# Liang Luar Cave, Indonesia Stalagmite Oxygen Isotope Data #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # # Online_Resource: https://www.ncdc.noaa.gov/cdo/f?p=519:1:::::P1_STUDY_ID:8632 # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611 # # Original_Source_URL: http://www1.ncdc.noaa.gov/pub/data/paleo/speleothem/pacific/liang-luar2009.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Speleothems #-------------------- # Contribution_Date # Date: 2011-03-17 #-------------------- # Title # Study_Name: Liang Luar Cave, Indonesia Stalagmite Oxygen Isotope Data #-------------------- # Investigators # Investigators: Griffiths, M.L.; Drysdale, R.N.; Gagan, M.K.; Zhao, J.-x.; Ayliffe, L.K.; Hellstrom, J.C.; Hantoro, W.S.; Frisia, S.; Feng, Y.-x.; Cartwright, I.; Pierre, E. St.; Fischer, M.J.; Suwargadi, B.W. #-------------------- # Description_and_Notes # Description: Oxygen isotope data from two combined stalagmite records covering the period 12.7 ka to present from Liang Luar cave, Flores, Indonesia. Stalagmite d18O analyses were carried out on a GV2003 continuous-flow isotope ratio mass spectrometer at the University of Newcastle, Australia, and an automated Kiel carbonate preparation device coupled to a Finnigan MAT-251 mass spectrometer at the Research School of Earth Sciences, The Australian National University. 62 Th230/U234 dates were determined using TIMS and multi-collector ICP-MS at the University of Queensland and the University of Melbourne, Australia, respectively (see Suppl. Info in Griffiths et al., 2009). The d18O profile (Table S4 in Griffiths et al., 2009) consists of the spliced d18O records of stalagmites LR06-B1 (~8 year resolution) and LR06-B3 (~70 year resolution). #-------------------- # Publication # Authors: Griffiths, M.L.; Drysdale, R.N.; Gagan, M.K.; Zhao, J.-x.; Ayliffe, L.K.; Hellstrom, J.C.; Hantoro, W.S.; Frisia, S.; Feng, Y.-x.; Cartwright, I.; Pierre, E. St.; Fischer, M.J.; Suwargadi, B.W. # Published_Date_or_Year: 2009 # Published_Title: Increasing Australian-Indonesian monsoon rainfall linked to early Holocene sea-level rise. # Journal_Name: Nature GeoScience # Volume: 2 # Edition: # Issue: 9 # Pages: 597-664 # DOI: 10.1038/ngeo605 # Online_Resource: # Full_Citation: # Abstract: The Australian-Indonesian summer monsoon affects rainfall variability and hence terrestrial productivity in the densely populated tropical Indo-Pacific region. It has been proposed that the main control of summer monsoon precipitation on millennial timescales is local insolation, but unravelling the mechanisms that have influenced monsoon variability and teleconnections has proven difficult, owing to the lack of high-resolution records of past monsoon behaviour. Here we present a precisely dated reconstruction of monsoon rainfall over the past 12,000 years, based on oxygen isotope measurements from two stalagmites collected in southeast Indonesia. We show that the summer monsoon precipitation increased during the Younger Dryas cooling event, when Atlantic meridional overturning circulation was relatively weak. Monsoon precipitation intensified even more rapidly from 11,000 to 7,000 years ago, when the Indonesian continental shelf was flooded by global sea-level rise. We suggest that the intensification during the Younger Dryas cooling was caused by enhanced winter monsoon outflow from Asia and a related southward migration of the intertropical convergence zone. However, the early Holocene intensification of monsoon precipitation was driven by sea-level rise, which increased the supply of moisture to the Indonesian archipelago. #------------------ # Authors: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G.J., Noone, D., Perkins, W.A., and E. Steig # Published_Date_or_Year: 2018 # Published_Title: Additions to the last millennium reanalysis multi-proxy database # Journal_Name: Data Science Journal # Volume: # Edition: # Issue: # Pages: # Report_Number: # DOI: # Online_Resource: # Full_Citation: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G., J., Noone, D., Perkins, W.A., and E. Steig, submitted. Additions to the last millennium reanalysis multi-proxy database. Data Science Journal. # Abstract: Progress in paleoclimatology increasingly occurs via data syntheses. We describe additions to a collection prepared for use in paleoclimate state estimation, specifically the Last Millennium Reanalysis (LMR). The 2290 additional series include 2152 tree ring chronologies and 138 other series. They supplement the collection used previously and together form a database titled LMRdb 1.0.0. The additional data draws from lake core, ice core, coral, speleothem, and tree ring archives, using published data primarily from the NOAA Paleoclimatology archive and a set of tree ring width chronologies standardized from raw International Tree Ring Data Bank ring width series. In contrast to many previous paleo compilations, the data were not selected (screened) on the basis of their environmental correlation, multi-century length, or other attributes. The inclusion of proxies sensitive to moisture and other environmental variables expands their use in data assimilation. A preliminary calibration using linear regression with mean annual temperature reveals characteristics of the proxy series and their relationship to temperature, as well as the noise and error characteristics of the records. The additional records are structured as individual files in the NOAA Paleoclimatology format and archived at NOAA Paleoclimatology (Anderson et al. 2018) and will continue to be improved and expanded as part of the LMR Project. The additions represent a four-fold increase in the number of records available for assimilation, provide expanded geographic coverage, and add additional proxy variables. Applications include data assimilation, proxy system model development, and paleoclimate reconstruction using climate field reconstruction and other methods. #------------------ # Funding_Agency # Funding_Agency_Name: Australian Postgraduate Award # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: Australian Research Council grant # Grant: DP0663274 #------------------ # Funding_Agency_Name: National Science Foundation # Grant:AGS-1304263 # Funding_Agency_Name: National Oceanic and Atmospheric Administration # Grant:NA14OAR4310176 #------------------ # Site_Information # Site_Name: Liang Luar Cave, Indonesia # Location: Ocean>Indian Ocean>Indonesia # Country: Indonesia # Northernmost_Latitude: -8.533 # Southernmost_Latitude: -8.533 # Easternmost_Longitude: 120.433 # Westernmost_Longitude: 120.433 # Elevation: 550 m #------------------ # Data_Collection # Collection_Name: 09indo01a # Earliest_Year: 12649 # Most_Recent_Year: -52 # Time_Unit: yb_1950 # Core_Length: # Notes: {"database":"LMR"} core LR06-B1 #------------------ # Species # Species_Name: # Common_Name: #------------------ # Chronology: # # # # # # # # #---------------- # Variables # # Data variables follow that are preceded by "##" in columns one and two. # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) # ##age age, , , years before 1950, , , , ,N ##d18O delta 18 Oxygen,,,permil VPDB,,Speleothems,,,N # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: NAN # age d18O -52 -6.26 -45.7 -6.51 -38.9 -6.59 -31 -5.93 -20.8 -6.35 -10.4 -6.14 0.1 -6.1 10.6 -6.3 21.3 -6.33 31.9 -6.23 42.6 -6.24 52.6 -6.33 62.4 -6.39 72 -6.12 81.5 -6.15 90.7 -6.14 99.8 -6.07 108.7 -6.44 117.7 -6.61 126.6 -6.17 135.6 -6.31 144.7 -6.03 153.4 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