Test Calibration of the Paleoclimatic Proxy Data with Chinese Historical Records

The calibration of paleoclimate proxies is one of the key problems in the study of paleoclimate at present. Historical documentary records of climate are suitable for calibration on dating and the climatic implication of the proxy data in a climatological sense. A test calibration on correcting the Delingha tree ring precipitation series using Chinese historical documentary records shows that among the 44 extreme dry cases in 1401 1950 AD, 42 cases （or 95.5%） are believable. Thus the long series of Delingha rings-denoted precipitation is highly reliable. Another test to validate the monsoon intensity proxy data based on the Zhanjiang Huguangyan sediments using historical records indicates that the years of Lake Maar Ti content series-designated winter monsoon intensities are entirely opposite to historical documents- depicted years of harsh winters in 800-900 AD. As a result, serious doubt is raised about the climatic implication of this paleo-monsoon proxy series.

A New High-resolution Late Glacial-Holocene Climatic Record from Eastern Nanling Mountains in South China

A 350-cm-long sediment core sequence from Dahu Swamp situated in the eastern Nanling Mountains was selected for high-resolution paleoclimatic reconstruction since the Late Glacial period. The multi-proxy records of this paper reveal several evidently dry and cold events that may coincide with the Oldest Dryas, the Older Dryas, the Younger Dryas in the late deglacial period. Two relatively wetter and warmer phases occurred in ca. 15,000-14,400 cal yr B.P. and 13,500-12,800 cal yr B.P. respectively may correspond to the Boiling and Allerod warming events. The Younger Dryas event （ca. 12,800-11,500 cal yr B.P.） revealed by multi-proxies was characterized by relatively colder and drier climate. A warmer and wetter climate, occurred in ca. 10,000~5000 cal yr B.P., was consistent with the Holoeene Optimum, which coincided with the maximum Northern Hemisphere insolation. The ＂8.2kyr cool event＂ and even the ＂8.8kyr cool event＂ were indicated as well from our sediment core. A dry mid-Holocene period （ca. 60000 3000 cal yr B.P.） indicated by multi-proxies does not follow the traditional concept of the wet mid-Holocene conditions observed in other regions in China.

A 16 ka climate record deduced from δ^(13)C and C/N ratio in Qinghai Lake sediments, northeastern Tibetan Plateau

On the basis of multi-proxy analysis on TOC, TN, C/N, organic δ^13C and grain size, sediment record from Qinghai Lake provides evidences of stepwise-pattemed climatic change since 16 ka BE Results show that Qinghai Lake underwent six environmental stages. From 16.2 to 14.3 ka BP and from 4.0 to 2.1 ka BP, the organic δ^13C value was controlled by the concentration of atmospheric CO2. Relative higher organic δ^13C values occurred between 14.3 to 10.4 ka BP indicative of water hardness decrease resulted from melting ice water, corresponding to two intervals of C/N peak values to the Boling and AIlerod warm periods in Europe respectively. From 10.4 ka BP, Qinghai Lake entered the Holocene and the climate was warm and a little dry. The Megathermal appeared at about 6.7 ka BP when the vegetation around the lake transformed into a forest. Between 6.3 ka BP and 4.0 ka BP, the temperature decreased and δ^13C value was controlled by the expansion of C3 plants and the retreat of C4 plants in river catchment. Since 4.0 ka BP, the climate gradually became cold and dry. From 2.1 ka BP, the cold-dry climate and human activity resulted in an abrupt increase in C/N with deceased δ^13C value; meanwhile, many coarse grains appeared in sediments.

The Mid-Pleistocene Climate Transition Recorded in a 1.6 Myr-Period Lacustrine Sediment Sequence from Mazatage, Tarim Basin

The mid-Pleistocene climate transition （MPT） has been widely reported in worldwide geological events. As a key issue of the Quaternary geology, it has attracted much attention from the paleoclimate community. MPT refers to a period lasting for several hundreds of thousand years, during which the dominant climate periodicity gradually extended from 41 kyr to 100 kyr （Ruddiman et al., 1989）,

Long time data series and data stewardship reference model

The need for accessing historical Earth Observation(EO)data series strongly increased in the last ten years,particularly for long-term science and environmental monitoring applications.This trend is likely to increase even more in the future,in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more,and also due to the need to support the United Nations Framework Convention on Climate Change(UNFCCC).While much of the satellite observations are accessible from different data centers,the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical.Climate research is a big data problem that involves high data volume of measurements,methods for on-the-fly extraction and reduction to keep up with the speed and data volume,and the ability to address uncertainties from data collections,processing,and analysis.The content of EO data archives is extending from a few years to decades and therefore,their value as a scientific time-series is continuously increasing.Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable.The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset.This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes.The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.