# Northern Russia Air Temperature Reconstructions during the Holocene #----------------------------------------------------------------------- # 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/32612 # Online_Resource_Description: NOAA Landing Page # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/27330 # Online_Resource_Description: NOAA Landing Page for Temperature-12k Database # # Online_Resource: https://www.ncei.noaa.gov/pub/data/paleo/reconstructions/climate12k/temperature/version1.0.0/Temp12k_directory_NOAA_files/Llet-T.liranta.2015.txt # Online_Resource_Description: NOAA location of the template # # Online_Resource: https://www.ncei.noaa.gov/pub/data/paleo/reconstructions/climate12k/temperature/version1.0.0/Temp12k_directory_LiPD_files/Llet-T.liranta.2015.lpd # Online_Resource_Description: Linked Paleo Data (LiPD) formatted file containing metadata and data related to this file, for version 1.0.0 of this dataset. # # Original_Source_URL: # Description/Documentation lines begin with # # Data lines have no # # # Data_Type: Climate Reconstructions # Parameter_Keywords: air temperature # Dataset_DOI: # #------------------ # Contribution_Date # Date: 2020-04-15 #------------------ # File_Last_Modified_Date # Date: 2020-05-16 #------------------ # Title # Study_Name: Northern Russia Air Temperature Reconstructions during the Holocene #------------------ # Investigators # Investigators: Väliranta, M.; Salonen, J. S.; Heikkilä, M.; Amon, L.; Helmens, K.; Klimaschewski, A.; Kuhry, P.; Kultti, S.; Poska, A.; Shala, S.; Veski, S.; Birks, H. H. #------------------ # Description_Notes_and_Keywords # Description: This dataset was contributed as part of the Temperature-12k project (https://doi.org/10.25921/4RY2-G808). Data were contributed to the project from the original data generators, who are listed in the Investigator field of this template file. Additional notes regarding the use of these data in the Temperature-12k project can be found in the LiPD file listed as an Online_Resource of this template file. Note added by Temperature-12k authors: untenable min age #------------------ # Publication # Authors: Väliranta, M.; Salonen, J. S.; Heikkilä, M.; Amon, L.; Helmens, K.; Klimaschewski, A.; Kuhry, P.; Kultti, S.; Poska, A.; Shala, S.; Veski, S.; Birks, H. H. # Published_Date_or_Year: 2015 # Published_Title: Plant macrofossil evidence for an early onset of the Holocene summer thermal maximum in northernmost Europe # Journal_Name: Nature Communications # Volume: 6 # Edition: # Issue: 1 # Pages: 6809 # Report: # DOI: 10.1038/ncomms7809 # Online_Resource: # Full_Citation: # Abstract: Holocene summer temperature reconstructions from northern Europe based on sedimentary pollen records suggest an onset of peak summer warmth around 9,000 years ago. However, pollen-based temperature reconstructions are largely driven by changes in the proportions of tree taxa, and thus the early-Holocene warming signal may be delayed due to the geographical disequilibrium between climate and tree populations. Here we show that quantitative summer-temperature estimates in northern Europe based on macrofossils of aquatic plants are in many cases ca. 2 °C warmer in the early Holocene (11,700–7,500 years ago) than reconstructions based on pollen data. When the lag in potential tree establishment becomes imperceptible in the mid-Holocene (7,500 years ago), the reconstructed temperatures converge at all study sites. We demonstrate that aquatic plant macrofossil records can provide additional and informative insights into early-Holocene temperature evolution in northernmost Europe and suggest further validation of early post-glacial climate development based on multi-proxy data syntheses. #------------------ # Publication # Authors: Kaufman, D., N. McKay, C. Routson, M. Erb, B. Davis, O. Heiri, S. Jaccard, J. Tierney, C. Dätwyler, Y. Axford, T. Brussel, O. Cartapanis, B. Chase, A. Dawson, A. de Vernal, S. Engels, L. Jonkers, J. Marsicek, P. Moffa-Sánchez, C. Morrill, A. Orsi, K. Rehfeld, K. Saunders, P. S. Sommer, E. Thomas, M. Tonello, M. Tóth, R. Vachula, A. Andreev, S. Bertrand, B. Biskaborn, M. Bringué, S. Brooks, M. Caniupán, M. Chevalier, L. Cwynar, J. Emile-Geay, J. Fegyveresi, A. Feurdean, W. Finsinger, M-C. Fortin, L. Foster, M. Fox, K. Gajewski, M. Grosjean, S. Hausmann, M. Heinrichs, N. Holmes, B. Ilyashuk, E. Ilyashuk, S. Juggins, D. Khider, K. Koinig, P. Langdon, I. Larocque-Tobler, J. Li, A. Lotter, T. Luoto, A. Mackay, E. Magyari, S. Malevich, B. Mark, J. Massaferro, V. Montade, L. Nazarova, E. Novenko, P. Paril, E. Pearson, M. Peros, R. Pienitz, M. Plóciennik, D. Porinchu, A. Potito, A. Rees, S. Reinemann, S. Roberts, N. Rolland, S. Salonen, A. Self, H. Seppä, S. Shala, J-M. St-Jacques, B. Stenni, L. Syrykh, P. Tarrats, K. Taylor, V. van den Bos, G. Velle, E. Wahl, I. Walker, J. Wilmshurst, E. Zhang, S. Zhilich # Published_Date_or_Year: 2020-04-14 # Published_Title: A global database of Holocene paleotemperature records # Journal_Name: Scientific Data # Volume: 7 # Edition: 115 # Issue: # Pages: # Report_Number: # DOI: 10.1038/s41597-020-0445-3 # Online_Resource: https://www.nature.com/articles/s41597-020-0445-3 # Full_Citation: # Abstract: A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format. #------------------ # Funding_Agency # Funding_Agency_Name: # Grant: #------------------ # Site_Information # Site_Name: Llet-Ti # Location: Europe>Eastern Europe>Russia # Country: Russia # Northernmost_Latitude: 66.52 # Southernmost_Latitude: 66.52 # Easternmost_Longitude: 59.3 # Westernmost_Longitude: 59.3 # Elevation: 50 #------------------ # Data_Collection # Collection_Name: Llet-T.liranta.2015 # Earliest_Year: 12836.0 # Most_Recent_Year: -90.0 # Time_Unit: cal yr BP # Core_Length: # Notes: #------------------ # Species # Species_Name: # Species_Code: # Common_Name: #------------------ # Chronology_Information # Chronology: # depth_top depth_bottom age_type age uncertainty_old uncertainty_young AdditionalNotes # 0.0 1.0 Core top -55.0 -55.0 -55.0 Lake cored in spring 2009; Modern age confirmed with Pb-210 dating # 63.0 64.0 age14C 4115.0 4150.0 4080.0 nan # 127.0 131.0 age14C 6090.0 6130.0 6050.0 nan # 211.0 212.0 age14C 7260.0 7300.0 7220.0 nan # 273.0 274.0 age14C 8580.0 8630.0 8530.0 nan # 307.0 309.0 age14C 9550.0 9600.0 9500.0 nan # 371.0 375.0 age14C 10020.0 10080.0 9960.0 nan # 415.0 419.0 age14C 11010.0 11070.0 10950.0 nan #------------------ # 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) # ## OriginalSampleID sample identification,,,,,pollen;climate reconstructions,,,C,OriginalSampleID ## depth depth,,,centimeter,,pollen;climate reconstructions,,,N, ## age age,,,calendar year before present,,pollen;climate reconstructions,,,N, ## temperature surface air temperature,,,degree Celsius,Jul,pollen;climate reconstructions,,,N,58-lake training set from European Russia; WAPLS ## uncertainty surface air temperature,,unspecified error upper bound,degree Celsius,Jul,pollen;climate reconstructions,,,N, ## uncertainty-1 surface air temperature,,unspecified error lower bound,degree Celsius,Jul,pollen;climate reconstructions,,,N, ## reliable notes,,,,,pollen;climate reconstructions,,,C,Data are reliable (Yes or No) # #------------------ # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing_Values: nan # OriginalSampleID depth age temperature uncertainty uncertainty-1 reliable LLET144 0.0 -90.0 14.228 15.117 13.339 Y LLET148 4.0 219.3 14.525 15.407 13.642 Y LLET152 8.0 528.0 14.128 15.03 13.226 Y LLET156 12.0 835.7 14.672 15.554 13.791 Y LLET160 16.0 1141.5 14.746 15.611 13.881 Y LLET164 20.0 1445.0 15.204 16.154 14.254 Y LLET168 24.0 1745.3 14.411 15.269 13.552 Y LLET172 28.0 2041.9 14.289 15.181 13.398 Y LLET176 32.0 2334.1 14.859 15.745 13.972 Y LLET180 36.0 2621.2 14.246 15.116 13.375 Y LLET184 40.0 2902.6 14.998 15.863 14.134 Y LLET188 44.0 3177.6 15.023 15.906 14.141 Y LLET192 48.0 3445.6 15.196 16.105 14.287 Y LLET196 52.0 3705.9 15.938 16.874 15.003 Y LLET200 56.0 3957.8 14.986 15.866 14.107 Y LLET204 60.0 4200.7 14.572 15.448 13.697 Y LLET208 64.0 4433.9 15.128 16.03 14.226 Y LLET212 68.0 4656.9 15.019 15.899 14.138 Y LLET216 72.0 4870.0 15.185 16.059 14.311 Y LLET220 76.0 5073.2 15.185 16.057 14.313 Y LLET224 80.0 5266.9 15.212 16.08 14.344 Y LLET228 84.0 5451.2 14.794 15.66 13.929 Y LLET232 94.0 5872.5 14.877 15.758 13.997 Y LLET236 98.0 6025.8 15.902 16.787 15.018 Y LLET240 102.0 6170.6 15.105 15.972 14.238 Y LLET242 110.0 6436.1 15.234 16.121 14.348 Y LLET246 114.0 6557.2 15.39 16.283 14.497 Y LLET250 118.0 6670.7 15.457 16.338 14.576 Y LLET254 122.0 6776.9 15.179 16.061 14.297 Y LLET258 126.0 6876.1 15.662 16.542 14.781 Y LLET262 130.0 6968.3 14.973 15.844 14.101 Y LLET266 134.0 7054.1 15.33 16.203 14.457 Y LLET270 138.0 7133.8 15.041 15.916 14.167 Y LLET274 142.0 7208.1 15.155 16.045 14.266 Y LLET278 146.0 7277.4 15.442 16.325 14.56 Y LLET282 150.0 7342.4 14.635 15.53 13.739 Y LLET286 154.0 7403.6 14.782 15.664 13.899 Y LLET290 158.0 7461.4 14.742 15.623 13.86 Y LLET294 162.0 7516.6 14.907 15.811 14.002 Y LLET298 166.0 7569.6 15.793 16.779 14.807 Y LLET302 176.0 7696.1 14.505 15.398 13.611 Y LLET306 181.0 7758.2 15.125 16.007 14.242 Y LLET310 184.0 7795.7 15.143 16.02 14.265 Y LLET314 194.0 7925.4 14.728 15.606 13.849 Y LLET318 196.0 7952.8 15.773 16.66 14.886 Y LLET322 200.0 8009.3 15.116 16.012 14.22 Y LLET326 204.0 8069.0 14.507 15.404 13.611 Y LLET330 208.0 8132.1 14.707 15.604 13.81 Y LLET334 212.0 8199.5 14.855 15.771 13.939 Y LLET338 216.0 8271.2 14.72 15.618 13.822 Y LLET342 220.0 8347.2 15.11 16.022 14.199 Y LLET346 224.0 8427.1 15.024 15.916 14.132 Y LLET350 228.0 8510.6 15.192 16.088 14.295 Y LLET354 232.0 8597.3 15.29 16.221 14.358 Y LLET358 236.0 8687.0 14.432 15.334 13.53 Y LLET362 240.0 8779.2 14.638 15.538 13.739 Y LLET366 244.0 8873.7 15.062 15.973 14.152 Y LLET370 248.0 8970.1 14.674 15.586 13.762 Y LLET374 252.0 9068.0 14.694 15.625 13.762 Y LLET378 256.0 9167.3 15.12 16.031 14.209 Y LLET382 260.0 9267.5 14.431 15.35 13.512 Y LLET386 264.0 9368.3 14.541 15.472 13.61 Y LLET390 268.0 9469.3 14.321 15.25 13.392 Y LLET404 274.0 9620.7 14.011 14.924 13.098 Y LLET408 278.0 9721.1 14.232 15.181 13.282 Y LLET412 282.0 9820.7 14.546 15.476 13.617 Y LLET416 286.0 9919.6 14.523 15.433 13.613 Y LLET420 290.0 10017.4 13.946 14.914 12.978 Y LLET424 294.0 10114.2 14.148 15.089 13.206 Y LLET428 298.0 10209.7 13.71 14.629 12.79 Y LLET432 302.0 10303.7 13.926 14.853 12.998 Y LLET436 306.0 10396.2 13.59 14.526 12.655 Y LLET440 310.0 10487.0 13.733 14.734 12.733 Y LLET444 314.0 10554.0 13.305 14.323 12.286 Y LLET448 318.0 10663.7 12.098 13.087 11.109 Y LLET452 322.0 10750.0 12.345 13.322 11.368 Y LLET456 326.0 10835.1 13.274 14.411 12.138 Y LLET460 330.0 10919.3 11.991 13.003 10.979 Y LLET464 337.0 11064.9 11.994 12.971 11.017 Y LLET468 344.0 11209.2 12.103 13.068 11.139 Y LLET472 348.0 11291.4 12.669 13.695 11.643 Y LLET476 352.0 11373.7 12.797 13.802 11.792 Y LLET480 356.0 11456.2 12.171 13.159 11.183 Y LLET484 360.0 11539.2 13.062 14.026 12.097 Y LLET488 364.0 11622.8 14.311 15.253 13.368 Y LLET492 368.0 11707.2 13.524 14.49 12.558 Y LLET496 372.0 11792.6 13.089 14.088 12.091 Y LLET500 376.0 11879.2 12.821 13.797 11.845 Y LLET504 380.0 11966.9 14.501 15.412 13.59 Y LLET508 384.0 12055.7 14.168 15.083 13.254 Y LLET512 388.0 12145.4 13.349 14.27 12.428 Y LLET516 392.0 12235.9 12.717 13.774 11.66 Y LLET520 396.0 12327.1 14.406 15.343 13.469 Y LLET524 400.0 12418.9 13.627 14.603 12.652 Y LLET528 404.0 12511.1 11.551 12.697 10.405 Y LLET532 408.0 12603.8 12.379 13.39 11.368 Y LLET536 412.0 12696.6 13.527 14.482 12.571 Y LLET540 416.0 12789.7 14.078 15.093 13.062 Y LLET544 418.0 12836.0 13.819 14.857 12.782 Y