# Chaohu and Pingwang, China Air Temperature Reconstruction over the Last 10,500 Years
#-----------------------------------------------------------------------            
#                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/31193
#     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/Pingwang.Li.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/Pingwang.Li.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: Chaohu and Pingwang, China Air Temperature Reconstruction over the Last 10,500 Years
#------------------
# Investigators
#     Investigators: Li, Jianyong; Dodson, John; Yan, Hong; Wang, Weiming; Innes, James B.; Zong, Yongqiang; Zhang, Xiaojian; Xu, Qinghai; Ni, Jian; Lu, Fengyan
#------------------
# 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: Li, Jianyong; Dodson, John; Yan, Hong; Wang, Weiming; Innes, James B.; Zong, Yongqiang; Zhang, Xiaojian; Xu, Qinghai; Ni, Jian; Lu, Fengyan
#     Published_Date_or_Year: 2018
#     Published_Title: Quantitative Holocene climatic reconstructions for the lower Yangtze region of China
#     Journal_Name: Climate Dynamics
#     Volume: 50
#     Edition: 
#     Issue: 3-4
#     Pages: 1101-1113
#     Report: 
#     DOI: 10.1007/s00382-017-3664-3
#     Online_Resource: 
#     Full_Citation: 
#     Abstract: Quantitative proxy-based and high-resolution palaeoclimatic datasets are scarce for the lower reaches of the Yangtze River (LYR) basin. This region is in a transitional vegetation zone which is climatologically sensitive; and as a birthplace for prehistorical civilization in China, it is important to understand how palaeoclimatic dynamics played a role in affecting cultural development in the region. We present a pollen-based and regionally-averaged Holocene climatic twin-dataset for mean total annual precipitation (PANN) and mean annual temperature (TANN) covering the last 10,000 years for the LYR region. This is based on the technique of weighted averaging-partial least squares regression to establish robust calibration models for obtaining reliable climatic inferences. The pollen-based reconstructions generally show an early Holocene climatic optimum with both abundant monsoonal rainfall and warm thermal conditions, and a declining pattern of both PANN and TANN values in the middle to late Holocene. The main driving forces behind the Holocene climatic changes in the LYR area are likely summer solar insolation associated with tropical or subtropical macro-scale climatic circulations such as the Intertropical Convergence Zone (ITCZ), Western Pacific Subtropical High (WPSH), and El Niño/Southern Oscillation (ENSO). Regional multi-proxy comparisons indicate that the Holocene variations in precipitation and temperature for the LYR region display an in-phase relationship with other related proxy records from southern monsoonal China and the Indian monsoon-influenced regions, but are inconsistent with the Holocene moisture or temperature records from northern monsoonal China and the westerly-dominated region in northwestern China. Overall, our comprehensive palaeoclimatic dataset and models may be significant tools for understanding the Holocene Asian monsoonal evolution and for anticipating its future dynamics in eastern Asia.
#------------------
# 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: Pingwang
#     Location: Asia>Eastern Asia>China
#     Country: China
#     Northernmost_Latitude: 30.96
#     Southernmost_Latitude: 30.96
#     Easternmost_Longitude: 120.64
#     Westernmost_Longitude: 120.64
#     Elevation: 1.6
#------------------
# Data_Collection
#     Collection_Name: Pingwang.Li.2018
#     Earliest_Year: 9006.0
#     Most_Recent_Year: 2271.0
#     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	1SD	*relject	IncludeYN	AdditionalNotes	
# 0.0	0.0	Core top	-62.0	0.0	0.0	Y	nan	
# 159.0	161.0	age14C	2700.0	40.0	40.0	Y	nan	
# 185.0	187.0	age14C	4430.0	40.0	40.0	Y	nan	
# 225.0	227.0	age14C	4720.0	40.0	40.0	Y	nan	
# 320.0	322.0	age14C	6800.0	50.0	50.0	Y	nan	
# 375.0	377.0	age14C	6290.0	50.0	50.0	N	Date based on pollen residue. Regarded as unreliable by authors	
#------------------
# 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,,pollen;climate reconstructions,,,N,back calculated from the age-depth model and 5cm sampling interval
## age	age,,,calendar year before present,,pollen;climate reconstructions,,,N,
## temperature	surface air temperature,,,degree Celsius,annual,pollen;climate reconstructions,,,N,WAPLS
## uncertaintyHigh	surface air temperature,,unspecified error upper bound,degree Celsius,annual,pollen;climate reconstructions,,,N,
## uncertaintyLow	surface air temperature,,unspecified error lower bound,degree Celsius,annual,pollen;climate reconstructions,,,N,
## ReliableYN1	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
#
depth	age	temperature	uncertaintyHigh	uncertaintyLow	ReliableYN1	
130.0	2271.0	9.06601782693163	12.6860178269316	5.44601782693163	Y	
135.0	2361.0	8.61466398838067	12.2346639883807	4.99466398838067	Y	
140.0	2450.0	7.66040409839183	11.2804040983918	4.04040409839183	Y	
145.0	2540.0	7.56540487791063	11.1854048779106	3.94540487791062	Y	
150.0	2629.0	6.67708142490243	10.2970814249024	3.05708142490243	Y	
155.0	2719.0	7.04470615990029	10.6647061599003	3.42470615990029	Y	
160.0	2808.0	6.79001420077691	10.4100142007769	3.17001420077691	Y	
165.0	3241.0	6.43730643019526	10.0573064301953	2.81730643019526	Y	
170.0	3673.0	6.36606747602685	9.98606747602685	2.74606747602685	Y	
175.0	4106.0	8.29264285824698	11.912642858247	4.67264285824698	Y	
180.0	4538.0	8.56635612773251	12.1863561277325	4.94635612773251	Y	
185.0	4971.0	8.75332967100514	12.3733296710051	5.13332967100514	Y	
190.0	5097.0	9.00160253778556	12.6216025377856	5.38160253778556	Y	
195.0	5146.0	7.82773865282987	11.4477386528299	4.20773865282987	Y	
200.0	5195.0	7.67831310099171	11.2983131009917	4.05831310099171	Y	
205.0	5245.0	7.98460939468839	11.6046093946884	4.36460939468839	Y	
210.0	5294.0	7.42130396469925	11.0413039646992	3.80130396469925	Y	
215.0	5344.0	7.83700289509066	11.4570028950907	4.21700289509066	Y	
220.0	5393.0	7.90741575063381	11.5274157506338	4.28741575063381	Y	
225.0	5442.0	8.19839200926864	11.8183920092686	4.57839200926864	Y	
230.0	5544.0	7.91416211020995	11.5341621102099	4.29416211020995	Y	
235.0	5660.0	9.01151192517462	12.6315119251746	5.39151192517462	Y	
240.0	5775.0	9.00762110653966	12.6276211065397	5.38762110653966	Y	
245.0	5890.0	8.52705614988212	12.1470561498821	4.90705614988212	Y	
250.0	6005.0	8.72759642706552	12.3475964270655	5.10759642706552	Y	
255.0	6120.0	9.12335525430579	12.7433552543058	5.50335525430579	Y	
260.0	6235.0	8.76618595936514	12.3861859593651	5.14618595936514	Y	
265.0	6351.0	8.49103803372825	12.1110380337282	4.87103803372825	Y	
270.0	6466.0	10.2840474044552	13.9040474044552	6.66404740445523	Y	
275.0	6581.0	9.91554890965485	13.5355489096548	6.29554890965485	Y	
280.0	6696.0	9.57216581962786	13.1921658196279	5.95216581962786	Y	
285.0	6811.0	10.0807521317373	13.7007521317373	6.46075213173729	Y	
290.0	6926.0	10.0335392536733	13.6535392536733	6.41353925367332	Y	
295.0	7041.0	9.66021174413063	13.2802117441306	6.04021174413063	Y	
300.0	7157.0	9.27917962895484	12.8991796289548	5.65917962895484	Y	
305.0	7272.0	10.0296752250644	13.6496752250644	6.40967522506436	Y	
310.0	7387.0	10.0490074052387	13.6690074052387	6.42900740523865	Y	
315.0	7502.0	9.99571207929986	13.6157120792999	6.37571207929986	Y	
320.0	7617.0	10.0867290644845	13.7067290644845	6.46672906448449	Y	
325.0	7733.0	10.1778928605116	13.7978928605116	6.55789286051163	Y	
330.0	7848.0	9.33239954144019	12.9523995414402	5.71239954144019	Y	
335.0	7964.0	10.0621601488294	13.6821601488294	6.44216014882938	Y	
340.0	8078.0	10.2463389291579	13.8663389291579	6.62633892915786	Y	
345.0	8197.0	10.3256756554024	13.9456756554024	6.70567565540237	Y	
350.0	8311.0	10.4934232560086	14.1134232560086	6.87342325600857	Y	
355.0	8422.0	9.55042551744685	13.1704255174469	5.93042551744685	Y	
360.0	8539.0	9.53541872612043	13.1554187261204	5.91541872612043	Y	
365.0	8658.0	7.55217529435905	11.1721752943591	3.93217529435905	Y	
370.0	8767.0	7.1591049165369	10.7791049165369	3.5391049165369	Y	
375.0	8883.0	5.11034510935482	8.73034510935482	1.49034510935482	Y	
380.0	9006.0	6.37764294480098	9.99764294480098	2.75764294480098	Y