintz, H., Hoffmann, F., Hoffmann, Markus, Hoffmann, Matthias, Holler, Y., Hüning, M., Ignatenko, A., Ilchen, M., Iluk, A., Iversen, J., Iversen, J., Izquierdo, M., Jachmann, L., Jardon, N., Jastrow, U., Jensch, K., Jensen, J., Jeżabek, M., Jidda, M., Jin, H., Johansson, N., Jonas, R., Kaabi, W., Kaefer, D., Kammering, R., Kapitza, H., Karabekyan, S., Karstensen, S., Kasprzak, K., Katalev, V., Keese, D., Keil, B., Kholopov, M., Killenberger, M., Kitaev, B., Klimchenko, Y., Klos, R., Knebel, L., Koch, A., Koepke, M., Köhler, S., Köhler, W., Kohlstrunk, N., Konopkova, Z., Konstantinov, A., Kook, W., Koprek, W., Körfer, M., Korth, O., Kosarev, A., Kosiński, K., Kostin, D., Kot, Y., Kotarba, A., Kozak, T., Kozak, V., Kramert, R., Krasilnikov, M., Krasnov, A., Krause, B., Kravchuk, L., Krebs, O., Kretschmer, R., Kreutzkamp, J., Kröplin, O., Krzysik, K., Kube, G., Kuehn, H., Kujala, N., Kulikov, V., Kuzminych, V., La Civita, D., Lacroix, M., Lamb, T., Lancetov, A., Larsson, M., Le Pinvidic, D., Lederer, S., Lensch, T., Lenz, D., Leuschner, A., Levenhagen, F., Li, Y., Liebing, J., Lilje, L., Limberg, T., Lipka, D., List, B., Liu, J., Liu, S., Lorbeer, B., Lorkiewicz, J., Lu, H. H., Ludwig, F., Machau, K., Maciocha, W., Madec, C., Magueur, C., Maiano, C., Maksimova, I., Malcher, K., Maltezopoulos, T., Mamoshkina, E., Manschwetus, B., Marcellini, F., Marinkovic, G., Martinez, T., Martirosyan, H., Maschmann, W., Maslov, M., Matheisen, A., Mavric, U., Meißner, J., Meissner, K., Messerschmidt, M., Meyners, N., Michalski, G., Michelato, P., Mildner, N., Moe, M., Moglia, F., Mohr, C., Mohr, S., Möller, W., Mommerz, M., Monaco, L., Montiel, C., Moretti, M., Morozov, I., Morozov, P., Mross, D., Mueller, J., Müller, C., Müller, J., Müller, K., Munilla, J., Münnich, A., Muratov, V., Napoly, O., Näser, B., Nefedov, N., Neumann, Reinhard, Neumann, Rudolf, Ngada, N., Noelle, D., Obier, F., Okunev, I., Oliver, J. A., Omet, M., Oppelt, A., Ottmar, A., Oublaid, M., Pagani, C., Paparella, R., Paramonov, V., Peitzmann, C., Penning, J., Perus, A., Peters, F., Petersen, B., Petrov, A., Petrov, I., Pfeiffer, S., Pflüger, J., Philipp, S., Pienaud, Y., Pierini, P., Pivovarov, S., Planas, M., Pławski, E., Pohl, M., Polinski, J., Popov, V., Prat, S., Prenting, J., Priebe, G., Pryschelski, H., Przygoda, K., Pyata, E., Racky, B., Rathjen, A., Ratuschni, W., Regnaud-Campderros, S., Rehlich, K., Reschke, D., Robson, C., Roever, J., Roggli, M., Rothenburg, J., Rusiński, E., Rybaniec, R., Sahling, H., Salmani, M., Samoylova, L., Sanzone, D., Saretzki, F., Sawlanski, O., Schaffran, J., Schlarb, H., Schlösser, M., Schlott, V., Schmidt, C., Schmidt-Foehre, F., Schmitz, M., Schmökel, M., Schnautz, T., Schneidmiller, E., Scholz, M., Schöneburg, B., Schultze, J., Schulz, C., Schwarz, A., Sekutowicz, J., Sellmann, D., Semenov, E., Serkez, S., Sertore, D., Shehzad, N., Shemarykin, P., Shi, L., Sienkiewicz, M., Sikora, D., Sikorski, M., Silenzi, A., Simon, C., Singer, W., Singer, X., Sinn, H., Sinram, K., Skvorodnev, N., Smirnow, P., Sommer, T., Sorokin, A., Stadler, M., Steckel, M., Steffen, B., Steinhau-Kühl, N., Stephan, F., Stodulski, M., Stolper, M., Sulimov, A., Susen, R., Świerblewski, J., Sydlo, C., Syresin, E., Sytchev, V., Szuba, J., Tesch, N., Thie, J., Thiebault, A., Tiedtke, K., Tischhauser, D., Tolkiehn, J., Tomin, S., Tonisch, F., Toral, F., Torbin, I., Trapp, A., Treyer, D., Trowitzsch, G., Trublet, T., Tschentscher, T., Ullrich, F., Vannoni, M., Varela, P., Varghese, G., Vashchenko, G., Vasic, M., Vazquez-Velez, C., Verguet, A., Vilcins-Czvitkovits, S., Villanueva, R., Visentin, B., Viti, M., Vogel, E., Volobuev, E., Wagner, R., Walker, N., Wamsat, T., Weddig, H., Weichert, G., Weise, H., Wenndorf, R., Werner, M., Wichmann, R., Wiebers, C., Wiencek, M., Wilksen, T., Will, I., Winkelmann, L., Winkowski, M., Wittenburg, K., Witzig, A., Wlk, P., Wohlenberg, T., Wojciechowski, M., Wolff-Fabris, F., Wrochna, G., Wrona, K., Yakopov, M., Yang, B., Yang, F., Yurkov, M., Zagorodnov, I., Zalden, P., Zavadtsev, A., Zavadtsev, D., Zhirnov, A., Zhukov, A., Ziemann, V., Zolotov, A., Zolotukhina, N., Zummack, F., and Zybin, D.

Published
2020
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2. Iconic CO2 time series at risk

Author

Houweling, S, Badawy, B, Baker, DF, Basu, S, Belikov, D, Bergamaschi, P, Bousquet, P, Broquet, G, Butler, T, Canadell, JG, Chen, J, Chevallier, F, Ciais, P, Collatz, JG, Denning, S, Engelen, R, Enting, IG, Fischer, ML, Fraser, A, Gerbig, C, Gloor, M, Jacobson, AR, Jones, DBA, Heimann, M, Khalil, A, Kaminski, T, Kasibhatla, PS, Krakauer, NY, Krol, M, Maki, T, Maksyutov, S, Manning, A, Meesters, A, Miller, JB, Palmer, PI, Patra, P, Peters, W, Peylin, P, Poussi, Z, Prather, MJ, Randerson, JT, Röckmann, T, Rödenbeck, C, Sarmiento, JL, Schimel, DS, Scholze, M, Schuh, A, Suntharalingam, P, Takahashi, T, Turnbull, J, Yurganov, L, and Vermeulen, A

Published
2012

4. Consistent Land- and Atmosphere-Based U.S. Carbon Sink Estimates

Author

Pacala, S. W., Hurtt, G. C., Baker, D., Peylin, P., Houghton, R. A., Birdsey, R. A., Heath, L., Sundquist, E. T., Stallard, R. F., Ciais, P., Moorcroft, P., Caspersen, J. P., Shevliakova, E., Moore, B., Kohlmaier, G., Holland, E., Gloor, M., Harmon, M. E., Sarmiento, J. L., Goodale, C. L., Schimel, D., and Field, C. B.

Published
2001

5. Impact of Fraserdale CO 2 observations on annual flux inversion of the North American boreal region

Author

Yuen, C.-W., Higuchi, K., Baker, D., Bousquet, P, Bruhwiler, L., Chen, Y.-H., Ciais, P., Denning, A. S., Fan, S., Fung, L., Gloor, M., Gurney, K. R., Heimann, M., John, J., Law, R. M., Maki, T,, Maksyutov, S., Pak, B., Peylin, P., Prather, M., Rayner, P., Sarmiento, J,, Taguchi, S., and Takahashi, T.

Subjects
boreal forest, carbon dioxide, carbon flux, monitoring system
Abstract

In TransCom-3 (Level 1), atmospheric CO2 measurements from 76 monitoring stations for the period 1992–1996 and 16 atmospheric transport models were used to constrain annual mean CO2 fluxes over 11 land and 11 ocean regions. The tower measurements of atmospheric CO2 from Fraserdale, a continental site in northern Ontario, Canada are now available and processed for use in the TransCom-3 inverse modelling framework. In this short study, we show that by including this set of continental CO2 data, the estimated flux for the North American boreal region becomes nearly zero, a reduction of about 0.26 Pg C yr−1 from the previous estimate. The uncertainty of the estimated flux for this region is also reduced by ∼30%. All transport models show negative changes for boreal North America, with the strongest responses (∼−0.5 Pg C yr−1) shown by NIRE, NIES, CSU and SKYHI. Furthermore, models showing a strong response in boreal North America tend to show strong sensitivity in middle- and high-latitude Asian regions.

Published
2005

8. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models

Author

Randerson, JT, Gurney, KR, Law, RM, Denning, AS, Rayner, PJ, Baker, D, Bousquet, P, Bruhwiler, L, Chen, YH, Ciais, P, Fan, S, Fung, IY, Gloor, M, Heimann, M, Higuchi, K, John, J, Maki, T, Maksyutov, S, Masarie, K, Peylin, P, Prather, M, Pak, BC, Sarmiento, J, Taguchi, S, Takahashi, T, and Yuen, CW

Subjects
General Science & Technology
Abstract

Information about regional carbon sources and sinks can be derived from variations in observed atmospheric CO2 concentrations via inverse modelling with atmospheric tracer transport models. A consensus has not yet been reached regarding the size and distribution of regional carbon fluxes obtained using this approach, partly owing to the use of several different atmospheric transport models(1-9). Here we report estimates of surface- atmosphere CO2 fluxes from an intercomparison of atmospheric CO2 inversion models (the TransCom 3 project), which includes 16 transport models and model variants. We find an uptake of CO2 in the southern extratropical ocean less than that estimated from ocean measurements, a result that is not sensitive to transport models or methodological approaches. We also find a northern land carbon sink that is distributed relatively evenly among the continents of the Northern Hemisphere, but these results show some sensitivity to transport differences among models, especially in how they respond to seasonal terrestrial exchange of CO2. Overall, carbon fluxes integrated over latitudinal zones are strongly constrained by observations in the middle to high latitudes. Further significant constraints to our understanding of regional carbon fluxes will therefore require improvements in transport models and expansion of the CO2 observation network within the tropics.

Published
2002

9. On error estimation in atmospheric CO 2 inversions

Author

Engelen, Richard J, Denning, A S, Gurney, K R, Law, R M, Rayner, P J, Baker, D, Bousquet, P, Bruhwiler, L, Chen, Y H, Ciais, P, Fan, S, Fung, I Y, Gloor, M, Heimann, M, Higuchi, K, John, J, Maki, T, Maksyutov, S, Masarie, K, Peylin, P, Prather, M, Pak, B C, Sarmiento, J, Taguchi, S, Takahashi, T, and Yuen, C W

Subjects
atmospheric movements, carbon dioxide, earth atmosphere, error analysis, inverse problems, problem solving, transport properties, error, fluxes, model inversion
Abstract

This paper explores various sources of error in atmospheric CO2 synthesis inversions using global circulation models. The estimation of prior, observation, model transport, and representation errors is described, and the latter two error sources are explored in more detail. Not accounting for these errors will act as a hard constraint on the inversion and will produce incorrect solutions to the problem as is shown in some example inversions. The magnitude of these errors falls generally between about 10% and 100% in the retrieved fluxes but can be even larger. This makes it highly desirable to avoid hard constraints and apply any prior information we have about the surface fluxes as a weak constraint to the inversion problem.

Published
2002

10. Author Correction: A MHz-repetition-rate hard X-ray free-electron laser driven by a superconducting linear accelerator

Author

Decking, W., Abeghyan, S., Abramian, P., Abramsky, A., Aguirre, A., Albrecht, C., Alou, P., Altarelli, M., Altmann, P., Amyan, K., Anashin, V., Apostolov, E., Appel, K., Auguste, D., Ayvazyan, V., Baark, S., Babies, F., Baboi, N., Bak, P., Balandin, V., Baldinger, R., Baranasic, B., Barbanotti, S., Belikov, O., Belokurov, V., Belova, L., Belyakov, V., Berry, S., Bertucci, M., Beutner, B., Block, A., Blöcher, M., Böckmann, T., Bohm, C., Böhnert, M., Bondar, V., Bondarchuk, E., Bonezzi, M., Borowiec, P., Bösch, C., Bösenberg, U., Bosotti, A., Böspflug, R., Bousonville, M., Boyd, E., Bozhko, Y., Brand, A., Branlard, J., Briechle, S., Brinker, F., Brinker, S., Brinkmann, R., Brockhauser, S., Brovko, O., Brück, H., Brüdgam, A., Butkowski, L., Büttner, T., Calero, J., Castro-Carballo, E., Cattalanotto, G., Charrier, J., Chen, J., Cherepenko, A., Cheskidov, V., Chiodini, M., Chong, A., Choroba, S., Chorowski, M., Churanov, D., Cichalewski, W., Clausen, M., Clement, W., Cloué, C., Cobos, J. A., Coppola, N., Cunis, S., Czuba, K., Czwalinna, M., D’Almagne, B., Dammann, J., Danared, H., de Zubiaurre Wagner, A., Delfs, A., Delfs, T., Dietrich, F., Dietrich, T., Dohlus, M., Dommach, M., Donat, A., Dong, X., Doynikov, N., Dressel, M., Duda, M., Duda, P., Eckoldt, H., Ehsan, W., Eidam, J., Eints, F., Engling, C., Englisch, U., Ermakov, A., Escherich, K., Eschke, J., Saldin, E., Faesing, M., Fallou, A., Felber, M., Fenner, M., Fernandes, B., Fernández, J. M., Feuker, S., Filippakopoulos, K., Floettmann, K., Fogel, V., Fontaine, M., Francés, A., Martin, I. Freijo, Freund, W., Freyermuth, T., Friedland, M., Fröhlich, L., Fusetti, M., Fydrych, J., Gallas, A., García, O., Garcia-Tabares, L., Geloni, G., Gerasimova, N., Gerth, C., Geßler, P., Gharibyan, V., Gloor, M., Głowinkowski, J., Goessel, A., Gołębiewski, Z., Golubeva, N., Grabowski, W., Graeff, W., Grebentsov, A., Grecki, M., Grevsmuehl, T., Gross, M., Grosse-Wortmann, U., Grünert, J., Grunewald, S., Grzegory, P., Feng, G., Guler, H., Gusev, G., Gutierrez, J. L., Hagge, L., Hamberg, M., Hanneken, R., Harms, E., Hartl, I., Hauberg, A., Hauf, S., Hauschildt, J., Hauser, J., Havlicek, J., Hedqvist, A., Heidbrook, N., Hellberg, F., Henning, D., Hensler, O., Hermann, T., Hidvégi, A., Hierholzer, M., Hintz, H., Hoffmann, F., Hoffmann, Markus, Hoffmann, Matthias, Holler, Y., Hüning, M., Ignatenko, A., Ilchen, M., Iluk, A., Iversen, J., Iversen, J., Izquierdo, M., Jachmann, L., Jardon, N., Jastrow, U., Jensch, K., Jensen, J., Jeżabek, M., Jidda, M., Jin, H., Johansson, N., Jonas, R., Kaabi, W., Kaefer, D., Kammering, R., Kapitza, H., Karabekyan, S., Karstensen, S., Kasprzak, K., Katalev, V., Keese, D., Keil, B., Kholopov, M., Killenberger, M., Kitaev, B., Klimchenko, Y., Klos, R., Knebel, L., Koch, A., Koepke, M., Köhler, S., Köhler, W., Kohlstrunk, N., Konopkova, Z., Konstantinov, A., Kook, W., Koprek, W., Körfer, M., Korth, O., Kosarev, A., Kosiński, K., Kostin, D., Kot, Y., Kotarba, A., Kozak, T., Kozak, V., Kramert, R., Krasilnikov, M., Krasnov, A., Krause, B., Kravchuk, L., Krebs, O., Kretschmer, R., Kreutzkamp, J., Kröplin, O., Krzysik, K., Kube, G., Kuehn, H., Kujala, N., Kulikov, V., Kuzminych, V., La Civita, D., Lacroix, M., Lamb, T., Lancetov, A., Larsson, M., Le Pinvidic, D., Lederer, S., Lensch, T., Lenz, D., Leuschner, A., Levenhagen, F., Li, Y., Liebing, J., Lilje, L., Limberg, T., Lipka, D., List, B., Liu, J., Liu, S., Lorbeer, B., Lorkiewicz, J., Lu, H. H., Ludwig, F., Machau, K., Maciocha, W., Madec, C., Magueur, C., Maiano, C., Maksimova, I., Malcher, K., Maltezopoulos, T., Mamoshkina, E., Manschwetus, B., Marcellini, F., Marinkovic, G., Martinez, T., Martirosyan, H., Maschmann, W., Maslov, M., Matheisen, A., Mavric, U., Meißner, J., Meissner, K., Messerschmidt, M., Meyners, N., Michalski, G., Michelato, P., Mildner, N., Moe, M., Moglia, F., Mohr, C., Mohr, S., Möller, W., Mommerz, M., Monaco, L., Montiel, C., Moretti, M., Morozov, I., Morozov, P., Mross, D., Mueller, J., Müller, C., Müller, J., Müller, K., Munilla, J., Münnich, A., Muratov, V., Napoly, O., Näser, B., Nefedov, N., Neumann, Reinhard, Neumann, Rudolf, Ngada, N., Noelle, D., Obier, F., Okunev, I., Oliver, J. A., Omet, M., Oppelt, A., Ottmar, A., Oublaid, M., Pagani, C., Paparella, R., Paramonov, V., Peitzmann, C., Penning, J., Perus, A., Peters, F., Petersen, B., Petrov, A., Petrov, I., Pfeiffer, S., Pflüger, J., Philipp, S., Pienaud, Y., Pierini, P., Pivovarov, S., Planas, M., Pławski, E., Pohl, M., Polinski, J., Popov, V., Prat, S., Prenting, J., Priebe, G., Pryschelski, H., Przygoda, K., Pyata, E., Racky, B., Rathjen, A., Ratuschni, W., Regnaud-Campderros, S., Rehlich, K., Reschke, D., Robson, C., Roever, J., Roggli, M., Rothenburg, J., Rusiński, E., Rybaniec, R., Sahling, H., Salmani, M., Samoylova, L., Sanzone, D., Saretzki, F., Sawlanski, O., Schaffran, J., Schlarb, H., Schlösser, M., Schlott, V., Schmidt, C., Schmidt-Foehre, F., Schmitz, M., Schmökel, M., Schnautz, T., Schneidmiller, E., Scholz, M., Schöneburg, B., Schultze, J., Schulz, C., Schwarz, A., Sekutowicz, J., Sellmann, D., Semenov, E., Serkez, S., Sertore, D., Shehzad, N., Shemarykin, P., Shi, L., Sienkiewicz, M., Sikora, D., Sikorski, M., Silenzi, A., Simon, C., Singer, W., Singer, X., Sinn, H., Sinram, K., Skvorodnev, N., Smirnow, P., Sommer, T., Sorokin, A., Stadler, M., Steckel, M., Steffen, B., Steinhau-Kühl, N., Stephan, F., Stodulski, M., Stolper, M., Sulimov, A., Susen, R., Świerblewski, J., Sydlo, C., Syresin, E., Sytchev, V., Szuba, J., Tesch, N., Thie, J., Thiebault, A., Tiedtke, K., Tischhauser, D., Tolkiehn, J., Tomin, S., Tonisch, F., Toral, F., Torbin, I., Trapp, A., Treyer, D., Trowitzsch, G., Trublet, T., Tschentscher, T., Ullrich, F., Vannoni, M., Varela, P., Varghese, G., Vashchenko, G., Vasic, M., Vazquez-Velez, C., Verguet, A., Vilcins-Czvitkovits, S., Villanueva, R., Visentin, B., Viti, M., Vogel, E., Volobuev, E., Wagner, R., Walker, N., Wamsat, T., Weddig, H., Weichert, G., Weise, H., Wenndorf, R., Werner, M., Wichmann, R., Wiebers, C., Wiencek, M., Wilksen, T., Will, I., Winkelmann, L., Winkowski, M., Wittenburg, K., Witzig, A., Wlk, P., Wohlenberg, T., Wojciechowski, M., Wolff-Fabris, F., Wrochna, G., Wrona, K., Yakopov, M., Yang, B., Yang, F., Yurkov, M., Zagorodnov, I., Zalden, P., Zavadtsev, A., Zavadtsev, D., Zhirnov, A., Zhukov, A., Ziemann, V., Zolotov, A., Zolotukhina, N., Zummack, F., and Zybin, D.

Published
2020
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30. Tree-ring oxygen isotopes record a decrease in Amazon dry season rainfall over the past 40 years

Author

Cintra, BBL, Gloor, M, Boom, A, Schöngart, J, Baker, JCA, Cruz, FW, Clerici, S, and Brienen, RJW

Abstract

Extant climate observations suggest the dry season over large parts of the Amazon Basin has become longer and drier over recent decades. However, such possible intensification of the Amazon dry season and its underlying causes are still a matter of debate. Here we used oxygen isotope ratios in tree rings (δ18OTR) from six floodplain trees from the western Amazon to assess changes in past climate. Our analysis shows that δ18OTR of these trees is negatively related to inter-annual variability of precipitation during the dry season over large parts of the Amazon Basin, consistent with a Rayleigh rainout model. Furthermore δ18OTR increases by approximately 2‰ over the last four decades (~ 1970–2014) providing evidence of an Amazon drying trend independent from satellite and in situ rainfall observations. Using a Rayleigh rainout framework, we estimate basin-wide dry season rainfall to have decreased by up to 30%. The δ18OTR record further suggests such drying trend may not be unprecedented over the past 80 years. Analysis of δ18OTR with sea surface temperatures indicates a strong role of a warming Tropical North Atlantic Ocean in driving this long-term increase in δ18OTR and decrease in dry season rainfall.

Published
2021

32. Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH₄ Profiles

Author

Gloor, M, Gatti, LV, Wilson, C, Parker, RJ, Boesch, H, Popa, E, Chipperfield, MP, Poulter, B, Zhang, Z, Basso, L, Miller, J, McNorton, J, Jimenez, C, and Prigent, C

Abstract

The Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer-free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer-term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks.

Published
2021

33. Large Methane Emissions From the Pantanal During Rising Water-Levels Revealed by Regularly Measured Lower Troposphere CH4 Profiles

Author

Gloor, M., Gatti, L. V., Wilson, C., Parker, R. J., Boesch, H., Popa, E., Chipperfield, M. P., Poulter, B., Zhang, Z., Basso, L., Miller, J., McNorton, J., Jimenez, C., Prigent, C., Afd Marine and Atmospheric Research, Sub Atmospheric physics and chemistry, Marine and Atmospheric Research, and Nanophotonics

Subjects
Global and Planetary Change, Atmospheric Science, seasonally flooded savannah, Environmental Science(all), Taverne, climate feedback, Environmental Chemistry, Pantanal, atmospheric methane, wetlands
Abstract

The Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer-free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer-term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks.

Published
2021

34. Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH 4 Profiles

Author

Gloor, M., primary, Gatti, L. V., additional, Wilson, C., additional, Parker, R. J., additional, Boesch, H., additional, Popa, E., additional, Chipperfield, M. P., additional, Poulter, B., additional, Zhang, Z., additional, Basso, L., additional, Miller, J., additional, McNorton, J., additional, Jimenez, C., additional, and Prigent, C., additional

Published
2021
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35. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements

Author

Gatti, L.V., Gloor, M., Miller, J.B., Doughty, C.E., Malhi, Y., Domingues, L.G., Basso, L.S., Martinewski, A., Correia, C.S.C., Borges, V.F., Freitas, S., Braz, R., Anderson, L.O., Rocha, H., Grace, J., Phillips, O.L., and Lloyd, J.

Subjects
Carbon cycle (Biogeochemistry) -- Research, Environment -- Research, Droughts -- Environmental aspects -- Brazil, Atmospheric carbon dioxide -- Environmental aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Feedbacks between land carbon pools and climate provide one of the largest sources of uncertainty in our predictions of global climate (1,2). Estimates of the sensitivity of the terrestrial carbon budget to climate anomalies in the tropics and the identification of the mechanisms responsible for feedback effects remain uncertain (3,4). The Amazon basin stores a vast amount of carbon (5), and has experienced increasingly higher temperatures and more frequent floods and droughts over the past two decades (6). Here we report seasonal and annual carbon balances across the Amazon basin, based on carbon dioxide and carbon monoxide measurements for the anomalously dry and wet years 2010 and 2011, respectively. We find that the Amazon basin lost 0.48 ± 0.18 petagrams of carbon per year (Pg C [yr.sup.-1]) during the dry year but was carbon neutral (0.06 ± 0.1 Pg C [yr.sup.-1]) during the wet year. Taking into account carbon losses from fire by using carbon monoxide measurements, we derived the basin net biome exchange (that is, the carbon flux between the non-burned forest and the atmosphere) revealing that during the dry year, vegetation was carbon neutral. During the wet year, vegetation was a net carbon sink of 0.25 ± 0.14 Pg C [yr.sup.-1], which is roughly consistent with the mean long-term intact-forest biomass sink of 0.39 ± 0.10 Pg C [yr.sup.-1] previously estimated from forest censuses (7). Observations from Amazonian forest plots suggest the suppression of photosynthesis during drought as the primary cause for the 2010 sink neutralization. Overall, our results suggest that moisture has an important role in determining the Amazonian carbon balance. If the recent trend of increasing precipitation extremes persists (6), the Amazon may become an increasing carbon source as a result of both emissions from fires and the suppression of net biome exchange by drought., To observe the state, changes and climate sensitivity of the Amazon carbon pools we initiated a lower-troposphere greenhouse-gas sampling programme over the Amazon basin in 2010, measuring bi-weekly vertical profiles [...]

Published
2014

44. Métodos de Estimativas de Fluxo de Gases de Efeito Estufa e a Influência da Ação Humana na Redução da Capacidade de Remoção de CO2 na Floresta Amazônica

Author

Marani, L., Gatti, L., Miller, J. B., Domingues, L. G., Correia, C. C. S., Gloor, M., Peters, W., Basso, L. S., Crispim, S. P., Neves, R. A. L., and Isotopenonderzoek

Subjects
SOUTH-AMERICA, CLIMATE, Greenhouse gases, climate changes, amazon, WATER, CO2, DROUGHT SENSITIVITY, CYCLE, ATMOSPHERE, CARBON BALANCE
Abstract

The Amazon Forest play an important rule to tropical climate of South America, particularly to the water vapor atmospheric recirculation and represent a potential strong carbon storage that if totally released, will contribute largely to the global warming. The entire region is over strong human pressure, through logging, forest conversion and others resources explorations. In this work, we present a way to study the amazon carbon fluxes using regular CO2 vertical atmospheric profiles with aircrafts with regional representability. By combining these measurements with the background measurements in Barbados and Ascension, the carbon flux for 20 % of the Amazon area can be estimated. There are two primary challenges in these CO2 measurements: precision and accuracy. The methodology developed to achieve both precision and accuracy is presented. The analyses of the differences in trend lines of the measurements bellow the Planetary Boundary Layer and the top of the profile show a change in the contribution over the study period, confirmed also by the analysis of the annual mean CO2 fluxes. The trend in the fluxes show a similar behavior to that observed in the trends of the land use change over the influenced area of SAN, mainly the conversion of forest to agriculture and livestock areas, highlighting the influence of human actions in the conversion of the East Amazon from a sink to a source of atmospheric CO2.

Published
2020

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