# Lake Lyadhej-To Air and Lake Surface Temperature Reconstructions during the last 11 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/30652
#     Online_Resource_Description:  NOAA Landing Page
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# 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/LakeLyadhej-To.Andreev.2005.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/LakeLyadhej-To.Andreev.2005.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 #
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# Data_Type: Climate Reconstructions
# Parameter_Keywords: air temperature
# Dataset_DOI: 
#
#------------------
# Contribution_Date
#     Date: 2020-04-15
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# File_Last_Modified_Date
#     Date: 2020-05-16
#------------------
# Title
#     Study_Name: Lake Lyadhej-To Air and Lake Surface Temperature Reconstructions during the last 11 ka
#------------------
# Investigators
#     Investigators: Andreev, Andrei A.; Tarasov, Pavel E.; Ilyashuk, Boris P.; Ilyashuk, Elena A.; Cremer, Holger; Hermichen, Wolf-Dieter; Wischer, Frank; Hubberten, Hans-Wolfgang
#------------------
# 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: Andreev, Andrei A.; Tarasov, Pavel E.; Ilyashuk, Boris P.; Ilyashuk, Elena A.; Cremer, Holger; Hermichen, Wolf-Dieter; Wischer, Frank; Hubberten, Hans-Wolfgang
#     Published_Date_or_Year: 2005
#     Published_Title: Holocene environmental history recorded in Lake Lyadhej-To sediments, polar Urals, Russia
#     Journal_Name: Palaeogeography, Palaeoclimatology, Palaeoecology
#     Volume: 223
#     Edition: 
#     Issue: 3-4
#     Pages: 181-203
#     Report: 
#     DOI: 10.1016/j.palaeo.2005.04.004
#     Online_Resource: 
#     Full_Citation: 
#     Abstract: An 1180-cm long core recovered from Lake Lyadhej-To (68°15′ N, 65°45′ E, 150 m a.s.l.) at the NW rim of the Polar Urals Mountains reflects the Holocene environmental history from ca. 11,000 cal. yr BP. Pollen assemblages from the diamicton (ca. 11,000–10,700 cal. yr BP) are dominated by Pre-Quaternary spores and redeposited Pinaceae pollen, pointing to a high terrestrial input. Turbid and nutrient-poor conditions existed in the lake ca. 10,700–10,550 cal. yr BP. The chironomid-inferred reconstructions suggest that mean July temperature increased rapidly from 10.0 to 11.8 °C during this period. Sparse, treeless vegetation dominated on the disturbed and denuded soils in the catchment area. A distinct dominance of planktonic diatoms ca. 10,500–8800 cal. yr BP points to the lowest lake-ice coverage, the longest growing season and the highest bioproductivity during the lake history. Birch forest with some shrub alder grew around the lake reflecting the warmest climate conditions during the Holocene. Mean July temperature was likely 11–13 °C and annual precipitation—400–500 mm. The period ca. 8800–5500 cal. yr BP is characterized by a gradual deterioration of environmental conditions in the lake and lake catchment. The pollen- and chironomid-inferred temperatures reflect a warm period (ca. 6500–6000 cal. BP) with a mean July temperature at least 1–2 °C higher than today. Birch forests disappeared from the lake vicinity after 6000 cal. yr BP. The vegetation in the Lyadhej-To region became similar to the modern one. Shrub (Betula nana, Salix) and herb tundra have dominated the lake catchment since ca. 5500 cal. yr BP. All proxies suggest rather harsh environmental conditions. Diatom assemblages reflect relatively short growing seasons and a longer persistence of lake-ice ca. 5500–2500 cal. yr BP. Pollen-based climate reconstructions suggest significant cooling between ca. 5500 and 3500 cal. yr BP with a mean July temperature 8–10 °C and annual precipitation—300–400 mm. The bioproductivity in the lake remained low after 2500 cal. yr BP, but biogeochemical proxies reflect a higher terrestrial influx. Changes in the diatom content may indicate warmer water temperatures and a reduced ice cover on the lake. However, chironomid-based reconstructions reflect a period with minimal temperatures during the lake history.
#------------------
# 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.
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# Funding_Agency
#     Funding_Agency_Name: 
#     Grant: 
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# Site_Information
#     Site_Name: Lyadhej-To
#     Location: Europe>Eastern Europe>Russia
#     Country: Russia
#     Northernmost_Latitude: 68.26
#     Southernmost_Latitude: 68.26
#     Easternmost_Longitude: 65.8
#     Westernmost_Longitude: 65.8
#     Elevation: 150
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# Data_Collection
#     Collection_Name: LakeLyadhej-To.Andreev.2005
#     Earliest_Year: 10888.064
#     Most_Recent_Year: 68.889
#     Time_Unit: cal yr BP
#     Core_Length: 
#     Notes: 
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# Species
#     Species_Name: 
#     Species_Code: 
#     Common_Name: 
#------------------
# Chronology_Information
#     Chronology:
# OriginalDateID	depth_top	depth_bottom	age_type	age	uncertainty_old	uncertainty_young	IncludeYN	material	age_type-1	
# nan	0.0	0.0	Core top	-48.0	nan	nan	Y	nan	core top	
# KIA-10040	8.0	10.0	C14 Uncalibrated	690.0	720.0	660.0	Y	Non-identified macrofossils	C14	
# KIA-8915	98.0	100.0	C14 Uncalibrated	2460.0	2500.0	2420.0	Y	Non-identified macrofossils	C14	
# KIA-10041	200.0	202.0	C14 Uncalibrated	5135.0	5195.0	5075.0	Y	Non-identified macrofossils	C14	
# KIA-8916	298.0	300.0	C14 Uncalibrated	6730.0	6800.0	6660.0	Y	Non-identified macrofossils	C14	
# KIA-8920	398.0	400.0	C14 Uncalibrated	8550.0	8650.0	8450.0	Y	Non-identified macrofossils	C14	
# KIA-8917	500.0	502.0	C14 Uncalibrated	9230.0	9320.0	9140.0	Y	Non-identified macrofossils	C14	
# KIA-12131	596.0	598.0	C14 Uncalibrated	10780.0	10920.0	10640.0	N	Non-identified macrofossils	C14	
# KIA-8759	652.0	654.0	C14 Uncalibrated	11230.0	11380.0	11080.0	N	Non-identified macrofossils	C14	
# KIA-8760	670.0	672.0	C14 Uncalibrated	14210.0	14300.0	14120.0	N	Non-identified macrofossils	C14	
# KIA-8761	717.0	719.0	C14 Uncalibrated	9600.0	9660.0	9540.0	N	Shrub twig	C14	
# KIA-12132	734.0	736.0	C14 Uncalibrated	10940.0	11030.0	10850.0	N	Non-identified macrofossils	C14	
# KIA-12133	793.0	795.0	C14 Uncalibrated	11850.0	11930.0	11770.0	N	Non-identified macrofossils	C14	
# KIA-12134	993.0	995.0	C14 Uncalibrated	9880.0	9930.0	9830.0	N	Non-identified macrofossils	C14	
# KIA-12135	1034.0	1036.0	C14 Uncalibrated	9490.0	9550.0	9430.0	Y	Moss remains	C14	
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# 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)
#
## depth	depth,,,centimeter,,insect;paleolimnology;climate reconstructions,,,N,
## age	age,,,calendar year before present,,insect;paleolimnology;climate reconstructions,,,N,
## temperature	lake surface temperature,midge assemblage,,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,100-lake training set from Sweden (Larocque et al. 2001); WAPLS
## uncertaintyHigh	lake surface temperature,midge assemblage,unspecified error upper bound,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,
## uncertaintyLow	lake surface temperature,midge assemblage,unspecified error lower bound,degree Celsius,Jul,insect;paleolimnology;climate reconstructions,,,N,
## ReliabIeYN1	notes,,,,,insect;paleolimnology;climate reconstructions,,,C,Data are reliable (Yes or No)
## Commentregardingreliability1	notes,,,,,insect;paleolimnology;climate reconstructions,,,C,comment regarding reliability
#
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# Data:        
# Data lines follow (have no #)        
# Data line format - tab-delimited text, variable short name as header
# Missing_Values: nan
#
depth	age	temperature	uncertaintyHigh	uncertaintyLow	ReliabIeYN1	Commentregardingreliability1	
1.0	68.889	8.15	9.526	6.774	Y	nan	
5.0	344.444	9.168	10.548	7.788	Y	nan	
11.0	620.0	8.405	9.802	7.008	Y	very poor' fit	
19.0	834.444	9.206	10.574	7.838	Y	nan	
27.0	963.111	8.388	9.797	6.979	Y	very poor' fit	
33.0	1134.667	7.997	9.371	6.624	Y	very poor' fit	
43.0	1349.111	8.724	10.134	7.315	Y	very poor' fit	
51.0	1520.667	8.477	9.895	7.06	Y	very poor' fit	
57.0	1649.333	8.463	9.867	7.06	Y	very poor' fit	
67.0	1863.778	8.751	10.126	7.376	Y	nan	
73.0	1992.444	8.699	10.08	7.318	Y	nan	
87.0	2292.667	8.542	9.974	7.11	Y	very poor' fit	
95.0	2464.222	8.895	10.257	7.533	Y	nan	
111.0	2940.588	9.164	10.509	7.818	Y	nan	
121.0	3266.078	8.608	9.958	7.258	Y	very poor' fit	
127.0	3461.373	8.678	10.042	7.314	Y	nan	
135.0	3721.765	9.973	11.28	8.667	Y	nan	
147.0	4112.353	9.093	10.446	7.74	Y	nan	
151.0	4242.549	8.024	9.427	6.621	Y	very poor' fit	
163.0	4633.137	8.476	9.872	7.08	Y	nan	
171.0	4893.529	8.877	10.188	7.565	Y	nan	
177.0	5088.824	9.765	11.102	8.427	Y	nan	
187.0	5414.314	9.201	10.555	7.847	Y	nan	
195.0	5674.706	8.466	9.813	7.119	Y	very poor' fit	
207.0	5974.694	9.579	10.896	8.263	Y	nan	
219.0	6184.082	10.154	11.503	8.804	Y	nan	
227.0	6323.673	10.449	11.773	9.125	Y	nan	
239.0	6498.163	9.569	10.933	8.205	Y	nan	
247.0	6672.653	8.867	10.228	7.507	Y	nan	
255.0	6812.245	9.49	10.836	8.144	Y	nan	
263.0	6951.837	9.521	10.86	8.182	Y	nan	
279.0	7231.021	9.516	10.846	8.186	Y	nan	
287.0	7370.612	9.684	11.015	8.354	Y	nan	
295.0	7510.204	10.368	11.647	9.089	Y	nan	
305.0	7696.4	9.8	11.092	8.508	Y	nan	
315.0	7890.4	10.601	11.883	9.32	Y	nan	
327.0	8123.2	9.507	10.811	8.203	Y	nan	
343.0	8433.6	10.041	11.315	8.767	Y	nan	
351.0	8588.8	10.794	12.093	9.495	Y	nan	
363.0	8821.6	10.654	11.947	9.361	Y	nan	
371.0	8976.8	11.276	12.585	9.966	Y	nan	
379.0	9132.0	10.565	11.855	9.275	Y	nan	
391.0	9364.8	10.904	12.186	9.623	Y	nan	
407.0	9592.941	11.163	12.464	9.863	Y	nan	
415.0	9665.883	11.419	12.712	10.126	Y	nan	
431.0	9811.765	11.427	12.731	10.124	Y	nan	
447.0	9957.647	11.63	12.928	10.331	Y	nan	
455.0	10030.588	11.272	12.54	10.005	Y	nan	
463.0	10103.529	11.151	12.437	9.864	Y	nan	
475.0	10212.941	11.893	13.191	10.596	Y	nan	
489.0	10340.588	9.691	11.009	8.372	Y	nan	
513.0	10477.097	11.602	12.861	10.342	Y	nan	
545.0	10549.354	11.669	12.954	10.383	Y	nan	
569.0	10603.549	11.638	12.926	10.349	Y	nan	
599.0	10671.29	12.461	13.757	11.165	Y	nan	
623.0	10725.483	11.909	13.178	10.64	Y	nan	
645.0	10775.161	10.387	11.691	9.083	Y	nan	
663.0	10815.807	9.212	10.575	7.849	Y	nan	
681.0	10856.451	11.265	12.895	9.634	Y	very poor' fit	
695.0	10888.064	10.02	11.448	8.592	Y	very poor' fit