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Trends in Total Cloud Amount Over China

DOI: 10.3334/CDIAC/cli.008

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Dale P. Kaiser
Carbon Dioxide Information Analysis Center,
Environmental Sciences Division,
Oak Ridge National Laboratory, Oak Ridge, Tennessee

Period of Record



These total cloud amount time series for China are derived from the work of Kaiser (1998). The cloud data were extracted from a database of 6-hourly weather observations provided by the National Climate Center of the China Meteorological Administration (CMA) to the U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) through a bilateral research agreement. Surface-observed (visual) six-hourly observations [0200, 0800, 1400, and 2000 Beijing Time (BT)] of cloud amount (0-10 tenths of sky cover) were available from 196 Chinese stations covering the period 1954-94. Data from 1951-1953 were also available; however, they only included 0800, 1400, and 2000 BT observations.

Since it has long been recognized that it is much more difficult to accurately estimate cloud amount at night [e.g., Schneider(1972)], especially if thin cirrus clouds are present, Kaiser (1998) analysed daytime observations separately from nighttime observations. Daytime analysis was for the period 1951-1994 and the nighttime analysis was for 1954-1994 (because of the lack of 0200 BT observations prior to 1954). It was assumed that mean daytime and nighttime cloud amount would differ significantly, not only because the presence of clouds is easier to detect in daylight, but due to the diurnal variations typically observed over land for specific cloud types [e.g., cumulus cloud amount over China typically peaks in the early afternoon across all seasons (Warren et al. 1986)]. Kaiser (1998) used 1400 BT cloud amount observations to represent what was termed "midday" cloud amount, and 0200 BT observations to represent "midnight" cloud amount. Since BT is used throughout the whole of China, some western China stations would actually take observations closer to about 1100-1200 hours local solar time (LST) (coresponding to the 1400 BT observation), and about 2300-2400 hours LST (corresponding to 0200 BT). Likewise, midday and midnight observations at stations in far northeastern China would be taken closer to 1500 LST and 0300 LST, respectively. Using the 1400 BT observations assures that these cloud amounts are estimated in daylight at all stations throughout the year. Likewise, the 0200 BT observations ensure nighttime observations at all stations. Observations made at 0800 and 2000 BT were not used, as these would mix day and night observations depending on season and location.

The midday and midnight cloud amount observations were averaged for each station over the four 3-month meteorological seasons (i.e., winter: months 12, 1, and 2; spring: months 3, 4, and 5, etc.) and over each year. Linear regression analysis was used to characterize trends in the seasonal and annual means over 1951-1994 (midday observations) and 1954-1994 (midnight observations) at individual stations, and for eight regions adopted in a recent analysis of precipitation trends over China (Zhai et al. 1997). The eight regions of China are: (1) Western Northwest (WNW), (2) Eastern Northwest (ENW), (3) North, (4) Northeast (NE), (5) Tibet, (6) Southwest (SW), (7) East, and (8) South. Statistics for the eight regions were calculated based upon simple unweighted arithmetic averages of the parameters from all stations within each region. The trends in total cloud amount over China reported by Kaiser (1998), encompassing the period 1951 through 1994, are described in the Trends section below.


Station and regional trends in annual and seasonal mean cloud amount clearly indicate decreasing total cloud amount over much of China from the early to mid-1950s through 1994. Trends in annual mean total cloud amount were found to be quite similar for midday and midnight observations. Since daytime cloud observations are generally considered more trustworthy, the analysis emphasis was placed on midday data. Most stations in central, eastern, and northeastern China show statistically significant decreases of 1-3% sky cover per decade. Annual and seasonal trends for selected regions of China show that the strongest and most consistent evidence for decreasing cloud amount is seen for the North and Northeast regions of China, where decreases ranging from 1-2% sky cover per decade (about 4.5-9% over the period of record) are observed.

The relative percentage change in cloud amount for the eight China regions may be assessed using the table of regional mean cloud amounts in conjunction with the table of regional cloud amount trends. For example, the autumn midday cloud amount trend for North China is -1.0% per decade. Over 1951-1994, the autumn mean midday total cloud amount for North China is 47.0%. Therefore the roughly 4.4% decrease in total cloud amount over the period (percent sky cover) may interpreted as a decrease of nearly 10% in the amount of autumn cloud amount observed over the region referred to as North China.

The decreasing trends in cloud amount over some China regions are especially interesting in light of recent temperature trends observed over China. Several studies (e.g., Karl et al. 1993; Easterling et al. 1997) have shown significant increasing trends in daily minimum temperatures over much of China since 1951. The largest increases in minimum temperature have been observed in the northeastern part of the country, precisely where the strongest decreasing trends in total cloud amount are observed. Increases in cloud amount have been offered as a possible explanation for increased minimum temperatures in other parts of the world (e.g., Karl et al. 1993). In China, it seems that some different mechanism(s) must be considered for understanding the observed increase in minimum temperatures, perhaps relating to atmospheric circulation or urbanization effects not fully removed from the temperature record.


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CITE AS: Kaiser, D.P. 1999. Trends in Total Cloud Amount Over China. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi: 10.3334/CDIAC/cli.008