# Windy Lake Holocene Surface Air Temperature 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/30835 # 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/Windy.Chase.2008.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/Windy.Chase.2008.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: 2020-08-16 #------------------ # Title # Study_Name: Windy Lake Holocene Surface Air Temperature Reconstructions #------------------ # Investigators # Investigators: Chase, Marianne; Bleskie, Christina; Walker, Ian R.; Gavin, Daniel G.; Hu, Feng Sheng #------------------ # 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: Chase, Marianne; Bleskie, Christina; Walker, Ian R.; Gavin, Daniel G.; Hu, Feng Sheng # Published_Date_or_Year: 2008 # Published_Title: Midge-inferred Holocene summer temperatures in southeastern British Columbia, Canada # Journal_Name: Palaeogeography, Palaeoclimatology, Palaeoecology # Volume: 257 # Edition: # Issue: 2-Jan # Pages: 244-259 # Report: # DOI: 10.1016/j.palaeo.2007.10.020 # Online_Resource: # Full_Citation: # Abstract: Using fossil midge stratigraphies, we inferred Holocene summer temperatures at three subalpine lakes in eastern British Columbia. The late-glacial sediment indicated cool conditions, with an abundance of Microspectra atrofasciata/radialis type fossils at Thunder Lake and Redmountain Lake, and Sergentia at Windy Lake. Sergentia and Tanytarsus lugens/Corynocera oliveri type were dominant in the early Holocene, together with Chironomus at Redmountain Lake. At Thunder and Windy lakes, the early Holocene was dominated by warm-adapted taxa such as Microtendipes. Quantitative midge-temperature inference models reconstruct a 4 to 8 °C rise in mean July air temperature for Windy and Thunder lakes at the Pleistocene/Holocene transition. Early-Holocene temperatures averaged 3 to 4 °C warmer than those extant today. In contrast, no long-term temperature trend was evident at Redmountain Lake. This site may not reflect actual trends in air temperature due to runoff from a persistent snow pack in the watershed. Comparison of midge and pollen data suggests an inverse relationship between summer temperature and precipitation through the middle to late Holocene. #------------------ # Publication # Authors: Daniel G. Gavin Linda B. Brubaker D. Noah Greenwald # Published_Date_or_Year: 2013 # Published_Title: Postglacial climate and fire-mediated vegetation change on the western Olympic Peninsula, Washington (USA) # Journal_Name: Ecological Monographs, Ecological Society of America # Volume: 83 # Edition: # Issue: 4 # Pages: 471-489 # Report: # DOI: 10.1890/12-1742.1 # Online_Resource: # Full_Citation: # Abstract: The mode and tempo of forest compositional change during periods of rapid climate change, including the potential for the fire regime to produce nonlinear relationships between climate and vegetation, is a long-standing theme of forest ecological research. In the old conifer forests of the coastal Pacific Northwest, fire disturbances are sufficiently rare that their relation to climate and their ecological effects are poorly understood. We used a 14700-year high-resolution sediment record from Yahoo Lake on the Olympic Peninsula, Washington, USA, to examine vegetation (landscape vegetation from pollen and local vegetation from macrofossils) and fire (landscape fire from total charcoal and local fire from charcoal peaks) in conjunction with independent records of climate. We hypothesized that the successional stage of the local forest will exhibit alternate stable states over a range of fire activity, that species turnover will increase abruptly above a certain level of fire activity and that both responses would be more gradual at the landscape scale than the local scale. Supporting these hypotheses, at the local scale, we found strong evidence for alternate stable states of late vs. early successional communities and inertia of species turnover to changing fire activity. At the landscape scale, vegetation responded more gradually to changing fire activity. From 14700 to 7000 years ago, high landscape vegetation turnover occurred along with high landscape fire activity, especially during the warm summers of the early Holocene. In several instances, local species turned over completely following fire events but several centuries after climate change. In contrast, during the last 7000 years, the local forest composition was dominated by late-successional species with little species turnover, despite periods of moderate fire activity. We suggest that the relatively minor climate fluctuations of the past 7000 years were not sufficient to cause large-scale species turnover after fire. The Yahoo Lake fire and vegetation record of the early Holocene provides a model for dramatic ecosystem change following an anticipated shift to warmer summer temperatures. #------------------ # 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: Windy Lake CA # Location: North America>Canada>British Columbia # Country: # Northernmost_Latitude: 49.8133 # Southernmost_Latitude: 49.8133 # Easternmost_Longitude: -117.8772 # Westernmost_Longitude: -117.8772 # Elevation: 1814 #------------------ # Data_Collection # Collection_Name: Windy.Chase.2008 # Earliest_Year: 13048.0 # Most_Recent_Year: 507.0 # Time_Unit: cal yr BP # Core_Length: # Notes: #------------------ # Species # Species_Name: # Species_Code: # Common_Name: #------------------ # Chronology_Information # Chronology: # depth notes age SD material age_type # 42.25 has depth range of 442-5 471.0 nan tephra tephra # 115.0 nan 2245.0 70.0 cone bract AMS14C # 173.0 nan 4020.0 100.0 wood AMS14C # 223.0 nan 4800.0 60.0 Abies wood AMS14C # 330.0 has depth range 287-373 7627.0 nan tephra tephra # 420.0 nan 8350.0 80.0 bark AMS14C # 475.0 nan 9950.0 120.0 pinus needle AMS14C #------------------ # 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) # ## sampleID sample identification,,,,,insect;paleolimnology;climate reconstructions,,,C,sampleID ## age age,,,calendar year before present,,insect;paleolimnology;climate reconstructions,,,N, ## depth depth,,,centimeter,,insect;paleolimnology;climate reconstructions,,,N, ## Meanconsensus surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,average of 7 inference models ## WAINV surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= WAINV ## WAINV_total surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= WAINV_total ## WACLS surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= WACLS ## WACLS_total surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= WACLS_total ## WAPLS-2 surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= WAPLS-2 ## PLS-1 surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= PLS-1 ## PLS-2 surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,calibration method= PLS-2 ## temperature surface air temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,53 lakes from British Columbia training set (Rosenberg et al 2004);Method used in Chase 2008 based on 7 inference models and a composite # #------------------ # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing_Values: nan # sampleID age depth Meanconsensus WAINV WAINV_total WACLS WACLS_total WAPLS-2 PLS-1 PLS-2 temperature WIN 45 507.0 45.0 10.612 10.404 10.544 10.48 10.647 10.655 10.589 10.966 10.57 WIN_50 595.0 50.0 9.932 9.816 10.028 9.742 10.01 10.073 9.566 10.285 9.95 WIN 55 689.0 55.0 8.892 9.223 9.266 8.997 9.07 9.048 8.488 8.153 9.07 WIN 60 789.0 60.0 8.903 9.379 9.447 9.193 9.293 8.646 8.906 7.456 9.17 WIN 65 894.0 65.0 9.1 9.293 9.552 9.085 9.422 8.851 8.784 8.716 9.09 WIN 70 1004.0 70.0 9.244 9.367 10.018 9.178 9.998 9.124 8.476 8.55 9.23 WIN 75 1120.0 75.0 8.85 9.112 9.041 8.858 8.792 8.596 8.997 8.556 8.86 WIN 80 1241.0 80.0 8.052 8.771 8.382 8.43 7.978 8.236 7.475 7.095 8.25 WIN 85 1368.0 85.0 9.295 9.68 9.794 9.571 9.721 8.956 9.269 8.074 9.55 WIN 90 1500.0 90.0 8.238 8.774 9.053 8.433 8.807 7.771 7.912 6.918 8.35 WIN 95 1638.0 95.0 8.834 9.168 9.765 8.928 9.685 8.331 8.15 7.813 8.85 WIN_100 1781.0 100.0 8.92 9.278 9.729 9.066 9.641 8.643 8.209 7.874 8.97 WIN 105 1929.0 105.0 9.312 9.785 10.123 9.704 10.128 8.756 8.669 8.018 9.59 WIN 110 2083.0 110.0 10.246 10.174 10.705 10.192 10.845 10.056 9.72 10.033 10.11 WIN115 2242.0 115.0 9.348 9.54 10.571 9.396 10.681 9.288 8.145 7.815 9.36 WIN 120 2407.0 120.0 9.053 9.466 9.875 9.302 9.821 9.09 8.381 7.439 9.27 WIN_125 2577.0 125.0 9.907 10.082 10.556 10.076 10.662 9.719 9.243 9.014 9.96 WIN 130 2753.0 130.0 9.719 10.061 10.321 10.05 10.371 9.437 9.268 8.525 9.98 WIN 135 2937.0 135.0 9.584 9.743 10.016 9.651 9.995 9.408 9.267 9.005 9.6 WIN 140 3136.0 140.0 8.867 9.385 9.796 9.201 9.724 8.36 8.577 7.027 9.11 WIN 145 3348.0 145.0 9.095 9.44 9.624 9.269 9.512 8.967 8.625 8.23 9.22 WIN_150 3566.0 150.0 9.759 9.983 10.578 9.952 10.689 9.49 9.1 8.519 9.82 WIN 155 3784.0 155.0 9.705 9.937 10.165 9.894 10.179 9.325 9.179 9.254 9.78 WIN 161 3998.0 160.0 9.218 9.659 10.099 9.544 10.098 8.804 8.6 7.721 9.4 WIN 165 4201.0 165.0 8.779 9.092 9.476 8.833 9.329 8.356 8.665 7.704 8.83 WIN 170 4388.0 170.0 7.984 8.617 8.252 8.236 7.818 8.047 7.989 6.928 8.12 WIN_175 4553.0 175.0 9.676 9.901 10.049 9.849 10.036 9.614 8.928 9.356 9.83 WIN 180 4701.0 180.0 7.806 8.279 8.599 7.811 8.247 7.641 7.491 6.574 7.87 WIN 190 4922.0 190.0 10.01 10.135 10.603 10.143 10.72 9.713 9.552 9.201 10.04 WIN 195 5010.0 195.0 9.673 9.806 10.61 9.73 10.729 9.368 9.253 8.217 9.75 WIN_200 5090.0 200.0 10.894 10.755 11.671 10.922 12.037 10.705 10.105 10.061 10.85 WIN 205 5168.0 205.0 9.995 10.12 10.799 10.124 10.961 9.458 9.563 8.938 10.02 WIN 210 5248.0 210.0 9.319 9.659 9.752 9.545 9.67 9.142 9.049 8.416 9.51 WIN 215 5338.0 215.0 9.373 9.797 10.499 9.719 10.591 8.877 8.63 7.496 9.51 WIN 220 5442.0 220.0 9.397 9.731 10.457 9.635 10.54 8.653 8.792 7.974 9.45 WIN_225 5566.0 225.0 9.491 10.015 10.21 9.992 10.235 9.169 8.78 8.034 10.04 WIN 231 5728.0 231.0 10.791 10.927 10.932 11.137 11.125 10.615 10.668 10.136 10.88 WIN 235 5841.0 235.0 9.603 9.907 10.299 9.856 10.344 9.146 9.373 8.3 9.75 WIN 240 5986.0 240.0 9.9 10.258 10.773 10.297 10.929 9.725 8.938 8.379 10.15 WIN 245 6138.0 245.0 9.058 9.415 9.511 9.238 9.372 8.744 8.822 8.302 9.16 WIN_250 6295.0 250.0 11.088 11.126 11.455 11.387 11.772 10.938 10.528 10.41 11.11 WIN 255 6457.0 255.0 10.656 10.931 11.153 11.143 11.399 10.393 10.03 9.543 10.79 WIN 260 6625.0 260.0 12.244 12.265 12.689 12.819 13.294 12.443 10.933 11.266 12.51 WIN 265 6798.0 265.0 12.549 12.45 12.999 13.051 13.676 12.277 11.798 11.591 12.52 WIN 270 6984.0 270.0 11.613 12.072 12.201 12.576 12.692 11.515 10.375 9.863 12.01 WIN_275 7175.0 275.0 11.851 12.123 12.337 12.639 12.86 11.697 10.724 10.575 12.05 WIN_280 7367.0 280.0 11.488 11.875 12.325 12.329 12.845 11.482 10.142 9.419 12.05 WIN_375 7705.0 375.0 11.132 11.43 12.064 11.769 12.523 10.757 10.448 8.937 11.4 WIN 380 7889.0 380.0 12.677 12.765 13.223 13.447 13.953 12.533 11.81 11.008 12.77 WIN 385 8073.0 385.0 10.907 11.559 11.766 11.932 12.155 10.373 9.935 8.63 11.46 WIN 390 8258.0 390.0 10.067 10.535 10.794 10.645 10.955 9.656 9.542 8.341 10.45 WIN 395 8442.0 395.0 11.226 11.881 12.354 12.336 12.88 10.575 10.791 7.765 11.76 WIN_401 8663.0 401.0 11.034 11.177 11.31 11.452 11.592 10.95 10.639 10.119 11.17 WIN 405 8810.0 405.0 12.648 12.535 12.344 13.158 12.868 13.106 11.93 12.597 12.65 WIN 410 8994.0 410.0 14.186 14.144 14.646 15.18 15.71 14.675 12.714 12.232 14.71 WIN 415 9179.0 415.0 13.576 13.4 13.655 14.244 14.487 13.816 12.506 12.925 13.61 WIN 420 9363.0 420.0 12.378 12.38 12.998 12.963 13.676 12.165 11.637 10.828 12.42 WIN_425 9547.0 425.0 12.032 12.408 12.674 12.998 13.276 11.898 11.175 9.796 12.42 WIN 430 9731.0 430.0 13.332 13.182 13.804 13.971 14.671 13.456 11.88 12.363 13.44 WIN435 9916.0 435.0 14.556 14.359 13.625 15.449 14.449 15.451 13.752 14.806 14.54 WIN 440 10100.0 440.0 12.487 13.35 13.184 14.182 13.905 12.762 10.604 9.422 13.32 WIN 445 10284.0 445.0 12.887 13.156 13.545 13.938 14.351 13.094 11.444 10.684 13.16 WIN_451 10505.0 451.0 13.451 12.95 13.679 13.679 14.516 13.195 13.103 13.034 13.24 WIN 455 10653.0 455.0 13.574 13.734 13.948 14.664 14.848 13.334 13.204 11.289 13.88 WIN 460 10837.0 460.0 11.659 11.703 12.461 12.112 13.012 11.473 11.081 9.771 11.78 WIN 465 11021.0 465.0 10.423 10.904 10.684 11.109 10.82 10.043 10.731 8.669 10.81 WIN_475 11389.0 475.0 11.259 11.576 11.203 11.954 11.46 10.919 10.811 10.891 11.22 WIN 480 11574.0 480.0 7.519 8.027 7.964 7.494 7.463 7.256 7.522 6.907 7.51 WIN485 11758.0 485.0 9.525 10.115 10.476 10.117 10.562 8.726 9.75 6.931 10.15 WIN 495 12126.0 495.0 9.46 9.77 10.597 9.684 10.713 8.776 9.006 7.675 9.54 WIN_500 12311.0 500.0 6.571 7.166 7.27 6.414 6.607 5.763 7.238 5.536 6.6 WIN 505 12495.0 505.0 7.31 7.821 7.873 7.236 7.351 6.47 7.879 6.542 7.34 WIN 510 12679.0 510.0 9.639 10.407 10.236 10.484 10.267 8.84 9.92 7.318 10.27 WIN 515 12863.0 515.0 11.911 12.376 12.216 12.958 12.711 11.529 11.299 10.29 12.05 WIN 520 13048.0 520.0 7.741 8.391 8.328 7.951 7.912 7.028 7.5 7.076 7.8 WIN_525 13048.0 525.0 7.837 8.257 8.2 7.784 7.754 7.045 8.486 7.335 7.84