ALBERT

Description: Diverse microbial communities and numerous energy-yielding activities occur in deeply buried sediments of the eastern Pacific Ocean. Distributions of metabolic activities often deviate from the standard model. Rates of activities, cell concentrations, and populations of cultured bacteria vary consistently from one subseafloor environment to another. Net rates of major activities principally rely on electron acceptors and electron donors from the photosynthetic surface world. At open-ocean sites, nitrate and oxygen are supplied to the deepest sedimentary communities through the underlying basaltic aquifer. In turn, these sedimentary communities may supply dissolved electron donors and nutrients to the underlying crustal biosphere.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉D'Hondt, Steven -- Jorgensen, Bo Barker -- Miller, D Jay -- Batzke, Anja -- Blake, Ruth -- Cragg, Barry A -- Cypionka, Heribert -- Dickens, Gerald R -- Ferdelman, Timothy -- Hinrichs, Kai-Uwe -- Holm, Nils G -- Mitterer, Richard -- Spivack, Arthur -- Wang, Guizhi -- Bekins, Barbara -- Engelen, Bert -- Ford, Kathryn -- Gettemy, Glen -- Rutherford, Scott D -- Sass, Henrik -- Skilbeck, C Gregory -- Aiello, Ivano W -- Guerin, Gilles -- House, Christopher H -- Inagaki, Fumio -- Meister, Patrick -- Naehr, Thomas -- Niitsuma, Sachiko -- Parkes, R John -- Schippers, Axel -- Smith, David C -- Teske, Andreas -- Wiegel, Juergen -- Padilla, Christian Naranjo -- Acosta, Juana Luz Solis -- New York, N.Y. -- Science. 2004 Dec 24;306(5705):2216-21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Ocean Drilling Program Leg 201 Shipboard Scientific Party, NASA Astrobiology Institute, University of Rhode Island Graduate School of Oceanography, South Ferry Road, Narragansett, RI 02882, USA. dhondt@gso.uri.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/15618510" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Houweling, Sander -- Badawy, Bakr -- Baker, David F -- Basu, Sourish -- Belikov, Dmitry -- Bergamaschi, Peter -- Bousquet, Philippe -- Broquet, Gregoire -- Butler, Tim -- Canadell, Josep G -- Chen, Jing -- Chevallier, Frederic -- Ciais, Philippe -- Collatz, G James -- Denning, Scott -- Engelen, Richard -- Enting, Ian G -- Fischer, Marc L -- Fraser, Annemarie -- Gerbig, Christoph -- Gloor, Manuel -- Jacobson, Andrew R -- Jones, Dylan B A -- Heimann, Martin -- Khalil, Aslam -- Kaminski, Thomas -- Kasibhatla, Prasad S -- Krakauer, Nir Y -- Krol, Maarten -- Maki, Takashi -- Maksyutov, Shamil -- Manning, Andrew -- Meesters, Antoon -- Miller, John B -- Palmer, Paul I -- Patra, Prabir -- Peters, Wouter -- Peylin, Philippe -- Poussi, Zegbeu -- Prather, Michael J -- Randerson, James T -- Rockmann, Thomas -- Rodenbeck, Christian -- Sarmiento, Jorge L -- Schimel, David S -- Scholze, Marko -- Schuh, Andrew -- Suntharalingam, Parv -- Takahashi, Taro -- Turnbull, Jocelyn -- Yurganov, Leonid -- Vermeulen, Alex -- New York, N.Y. -- Science. 2012 Aug 31;337(6098):1038-40. doi: 10.1126/science.337.6098.1038-b.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22936755" target="_blank"〉PubMed〈/a〉

Description: Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with 〉40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing 〉35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that 〉73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sunagawa, Shinichi -- Coelho, Luis Pedro -- Chaffron, Samuel -- Kultima, Jens Roat -- Labadie, Karine -- Salazar, Guillem -- Djahanschiri, Bardya -- Zeller, Georg -- Mende, Daniel R -- Alberti, Adriana -- Cornejo-Castillo, Francisco M -- Costea, Paul I -- Cruaud, Corinne -- d'Ovidio, Francesco -- Engelen, Stefan -- Ferrera, Isabel -- Gasol, Josep M -- Guidi, Lionel -- Hildebrand, Falk -- Kokoszka, Florian -- Lepoivre, Cyrille -- Lima-Mendez, Gipsi -- Poulain, Julie -- Poulos, Bonnie T -- Royo-Llonch, Marta -- Sarmento, Hugo -- Vieira-Silva, Sara -- Dimier, Celine -- Picheral, Marc -- Searson, Sarah -- Kandels-Lewis, Stefanie -- Tara Oceans coordinators -- Bowler, Chris -- de Vargas, Colomban -- Gorsky, Gabriel -- Grimsley, Nigel -- Hingamp, Pascal -- Iudicone, Daniele -- Jaillon, Olivier -- Not, Fabrice -- Ogata, Hiroyuki -- Pesant, Stephane -- Speich, Sabrina -- Stemmann, Lars -- Sullivan, Matthew B -- Weissenbach, Jean -- Wincker, Patrick -- Karsenti, Eric -- Raes, Jeroen -- Acinas, Silvia G -- Bork, Peer -- New York, N.Y. -- Science. 2015 May 22;348(6237):1261359. doi: 10.1126/science.1261359.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; CEA-Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91057 Evry, France. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. ; Sorbonne Universites, UPMC, Universite Paris 06, CNRS-IRD-MNHN, LOCEAN Laboratory, 4 Place Jussieu, 75005 Paris France. ; CNRS, UMR 7093, Laboratoire d'Oceanographie de Villefranche-sur-Mer, Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7093, LOV, Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. Laboratoire de Physique des Oceans UBO-IUEM, Place Copernic 29820 Plouzane, France. ; Aix Marseille Universite CNRS IGS UMR 7256, 13288 Marseille, France. ; Department of Ecology and Evolutionary Biology, University of Arizona, 1007 East Lowell Street, Tucson, AZ 85721, USA. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. Department of Hydrobiology, Federal University of Sao Carlos (UFSCar), Rodovia Washington Luiz, 13565-905 Sao Carlos, Sao Paulo, Brazil. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Directors' Research, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. ; CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; CNRS UMR 7232, BIOM, Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. Sorbonne Universites Paris 06, OOB UPMC, Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. ; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy. ; CEA-Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91057 Evry, France. CNRS, UMR 8030, CP5706, Evry, France. Universite d'Evry, UMR 8030, CP5706, Evry, France. ; Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-001, Japan. ; PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany. ; Department of Geosciences, Laboratoire de Meteorologie Dynamique (LMD), Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris Cedex 05, France. Laboratoire de Physique des Oceans UBO-IUEM, Place Copernic, 29820 Plouzane, France. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. Directors' Research, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Max-Delbruck-Centre for Molecular Medicine, 13092 Berlin, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25999513" target="_blank"〉PubMed〈/a〉

Description: Marine plankton support global biological and geochemical processes. Surveys of their biodiversity have hitherto been geographically restricted and have not accounted for the full range of plankton size. We assessed eukaryotic diversity from 334 size-fractionated photic-zone plankton communities collected across tropical and temperate oceans during the circumglobal Tara Oceans expedition. We analyzed 18S ribosomal DNA sequences across the intermediate plankton-size spectrum from the smallest unicellular eukaryotes (protists, 〉0.8 micrometers) to small animals of a few millimeters. Eukaryotic ribosomal diversity saturated at ~150,000 operational taxonomic units, about one-third of which could not be assigned to known eukaryotic groups. Diversity emerged at all taxonomic levels, both within the groups comprising the ~11,200 cataloged morphospecies of eukaryotic plankton and among twice as many other deep-branching lineages of unappreciated importance in plankton ecology studies. Most eukaryotic plankton biodiversity belonged to heterotrophic protistan groups, particularly those known to be parasites or symbiotic hosts.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉de Vargas, Colomban -- Audic, Stephane -- Henry, Nicolas -- Decelle, Johan -- Mahe, Frederic -- Logares, Ramiro -- Lara, Enrique -- Berney, Cedric -- Le Bescot, Noan -- Probert, Ian -- Carmichael, Margaux -- Poulain, Julie -- Romac, Sarah -- Colin, Sebastien -- Aury, Jean-Marc -- Bittner, Lucie -- Chaffron, Samuel -- Dunthorn, Micah -- Engelen, Stefan -- Flegontova, Olga -- Guidi, Lionel -- Horak, Ales -- Jaillon, Olivier -- Lima-Mendez, Gipsi -- Lukes, Julius -- Malviya, Shruti -- Morard, Raphael -- Mulot, Matthieu -- Scalco, Eleonora -- Siano, Raffaele -- Vincent, Flora -- Zingone, Adriana -- Dimier, Celine -- Picheral, Marc -- Searson, Sarah -- Kandels-Lewis, Stefanie -- Tara Oceans Coordinators -- Acinas, Silvia G -- Bork, Peer -- Bowler, Chris -- Gorsky, Gabriel -- Grimsley, Nigel -- Hingamp, Pascal -- Iudicone, Daniele -- Not, Fabrice -- Ogata, Hiroyuki -- Pesant, Stephane -- Raes, Jeroen -- Sieracki, Michael E -- Speich, Sabrina -- Stemmann, Lars -- Sunagawa, Shinichi -- Weissenbach, Jean -- Wincker, Patrick -- Karsenti, Eric -- New York, N.Y. -- Science. 2015 May 22;348(6237):1261605. doi: 10.1126/science.1261605.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. vargas@sb-roscoff.fr pwincker@genoscope.cns.fr karsenti@embl.de. ; CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Department of Ecology, University of Kaiserslautern, Erwin-Schroedinger Street, 67663 Kaiserslautern, Germany. CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Department of Marine Biology and Oceanography, Institute of Marine Science (ICM)-Consejo Superior de Investigaciones Cientificas (CSIC), Passeig Maritim de la Barceloneta 37-49, Barcelona E08003, Spain. ; Laboratory of Soil Biology, University of Neuchatel, Rue Emile-Argand 11, 2000 Neuchatel, Switzerland. ; CNRS, FR2424, Roscoff Culture Collection, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, UPMC Paris 06, FR 2424, Roscoff Culture Collection, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, F-75005 France. ; Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91000 Evry, France. ; CNRS FR3631, Institut de Biologie Paris-Seine, F-75005, Paris, France. Sorbonne Universites, UPMC Paris 06, Institut de Biologie Paris-Seine, F-75005, Paris, France. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, F-75005 France. CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; Department of Ecology, University of Kaiserslautern, Erwin-Schroedinger Street, 67663 Kaiserslautern, Germany. ; Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branisovska 31, 37005 Ceske Budejovice, Czech Republic. Faculty of Science, University of South Bohemia, Branisovska 31, 37005 Ceske Budejovice, Czech Republic. ; CNRS, UMR 7093, Laboratoire d'Oceanographie de Villefranche-sur-Mer (LOV), Observatoire Oceanologique, F-06230, Villefranche-sur-Mer, France. Sorbonne Universites, UPMC Paris 06, UMR 7093, LOV, Observatoire Oceanologique, F-06230, Villefranche-sur-Mer, France. ; Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91000 Evry, France. CNRS, UMR 8030, CP5706, Evry, France. Universite d'Evry, UMR 8030, CP5706, Evry, France. ; Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branisovska 31, 37005 Ceske Budejovice, Czech Republic. Faculty of Science, University of South Bohemia, Branisovska 31, 37005 Ceske Budejovice, Czech Republic. Canadian Institute for Advanced Research, 180 Dundas Street West, Suite 1400, Toronto, Ontario M5G 1Z8, Canada. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, F-75005 France. ; MARUM, Center for Marine Environmental Sciences, University of Bremen, 28359 Bremen, Germany. CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy. ; Ifremer, Centre de Brest, DYNECO/Pelagos CS 10070, 29280 Plouzane, France. ; Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, F-75005 France. ; Structural and Computational Biology, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany. Directors' Research, EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Structural and Computational Biology, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany. Max-Delbruck-Centre for Molecular Medicine, 13092 Berlin, Germany. ; CNRS UMR 7232, Biologie Integrative des Organismes Marins (BIOM), Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. Sorbonne Universites Paris 06, Observatoire Oceanologique de Banyuls (OOB) UPMC, Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. ; Aix Marseille Universite, CNRS IGS UMR 7256, 13288 Marseille, France. ; Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. ; PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany. MARUM, Center for Marine Environmental Sciences, University of Bremen, 28359 Bremen, Germany. ; Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA. National Science Foundation, Arlington, VA 22230, USA. ; Department of Geosciences, Laboratoire de Meteorologie Dynamique (LMD), Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris Cedex 05, France. Laboratoire de Physique des Oceans, Universite de Bretagne Occidentale (UBO)-Institut Universitaire Europeen de la Mer (IUEM), Place Copernic, 29820 Plouzane, France. ; Structural and Computational Biology, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91000 Evry, France. CNRS, UMR 8030, CP5706, Evry, France. Universite d'Evry, UMR 8030, CP5706, Evry, France. vargas@sb-roscoff.fr pwincker@genoscope.cns.fr karsenti@embl.de. ; Directors' Research, EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, F-75005 France. vargas@sb-roscoff.fr pwincker@genoscope.cns.fr karsenti@embl.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25999516" target="_blank"〉PubMed〈/a〉

Description: Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatio-temporal scales. Inversions require prior flux, a forward model and observation errors to estimate posterior fluxes and uncertainties. Here, we investigate the forward transport error and the associated biogenic feedback in an Earth system model (ESM) context. These errors can occur from uncertainty in the initial meteorology, the analysis fields used, or the advection schemes and physical parameterisation of the model. We also explore the spatio-temporal variability and flow-dependent error covariances. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total-column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed, prior monthly anthropogenic flux uncertainties projected onto the same space (0.1–1.4 ppm). Average near-surface transport errors at three sites (Paris, Caltech and Tsukuba) range from 1.7 to 7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ∼0.1 ppm after 2–3 d, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow dependent, with XCO2 spatio-temporal correlation length scales ranging from 0 to 700 km and 0 to 260 min. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (

Description: Holocene marine transgressions are often put forward to explain observed groundwater salinities that extend far inland in deltas. This hypothesis was also proposed in the literature to explain the large land-inward extent of saline groundwater in the Nile Delta. The groundwater models previously built for the area used very large dispersivities to reconstruct this saline and brackish groundwater zone. However, this approach cannot explain the observed freshening of this zone. Here, we investigated the physical plausibility of the Holocene-transgression hypothesis to explain observed salinities by conducting a palaeohydrogeological reconstruction of groundwater salinity for the last 32 ka with a complex 3-D variable-density groundwater flow model, using a state-of-the-art version of the SEAWAT computer code that allows for parallel computation. Several scenarios with different lithologies and hypersaline groundwater provenances were simulated, of which five were selected that showed the best match with the observations. Amongst these selections, total freshwater volumes varied strongly, ranging from 1526 to 2659 km3, mainly due to uncertainties in the lithology offshore and at larger depths. This range is smaller (1511–1989 km3) when we only consider the volumes of onshore fresh groundwater within 300 m depth. In all five selected scenarios the total volume of hypersaline groundwater exceeded that of seawater. We also show that during the last 32 ka, total freshwater volumes significantly declined, with a factor ranging from 2 to 5, due to the rising sea level. Furthermore, the time period required to reach a steady state under current boundary conditions exceeded 5.5 ka for all scenarios. Finally, under highly permeable conditions the marine transgression simulated with the palaeohydrogeological reconstruction led to a steeper fresh–salt interface compared to its steady-state equivalent, while low-permeable clay layers allowed for the preservation of fresh groundwater volumes. This shows that long-term transient simulations are needed when estimating present-day fresh–salt groundwater distributions in large deltas. The insights of this study are also applicable to other major deltaic areas, since many also experienced a Holocene marine transgression.

Description: Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

Description: The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

Description: The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of biomass burning emissions. It has been extended to include information about injection heights derived from fire observations and meteorological information from the operational weather forecasts of ECMWF. Injection heights are provided by two distinct methods: the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) parameterisation and the one-dimensional plume rise model (PRM). A global database of daily biomass burning emissions and injection heights at 0.1° resolution has been produced for 2003–2015 and is continuously extended in near-real time with the operational GFAS service of the Copernicus Atmospheric Monitoring Service (CAMS). In this study, the two injection height data sets were compared with the new MPHP2 (MISR Plume Height Project 2) satellite-based plume height retrievals. The IS4FIRES parameterisation showed a better overall agreement than the observations, while the PRM was better at capturing the variability of injection heights. The performance of both parameterisations is also dependent on the type of vegetation. Furthermore, the use of biomass burning emission heights from GFAS in atmospheric composition forecasts was assessed in two case studies: the South AMerican Biomass Burning Analysis (SAMBBA) campaign which took place in September 2012 in Brazil, and a series of large fire events in the western USA in August 2013. For these case studies, forecasts of biomass burning aerosol species by the Composition Integrated Forecasting System (C-IFS) of CAMS were found to better reproduce the observed vertical distribution when using PRM injection heights from GFAS compared to aerosols emissions being prescribed at the surface. The globally available GFAS injection heights introduced and evaluated in this study provide a comprehensive data set for future fire and atmospheric composition modelling studies.

Description: A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.