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  • PANGAEA  (21,398)
  • American Journal of Science
  • American Meteorological Society
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  • 1
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Daniau, Anne-Laure; Bartlein, Patrick J; Harrison, S P; Prentice, Iain Colin; Brewer, Simon; Friedlingstein, Pierre; Harrison-Prentice, T I; Inoue, J; Izumi, K; Marlon, Jennifer R; Mooney, Scott D; Power, Mitchell J; Stevenson, J; Tinner, Willy; Andric, M; Atanassova, J; Behling, Hermann; Black, M; Blarquez, O; Brown, K J; Carcaillet, C; Colhoun, Eric A; Colombaroli, Daniele; Davis, Basil A S; D'Costa, D; Dodson, John; Dupont, Lydie M; Eshetu, Z; Gavin, D G; Genries, A; Haberle, Simon G; Hallett, D J; Hope, Geoffrey; Horn, S P; Kassa, T G; Katamura, F; Kennedy, L M; Kershaw, A Peter; Krivonogov, S; Long, C; Magri, Donatella; Marinova, E; McKenzie, G Merna; Moreno, P I; Moss, Patrick T; Neumann, F H; Norstrom, E; Paitre, C; Rius, D; Roberts, Neil; Robinson, G S; Sasaki, N; Scott, Louis; Takahara, H; Terwilliger, V; Thevenon, Florian; Turner, R; Valsecchi, V G; Vannière, Boris; Walsh, M; Williams, N; Zhang, Yancheng (2012): Predictability of biomass burning in response to climate changes. Global Biogeochemical Cycles, 26(4), https://doi.org/10.1029/2011GB004249
    Publication Date: 2024-05-27
    Description: We analyze sedimentary charcoal records to show that the changes in fire regime over the past 21,000 yrs are predictable from changes in regional climates. Analyses of paleo- fire data show that fire increases monotonically with changes in temperature and peaks at intermediate moisture levels, and that temperature is quantitatively the most important driver of changes in biomass burning over the past 21,000 yrs. Given that a similar relationship between climate drivers and fire emerges from analyses of the interannual variability in biomass burning shown by remote-sensing observations of month-by-month burnt area between 1996 and 2008, our results signal a serious cause for concern in the face of continuing global warming.
    Keywords: Center for Marine Environmental Sciences; MARUM
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Expected Availability
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  • 2
    facet.materialart.
    Unknown
    American Meteorological Society
    In:  EPIC3Bulletin of the American Meteorological Society, American Meteorological Society, 104(9), pp. s1-s10, ISSN: 0003-0007
    Publication Date: 2024-05-29
    Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉—J. BLUNDEN, T. BOYER, AND E. BARTOW-GILLIES〈/jats:p〉 〈jats:p〉Earth’s global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the “triple dip” La Niña conditions that prevailed in the eastern Pacific Ocean nearly continuously from mid-2020 through all of 2022; by regional phenomena such as the positive winter and summer North Atlantic Oscillation that impacted weather in parts the Northern Hemisphere and the negative Indian Ocean dipole that impacted weather in parts of the Southern Hemisphere; and by more localized systems such as high-pressure heat domes that caused extreme heat in different areas of the world. Underlying all these natural short-term variabilities are long-term climate trends due to continuous increases since the beginning of the Industrial Revolution in the atmospheric concentrations of Earth’s major greenhouse gases.〈/jats:p〉 〈jats:p〉In 2022, the annual global average carbon dioxide concentration in the atmosphere rose to 417.1±0.1 ppm, which is 50% greater than the pre-industrial level. Global mean tropospheric methane abundance was 165% higher than its pre-industrial level, and nitrous oxide was 24% higher. All three gases set new record-high atmospheric concentration levels in 2022.〈/jats:p〉 〈jats:p〉Sea-surface temperature patterns in the tropical Pacific characteristic of La Niña and attendant atmospheric patterns tend to mitigate atmospheric heat gain at the global scale, but the annual global surface temperature across land and oceans was still among the six highest in records dating as far back as the mid-1800s. It was the warmest La Niña year on record. Many areas observed record or near-record heat. Europe as a whole observed its second-warmest year on record, with sixteen individual countries observing record warmth at the national scale. Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record.〈/jats:p〉 〈jats:p〉While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia. Cold outbreaks in the first half of December led to many record-low daily minimum temperature records in eastern Australia.〈/jats:p〉 〈jats:p〉The effects of rising temperatures and extreme heat were apparent across the Northern Hemisphere, where snow-cover extent by June 2022 was the third smallest in the 56-year record, and the seasonal duration of lake ice cover was the fourth shortest since 1980. More frequent and intense heatwaves contributed to the second-greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Glaciers in the Swiss Alps lost a record 6% of their volume. In South America, the combination of drought and heat left many central Andean glaciers snow free by mid-summer in early 2022; glacial ice has a much lower albedo than snow, leading to accelerated heating of the glacier. Across the global cryosphere, permafrost temperatures continued to reach record highs at many high-latitude and mountain locations.〈/jats:p〉 〈jats:p〉In the high northern latitudes, the annual surface-air temperature across the Arctic was the fifth highest in the 123-year record. The seasonal Arctic minimum sea-ice extent, typically reached in September, was the 11th-smallest in the 43-year record; however, the amount of multiyear ice—ice that survives at least one summer melt season—remaining in the Arctic continued to decline. Since 2012, the Arctic has been nearly devoid of ice more than four years old.〈/jats:p〉 〈jats:p〉In Antarctica, an unusually large amount of snow and ice fell over the continent in 2022 due to several landfalling atmospheric rivers, which contributed to the highest annual surface mass balance, 15% to 16% above the 1991–2020 normal, since the start of two reanalyses records dating to 1980. It was the second-warmest year on record for all five of the long-term staffed weather stations on the Antarctic Peninsula. In East Antarctica, a heatwave event led to a new all-time record-high temperature of −9.4°C—44°C above the March average—on 18 March at Dome C. This was followed by the collapse of the critically unstable Conger Ice Shelf. More than 100 daily low sea-ice extent and sea-ice area records were set in 2022, including two new all-time annual record lows in net sea-ice extent and area in February.〈/jats:p〉 〈jats:p〉Across the world’s oceans, global mean sea level was record high for the 11th consecutive year, reaching 101.2 mm above the 1993 average when satellite altimetry measurements began, an increase of 3.3±0.7 over 2021. Globally-averaged ocean heat content was also record high in 2022, while the global sea-surface temperature was the sixth highest on record, equal with 2018. Approximately 58% of the ocean surface experienced at least one marine heatwave in 2022. In the Bay of Plenty, New Zealand’s longest continuous marine heatwave was recorded.〈/jats:p〉 〈jats:p〉A total of 85 named tropical storms were observed during the Northern and Southern Hemisphere storm seasons, close to the 1991–2020 average of 87. There were three Category 5 tropical cyclones across the globe—two in the western North Pacific and one in the North Atlantic. This was the fewest Category 5 storms globally since 2017. Globally, the accumulated cyclone energy was the lowest since reliable records began in 1981. Regardless, some storms caused massive damage. In the North Atlantic, Hurricane Fiona became the most intense and most destructive tropical or post-tropical cyclone in Atlantic Canada’s history, while major Hurricane Ian killed more than 100 people and became the third costliest disaster in the United States, causing damage estimated at $113 billion U.S. dollars. In the South Indian Ocean, Tropical Cyclone Batsirai dropped 2044 mm of rain at Commerson Crater in Réunion. The storm also impacted Madagascar, where 121 fatalities were reported.〈/jats:p〉 〈jats:p〉As is typical, some areas around the world were notably dry in 2022 and some were notably wet. In August, record high areas of land across the globe (6.2%) were experiencing extreme drought. Overall, 29% of land experienced moderate or worse categories of drought during the year. The largest drought footprint in the contiguous United States since 2012 (63%) was observed in late October. The record-breaking megadrought of central Chile continued in its 13th consecutive year, and 80-year record-low river levels in northern Argentina and Paraguay disrupted fluvial transport. In China, the Yangtze River reached record-low values. Much of equatorial eastern Africa had five consecutive below-normal rainy seasons by the end of 2022, with some areas receiving record-low precipitation totals for the year. This ongoing 2.5-year drought is the most extensive and persistent drought event in decades, and led to crop failure, millions of livestock deaths, water scarcity, and inflated prices for staple food items.〈/jats:p〉 〈jats:p〉In South Asia, Pakistan received around three times its normal volume of monsoon precipitation in August, with some regions receiving up to eight times their expected monthly totals. Resulting floods affected over 30 million people, caused over 1700 fatalities, led to major crop and property losses, and was recorded as one of the world’s costliest natural disasters of all time. Near Rio de Janeiro, Brazil, Petrópolis received 530 mm in 24 hours on 15 February, about 2.5 times the monthly February average, leading to the worst disaster in the city since 1931 with over 230 fatalities.〈/jats:p〉 〈jats:p〉On 14–15 January, the Hunga Tonga-Hunga Ha'apai submarine volcano in the South Pacific erupted multiple times. The injection of water into the atmosphere was unprecedented in both magnitude—far exceeding any previous values in the 17-year satellite record—and altitude as it penetrated into the mesosphere. The amount of water injected into the stratosphere is estimated to be 146±5 Terragrams, or ∼10% of the total amount in the stratosphere. It may take several years for the water plume to dissipate, and it is currently unknown whether this eruption will have any long-term climate effect.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
    Location Call Number Expected Availability
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  • 3
    Publication Date: 2023-05-12
    Description: At present time, there is a lack of knowledge on the interannual climate-related variability of zooplankton communities of the tropical Atlantic, central Mediterranean Sea, Caspian Sea, and Aral Sea, due to the absence of appropriate databases. In the mid latitudes, the North Atlantic Oscillation (NAO) is the dominant mode of atmospheric fluctuations over eastern North America, the northern Atlantic Ocean and Europe. Therefore, one of the issues that need to be addressed through data synthesis is the evaluation of interannual patterns in species abundance and species diversity over these regions in regard to the NAO. The database has been used to investigate the ecological role of the NAO in interannual variations of mesozooplankton abundance and biomass along the zonal array of the NAO influence. Basic approach to the proposed research involved: (1) development of co-operation between experts and data holders in Ukraine, Russia, Kazakhstan, Azerbaijan, UK, and USA to rescue and compile the oceanographic data sets and release them on CD-ROM, (2) organization and compilation of a database based on FSU cruises to the above regions, (3) analysis of the basin-scale interannual variability of the zooplankton species abundance, biomass, and species diversity.
    Keywords: AAK106; AAK62; AAK65; AAK7/1; AAK7/2; AAK7/3; AAK7/4; AAK7/5; AAK71; AAK88; AAK90; AAK95; Adriatic Sea; Aegean Sea; Akademik A Kovalyevskiy; Akademik Vernadsky; AKov_106-track; AKov_62-track; AKov_65-track; AKov_7/1-track; AKov_7/2-track; AKov_7/3-track; AKov_7/4-track; AKov_7/5-track; AKov_71-track; AKov_88-track; AKov_90-track; AKov_95-track; Arabian Sea; Aral_Sea; Atlantic Ocean; AV10; AV10_938-1; AV10_938-2; AV10_939-1; AV10_939-2; AV10_940-1; AV10_940-2; AV10_941-1; AV10_941-2; AV10_942-1; AV10_942-2; AV10_943-1; AV10_943-2; AV10_944-1; AV10_944-2; AV10_945-1; AV10_945-2; AV10_946-1; AV10_947-1; AV10_948-1; AV10_948-2; AV10_949-1; AV10_949-2; AV10_950-1; AV10_950-2; AV10_950-3; AV10_950-4; AV10_951-1; AV10_951-2; AV10_952-1; AV10_952-2; AV10_953-1; AV10_953-2; AV10_954-1; AV10_954-2; AV10_955-1; AV10_955-2; AV10_956-1; AV10_956-2; AV10_957-1; AV10_958-1; AV10_959-1; AV10_960-1; AV10_961-1; AV10_961-10; AV10_961-100; AV10_961-101; AV10_961-102; AV10_961-103; AV10_961-104; AV10_961-105; AV10_961-106; AV10_961-107; AV10_961-108; AV10_961-109; AV10_961-11; AV10_961-110; AV10_961-111; AV10_961-112; AV10_961-113; AV10_961-114; AV10_961-115; AV10_961-116; AV10_961-117; AV10_961-118; AV10_961-119; AV10_961-12; AV10_961-120; AV10_961-121; AV10_961-122; AV10_961-123; AV10_961-124; AV10_961-125; AV10_961-126; AV10_961-127; AV10_961-128; AV10_961-129; AV10_961-13; AV10_961-130; AV10_961-131; AV10_961-132; AV10_961-133; AV10_961-134; AV10_961-135; AV10_961-136; AV10_961-137; AV10_961-138; AV10_961-139; AV10_961-14; AV10_961-140; AV10_961-141; AV10_961-142; AV10_961-143; AV10_961-144; AV10_961-145; AV10_961-146; AV10_961-147; AV10_961-148; AV10_961-149; AV10_961-15; AV10_961-150; AV10_961-151; AV10_961-152; AV10_961-153; AV10_961-154; AV10_961-155; AV10_961-156; AV10_961-157; AV10_961-158; AV10_961-159; AV10_961-16; AV10_961-160; AV10_961-161; AV10_961-162; AV10_961-163; AV10_961-164; AV10_961-165; AV10_961-166; AV10_961-167; AV10_961-168; AV10_961-169; AV10_961-17; AV10_961-170; AV10_961-171; AV10_961-172; AV10_961-173; AV10_961-174; AV10_961-175; AV10_961-176; AV10_961-177; AV10_961-178; AV10_961-179; AV10_961-18; AV10_961-180; AV10_961-181; AV10_961-182; AV10_961-183; AV10_961-184; AV10_961-185; AV10_961-186; AV10_961-187; AV10_961-188; AV10_961-189; AV10_961-19; AV10_961-190; AV10_961-191; AV10_961-192; AV10_961-193; AV10_961-194; AV10_961-195; AV10_961-196; AV10_961-197; AV10_961-198; AV10_961-199; AV10_961-2; AV10_961-20; AV10_961-200; AV10_961-201; AV10_961-202; AV10_961-203; AV10_961-204; AV10_961-205; AV10_961-206; AV10_961-207; AV10_961-208; AV10_961-209; AV10_961-21; AV10_961-210; AV10_961-211; AV10_961-212; AV10_961-213; AV10_961-214; AV10_961-215; AV10_961-216; AV10_961-217; AV10_961-218; AV10_961-219; AV10_961-22; AV10_961-220; AV10_961-221; AV10_961-222; AV10_961-223; AV10_961-224; AV10_961-225; AV10_961-226; AV10_961-227; AV10_961-228; AV10_961-229; AV10_961-23; AV10_961-230; AV10_961-231; AV10_961-232; AV10_961-233; AV10_961-234; AV10_961-235; AV10_961-236; AV10_961-237; AV10_961-238; AV10_961-239; AV10_961-24; AV10_961-240; AV10_961-241; AV10_961-242; AV10_961-243; AV10_961-244; AV10_961-245; AV10_961-246; AV10_961-247; AV10_961-248; AV10_961-249; AV10_961-25; AV10_961-250; AV10_961-251; AV10_961-252; AV10_961-253; AV10_961-254; AV10_961-255; AV10_961-256; AV10_961-257; AV10_961-258; AV10_961-259; AV10_961-26; AV10_961-260; AV10_961-261; AV10_961-262; AV10_961-263; AV10_961-264; AV10_961-265; AV10_961-266; AV10_961-267; AV10_961-268; AV10_961-269; AV10_961-27; AV10_961-270; AV10_961-271; AV10_961-272; AV10_961-273; AV10_961-274; AV10_961-275; AV10_961-276; AV10_961-277; AV10_961-2771; AV10_961-278; AV10_961-279; AV10_961-28; AV10_961-280; AV10_961-281; AV10_961-282; AV10_961-283; AV10_961-284; AV10_961-285; AV10_961-286; AV10_961-287; AV10_961-288; AV10_961-289; AV10_961-29; AV10_961-290; AV10_961-291; AV10_961-292; AV10_961-293; AV10_961-294; AV10_961-295; AV10_961-296; AV10_961-297; AV10_961-298; AV10_961-299; AV10_961-3; AV10_961-30; AV10_961-300; AV10_961-301; AV10_961-302; AV10_961-303; AV10_961-304; AV10_961-305; AV10_961-306; AV10_961-307; AV10_961-308; AV10_961-309; AV10_961-31; AV10_961-310; AV10_961-311; AV10_961-312; AV10_961-313; AV10_961-314; AV10_961-315; AV10_961-316; AV10_961-317; AV10_961-318; AV10_961-319; AV10_961-32; AV10_961-320; AV10_961-321; AV10_961-322; AV10_961-323; AV10_961-324; AV10_961-325; AV10_961-326; AV10_961-327; AV10_961-328; AV10_961-329; AV10_961-33; AV10_961-330; AV10_961-331; AV10_961-332; AV10_961-333; AV10_961-334; AV10_961-34; AV10_961-35; AV10_961-36; AV10_961-37; AV10_961-38; AV10_961-39; AV10_961-4; AV10_961-40; AV10_961-41; AV10_961-42; AV10_961-43; AV10_961-44; AV10_961-45; AV10_961-46; AV10_961-47; AV10_961-48; AV10_961-49; AV10_961-5; AV10_961-50; AV10_961-51; AV10_961-52; AV10_961-53; AV10_961-54; AV10_961-55; AV10_961-56; AV10_961-57; AV10_961-58; AV10_961-59; AV10_961-6; AV10_961-60; AV10_961-61; AV10_961-62; AV10_961-63; AV10_961-64; AV10_961-65; AV10_961-66; AV10_961-67; AV10_961-68; AV10_961-69; AV10_961-7; AV10_961-70; AV10_961-71; AV10_961-72; AV10_961-73; AV10_961-74; AV10_961-75; AV10_961-76; AV10_961-77; AV10_961-78; AV10_961-79; AV10_961-8; AV10_961-80; AV10_961-81; AV10_961-82; AV10_961-83; AV10_961-84; AV10_961-85; AV10_961-86; AV10_961-862; AV10_961-87; AV10_961-88; AV10_961-89; AV10_961-9; AV10_961-90; AV10_961-91; AV10_961-92; AV10_961-93; AV10_961-94; AV10_961-95; AV10_961-96; AV10_961-97; AV10_961-98; AV10_961-99; AV10_962-1; AV10_963-1; AV10_964-1; AV10_965-1; AV10_966-1; AV10_967-1; AV10_968-2; AV10_969-2; AV10_970-1; AV10_970-2; AV10_971-2; AV10_974-1; AV11; AV11_1000-2; AV11_1001-2; AV11_1002-2; AV11_1003-1; AV11_1005-1; AV11_1006-1; AV11_1006-10; AV11_1006-11; AV11_1006-12; AV11_1006-13; AV11_1006-15; AV11_1006-16; AV11_1006-17; AV11_1006-18; AV11_1006-19; AV11_1006-2; AV11_1006-20; AV11_1006-21; AV11_1006-22; AV11_1006-23; AV11_1006-24; AV11_1006-25; AV11_1006-26; AV11_1006-27; AV11_1006-28; AV11_1006-29; AV11_1006-3; AV11_1006-30; AV11_1006-31; AV11_1006-32; AV11_1006-33; AV11_1006-34; AV11_1006-35; AV11_1006-36; AV11_1006-37; AV11_1006-38; AV11_1006-39; AV11_1006-4; AV11_1006-40; AV11_1006-41; AV11_1006-42; AV11_1006-43; AV11_1006-44; AV11_1006-45; AV11_1006-46; AV11_1006-47; AV11_1006-48; AV11_1006-49; AV11_1006-5; AV11_1006-50; AV11_1006-51; AV11_1006-52; AV11_1006-6; AV11_1006-7; AV11_1006-8; AV11_1006-9; AV11_1007-10; AV11_1007-11; AV11_1007-12; AV11_1007-13; AV11_1007-14; AV11_1007-15; AV11_1007-16; AV11_1007-17; AV11_1007-18; AV11_1007-19; AV11_1007-2; AV11_1007-20; AV11_1007-21; AV11_1007-22; AV11_1007-23; AV11_1007-24; AV11_1007-25; AV11_1007-26; AV11_1007-27; AV11_1007-28; AV11_1007-29; AV11_1007-3; AV11_1007-30; AV11_1007-31; AV11_1007-32; AV11_1007-33; AV11_1007-34; AV11_1007-35; AV11_1007-36; AV11_1007-37; AV11_1007-38; AV11_1007-39; AV11_1007-4; AV11_1007-40; AV11_1007-41; AV11_1007-42; AV11_1007-43; AV11_1007-44; AV11_1007-45; AV11_1007-46; AV11_1007-47; AV11_1007-48; AV11_1007-49; AV11_1007-5; AV11_1007-50; AV11_1007-51; AV11_1007-52; AV11_1007-53; AV11_1007-54; AV11_1007-55; AV11_1007-56; AV11_1007-57; AV11_1007-58; AV11_1007-59; AV11_1007-6; AV11_1007-60; AV11_1007-61; AV11_1007-62; AV11_1007-63; AV11_1007-7; AV11_1007-8; AV11_1007-9; AV11_1008-1; AV11_1010-1; AV11_1010-2; AV11_1011-1; AV11_1011-2; AV11_1012-1; AV11_1013-1; AV11_1013-2; AV11_1014-1; AV11_1014-2; AV11_1015-1; AV11_1015-2; AV11_1016-1; AV11_1016-2; AV11_1017-1; AV11_1017-2; AV11_1018-1; AV11_1018-2; AV11_1019-1; AV11_1019-2; AV11_1021-2; AV11_1022-2; AV11_1023-2; AV11_1024-2; AV11_1025-2; AV11_1026-2; AV11_1027-2; AV11_1028-2; AV11_1029-2; AV11_1030-2; AV11_1032-2; AV11_1033-2; AV11_1034-2; AV11_1035-2; AV11_1036-2; AV11_1037-2; AV11_1038-2; AV11_1039-2; AV11_1040-2; AV11_1041-2; AV11_1042-2; AV11_1043-2; AV11_1044-2; AV11_1045-2; AV11_1046-2; AV11_1051-1; AV11_1051-10; AV11_1051-11; AV11_1051-12; AV11_1051-13; AV11_1051-14; AV11_1051-15; AV11_1051-16; AV11_1051-17; AV11_1051-18;
    Type: Dataset
    Format: application/zip, 752 datasets
    Location Call Number Expected Availability
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  • 4
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Herbert, Timothy D; Schuffert, Jeffrey D; Thomas, D; Lange, Carina Beatriz; Weinheimer, Amy L; Peleo-Alampay, Alyssa; Herguera, Juan-Carlos (1998): Depth and seasonality of alkenone production along the California Margin inferred from a core top transect. Paleoceanography, 13(3), 263-271, https://doi.org/10.1029/98PA00069
    Publication Date: 2023-05-12
    Description: Alkenone unsaturation indices (Uk'37) of marine sediment could prove particularly useful on organic-rich continental margins where carbonate dissolution hampers the use of other paleoclimatic proxies [McCaffrey et al., 1990, doi:10.1016/0016-7037(90)90399-6; Kennedy and Brassell, 1992, doi:10.1016/0146-6380(92)90040-5]. Forty core top samples of Recent sediment from a latitudinal transect (23°-40°N) along the California margin yield Uk'37 values that correlate linearly with modern mean annual sea surface temperatures (SSTs) in the range of 12°-23°C. Reproducibility of the unsaturation value in closely spaced cores is near analytical error. Uk'37 data define a relationship to temperature nearly identical to the Prahl et al. [1988, doi:10.1016/0016-7037(88)90132-9] laboratory cultures of Emiliania huxleyi. The close agreement is particularly significant in light of the nannofossil composition of the sediments, where the abundance of the coccolith taxon Gephyrocapsa oceanica (known to synthesize alkenones) equals or exceeds that of E. huxleyi. Comparison with seasonal temperature variations at different depths indicates that little if any alkenone production occurs at depths 〉30 m along the continental margin (water depths 〈2 km). Sediments in more pelagic locations exhibit small but consistent biases toward winter and/or subsurface production similar to previously reported sediment trap and core top data from the Oregon margin [Prahl et al., 1993, doi:10.1016/0967-0637(93)90045-5; Doose et al., 1997, doi:10.1029/97PA00821].
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 5
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    Unknown
    PANGAEA
    In:  Supplement to: Paton-Walsh, Clare; Guérette, Elise-Andrée; Kubistin, Dagmar; Humphries, Ruhi S; Wilson, Stephen R; Dominick, Doreena; Galbally, Ian; Buchholz, Rebecca R; Bhujel, Mahendra; Chambers, Scott D; Cheng, Min; Cope, Martin; Davy, Perry; Emmerson, Kathryn M; Griffith, David W T; Griffiths, Alan D; Keywood, Melita D; Lawson, Sarah; Molloy, Suzie; Rea, Geraldine; Selleck, Paul; Shi, Xue; Simmons, Jack B; Velazco, Voltaire (2017): The MUMBA Campaign: Measurements of Urban, Marine and Biogenic Air. Earth System Science Data, 9(1), 349-362, https://doi.org/10.5194/essd-9-349-2017
    Publication Date: 2023-01-13
    Description: The Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign took place in Wollongong, New South Wales (a small coastal city approximately 80 km south of Sydney, Australia), from 21st December 2012 to 15th February 2013. Instruments were deployed during MUMBA to measure the gaseous and aerosol composition of the atmosphere with the aim of providing a detailed characterisation of the complex environment of the ocean/forest/urban interface that could be used to test the skill of atmospheric models. Gases measured included ozone, oxides of nitrogen, carbon monoxide, carbon dioxide, methane and many of the most abundant volatile organic compounds. Aerosol characterisation included total particle counts above 3 nm, total cloud condensation nuclei counts; mass concentration of PM2.5, number concentration size distribution, aerosol chemical analyses and elemental analysis. Meteorological measurements and LIDAR measurements were also performed. The campaign captured varied meteorological conditions, including two extreme heat events, providing a potentially valuable test for models of future air quality in a warmer climate. There was also an episode when the site sampled clean marine air for many hours, providing a useful additional measure of background concentrations of these trace gases within this poorly sampled region of the globe. Here we present the observations recorded at the MUMBA site during the campaign, as well as radon and air quality data from nearby sites. These records can be used for testing chemical transport models.
    Type: Dataset
    Format: application/zip, 17 datasets
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  • 6
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Byrne, James M; Coker, V S; Moise, S; Wincott, P L; Vaughan, D J; Tuna, F; Arenholz, E; van der Laan, G; Pattrick, R A D P; Lloyd, J R; Telling, N D (2013): Controlled cobalt doping in biogenic magnetite nanoparticles. Journal of The Royal Society Interface, 10(83), 20130134-20130134, https://doi.org/10.1098/rsif.2013.0134
    Publication Date: 2023-01-13
    Description: Cobalt doped magnetite (CoxFe3-xO4) nanoparticles have been produced through the microbial reduction of cobalt-iron oxyhydroxide by the bacterium Geobacter sulfurreducens. The materials produced, as measured by SQUID, x-ray magnetic circular dichroism, Mössbauer spectroscopy, etc., show dramatic increases in coercivity with increasing cobalt content without a major decrease in overall saturation magnetization. Structural and magnetization analyses reveal a reduction in particle size to 〈4 nm at the highest Co content, combined with an increase in the effective anisotropy of the magnetic nanoparticles. The potential use of these biogenic nanoparticles in aqueous suspensions for magnetic hyperthermia applications is demonstrated. Further analysis of the distribution of cations within the ferrite spinel indicates that the cobalt is predominantly incorporated in octahedral coordination, achieved by the substitution of Fe2+ site with Co2+, with up to 17 per cent Co substituted into tetrahedral sites.
    Type: Dataset
    Format: application/zip, 695.2 kBytes
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  • 7
    Publication Date: 2023-05-12
    Keywords: BC; Box corer; CALGRK.K-173; CH9416; DEPTH, sediment/rock; Elevation of event; Emiliania huxleyi; Event label; Florisphaera profunda; Gephyrocapsa spp., large; Gephyrocapsa spp., small; GRKK-166; Latitude of event; Longitude of event; RGS0487BC-19; RGS0487BC-47; RGS0487BC-50; RGS0487BC-9; SBBX-1
    Type: Dataset
    Format: text/tab-separated-values, 32 data points
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  • 8
    Publication Date: 2023-05-12
    Keywords: Conductivity, average; ELEVATION; Heat flow; LATITUDE; LONGITUDE; Method comment; Number; Sample, optional label/labor no; Temperature gradient
    Type: Dataset
    Format: text/tab-separated-values, 726 data points
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  • 9
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Inagaki, F; Hinrichs, Kai-Uwe; Kubo, Y; Bowles, Marshall W; Heuer, Verena B; Hong, W-L; Hoshino, Tatsuhiko; Ijiri, Akira; Imachi, H; Ito, M; Kaneko, Masanori; Lever, Mark A; Lin, Yu-Shih; Methe, B A; Morita, S; Morono, Yuki; Tanikawa, Wataru; Bihan, M; Bowden, Stephen A; Elvert, Marcus; Glombitza, Clemens; Gross, D; Harrington, G J; Hori, T; Li, K; Limmer, D; Liu, Chiung-Hui; Murayama, M; Ohkouchi, Naohiko; Ono, Shuhei; Park, Young-Soo; Phillips, S C; Prieto-Mollar, Xavier; Purkey, M; Riedinger, Natascha; Sanada, Yoshinori; Sauvage, J; Snyder, Glen T; Susilawati, R; Takano, Yoshinori; Tasumi, E; Terada, Takeshi; Tomaru, Hitoshi; Trembath-Reichert, E; Wang, D T; Yamada, Y (2015): Exploring deep microbial life in coal-bearing sediment down to ~2.5 km below the ocean floor. Science, 439 (6246), 420-424, https://doi.org/10.1126/science.aaa6882
    Publication Date: 2023-04-29
    Description: Microbial life inhabits deeply buried marine sediments, but the extent of this vast ecosystem remains poorly constrained. Here we provide evidence for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan. Microbial methanogenesis was indicated by the isotopic compositions of methane and carbon dioxide, biomarkers, cultivation data, and gas compositions. Concentrations of indigenous microbial cells below 1.5 km ranged from 〈10 to ~10**4 cells cm**-3. Peak concentrations occurred in lignite layers, where communities differed markedly from shallower subseafloor communities and instead resembled organotrophic communities in forest soils. This suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.
    Keywords: Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 10
    Publication Date: 2023-05-12
    Keywords: Area/locality; Conductivity, average; Depth, bottom/max; Depth, top/min; ELEVATION; Heat flow; LATITUDE; LONGITUDE; Number; Number of conductivity measurements; Sample, optional label/labor no; Temperature gradient
    Type: Dataset
    Format: text/tab-separated-values, 54 data points
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