# Northeastern United States 11,000 Year Precipitation Reconstructions #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 3.0 # Encoding: UTF-8 # 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/23072 # Description: NOAA Landing Page # # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/nam2k-hydro-v1-1.0.0/noaa-templates/data-version-1.0.0/Deep.Marsicek.2013-2.txt # Online_Resource_Description: This file. NOAA WDS Paleo formatted metadata and data for version v1-1.0.0 of this dataset. # # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/nam2k-hydro-v1-1.0.0/data-version-1.0.0/Deep.Marsicek.2013.lpd # Online_Resource_Description: Linked Paleo Data (LiPD) formatted file containing the same metadata and data as this file, for version v1-1.0.0 of this dataset. # # Original_Source_URL: https://www.ncdc.noaa.gov/paleo/study/16095 # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstruction # Parameter_Keywords: precipitation # Dataset_DOI: # #------------------ # Contribution_Date # Date: 2018-01-08 #------------------ # File_Last_Modified_Date # Modified_Date: 2018-01-08 #------------------ # Title # Study_Name: Northeastern United States 11,000 Year Precipitation Reconstructions #------------------ # Investigators # Investigators: Marsicek, Jeremiah P.; Shuman, Bryan; Brewer, Simon; Foster, David R.; Oswald, W. Wyatt #------------------ # Description_Notes_and_Keywords # Description: Precipitation reconstructions for 5 locations in the northeastern United States over the Holocene. #------------------ # Publication # Authors: Jeremiah P. Marsicek, Bryan Shuman, Simon Brewer, David R. Foster, W. Wyatt Oswald # Published_Date_or_Year: 2013-11-15 # Published_Title: Moisture and temperature changes associated with the mid-Holocene Tsuga decline in the northeastern United States # Journal_Name: Quaternary Science Reviews # Volume: 80 # Edition: # Issue: # Pages: 129-142 # Report: # DOI: 10.1016/j.quascirev.2013.09.001 # Online_Resource: https://www.sciencedirect.com/science/article/pii/S0277379113003375 # Full_Citation: # Abstract: A decline of hemlock (Tsuga) populations at ca 5.5 ka (thousands of calibrated radiocarbon years before 1950 AD) stands out as the most abrupt vegetation change of the Holocene in North America, but remains poorly understood after decades of study. Recent analyses of fossil pollen have revealed a concurrent, abrupt oak (Quercus) decline and increases in the abundance of beech (Fagus) and pine (Pinus) on Cape Cod in eastern Massachusetts, but the replacement of drought-tolerant oaks by moisture-sensitive beeches appears inconsistent with low lake levels in the region at the same time. The oak and beech changes are also limited to coastal areas, and the coastal-inland differences require an explanation. Here, we develop a new lake-level reconstruction from Deep Pond, Cape Cod by using a transect of sediment cores and ground-penetrating radar (GPR) profiles to constrain the past elevations of the sandy, littoral zone of the pond. The reconstruction shows that a series of multi-century episodes of low water coincide with the abrupt hemlock and oak declines, and interrupt subsequent phases of hemlock recovery. The lake-level variations equal precipitation deficits of ~100 mm superimposed on a Holocene long moisture increase of >400 mm. However, because moisture deficits do not easily explain the oak and beech changes, we also evaluate how the climate preferences of the regional vegetation changed over time by matching the fossil pollen assemblages from Deep Pond with their modern equivalents. Reconstructions of the precipitation requirements of the vegetation correlate well even in detail with the lake-level record (r = 0.88 at Deep Pond), and indicate close tracking of effective moisture (precipitation minus evapotranspiration) by the vegetation despite the abrupt species declines, which could have decoupled climate and vegetation trends. Reconstructions of the temperature preferences of the vegetation indicate that coastal sites may have cooled by 0.5-2.5C after ca 5.5 ka, while inland sites warmed by 0.5-1C. The change in coastal temperature preferences agrees with sea surface cooling in the western Atlantic Ocean of >1C. Consequently, the persistence of low hemlock abundance after 5.5 ka in the northeast U.S. may have resulted from oceanic changes that produced multi-century droughts and thus delayed the post-decline recovery of hemlock populations. #------------------ # Funding_Agency # Funding_Agency_Name: US National Science Foundation # Grant: DEB-0816731, DEB-0815036 #------------------ # Site_Information # Site_Name: Deep Lake # Location: North America>United States Of America>Massachusetts # Country: United States Of America # Northernmost_Latitude: 41.564 # Southernmost_Latitude: 41.564 # Easternmost_Longitude: -70.635 # Westernmost_Longitude: -70.635 # Elevation: 23 #------------------ # Data_Collection # Collection_Name: Deep.Marsicek.2013 # Earliest_Year: 11000.0 # Most_Recent_Year: 0.0 # Time_Unit: BP # Core_Length: # Notes: #------------------ # 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 # ## age_CE age, , , year CE, , , , ,N, ## precip Precipitation, ,,mm/yr, annual, climate reconstruction, ,,N, ## age_calBP age, , , calendar years before present, , , , ,N, # #------------------ # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header) # Missing_Values: nan # age_CE precip age_calBP 1950.0 1208.974 0 1900.0 1271.772 50 1850.0 1170.793 100 1800.0 1221.547 150 1750.0 1238.312 200 1700.0 1217.176 250 1650.0 1267.298 300 1600.0 1276.644 350 1550.0 1247.959 400 1500.0 1263.068 450 1450.0 1262.937 500 1400.0 1253.531 550 1350.0 1271.385 600 1300.0 1269.299 650 1250.0 1252.034 700 1200.0 1269.931 750 1150.0 1275.575 800 1100.0 1256.027 850 1050.0 1251.702 900 1000.0 1243.065 950 950.0 1232.687 1000 900.0 1244.879 1050 850.0 1227.019 1100 800.0 1204.695 1150 750.0 1223.076 1200 700.0 1218.488 1250 650.0 1199.553 1300 600.0 1180.619 1350 550.0 1196.892 1400 500.0 1245.383 1450 450.0 1261.799 1500 400.0 1259.307 1550 350.0 1232.706 1600 300.0 1245.786 1650 250.0 1250.393 1700 200.0 1265.868 1750 150.0 1235.705 1800 100.0 1215.636 1850 50.0 1238.292 1900 0.0 1249.514 1950 -50.0 1269.838 2000 -100.0 1242.824 2050 -150.0 1167.121 2100 -200.0 1119.212 2150 -250.0 1130.112 2200 -300.0 1141.012 2250 -350.0 1151.911 2300 -400.0 1162.811 2350 -450.0 1173.71 2400 -500.0 1184.61 2450 -550.0 1195.234 2500 -600.0 1169.03 2550 -650.0 1142.826 2600 -700.0 1116.623 2650 -750.0 1111.004 2700 -800.0 1202.548 2750 -850.0 1257.382 2800 -900.0 1206.914 2850 -950.0 1187.719 2900 -1000.0 1228.646 2950 -1050.0 1252.143 3000 -1100.0 1252.143 3050 -1150.0 1258.385 3100 -1200.0 1273.647 3150 -1250.0 1244.321 3200 -1300.0 1170.318 3250 -1350.0 1175.873 3300 -1400.0 1245.571 3350 -1450.0 1186.138 3400 -1500.0 1194.539 3450 -1550.0 1209.409 3500 -1600.0 1167.648 3550 -1650.0 1098.878 3600 -1700.0 1125.596 3650 -1750.0 1004.904 3700 -1800.0 1065.639 3750 -1850.0 961.576 3800 -1900.0 1027.671 3850 -1950.0 934.139 3900 -2000.0 1068.474 3950 -2050.0 1056.833 4000 -2100.0 1141.944 4050 -2150.0 1005.432 4100 -2200.0 1180.018 4150 -2250.0 1045.123 4200 -2300.0 1103.345 4250 -2350.0 1064.881 4300 -2400.0 1201.112 4350 -2450.0 1079.205 4400 -2500.0 1222.809 4450 -2550.0 1026.515 4500 -2600.0 1128.202 4550 -2650.0 1079.056 4600 -2700.0 1102.666 4650 -2750.0 1108.69 4700 -2800.0 1114.715 4750 -2850.0 1113.787 4800 -2900.0 1080.817 4850 -2950.0 1136.014 4900 -3000.0 983.611 4950 -3050.0 1112.396 5000 -3100.0 1057.542 5050 -3150.0 1016.396 5100 -3200.0 1051.44 5150 -3250.0 1099.604 5200 -3300.0 1183.132 5250 -3350.0 1259.872 5300 -3400.0 1204.721 5350 -3450.0 1149.643 5400 -3500.0 1108.684 5450 -3550.0 1067.725 5500 -3600.0 1059.186 5550 -3650.0 1052.828 5600 -3700.0 1140.324 5650 -3750.0 1213.45 5700 -3800.0 1185.497 5750 -3850.0 1145.866 5800 -3900.0 1077.958 5850 -3950.0 1028.929 5900 -4000.0 1003.221 5950 -4050.0 979.939 6000 -4100.0 958.258 6050 -4150.0 990.713 6100 -4200.0 1040.502 6150 -4250.0 1090.29 6200 -4300.0 1130.744 6250 -4350.0 1042.394 6300 -4400.0 967.474 6350 -4450.0 939.444 6400 -4500.0 932.586 6450 -4550.0 960.617 6500 -4600.0 986.09 6550 -4650.0 1009.319 6600 -4700.0 1027.702 6650 -4750.0 1043.886 6700 -4800.0 1048.63 6750 -4850.0 1051.241 6800 -4900.0 1061.718 6850 -4950.0 1084.955 6900 -5000.0 1108.192 6950 -5050.0 1130.076 7000 -5100.0 1151.73 7050 -5150.0 1158.038 7100 -5200.0 1132.878 7150 -5250.0 1107.719 7200 -5300.0 1122.869 7250 -5350.0 1148.028 7300 -5400.0 1158.27 7350 -5450.0 1129.052 7400 -5500.0 1099.833 7450 -5550.0 1070.614 7500 -5600.0 1041.395 7550 -5650.0 1012.177 7600 -5700.0 982.958 7650 -5750.0 1106.974 7700 -5800.0 1135.11 7750 -5850.0 1117.979 7800 -5900.0 1100.282 7850 -5950.0 1079.771 7900 -6000.0 1059.259 7950 -6050.0 1040.965 8000 -6100.0 1022.984 8050 -6150.0 1014.034 8100 -6200.0 1013.546 8150 -6250.0 1013.058 8200 -6300.0 1024.56 8250 -6350.0 1037.679 8300 -6400.0 1050.799 8350 -6450.0 1054.436 8400 -6500.0 1051.556 8450 -6550.0 1048.676 8500 -6600.0 1043.041 8550 -6650.0 1001.302 8600 -6700.0 959.562 8650 -6750.0 917.822 8700 -6800.0 931.829 8750 -6850.0 991.62 8800 -6900.0 972.443 8850 -6950.0 934.018 8900 -7000.0 966.144 8950 -7050.0 975.95 9000 -7100.0 924.272 9050 -7150.0 941.892 9100 -7200.0 1027.318 9150 -7250.0 985.661 9200 -7300.0 900.397 9250 -7350.0 893.095 9300 -7400.0 902.124 9350 -7450.0 963.618 9400 -7500.0 968.279 9450 -7550.0 906.703 9500 -7600.0 888.058 9550 -7650.0 887.734 9600 -7700.0 914.862 9650 -7750.0 943.395 9700 -7800.0 970.329 9750 -7850.0 962.863 9800 -7900.0 907.534 9850 -7950.0 898.173 9900 -8000.0 912.781 9950 -8050.0 927.389 10000 -8100.0 916.585 10050 -8150.0 904.398 10100 -8200.0 892.211 10150 -8250.0 880.024 10200 -8300.0 867.836 10250 -8350.0 863.87 10300 -8400.0 863.68 10350 -8450.0 863.49 10400 -8500.0 863.3 10450 -8550.0 863.11 10500 -8600.0 863.046 10550 -8650.0 863.154 10600 -8700.0 863.263 10650 -8750.0 863.372 10700 -8800.0 863.48 10750 -8850.0 861.188 10800 -8900.0 846.178 10850 -8950.0 831.168 10900 -9000.0 816.158 10950 -9050.0 801.148 11000