# Peruvian Andes Proglacial Lakes 15,000 Year Sediment Data #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 2.0 # 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/paleo/study/21951 # Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/paleolimnology/southamerica/peru/queshquecocha2017gff.txt # # Original_Source_URL: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleolimnology # # Parameter_Keywords: geochemistry, physical properties #-------------------- # Contribution_Date # Date: 2017-04-18 #-------------------- # Title # Study_Name: Peruvian Andes Proglacial Lakes 15,000 Year Sediment Data #-------------------- # Investigators # Investigators: Stansell, N.D.; Licciardi, J.M.; Rodbell, D.T.; Mark, B.G. #-------------------- # Description_and_Notes # Description: Sediment geochemical and physical properties data from 2 proglacial lakes in the Queshque valley, Peruvian Andes. #-------------------- # Publication # Authors: Nathan D. Stansell, Joseph M. Licciardi, Donald T. Rodbell, Bryan G. Mark # Published_Date_or_Year: 2017-04-18 # Published_Title: Tropical ocean-atmospheric forcing of Late Glacial and Holocene glacier fluctuations in the Cordillera Blanca, Peru # Journal_Name: Geophysical Research Letters # Volume: # Edition: # Issue: # Pages: # Report_Number: # DOI: 10.1002/2016GL072408 # Online_Resource: http://onlinelibrary.wiley.com/doi/10.1002/2016GL072408/full # Full_Citation: # Abstract: Evaluating the timing and style of past glacier fluctuations in the tropical Andes is important for our scientific understanding of global environmental change. Terrestrial cosmogenic nuclide (TCN) ages on moraine boulders combined with 14C-dated clastic sediment records from alpine lakes document glacial variability in the Cordillera Blanca of Peru during the last ca. 16 ka. Late Glacial ice extents culminated at the start of the Antarctic Cold Reversal (ACR) and began retracting prior to the Younger Dryas (YD). Multiple moraine crests dating to the early Holocene mark brief readvances or stillstands that punctuated overall retreat of the Queshque valley glacier terminus during this interval. Glaciers were less extensive during the middle Holocene before readvancing during the latest Holocene. These records suggest that tropical Atlantic and Pacific ocean-atmospheric processes exerted temporally variable forcing of Late Glacial and Holocene glacial changes in the Peruvian Andes. #------------------ # Funding_Agency # Funding_Agency_Name: US National Science Foundation # Grant: EAR-1003711, EAR-1003780, EAR-1344476 #------------------ # Site_Information # Site_Name: Upper Laguna Queshquecocha # Location: South America>Peru # Country: Peru # Northernmost_Latitude: -9.8167 # Southernmost_Latitude: -9.8167 # Easternmost_Longitude: -77.3 # Westernmost_Longitude: -77.3 # Elevation: 4290 m #------------------ # Data_Collection # Collection_Name: Queshquecocha2017GFF # Earliest_Year: 8286 # Most_Recent_Year: -62 # Time_Unit: CalYrBP # Core_Length: # Notes: #------------------ # Chronology_Information # Chronology: # #---------------- # Variables # # Data variables follow 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) # ## depth_cm depth, , , cm, , , , ,N ## age_calBPmin age minimum, , , calendar years before present, , , , ,N ## age_calBPmax age maximum, , , calendar years before present, , , Stansell BACON max ages, ,N ## gffmin Glacial Flour Flux at minimum possible age, , , gm cm-2 yr-1, , , , ,N ## gffmax Glacial Flour Flux at maximum possible age, , , gm cm-2 yr-1, , , , ,N # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # depth_cm age_calBPmin age_calBPmax gffmin gffmax 16.5 -62.7 7.6 0.077800 19.5 -28 34.1 0.068556 0.152210 20 11.6 38.1 0.167370 0.175407 25 35.2 81.6 0.271024 0.148316 30 61.6 119.3 0.289258 0.164578 33.5 90.5 144.6 0.236318 0.185141 38 122.2 179.1 0.211376 0.176826 43 155.2 220.3 0.154536 0.179198 48 187.4 258.5 0.186930 0.167571 53 208.8 297.7 0.182942 0.164194 58 246.5 336.5 0.186803 0.203550 63 256.3 374.6 0.206653 0.206033 68 266.8 413.4 0.209286 0.176867 71.5 302.3 439 0.189025 0.157424 73.5 331.3 451.3 0.194153 0.193407 78.5 367.5 492 0.213136 0.162846 83.5 405.2 533.9 0.177564 0.152653 88.5 440 575.7 0.184711 0.182689 93.5 476.3 617.9 0.186479 0.161839 98.5 510.5 659.2 0.186640 0.162243 103.5 549.9 698.6 0.163872 0.129434 108.5 587.9 736.3 0.173628 0.159659 111.5 625.1 760 0.190902 0.165188 116.5 666.6 801.8 0.130332 0.168480 118 707.6 812.8 0.138629 0.184109 119.5 744.1 826 0.174902 0.166477 124.5 750.4 865.8 0.221731 0.168603 128.5 786 897.9 0.173359 0.175403 133.5 823.4 933.3 0.196113 0.217953 138.5 861.9 971.8 0.177362 0.173875 143.5 903.3 1008.6 0.173413 0.174672 148.5 915.2 1045.3 0.167834 0.184447 153.5 927 1084.6 0.188941 0.162419 158.5 937.6 1123.2 0.213731 0.167268 163.5 954.2 1162.1 0.170650 0.169611 168.5 1008.8 1197.5 0.158350 0.200609 173.5 1031.5 1233.9 0.175269 0.148592 178.5 1066.7 1272.4 0.151971 0.147631 183.5 1100.1 1307.6 0.158257 0.181362 184.5 1132.3 1315.6 0.160360 0.174613 189 1187.1 1349.3 0.153958 0.183133 194 1257.7 1383.1 0.178282 0.217001 199 1319.7 1417.2 0.120793 0.200248 204 1339.4 1454.3 0.112394 0.193512 205.5 1386.6 1465.2 0.110179 0.183231 207 1451.8 1476.2 0.095456 0.202682 208.5 1525.2 1486.7 0.088917 0.215767 210.5 1557.2 1501.9 0.099273 0.186368 217 1589.2 1545.3 0.063923 0.199215 220 1615.9 1563.9 0.051372 0.213903 224 1643.9 1591 0.085270 0.197394 228 1659 1621 0.142420 0.176193 232 1671.3 1649.8 0.105215 0.179291 239 1690.3 1697.1 0.073725 0.178369 248 1709.4 1759.6 0.091050 0.201387 255 1740.8 1809.4 0.041196 0.150385 257 1783.3 1819.8 0.059456 0.212900 263 1896.2 1856.3 0.068303 0.142478 271 1921.5 1908.1 0.079203 0.120149 279 1953.3 1959.5 0.065792 0.126975 282.5 1992.9 1981.6 0.109091 0.143744 286 2028.7 2004.4 0.072578 0.089716 289 2077.6 2024.2 0.063523 0.069275 292 2120.3 2043.7 0.087088 0.122438 294 2170.5 2057.8 0.104574 0.152520 295.5 2190.8 2068.1 0.149661 0.125645 297.5 2223 2079.2 0.079228 0.126196 300 2247.6 2094.9 0.064977 0.110768 302 2289.7 2106.1 0.092401 0.115497 306 2326.4 2133.5 0.102006 0.092222 318 2348.6 2235 0.118500 0.075974 321 2372.1 2262.7 0.095724 0.072341 325 2421 2296.3 0.098276 0.062268 329.5 2496.2 2334.5 0.089973 0.113089 333 2548.5 2364.6 0.072661 0.086322 338 2601.3 2406.3 0.154848 0.074490 342 2680.7 2440.4 0.119133 0.109052 347.5 2690.8 2488.7 0.138221 0.108688 350.5 2714.3 2520.7 0.135424 0.094941 354 2742.7 2557 0.120712 0.070279 357 2760.7 2586.7 0.150528 0.053819 362 2799.8 2631.4 0.136242 0.087027 366.5 2851.3 2669.5 0.104562 0.098258 369.5 2903.9 2695.3 0.100032 0.101965 372 2918.3 2715.1 0.108203 0.113612 379 2965.2 2795.2 0.094860 0.059996 390 3031.7 2945.2 0.115936 0.045106 397 3099.8 3029.8 0.119673 0.044919 405 3189.1 3131.5 0.116615 0.080393 416.5 3237.1 3265.8 0.106867 0.070433 418 3302.5 3283.9 0.117715 0.077129 421.5 3411.3 3325.4 0.109705 0.076686 425.5 3531 3373.5 0.156314 0.071273 428 3583.2 3401.2 0.111037 0.097816 434 3635 3468.1 0.144060 0.079627 441 3685.7 3548.3 0.144420 0.067144 448 3711.8 3628.8 0.082321 0.065362 450 3739.5 3650.6 0.124623 0.071473 456 3778.9 3715.8 0.140226 0.068235 465 3876.5 3814.4 0.087675 0.078192 473 4104.1 3900.1 0.134850 0.095096 483 4247.3 4036.7 0.096014 0.076235 488 4341.4 4108.7 0.096590 0.071245 495 4369 4207.2 0.130862 0.078158 505 4428 4335.9 0.115352 0.092742 517 4481.1 4493.9 0.102224 0.118422 522 4532.6 4561 0.115919 0.086381 527 4628.8 4626.4 0.104773 0.114102 532 4653.7 4693.1 0.110732 0.109777 534.5 4681.2 4726.2 0.110683 0.064912 537 4728.3 4755.6 0.114806 0.117417 541 4863.3 4807.6 0.088638 0.106249 550 4982.1 4922.9 0.077733 0.074216 571 5079.7 5196.3 0.108737 0.112260 585 5141.9 5375.6 0.097202 0.076683 595 5199.8 5499.8 0.117436 0.073181 597.5 5345.8 5531.1 0.105902 0.115392 602 5473.7 5593.5 0.094419 0.109066 606 5574.4 5650.6 0.080002 0.095063 610 5601.9 5710.7 0.110429 0.099332 617 5657.8 5814.3 0.123978 0.097289 619 5730.4 5843.3 0.093502 0.095077 621 5800.5 5869.4 0.099032 0.116620 625 6008.4 5925.9 0.090264 0.095705 634 6109.9 6053.8 0.093573 0.093559 642.5 6188.7 6173.3 0.099901 0.077277 650 6285.4 6279.1 0.080997 0.100309 654.5 6332.5 6342.4 0.109119 0.095513 659 6398.7 6400.5 0.079441 0.117032 670 6461.9 6553.7 0.082682 0.100925 679 6588.2 6675.4 0.085005 0.099229 686 6717.7 6779.1 0.096568 0.077687 688 6864.2 6803.4 0.090864 0.124971 693 7006.4 6868.2 0.093900 0.106950 698 7075 6930.6 0.074014 0.108786 703 7135 6995.3 0.084855 0.107298 718 7194.7 0.094112 725 7289.1 0.100611 731 7363.1 0.106381 737 7442.7 0.098397 741 7494.4 0.099410 746 7555.1 0.086640 750 7606.1 0.102460 759 7717.5 0.096375 769 7845.7 0.097548 779 7970.4 0.106749 789 8087.8 0.113736 794 8150.5 0.080978 799 8209.5 0.086293 805 8286.3 0.089881