# Staroselsky Moch, Russia Air Temperature Reconstruction over the Last 10 ka #----------------------------------------------------------------------- # 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/30936 # 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/StaroselskyMoch.Novenko.2018.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/StaroselskyMoch.Novenko.2018.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: Staroselsky Moch, Russia Air Temperature Reconstruction over the Last 10 ka #------------------ # Investigators # Investigators: Novenko, E. Yu; Tsyganov, A.N.; Olchev, A.V. #------------------ # 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. #------------------ # Publication # Authors: Novenko, E. Yu; Tsyganov, A.N.; Olchev, A.V. # Published_Date_or_Year: 2018 # Published_Title: Palaeoecological data as a tool to predict possible future vegetation changes in the boreal forest zone of European Russia: A case study from the Central Forest Biosphere Reserve # Journal_Name: IOP Conference Series: Earth and Environmental Science # Volume: 107 # Edition: # Issue: # Pages: 12104 # Report: # DOI: 10.1088/1755-1315/107/1/012104 # Online_Resource: # Full_Citation: # Abstract: New multi-proxy records (pollen, testate amoebae, and charcoal) were applied to reconstruct the vegetation dynamics in the boreal forest area of the southern part of Valdai Hills (the Central Forest Biosphere Reserve) during the Holocene. The reconstructions of the mean annual temperature and precipitation, the climate moisture index (CMI), peatland surface moisture, and fire activity have shown that climate change has a significant impact on the boreal forests of European Russia. Temperature growth and decreased moistening during the warmest phases of the Holocene Thermal Maximum in 7.0-6.2 ka BP and 6.0-5.5 ka BP and in the relatively warm phase in 3.4-2.5 ka BP led to structural changes in plant communities, specifically an increase in the abundance of broadleaf tree species in forest stands and the suppression of Picea. The frequency of forest fires was higher in that period, and it resulted in the replacement of spruce forests by secondary stands with Betula and Pinus. Despite significant changes in the climatic parameters projected for the 21st century using even the optimistic RCP2.6 scenario, the time lag between climate changes and vegetation responses makes any catastrophic vegetation disturbances (due to natural reasons) in the area in the 21st century unlikely. #------------------ # Publication # Authors: Novenko, E.Yu.; Volkova, E.M.; Nosova, N.B.; Zuganova, I.S. # Published_Date_or_Year: 2018 # Published_Title: Late Glacial and Holocene landscape dynamics in the southern taiga zone of East European Plain according to pollen and macrofossil records from the Central Forest State Reserve (Valdai Hills, Russia) # Journal_Name: Quaternary International # Volume: 207 # Edition: # Issue: 1-2 # Pages: # Report: # DOI: 10.1016/j.quaint.2008.12.006 # Online_Resource: # Full_Citation: # Abstract: #------------------ # 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: Staroselsky Moch # Location: Europe>Eastern Europe>Russian Federation # Country: Russia # Northernmost_Latitude: 56.58 # Southernmost_Latitude: 56.58 # Easternmost_Longitude: 32.92 # Westernmost_Longitude: 32.92 # Elevation: 255 #------------------ # Data_Collection # Collection_Name: StaroselskyMoch.Novenko.2018 # Earliest_Year: 9792.0 # Most_Recent_Year: -19.0 # 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 # 0.0 0.0 Core top -58.0 -58.0 -58.0 # 40.0 45.0 age14C 1370.0 1440.0 1300.0 # 95.0 100.0 age14C 1620.0 1750.0 1490.0 # 165.0 170.0 age14C 1830.0 1900.0 1760.0 # 295.0 300.0 age14C 3860.0 3690.0 4030.0 # 395.0 400.0 age14C 5010.0 5140.0 4880.0 # 495.0 500.0 age14C 7190.0 8310.0 7070.0 # 545.0 550.0 age14C 8700.0 9880.0 8520.0 #------------------ # 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, ## ageMin age,,range lower bound,calendar year before present,,pollen;climate reconstructions,,,N, ## ageMax age,,range upper bound,calendar year before present,,pollen;climate reconstructions,,,N, ## temperature temperature,,,degree Celsius,annual,pollen;climate reconstructions,,modern analogue technique,N, ## uncertainty temperature,,unspecified error upper bound,degree Celsius,annual,pollen;climate reconstructions,,,N, ## uncertainty-1 temperature,,unspecified error lower bound,degree Celsius,annual,pollen;climate reconstructions,,,N, ## reliable notes,,,,,pollen;climate reconstructions,,,C,Data are reliable (Yes or No) ## temperature-1 temperature,,,degree Celsius,coldest month,pollen;climate reconstructions,,,N, ## uncertainty-2 temperature,,unspecified error upper bound,degree Celsius,coldest month,pollen;climate reconstructions,,,N, ## uncertainty-3 temperature,,unspecified error lower bound,degree Celsius,coldest month,pollen;climate reconstructions,,,N, ## reliable-1 notes,,,,,pollen;climate reconstructions,,,C,Data are reliable (Yes or No) ## temperature-2 temperature,,,degree Celsius,warmest month,pollen;climate reconstructions,,,N, ## uncertainty-4 temperature,,unspecified error upper bound,degree Celsius,warmest month,pollen;climate reconstructions,,,N, ## uncertainty-5 temperature,,unspecified error lower bound,degree Celsius,warmest month,pollen;climate reconstructions,,,N, ## reliable-2 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 ageMin ageMax temperature uncertainty uncertainty-1 reliable temperature-1 uncertainty-2 uncertainty-3 reliable-1 temperature-2 uncertainty-4 uncertainty-5 reliable-2 1.0 1.0 -19.0 -9.0 -29.0 4.085 7.035 3.889 Y -10.332 -0.576 -11.508 Y 16.585 18.602 16.156 Y 2.0 10.0 260.0 287.0 221.0 4.011 9.071 3.575 Y -9.336 -4.444 -10.806 Y 16.142 21.493 16.018 Y 3.0 20.0 570.0 620.0 494.0 4.431 5.751 3.889 Y -10.806 -3.588 -11.747 Y 16.465 18.552 16.235 Y 4.0 30.0 881.0 955.0 766.0 4.627 9.071 -0.142 Y -9.738 -1.663 -16.659 Y 17.742 21.493 11.593 Y 5.0 40.0 1191.0 1290.0 1039.0 4.414 9.071 -0.142 Y -8.749 -4.444 -16.659 Y 18.068 21.493 17.156 Y 6.0 50.0 1316.0 1412.0 1178.0 4.694 7.035 3.889 Y -6.929 -0.576 -8.746 Y 17.454 18.552 16.235 Y 7.0 60.0 1361.0 1465.0 1245.0 4.991 7.035 3.889 Y -5.759 -0.576 -8.508 Y 17.064 18.552 16.11 Y 8.0 70.0 1407.0 1532.0 1289.0 5.04 8.122 0.918 Y -6.916 -2.427 -14.682 Y 17.46 18.551 16.235 Y 9.0 80.0 1452.0 1602.0 1319.0 4.182 5.751 0.918 Y -8.016 -3.588 -14.682 Y 17.251 18.552 16.235 Y 10.0 90.0 1498.0 1676.0 1339.0 4.576 5.751 3.889 Y -7.509 -3.588 -8.747 Y 17.476 18.552 16.235 Y 11.0 100.0 1536.0 1724.0 1374.0 4.61 8.122 0.918 Y -7.457 -2.452 -14.682 Y 17.313 18.551 16.235 Y 12.0 110.0 1567.0 1731.0 1424.0 4.36 6.581 0.918 Y -7.704 -3.394 -14.682 Y 17.224 17.701 16.235 Y 13.0 120.0 1597.0 1747.0 1465.0 3.058 6.581 -0.142 Y -10.778 -3.394 -16.659 Y 17.574 19.122 17.056 Y 14.0 130.0 1628.0 1771.0 1501.0 4.261 6.581 0.918 Y -8.406 -3.394 -14.682 Y 17.493 19.122 16.235 Y 15.0 140.0 1659.0 1793.0 1537.0 4.871 8.122 0.918 Y -7.683 -2.427 -14.682 Y 17.774 18.613 17.056 Y 16.0 150.0 1689.0 1817.0 1560.0 3.379 8.122 -0.142 Y -10.2 -2.452 -16.659 Y 17.492 18.551 17.056 Y 17.0 160.0 1720.0 1843.0 1586.0 2.361 8.122 -0.896 Y -12.653 -2.452 -17.143 Y 17.824 19.122 16.389 Y 18.0 170.0 1751.0 1885.0 1605.0 3.795 9.443 -0.899 Y -9.615 -0.124 -17.146 Y 17.56 19.072 16.387 Y 19.0 180.0 1942.0 2080.0 1797.0 4.904 8.122 0.918 Y -7.651 -2.427 -14.682 Y 17.802 18.613 17.056 Y 20.0 190.0 2293.0 2423.0 2154.0 4.116 8.089 -0.14 Y -8.839 -2.427 -16.658 Y 17.533 18.498 17.056 Y 21.0 200.0 2644.0 2786.0 2506.0 4.933 8.122 0.918 Y -7.585 -2.427 -14.682 Y 17.79 18.613 17.056 Y 22.0 210.0 2995.0 3160.0 2841.0 4.96 8.122 0.918 Y -7.545 -2.427 -14.682 Y 17.809 18.613 17.056 Y 23.0 220.0 3346.0 3540.0 3171.0 4.967 8.122 0.918 Y -7.403 -2.427 -14.682 Y 17.67 18.551 17.056 Y 24.0 230.0 3697.0 3934.0 3492.0 4.723 8.122 -0.14 Y -8.07 -2.427 -16.658 Y 17.909 18.613 17.056 Y 25.0 240.0 3910.0 4147.0 3701.0 4.191 6.581 -0.14 Y -8.693 -3.394 -16.658 Y 17.714 19.122 17.301 Y 26.0 250.0 3984.0 4210.0 3775.0 6.374 9.443 1.677 Y -5.933 -0.124 -15.265 Y 18.7 19.122 18.489 Y 27.0 260.0 4058.0 4310.0 3822.0 4.473 8.122 -0.14 Y -8.287 -2.427 -16.658 Y 17.647 18.551 17.056 Y 28.0 270.0 4132.0 4432.0 3866.0 3.775 8.167 -0.14 Y -9.477 -1.389 -16.658 Y 17.462 17.726 17.301 Y 29.0 280.0 4206.0 4571.0 3893.0 5.888 8.122 0.918 Y -6.056 -2.423 -14.682 Y 18.075 18.613 17.056 Y 30.0 290.0 4280.0 4711.0 3922.0 4.901 8.122 0.918 Y -7.539 -2.427 -14.682 Y 17.659 18.551 17.056 Y 31.0 300.0 4390.0 4835.0 4025.0 4.528 8.122 -0.14 Y -8.296 -2.427 -16.658 Y 17.78 18.613 17.056 Y 32.0 310.0 4535.0 4935.0 4202.0 3.031 6.581 -0.142 Y -10.741 -3.394 -16.659 Y 17.462 18.095 17.056 Y 33.0 320.0 4679.0 5037.0 4376.0 5.948 8.122 0.918 Y -5.96 -2.423 -14.682 Y 18.086 18.613 17.056 Y 34.0 330.0 4824.0 5140.0 4543.0 5.185 6.581 4.188 Y -6.517 -3.394 -8.746 Y 17.529 17.701 17.406 Y 35.0 340.0 4969.0 5245.0 4713.0 5.242 6.581 4.188 Y -6.39 -3.394 -8.746 Y 17.536 17.701 17.406 Y 36.0 350.0 5113.0 5369.0 4876.0 4.923 8.122 0.918 Y -7.582 -2.427 -14.682 Y 17.78 18.613 17.056 Y 37.0 360.0 5258.0 5501.0 5021.0 5.076 8.122 0.918 Y -7.165 -2.427 -14.682 Y 17.727 18.551 17.056 Y 38.0 370.0 5403.0 5638.0 5148.0 5.031 6.581 4.186 Y -6.922 -3.394 -8.747 Y 17.634 18.095 17.405 Y 39.0 380.0 5548.0 5797.0 5286.0 3.679 6.581 -0.14 Y -9.36 -3.394 -16.658 Y 17.47 17.701 17.301 Y 40.0 390.0 5692.0 5976.0 5414.0 6.466 7.715 4.188 Y -3.58 -0.853 -8.746 Y 17.405 17.701 16.976 Y 41.0 400.0 5878.0 6160.0 5606.0 6.127 7.715 4.188 Y -4.435 -0.939 -8.746 Y 17.533 17.701 17.406 Y 42.0 410.0 6104.0 6362.0 5863.0 5.304 7.715 3.889 Y -6.467 -0.939 -9.002 Y 17.81 19.122 17.405 Y 43.0 420.0 6331.0 6569.0 6110.0 5.121 7.715 3.889 Y -6.489 -0.939 -8.747 Y 17.571 18.095 17.23 Y 44.0 430.0 6557.0 6776.0 6357.0 5.112 6.618 3.575 Y -6.199 -2.187 -8.508 Y 17.63 19.036 16.318 Y 45.0 440.0 6783.0 6980.0 6595.0 4.151 6.618 0.24 Y -7.054 -2.187 -8.806 Y 16.942 18.144 12.274 Y 46.0 450.0 7010.0 7197.0 6831.0 5.188 7.715 3.889 Y -6.53 -0.939 -9.002 Y 17.848 19.122 17.23 Y 47.0 460.0 7236.0 7434.0 7061.0 1.978 8.489 -2.233 Y -7.605 -2.187 -11.883 Y 14.202 19.591 10.764 Y 48.0 470.0 7463.0 7667.0 7267.0 3.98 6.581 -0.14 Y -9.062 -3.394 -16.658 Y 17.704 19.036 17.056 Y 49.0 480.0 7689.0 7905.0 7466.0 4.218 8.089 -0.14 Y -8.623 -2.427 -16.658 Y 17.602 18.498 17.056 Y 50.0 490.0 7915.0 8156.0 7667.0 4.131 6.618 0.24 Y -7.415 -2.187 -14.683 Y 16.995 19.036 12.274 Y 51.0 500.0 8205.0 8444.0 7979.0 2.607 6.618 -0.896 Y -9.079 -2.187 -17.143 Y 16.069 18.097 12.274 Y 52.0 510.0 8557.0 8777.0 8361.0 -0.575 0.671 -2.233 Y -10.062 -7.205 -11.883 Y 12.367 14.03 10.764 Y 53.0 520.0 8910.0 9149.0 8677.0 0.911 5.074 -2.39 Y -9.801 -6.765 -14.506 Y 14.112 20.087 10.253 Y 54.0 530.0 9263.0 9547.0 8971.0 -0.43 3.513 -2.39 Y -9.646 -6.765 -11.883 Y 12.16 15.811 10.253 Y 55.0 540.0 9615.0 9962.0 9236.0 0.87 7.949 -2.39 Y -8.982 -2.798 -11.883 Y 13.419 19.122 10.253 Y 56.0 545.0 9792.0 10180.0 9371.0 1.059 6.618 -2.233 Y -8.964 -2.187 -14.683 Y 13.833 17.457 10.764 Y