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  • 1
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Hessler, Ines; Harrison, S P; Kucera, Michal; Waelbroeck, Claire; Chen, Min-Te; Anderson, Carin; de Vernal, Anne; Fréchette, Bianca; Cloke-Hayes, Angela; Leduc, Guillaume; Londeix, Laurent (2014): Implication of methodological uncertainties for mid-Holocene sea surface temperature reconstructions. Climate of the Past, 10(6), 2237-2252, https://doi.org/10.5194/cp-10-2237-2014
    Publication Date: 2023-03-03
    Description: We present and examine a multi-sensor global compilation of mid-Holocene (MH) sea surface temperatures (SST), based on Mg/Ca and alkenone palaeothermometry and reconstructions obtained using planktonic foraminifera and organic-walled dinoflagellate cyst census counts. We assess the uncertainties originating from using different methodologies and evaluate the potential of MH SST reconstructions as a benchmark for climate-model simulations. The comparison between different analytical approaches (time frame, baseline climate) shows the choice of time window for the MH has a negligible effect on the reconstructed SST pattern, but the choice of baseline climate affects both the magnitude and spatial pattern of the reconstructed SSTs. Comparison of the SST reconstructions made using different sensors shows significant discrepancies at a regional scale, with uncertainties often exceeding the reconstructed SST anomaly. Apparent patterns in SST may largely be a reflection of the use of different sensors in different regions. Overall, the uncertainties associated with the SST reconstructions are generally larger than the MH anomalies. Thus, the SST data currently available cannot serve as a target for benchmarking model simulations.
    Keywords: Center for Marine Environmental Sciences; MARUM
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 2
    Publication Date: 2023-01-13
    Description: Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modelling. The China Plant Trait Database contains information on morphometric, physical, chemical and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site.
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 3
    Publication Date: 2023-01-13
    Keywords: China
    Type: Dataset
    Format: application/zip, 892.7 kBytes
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  • 4
    Publication Date: 2023-01-13
    Keywords: China
    Type: Dataset
    Format: application/zip, 312.2 kBytes
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  • 5
    Publication Date: 2023-07-20
    Keywords: Abrupt Climate Changes and Environmental Responses; ACER
    Type: Dataset
    Format: application/zip, 207.3 kBytes
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  • 6
    Publication Date: 2024-02-13
    Keywords: 101; 108-658C; 13078-016; 160-964; 160-967D; 160-969; 160-969E; 160-973; 161-974; 161-975; 161-976C; 161-977; 162-984; 165-1002C; 165-999; 167-1012B; 167-1017E; 167-1019C; 175-1078C; 175-1084B; 184-1145; 2; 2004-804-009; 2005-804-004; 2005-804-006; 202-1233; 202-1240; 202-1242; 225514; 225517; 255; 3664N/S; 36C; 79US/Fr; 89; A-7; AD91-17; Adriatic Sea; AGE; Alboran Sea; also published as VM28-122; Angola Basin; Arabian Sea; Arctic Ocean; Atlantic Ocean; AUSCAN; B-3GC; Barrow Canyon; Bay of Bengal; BENGAL FAN; Benguela Current, South Atlantic Ocean; BOFS31/1K; BOFS31#1; BS79-33; BS79-38; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Calypso square corer; Canarias Sea; Caribbean Sea; CASQ; Cayman Rise, Caribbean Sea; CD159-12; CD53; Center for Marine Environmental Sciences; CEPAG; CH07-98-GGC19; CH69-K09; CH77-02; Charles Darwin; Chatham Rise; CHIPAL; CLIVAMPcruises; Cocos Ridge; Comment; COMPCORE; Composite Core; Congo Fan; D13882; D226; D249; De Soto Canyon; Discovery (1962); DRILL; Drilling/drill rig; E017; East Bight Fracture Zone; East China Sea, Pacific Ocean; Eastern Basin; East Pacific; Emperor Seamounts; Equatorial East Pacific; Event label; FAEGAS_IV; GC; GeoB10029-4; GeoB10038-4; GeoB1023-5; GeoB1710-3; GeoB1711; GeoB1711-4; GeoB1712-4; GeoB3129-1; GeoB3313-1; GeoB3910-2; GeoB4905-4; GeoB5546-2; GeoB5844-2; GeoB5901-2; GeoB6007-2; GeoB6518-1; GeoB7112-5; GeoB7139-2; GeoB7926-2; GEOSCIENCES, MARMARCORE; GGC; GGC-15-1; Giant box corer; Giant gravity corer; Giant piston corer; GIK16773-1; GIK17748-2; GIK17938-2; GIK17940-2; GIK17964-1; GIK18252-3; GIK18287-3; GIK23258-2; GIK23323-1; GINCO 3; GKG; GPC; Gravity corer; Gravity corer (Kiel type); Greenland Rise; Hakuho-Maru; Healy; Healy-Oden Trans-Arctic Expedition; HLY0501-05JPC; HM03-133-25; HOTLINE, HYGAPE; HOTRAX_2005; HU-2003-033-011; HU-84-030-021PC; HU-84-030-021TWC; HU-90-031-019; HU-90-031-044; HU90-13-013; HU-91-039-008; HU-91-045-072; HU-91-045-080; HU-91-045-085; HU91-045-094; HUD90/13; Hudson; IMAGES I; IMAGES III - IPHIS; IMAGES IV-IPHIS III; IMAGES IX - PAGE; IMAGES V; IMAGES VIII - MONA; IMAGES VII - WEPAMA; IMAGES XIII - PECTEN; IMAGES XII - MARCO POLO; IN68-9; Indian Ocean; Indonesia; IOW225514; IOW225517; James Clark Ross; Jan Mayen; Jean Charcot; JM96; JM96-1207/1-GC; Joides Resolution; JOPSII-6; JR20000727; JR51; JR51GC-35; JT96-09; JT96-09PC; KAL; KAL20; KASTEN; Kasten corer; Kasten corer (1 m**2); Kasten corer 20 cm; KH-01-3; KH-01-3-19; KL; KL-74, AS-12; Knorr; KNR140; KNR140-2-51GGC; KNR140-51GGC; KNR176-2; KNR176-JPC32; KOL; KS310; Kurilen Trench; KY07-04-PC1; LAPAZ21P; LC21, LC-21; Leg108; Leg160; Leg161; Leg162; Leg165; Leg167; Leg175; Leg184; Leg202; Le Noroit; Le Suroît; LINK14; M20/2; M34/4; M35/1; M35003-4; M39/1; M39/1_08-3; M39008-3; M40/4; M40/4_SL78; M40/4_SL78-3; M40/4_SL80; M40/4_SL82; M41/1; M42/4b; M44/1; M44/1_71MC; M44/3; M45/1; M45/5a; M47/3; M53/1; M6/5; M6/6; M7/2; M7/4; Marge Ibérique; Marion Dufresne (1972); Marion Dufresne (1995); Marmara Sea; MARUM; Material; MD01-2334; MD012378; MD01-2378; MD012390; MD01-2390; MD012392; MD01-2392; MD012394; MD01-2394; MD012404; MD01-2404; MD012412; MD01-2412; MD012416; MD01-2416; MD01-2430; MD01-2443; MD022529; MD02-2529; MD022575; MD02-2575; MD032611G; MD03-2611G; MD03-2707; MD04-2747CQ; MD04-2797CQ; MD052904; MD05-2904; MD052928; MD05-2928; MD101; MD106; MD111; MD114; MD122; MD123; MD126; MD127; MD13; MD131; MD147; MD148; MD77-194; MD79-257; MD81; MD81-LC21; MD85674; MD94-103; MD952011; MD95-2011; MD952015; MD95-2015; MD952033; MD95-2033; MD952040; MD95-2040; MD952042; MD95-2042; MD952043; MD95-2043; MD972120; MD97-2120; MD972121; MD97-2121; MD972125; MD97-2125; MD972141; MD97-2141; MD972142; MD97-2142; MD972146; MD97-2146; MD972148; MD97-2148; MD972151; MD97-2151; MD982162; MD98-2162; MD982165; MD98-2165; MD982170; MD98-2170; MD982176; MD98-2176; MD982181; MD98-2181; MD982193; MD98-2193; MD992220; MD99-2220; MD99-2227; MD99-2251; MD99-2254; MD99-2269; MD99-2284; MD99-2346; ME0005A; ME0005A-24JC; Mediterranean Sea; Melville; Meteor (1986); MONITOR MONSUN; MR00-K03-PC01; MUC; MultiCorer; N.Iceland shelf, Reykjafjardarall; N. Shetland channel; NA87-22; Namibia continental slope; NEAP; NEAP-17K; NE-Brazilian continental margin; NEMO; Newfoundland margin; North Atlantic; Northeast Atlantic; Northeast Brasilian Margin; Northern Red Sea; North Pacific Ocean; North-West African margin; Northwest Atlantic; OCE326-GGC26; OCE326-GGC30; off Cameroon; off Chile; OSIRIS4; OSIRIS III; PABESIA; Pacific Ocean; PAKOMIN; PALAEOFLUX; Papua Plateau; PC; PC-17; PC-2; PC-4; Piston corer; Piston corer (BGR type); Piston corer (Kiel type); PL07-39PC; PL-96-112; Porto Seamount; Portuguese Margin; Professor Logachev; PUCK; RAPID-12-1K; RC24; RC24-16; Reference/source; Reykjanes Ridge; ride Calmar; RL11; RN88-PC5; RN92-PC3; RN92-PC4; RN93-PC1; RN93-PC3; RN93-PC4; RN93-PC6; RN94-PC3; RN96-PC1; Robert Conrad; Rockall; ROMANCHA; Sample code/label; SCS90-36; Sea of Marmara; Sea surface temperature, annual mean; Sea surface temperature, summer; Sea surface temperature, winter; Ship of opportunity; Sierra Leone Basin/Guinea Basin; SL; SO102/1; SO115; SO115_05; SO115_40; SO136; SO136_011GC; SO139; SO139-74KL; SO156/2; SO184/1; SO42; SO42-74KL; SO80_4; SO80a; SO90; SO90_93KL; SO93/3; SO93/3_126KL; SO95; Sonne; South Atlantic Ocean; South China Sea; South-East Pacific; Southern Ocean; Southern Okhotsk Sea; South Pacific Ocean; SSDP102; St.14; SU81-18; SUNDAFLUT; Sunda Shelf; TASQWA; TC; Timor Sea; Tirreno Sea; TN057-21; TR163-19; TR163-22; Trigger corer; TTR-17_MS419; TTR-17/1; TY93-905; upper Laurentien Island; V19; V19-27; V19-28; V19-30; V21; V21-30; V28; V28-122; V30; V30-36; Vema; Victor Hensen; Vietnam shelf; Voring Plateau; W8709A; W8709A-8TC; Wecoma; Western Basin
    Type: Dataset
    Format: text/tab-separated-values, 6357 data points
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  • 7
    Publication Date: 2024-02-02
    Keywords: 160-964A; 160-969A; 160-973A; 161-974B; 161-975B; Age, 14C AMS; Age, calibrated; Age, comment; Age, dated; Age, dated standard deviation; Age, maximum/old; Age, minimum/young; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Eastern Basin; Event label; GeoB3129-1; GeoB5546-2; Gravity corer (Kiel type); Joides Resolution; JOPSII-6; KL; Leg160; Leg161; M40/4; M40/4_SL80; M40/4_SL82; M42/4b; MARUM; Meteor (1986); NE-Brazilian continental margin; Piston corer (BGR type); Sample code/label; SL; Tirreno Sea; Victor Hensen; Western Basin
    Type: Dataset
    Format: text/tab-separated-values, 246 data points
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  • 8
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Bassinot, Franck C; Polanco-Martínez, Josué M; Harrison, Sandy P; Allen, Judy R M; Anderson, R Scott; Behling, Hermann; Bonnefille, Raymonde; Burjachs, Francesc; Carrión, José S; Cheddadi, Rachid; Clark, James S; Combourieu-Nebout, Nathalie; Courtney-Mustaphi, Colin J; DeBusk, Georg H; Dupont, Lydie M; Finch, Jemma M; Fletcher, William J; Giardini, Marco; González, Catalina; Gosling, William D; Grigg, Laurie D; Grimm, Eric C; Hayashi, Ryoma; Helmens, Karin F; Heusser, Linda E; Hill, Trevor R; Hope, Geoffrey; Huntley, Brian; Igarashi, Yaeko; Irino, Tomohisa; Jacobs, Bonnie Fine; Jiménez-Moreno, Gonzalo; Kawai, Sayuri; Kershaw, A Peter; Kumon, Fujio; Lawson, Ian T; Ledru, Marie-Pierre; Lézine, Anne-Marie; Liew, Ping-Mei; Magri, Donatella; Marchant, Robert; Margari, Vasiliki; Mayle, Francis E; McKenzie, G Merna; Moss, Patrick T; Müller, Stefanie; Müller, Ulrich C; Naughton, Filipa; Newnham, Rewi M; Oba, Tadamichi; Pérez-Obiol, Ramon P; Pini, Roberta; Ravazzi, Cesare; Roucoux, Katherine H; Rucina, Stephen M; Scott, Louis; Takahara, Hikaru; Tzedakis, Polychronis C; Urrego, Dunia H; van Geel, Bas; Valencia, Bryan G; Vandergoes, Marcus J; Vincens, Annie; Whitlock, Cathy L; Willard, Debra A; Yamamoto, Masanobu (2017): The ACER pollen and charcoal database: a global resource to document vegetation and fire response to abrupt climate changes during the last glacial period. Earth System Science Data, 9(2), 679-695, https://doi.org/10.5194/essd-9-679-2017
    Publication Date: 2024-03-02
    Description: Quaternary records provide an opportunity to examine the nature of the vegetation and fire responses to rapid past climate changes comparable in velocity and magnitude to those expected in the 21st century. The best documented examples of rapid climate change in the past are the warming events associated with the Dansgaard-Oeschger (D-O) cycles during the last glacial period, which were sufficiently large to have had a potential feedback through changes in albedo and greenhouse gas emissions on climate. Previous reconstructions of vegetation and fire changes during the D-O cycles used independently constructed age models, making it difficult to compare the changes between different sites and regions. Here we present the ACER (Abrupt Climate Changes and Environmental Responses) global database which includes 93 pollen records from the last glacial period (73-15 ka) with a temporal resolution better than 1,000 years, 32 of which also provide charcoal records. A harmonized and consistent chronology based on radiometric dating (14C, 234U/230Th, OSL, 40Ar/39Ar dated tephra layers) has been constructed for 86 of these records, although in some cases additional information was derived using common control points based on event stratigraphy. The ACER database compiles metadata including geospatial and dating information, pollen and charcoal counts and pollen percentages of the characteristic biomes, and is archived in Microsoft ACCESS(TM).
    Keywords: Abrupt Climate Changes and Environmental Responses; ACER
    Type: Dataset
    Format: application/zip, 6 datasets
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  • 9
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: The global vegetation response to climate and atmospheric CO2 changes between the last glacial maximum and recent times is examined using an equilibrium vegetation model (BIOME4), driven by output from 17 climate simulations from the Palaeoclimate Modelling Intercomparison Project. Features common to all of the simulations include expansion of treeless vegetation in high northern latitudes; southward displacement and fragmentation of boreal and temperate forests; and expansion of drought-tolerant biomes in the tropics. These features are broadly consistent with pollen-based reconstructions of vegetation distribution at the last glacial maximum. Glacial vegetation in high latitudes reflects cold and dry conditions due to the low CO2 concentration and the presence of large continental ice sheets. The extent of drought-tolerant vegetation in tropical and subtropical latitudes reflects a generally drier low-latitude climate. Comparisons of the observations with BIOME4 simulations, with and without consideration of the direct physiological effect of CO2 concentration on C3 photosynthesis, suggest an important additional role of low CO2 concentration in restricting the extent of forests, especially in the tropics. Global forest cover was overestimated by all models when climate change alone was used to drive BIOME4, and estimated more accurately when physiological effects of CO2 concentration were included. This result suggests that both CO2 effects and climate effects were important in determining glacial-interglacial changes in vegetation. More realistic simulations of glacial vegetation and climate will need to take into account the feedback effects of these structural and physiological changes on the climate.
    Type of Medium: Electronic Resource
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  • 10
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Ecosystem processes are important determinants of the biogeochemistry of the ocean, and they can be profoundly affected by changes in climate. Ocean models currently express ecosystem processes through empirically derived parameterizations that tightly link key geochemical tracers to ocean physics. The explicit inclusion of ecosystem processes in models will permit ecological changes to be taken into account, and will allow us to address several important questions, including the causes of observed glacial–interglacial changes in atmospheric trace gases and aerosols, and how the oceanic uptake of CO2 is likely to change in the future. There is an urgent need to assess our mechanistic understanding of the environmental factors that exert control over marine ecosystems, and to represent their natural complexity based on theoretical understanding. We present a prototype design for a Dynamic Green Ocean Model (DGOM) based on the identification of (a) key plankton functional types that need to be simulated explicitly to capture important biogeochemical processes in the ocean; (b) key processes controlling the growth and mortality of these functional types and hence their interactions; and (c) sources of information necessary to parameterize each of these processes within a modeling framework. We also develop a strategy for model evaluation, based on simulation of both past and present mean state and variability, and identify potential sources of validation data for each. Finally, we present a DGOM-based strategy for addressing key questions in ocean biogeochemistry. This paper thus presents ongoing work in ocean biogeochemical modeling, which, it is hoped will motivate international collaborations to improve our understanding of the role of the ocean in the climate system.
    Type of Medium: Electronic Resource
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