IDEAS home Printed from https://ideas.repec.org/p/wop/iasawp/ir99074.html
   My bibliography  Save this paper

Temperature and Precipitation Variability in China - A Gridded Monthly Time Series from 1958 to 1988

Author

Listed:
  • S. Prieler

Abstract

Wide climatic variability is characteristic for large parts of China including events of extreme anomalies. This paper presents a time series covering the period 1958 to 1988 for monthly temperature and precipitation in China for a 5x5 km grid cell size. Monthly station histories (265 for temperature and 310 for rainfall), long-term averages of mean monthly temperature and rainfall on a 5 km grid, and a digital elevation mode (DEM) are the input data used to build the time series data base. Individual station anomalies in terms of deviation from the 31-year average were calculated and interpolated throughout China using the Mollifier interpolation technique. It uses a statistical approach to non-parametric interpolation. As a result data is available for monthly anomaly surfaces for all the years. By linking these to the long-term average grid maps we derive a time series of temperature and rainfall for China. Maps were produced for anomalies, and for absolute temperature and precipitation in each year between 1958 and 1988. Along with maps indicating variability at the stations, others have been completed based on the interpolated time series. Due to surface smoothing of the interpolation the variability of the interpolated time series is usually lower than the one based upon station observations. Temperature variability is quite low during the summer half. Anomalies are mostly less than 2 C in nearly all of China. During the winter months the anomaly increases up to 6 C with the highest variability in modern China and on the plateau. the pattern of monthly anomalies is stable in that relatively large areas show the same trend of deviation. Variability of rainfall shows large differences in spatial and temporal terms. Rainfall variability is highest during winter when rainfall is low. Especially the monthly data offer a comprehensive insight into seasonal differences in regional rainfall variability. In northern China's agricultural productive areas variability is high during the spring months, decreases in summer and increases as of September. In the middle and lower reaches of the Changjiang river basin variability is high in July and August amounting to as much as over 50%. Variability is relatively low in Southwest China, which includes the fertile Sichuan basin. Also in China's northeastern agricultural areas variability is relatively low during the growing season. From a policy point of view it is also of interest to aggregate the data for certain geographic regions. Results for provinces and major watersheds are presented. The interpolated surfaces are validated by comparing them with the station observations available in this study. Anomaly surfaces validation is determined by the interpolation error. There is a good fit for temperature anomaly surfaces compared to observed station anomalies. Because of the high spatial variability of rainfall anomalies including the possibility of extreme events in selected stations, interpolated anomalies are usually reduced during the interpolation. The temperature and rainfall time series validation is, in addition by the interpolation error, influenced by the differences in the 31-year average observed at the stations and the average presented in the long-term average grids to which the anomaly surfaces are linked.

Suggested Citation

  • S. Prieler, 1999. "Temperature and Precipitation Variability in China - A Gridded Monthly Time Series from 1958 to 1988," Working Papers ir99074, International Institute for Applied Systems Analysis.
  • Handle: RePEc:wop:iasawp:ir99074
    as

    Download full text from publisher

    File URL: http://www.iiasa.ac.at/Publications/Documents/IR-99-074.pdf
    Download Restriction: no

    File URL: http://www.iiasa.ac.at/Publications/Documents/IR-99-074.ps
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wop:iasawp:ir99074. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/iiasaat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.