# China 1000 Year Multiproxy Temperature Reconstructions #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original reference when using these data, # plus the Online Resource and date accessed. # # Online_Resource: http://hurricane.ncdc.noaa.gov/pls/paleox/f?p=519:1:::::P1_STUDY_ID:14171 # # Original_Source_URL: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/contributions_by_author/shi2012b/shi2012china-west.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions #-------------------- # Contribution_Date # Date: (required) 2013-03-28 #-------------------- # Title # Study_Name: China 1000 Year Multiproxy Temperature Reconstructions #-------------------- # Investigators # Investigators: Shi, F.; Yang, B.; Von Gunten, L. #-------------------- # Description_and_Notes # Description: Surface temperature reconstructions for China, by both composite plus scale (CPS) # and Regularized errors-in-variables (EIV) methods. The reconstruction method is a modified form of # the point-by-point regression (PPR) approach, using a small search radius restricted to only one 5x5 # degree grid square. Data are reported as temperature anomalies with respect to AD 1961-1990, in degrees C. # Eastern and Western China region EIV reconstructions (divided along 105E Longitude) are also presented. # #-------------------- # Publication # Authors: SHI Feng, YANG Bao, and Lucien Von GUNTEN # Published_Date_or_Year: 2012-12-01 # Published_Title: Preliminary multiproxy surface air temperature field reconstruction for China over the past millennium # Journal_Name: Science China Earth Sciences # Volume: 55 # Issue: 12 # Pages: 2058-2067 # DOI: 10.1007/s11430-012-4374-7 # Abstract: We present the first millennial-length gridded field reconstruction of annual temperature for China, and analyze the reconstruction for spatiotemporal changes and associated uncertainties, based on a network of 415 well-distributed and accurately dated climatic proxy series. The new reconstruction method is a modified form of the point-by-point regression (PPR) approach. The main difference is the incorporation of the "composite plus scale" (CPS) and "Regularized errors-in-variables" (EIV) algorithms to allow for the assimilation of various types of the proxy data. Furthermore, the search radius is restricted to a grid size; this restriction helps effectively exclude proxy data possibly correlated with temperature but belonging to a different climate region. The results indicate that: 1) the past temperature record in China is spatially heterogenic, with variable correlations between cells in time; 2) the late 20th century warming in China probably exceeds mean temperature levels at any period of the past 1000 years, but the temperature anomalies of some grids in eastern China during the Medieval climate anomaly period are warmer than during the modern warming; 3) the climatic variability in the eastern and western regions of China was not synchronous during much of the last millennium, probably due to the influence of the Tibetan Plateau. Our temperature reconstruction may serve as a reference to test simulation results over the past millennium, and help to finely analyze the spatial characteristics and the driving mechanism of the past temperature variability. However, the lower reconstruction skill scores for some grid points underline that the present set of available proxy data series is not yet sufficient to accurately reconstruct the heterogeneous climate of China in all regions, and that there is the need for more highly resolved temperature proxies, particularly in the Tibetan Plateau. #------------------ # Funding_Agency # Funding_Agency_Name: Chinese Academy of Sciences Strategic Priority Research Program # Grant: XDA05080801 #------------------ # Funding_Agency # Funding_Agency_Name: National Basic Research Program of China # Grant: 2010CB950104 #------------------ # Funding_Agency # Funding_Agency_Name: Chinese Academy of Sciences 100 Talents Project # Grant: 29082762 #------------------ # Funding_Agency # Funding_Agency_Name: National Natural Science Foundation of China # Grant: 40871091 #------------------ # Funding_Agency # Funding_Agency_Name: Chinese Academy of Sciences West Light Program for Talent Cultivation # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: China Meteorological Administration Drought Research Fund # Grant: IAM201213, IAM201109 #------------------ # Funding_Agency # Funding_Agency_Name: Swiss National Science Foundation # Grant: PBBEP2-126056 #------------------ # Site_Information # Site_Name: Western China # Location: Asia>Eastern Asia>China # Country: China # Northernmost_Latitude: 45.0 # Southernmost_Latitude: 25.0 # Easternmost_Longitude: 105.0 # Westernmost_Longitude: 70.0 # Elevation: m #------------------ # Data_Collection # Collection_Name: Shi2012ChinaWest # Earliest_Year: 1000 # Most_Recent_Year: 1996 # Time_Unit: AD # Core_Length: m # Notes: #------------------ # Chronology: # # # #---------------- # Variables # # Data variables follow (have no #) # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) ## age_AD Age, , , years AD, , , , ,N ## tempanom-ann surface temperature anomaly Annual, , , deg C, Annual, , , EIV, N #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: age_AD tempanom-ann 1000 -0.275 1001 -0.284 1002 -0.291 1003 -0.295 1004 -0.296 1005 -0.295 1006 -0.292 1007 -0.291 1008 -0.297 1009 -0.314 1010 -0.343 1011 -0.382 1012 -0.427 1013 -0.472 1014 -0.509 1015 -0.535 1016 -0.547 1017 -0.547 1018 -0.538 1019 -0.527 1020 -0.517 1021 -0.508 1022 -0.498 1023 -0.484 1024 -0.464 1025 -0.436 1026 -0.402 1027 -0.360 1028 -0.317 1029 -0.276 1030 -0.240 1031 -0.212 1032 -0.192 1033 -0.178 1034 -0.165 1035 -0.152 1036 -0.136 1037 -0.119 1038 -0.107 1039 -0.105 1040 -0.117 1041 -0.146 1042 -0.185 1043 -0.229 1044 -0.271 1045 -0.304 1046 -0.323 1047 -0.329 1048 -0.323 1049 -0.309 1050 -0.293 1051 -0.279 1052 -0.268 1053 -0.263 1054 -0.263 1055 -0.266 1056 -0.270 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