# Southern Siberia Holocene Chironmid-Inferred July Air Temperature #----------------------------------------------------------------------- # 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/28371 # Description: NOAA Landing Page # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/insecta/chironomidae/asia/russia/siberia2012temp.txt # Description: NOAA location of the template # # Original_Source_URL: # Description: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions # # Dataset DOI: # # Parameter_Keywords: air temperature #-------------------- # Contribution_Date # Date: 2019-11-13 #-------------------- # File_Last_Modified_Date # Date: 2019-11-13 #-------------------- # Title # Study_Name: Southern Siberia Holocene Chironmid-Inferred July Air Temperature #-------------------- # Investigators # Investigators: Mackay, A.W.; Self, A. #-------------------- # Description_Notes_and_Keywords # Description: Chironmid-inferred July air temperature from Lake ESM-1, Eastern Sayan Mountains, Siberia, for the past 11,200 years. # This chironmid July temperature reconstruction data are part of a multiproxy dataset # Provided Keywords: pollen, diatoms, chironomids, organic geochemistry, Holocene, multiproxy, Siberia #-------------------- # Publication # Authors: Anson W. Mackay, Elena V. Bezrukova, Melanie J. Leng, Miriam Meaney, Ana Nunes, Natalia Piotrowska, Angela Self, Alexander Shchetnikov, Ewan Shilland, Pavel Tarasov, Luo Wang, Dustin White # Published_Date_or_Year: 2012-05-18 # Published_Title: Aquatic ecosystem responses to Holocene climate change and biome development in boreal, central Asia # Journal_Name: Quaternary Science Reviews # Volume: 41 # Edition: # Issue: # Pages:119-131 # Report_Number: # DOI: 10.1016/j.quascirev.2012.03.004 # Online_Resource: https://www.sciencedirect.com/science/article/pii/S0277379112001175 # Full_Citation: # Abstract: Boreal ecosystems are highly vulnerable to climate change, and severe ecological impacts in the near future are virtually certain to occur. We undertook a multiproxy study on an alpine lake (ESM-1) at the modern tree-line in boreal, southern Siberia. Steppe and tundra biomes were extensive in eastern Sayan landscapes during the early Holocene. Boreal forest quickly expanded by 9.1 ka BP, and dominated the landscape until c 0.7 ka BP, when the greatest period of compositional turnover occurred. At this time, alpine meadow landscape expanded and Picea obovata colonised new habitats along river valleys and lake shorelines, because of prevailing cool, moist conditions. During the early Holocene, chironomid assemblages were dominated by cold stenotherms. Diatoms for much of the Holocene were dominated by alkaliphilous, fragilarioid taxa, up until 0.2 ka BP, when epiphytic species expanded, indicative of increased habitat availability. C/N mass ratios ranged between 9.5 and 13.5 (11.1-15.8 C/N atomic ratios), indicative of algal communities dominating organic matter contributions to bottom sediments with small, persistent contributions from vascular plants. However, d13C values increased steadily from -34.9 permil during the early Holocene (9.3 ka BP) to -24.8 permil by 0.6 ka BP. This large shift in magnitude may be due to a number of factors, including increasing within-lake productivity, increasing disequilibrium between the isotopic balance of the lake with the atmosphere as the lake became isotopically 'mature', and declining soil respiration linked to small, but distinct retreat in forest biomes. The influence of climatic variables on landscape vegetation was assessed using redundancy analysis (RDA), a linear, direct ordination technique. Changes in July insolation at 60 N significantly explained over one-fifth of the variation in species composition, while changes in estimates of northern hemisphere temperature and ice-rafted debris events in the North Atlantic were also significant, but considerably less important. The potential importance of climate and biome development (tundra, steppe, cold deciduous forest and taiga) on different trophic levels (i.e. chironomid and diatom communities) in lake ESM-1 was also assessed using RDA. Climate predictors had a more significant influence on Holocene chironomid assemblages, especially July insolation at 60 N, estimates of regional precipitation and estimates of northern hemisphere temperature, while only the development of the taiga biome had a significant impact on these primary consumers. Diatom communities also had a small, but significant influence on Holocene chironomid populations, perhaps linked to variation in faunal feeding strategies. In contrast, climatic and biome predictors explained similar amounts of variation in the Holocene diatom assemblage (approximately 20% each), while chironomids themselves as predictors explained just under 7% of diatom variation. Lake acidity was inferred using a diatom inference model. Results suggest that after deglaciation, the lake did not undergo a process of gradual acidification, most likely due to the presence of continuous permafrost and low levels of precipitation, preventing base cations and dissolved organic carbon entering the lake (except for the period between 1.7 and 0.7 ka BP). We conclude that lakes in continental, boreal regions undergo different models of lake ontogeny than oceanic boreal regions. Unlike other regions discussed, climate is a more important driver of ecosystem change than catchment changes. We also demonstrate that the start of the period coincident with the onset of the Little Ice Age resulted in important thresholds crossed in catchment vegetation and aquatic communities. #------------------ # Funding_Agency # Funding_Agency_Name: Social Sciences and Humanities Research Council (SSHRC) Canada # Grant: MCRI-BAP #------------------ # Site_Information # Site_Name: Lake ESM-1 # Location: Europe>Eastern Europe>Russia # Country: Russia # Northernmost_Latitude: 52.0269 # Southernmost_Latitude: 52.0269 # Easternmost_Longitude: 101.0591 # Westernmost_Longitude: 101.0591 # Elevation: 1992 #------------------ # Data_Collection # Collection_Name: ESM-1-2012CIT # Earliest_Year: 11206 # Most_Recent_Year: 29 # Time_Unit: Cal Year BP # Core_Length: 3.71 # Notes: #------------------ # Chronology_Information # Chronology: # Labcode depth_top depth_bottom mat.dated 14C.raw 14C.raw_err datemeth reservoir delta-R delta-R_err_lo delta-R_err_up calib.14C calib.14C_2sig_lo calib.14C_2sig_up calib_method # # Poz-22126 27.5 28 bulk organic seds 720 30 14C AMS 200 458 497 373 IntCal09 # Poz-21076 51.2 52 bulk organic seds 820 30 14C AMS 200 641 694 580 IntCal09 # Poz-22158 83.5 84 bulk organic seds 1105 30 14C AMS 200 936 1003 862 IntCal09 # Poz-21077 114 115 bulk organic seds 1920 35 14C AMS 200 1543 1626 1452 IntCal09 # Poz-22127 149 150 bulk organic seds 2345 35 14C AMS 200 2136 2250 2049 IntCal09 # Poz-21078 184 185 bulk organic seds 2700 35 14C AMS 200 2555 2645 2474 IntCal09 # Poz-22160 209 210 bulk organic seds 2920 35 14C AMS 200 2855 2949 2773 IntCal09 # Poz-21080 244 245 bulk organic seds 3580 40 14C AMS 200 3439 3559 3219 IntCal09 # Poz-22159 279 280 bulk organic seds 3955±35 35 14C AMS 200 4245 4402 4111 IntCal09 # Poz-21081 304 305 bulk organic seds 4900 40 14C AMS 200 5537 5679 5404 IntCal09 # Poz-22161 329 330 bulk organic seds 7010 50 14C AMS 200 7479 7582 7306 IntCal09 # Poz-21082 359 360 bulk organic seds 9010 60 14C AMS 200 10126 10315 9950 IntCal09 # Poz-22162 370 371 bulk organic seds 10190 60 14C AMS 200 11159 11394 10935 IntCal09 # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: one per line, shortname-tab-variable components (what, material, error, units, seasonality, data type,detail, method, C or N for Character or Numeric data, free text) # ## depth_cm depth, , , centimeter, , climate reconstructions, , ,N, ## age_calBP age, , , calendar years before present, , climate reconstructions, , ,N, calibrated ages ## CI.July.Temp air temperature, midge assemblage, , degrees C, July,climate reconstructions, ,weighted-averaging.parial.least.squares,N, model further refined using a 2-component model ## RMSEP_+ air temperature, midge assemblage, one root mean square error upper bound, degrees C, July,climate reconstructions, ,jack-knifing,N, ## RMSEP_- air temperature, midge assemblage, one root mean square error lower bound, degrees C, July,climate reconstructions, ,jack-knifing,N, # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # depth_cm age_calBP CI.July.Temp RMSEP_+ RMSEP_- 3.75 29 13.392 0.954021 0.954021 7.75 115 12.3083 1.04713 1.04713 31.75 512 13.6415 1.01792 1.01792 55.75 668 13.3559 0.985291 0.985291 79.75 889 13.6763 1.00796 1.00796 91.75 1085 14.1626 0.968586 0.968586 103.75 1328 13.7781 1.0084 1.0084 124.5 1745 13.8327 0.965705 0.965705 134.5 1917 12.7916 1.02626 1.02626 154.5 2210 13.6995 0.985646 0.985646 174.5 2451 15.4117 0.964191 0.964191 194.5 2673 12.641 0.979355 0.979355 209.5 2861 12.0086 0.991387 0.991387 224.5 3095 14.892 0.990564 0.990564 239.5 3361 12.357 1.00178 1.00178 259.5 3715 13.1933 0.998842 0.998842 279.5 4262 12.6399 0.971029 0.971029 294.5 4956 12.1253 1.03439 1.03439 309.5 5920 13.412 0.99191 0.99191 314.5 6296 12.6942 0.972371 0.972371 319.5 6691 12.542 0.959301 0.959301 329.5 7521 13.912 0.992223 0.992223 334.5 7947 13.21 0.960901 0.960901 339.5 8377 13.1725 0.968686 0.968686 344.5 8816 14.1518 0.955961 0.955961 349.5 9258 14.5947 0.969291 0.969291 354.5 9712 13.8922 1.11034 1.11034 359.5 10171 14.0489 1.14133 1.14133 360.5 10265 14.5357 0.984844 0.984844 370.5 11206 14.893 1.01195 1.01195