# Indonesian Vegetation and d13C Fatty Acids Data over the Past 25,000 Years #----------------------------------------------------------------------- # 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: http://www.ncdc.noaa.gov/paleo/study/17290 # Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/contributions_by_authot/dubois2014-ng/dubois2014-ng-91ggc.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleoceanography #-------------------- # Contribution_Date # Date: 2014-10-14 #-------------------- # Title # Study_Name: Indonesian Vegetation and d13C Fatty Acids Data over the Past 25,000 Years #-------------------- # Investigators # Investigators: Dubois, N.; Oppo, D.W.; Galy, V.; Mohtadi, M.; van der Kaars, S.; Tierney, J.E.; Rosenthal, Y.; Eglinton, T.I.; Lueckge, A.; Linsley, B.K. #-------------------- # Description_and_Notes # Description: The data set contains fatty acids d13C data and pollen data from 3 cores from the Indo-Pacific Warm Pool covering the last 30 kyr. # # Methods: 5_-10 g of sediments were freeze-dried and homogenized before lipid extraction using a microwave-assisted reaction system (MARS). Fatty acids were isolated from the total lipid extract by aminopropyl-silica-gel column chromatography, methylated with methanol of known isotopic composition, and then further purified by aminopropyl-silica-gel chromatography and silver nitrate-silica-gel chromatography. Fatty acid methyl esters were analysed in triplicate for their carbon isotopic composition by means of gas chromatography-isotope ratio monitoring-mass spectrometry (GC-IR-MS) at the Woods Hole Oceanographic Institution. All _13C values were normalized to the Vienna Pee Dee Belemnite (VPDB) scale using multiple pulses of CO2 reference gas. The average standard deviation of replicate measurements was 0.19‰. # Measurements were corrected for the added methyl group on the basis of isotopic mass balance. # # For palynological processing 5.3_-7.5ml of sediment were suspended in approximately 40 ml of tetra-sodium-pyrophosphate (_±10%), sieved over 200 and # 7 micrometer screens. Following hydrochloric acid (10%) treatment, heavy liquid separation (sodium-polytungstate, SG 2.0, 20 min at 2,000 rpm, twice), acetolyis # and sodium carbonate (20%) treatment, the resulting organic residues were mounted in glycerol and slides sealed with para_n wax. Palynological slides were # counted along evenly spaced transects until a minimum count of 100 dryland rainforest pollen grains was reached. All percentage values presented here are based on the total dryland pollen sum made up of all terrestrial pollen grains counted (that is, excluding mangrove pollen and pteridophyta spores). This # pollen sum varied between 322 and 964, with an average of 527 pollen grains. Pollen taxa were placed into ecological groups according to where they most commonly occur. # #-------------------- # Publication # Authors: Dubois, N., D.W. Oppo, V. Galy, M. Mohtadi, S. van der Kaars, J.E. Tierney, Y. Rosenthal, T.I. Eglinton, A. Lueckge, and B.K. Linsley # Published_Date_or_Year: 2014 # Published_Title: Indonesian vegetation response to changes in rainfall seasonality over the past 25,000 years # Journal_Name: Nature Geoscience # Volume: 7 # Edition: # Issue: # Pages: 513-517 # DOI: 10.1038/ngeo2182 # Online_Resource: # Full_Citation: # Abstract: The hydrologic response to climate forcing in the Indo-Pacific warm pool region has varied spatially over the past 25,000 years1–5. For example, drier conditions are inferred,on Java and Borneo for the period following the end of,the Last Glacial Maximum, whereas wetter conditions are reconstructed for northwest Australia4. The response of vegetation to these past rainfall variations is poorly constrained. Using a suite of 30 surface marine sediment samples from throughout the Indo-Pacific warm pool, we demonstrate that today the stable isotopic composition of vascular plant fatty acids (_13CFA) reflects the regional vegetation composition. This in turn is controlled by the seasonality of rainfall consistent with dry season water stress6. Applying this proxy in a sediment core from o_shore northeast Borneo, we show broadly similar vegetation cover during the Last Glacial Maximum and the Holocene, suggesting that, despite generally drier glacial conditions1,7, there was no pronounced dry season. In contrast, _13CFA and pollen data from a core o_ the coast of Sumba indicate an expansion of C4 herbs during the most recent glaciation, implying enhanced aridity and water stress during the dry season. Holocene vegetation trends are also consistent with a response to dry season water stress.We therefore conclude that vegetation in tropical monsoon regions is susceptible to increases in water stress arising from an enhanced seasonality of rainfall, as has occurred8 in past decades. # #-------------------- # Publication # Authors: Steinke, S., M. Mohtadi, M. Prange, V. Varma, D. Pittauerova, and H.W. Fischer # Published_Date_or_Year: 2014 # Published_Title: Mid-to Late-Holocene Australian-Indonesian summer monsoon variability # Journal_Name: Quaternary Science Reviews # Volume: 93 # Edition: # Issue: # Pages: 142-154 # DOI: 10.1016/j.quascirev.2014.04.006 # Online_Resource: # Full_Citation: # Abstract: The Australian–Indonesian monsoon has a governing influence on the agricultural practices and livelihood in the highly populated islands of Indonesia. However, little is known about the factors that have influenced past monsoon activity in southern Indonesia. Here, we present a ~6000 years high-resolution record of Australian-Indonesian summer monsoon (AISM) rainfall variations based on bulk sediment element analysis in a sediment archive retrieved offshore northwest Sumba Island (Indonesia). The record suggests lower riverine detrital supply and hence weaker AISM rainfall between 6000 yr BP and ~3000 yr BP compared to the Late Holocene. We find a distinct shift in terrigenous sediment supply at around 2800 yr BP indicating a reorganization of the AISM from a drier Mid Holocene to a wetter Late Holocene in southern Indonesia. The abrupt increase in rainfall at around 2800 yr BP coincides with a grand solar minimum. An increase in southern Indonesian rainfall in response to a solar minimum is consistent with climate model simulations that provide a possible explanation of the underlying mechanism responsible for the monsoonal shift. We conclude that variations in solar activity play a significant role in monsoonal rainfall variability at multi-decadal and longer timescales. The combined effect of orbital and solar forcing explains important details in the temporal evolution of AISM rainfall during the last 6000 years. By contrast, we find neither evidence for volcanic forcing of AISM variability nor for a control by long-term variations in the El Niño-Southern Oscillation (ENSO). # #------------------ # Funding_Agency # Funding_Agency_Name: National Science Fundation (NSF) # Grant: ABR-86074300, OCE-1333387 #------------------ # Funding_Agency # Funding_Agency_Name: Bundesministerium für Bildung und Forschung (BMBF). # Grant: PABESIA #------------------ # Site_Information # Site_Name: BJ8-03-91GGC # Location: Indonesia # Northernmost_Latitude: 2.8739 # Southernmost_Latitude: 2.8739 # Easternmost_Longitude: 118.38555 # Westernmost_Longitude: 118.38555 # Elevation: -2326 #--------------------------------------- # Data_Collection # Collection_Name: BJ8-03-91GGC FA Du14 # First_Year: 20350 # Last_Year: 370 # Time_Unit: cal yr BP # Core_Length: # Notes: #--------------------------------------- # Chronology: # Radiocarbon data of core BJ8-03-91GGC # # Column 1: Core # Column 2: Foram. species # Column 3: Depth (cm) # Column 4: 14C age (yr) # Column 5: 14C age error (yr) # Column 6: Age (yr BP) # Column 7: Lower cal range (1s) # Column 8: Upper cal range (1s) # Column 9: Reservoir age (yr) # Column 10: Program used # Column 11: Calibration # Column 12: Label # Core species Depth(cm) 14C Age(yr) 14C age error Calendar Age(yr) Lower cal range (1s) Upper cal range (1s) Reservoir age(yr) Program Calibration Label # BJ8-03-91GGC mixed planktonics 6 860 25.00 288.00 243.00 360 625±40 CALIB 7 Marine13 OS-47781 # BJ8-03-91GGC mixed planktonics 48 4140 20.00 3902.00 3834.00 3962 625±40 CALIB 7 Marine13 OS-107516 # BJ8-03-91GGC mixed planktonics 248 10800 55.00 11858.00 11726.00 12023 625±40 CALIB 7 Marine13 OS-47782 # BJ8-03-91GGC mixed planktonics 296 12850 60.00 14132.00 14006.00 14231 625±40 CALIB 7 Marine13 OS-107665 # BJ8-03-91GGC mixed planktonics 312 13850 75.00 15896.00 15764.00 16030 625±40 CALIB 7 Marine13 OS-107667 # BJ8-03-91GGC G. sacc. 360 16650 60.00 19343.00 19228.00 19462 625±40 CALIB 7 Marine13 OS-47810 # #--------------------------------------- # Variables # Data variables follow that are preceded by "##" in columns one and two. # Variables list, one per line, shortname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) ## depth_cm Depth,,,cm,,,,,N ## age_calkaBP age,,,calendar kiloyears before present (1950AD),,,,,N ## d13C-C30FA delta 13C,Carbon30 Fatty Acids,,per mil VPDB,,Paleoceanography,,,N ## d13C-C30FA_err delta 13C,Carbon30 Fatty Acids,standard error,per mil VPDB,,Paleoceanography,,,N # # Data # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Value: -999 depth_cm age_calkaBP d13C-C30FA d13C-C30FA_err 7 0.37 -31.07 0.25 17 1.23 -30.86 0.38 49 3.94 -30.55 0.26 66 4.62 -32.09 0.27 82 5.25 -31.51 0.04 114 6.53 -31.97 0.19 130 7.16 -32.06 0.13 147 7.84 -32.00 0.28 163 8.48 -32.05 0.25 179 9.11 -31.69 0.34 196 9.79 -31.66 0.18 212 10.43 -31.07 0.05 228 11.06 -31.78 0.14 245 11.74 -31.68 0.30 261 12.47 -31.17 0.07 277 13.23 -31.30 0.19 293 13.99 -30.78 0.42 309 15.57 -32.47 0.19 325 16.83 -32.19 0.30 342 18.05 -32.27 0.13 359 19.27 -32.67 0.13 374 20.35 -32.93 0.11