The dataset is from the transient experiment TRN40ka in Zhang et al (2021, Nature Geoscience), spanning 40ka-32ka BP with changing orbital parameters. For detailed description of experimental design, please refer to the original paper. Model details: COSMOS (ECHAM5-JSBACH-MPI-OM), a comprehensive fully coupled atmosphere–ocean general circulation model (AOGCM), is used to generate the dataset. The atmospheric model ECHAM5, complemented by the land surface component JSBACH, is used at T31 resolution (∼3.75°), with 19 vertical layers. The ocean model MPI-OM, including sea-ice dynamics that is formulated using viscous-plastic rheology, has a resolution of GR30 (3°×1.8°) in the horizontal, with 40 uneven vertical layers.
ZHANG Xu
This dataset is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The dataset contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. The data contains fileds as follows: year Tmin.recon (℃) See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
HUANG Ru, ZHU Haifeng, LIANG Eryuan
This data set contains stable oxygen isotope data of daily precipitation in Lulang, Nuxia, and Guangzhou from 2007 to 2014. The precipitation data of the Lulang station are obtained via automatic weather station (AWS) rain gauges, and the precipitation data for Guangzhou and Nuxia are the manual records of meteorological or hydrological stations. Project source of the data: the general project of the National Natural Science Foundation of China “Exploring the impact of ENSO on the source of water vapor in the north and south of the ‘third pole' through stable isotope of precipitation and ice core” (41571074). Data processing related information can be found in the following reference: Yang, X, Mary E. Davis, Sunil Acharya, Tandong Yao. Asian Monsoon variations revealed from stable isotopes in precipitation. Climate Dynamics, 2017, doi:10.1007/s00382-017-4011 -4. Data collection sites: Lulang Station of Southeast Tibet, Chinese Academy of Sciences, Longitude: 94.73°E; Latitude: 29.77°N; Altitude: 3330 m. Guangzhou weather station, longitude: 113.32 °E; latitude: 23.13 ° N; altitude: 7 meters. Nuxia hydrological station, longitude: 94.65 °E; latitude: 29.47 ° N; elevation: 2920 m.
YANG Xiaoxin
This dataset contains data on the lake core sporopollen spectrum and temperature/precipitation reconstruction sequence of Yamdrog Yumtso Lake in the southern Tibetan Plateau. It is used to study the environmental changes in the Yamdrog Yumtso region by 20 ka. It is obtained by the sporopollen analysis method. This data set is obtained by laboratory measurement and calculation. The samples and data are collected and identified in strict accordance with relevant operating procedures at all stages. There are three subtables in this dataset. The first two tables comprise the following analysis data of TC1 pore sporopollen samples. Field 1: Sample Number Field 2: Sample Depth Unit: cm Field 3: Sample Age Unit: aBP Field 4: Total sporopollen concentration Units: granules/gram Field 5: Total Pollen Granules Unit: Number of grains Field 6: Total number of indicative pollen Unit: Number of grains Field 7: Identification of indicative pollen number Unit: Number of grains Field 8: Sample Weight Unit: Grams Field 9: Concentration Coefficient Units: granules / gram Field 1: Sample Number Field 2: Plant species Field 3: Pollen content Unit: % The third subtable is the reconstructed temperature precipitation and has 6 fields. Field 1: Sample Code Field 2: Sample Name Field 3: Depth Unit: cm Field 4: Age Unit: aBP Field 5: Average annual temperature Unit: 0.1 °C Field 6: Annual precipitation Unit: 0.1 mm The rock core was collected from the Yamdrog Yumtso Basin in the southern part of the Tibetan Plateau. The approximate sampling location is 90°27′E，28°56′N, and the altitude there is 4425 m.
WANG Junbo, LV Houyuan
The application of general circulation models (GCMs) can improve our understanding of climate forcing. In addition, longer climate records and a wider range of climate states can help assess the ability of the models to simulate climate differences from the present. First, we try to find a substitute index that combines the effects of temperature in different seasons and then combine it with the Beijing stalagmite layer sequence and the Qilian tree-ring sequence to carry out a large-scale temperature reconstruction of China over the past millennium. We then compare the results with the simulated temperature record based on a GCM and ECH-G for the past millennium. Based on the 31-year average, the correlation coefficient between the simulated and reconstructed temperature records was 0.61 (with P < 0.01). The asymmetric V-type low-frequency variation revealed by the combination of the substitute index and the simulation series is the main long-term model of China's millennium-scale temperature. Therefore, solar irradiance and greenhouse gases can account for most of the low-frequency variation. To preserve low-frequency information, conservative detrended methods were used to eliminate age-related growth trends in the experiment. Each tree-ring series has a negative exponential curve installed while retaining all changes. The four fields of the combined 1000-yr (1000 AD-2000 AD) reconstructed temperature records derived from stalagmite and tree-ring archives (excel table) are as follows: 1) Year 2) Annual average temperature reconstruction 3) Reconstructed temperature deviation 4) Simulated temperature deviation
TAN Ming
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