This study assesses the variability of the amounts of annual precipitation in global land areas (excluding Greenland and Antarctica) from 1951 to 2000. The analysis is based on 0.5° longitude/latitude gridded data. Three different data sets were analysed (University of East Anglia Climate Research Unit's (UEA CRU) TS 2.1 data set, the Global Precipitation Climatology Centre's (GPCC) Full Data Reanalysis version 5 data set, Variability Analysis of Surface Climate Observations (VASClimO) version 1.1 data set), and all led to very similar results. The results included here correspond to the VasClimO project data. Precipitation variability is examined through the anomaly of the coefficient of variation, which is shown to be a robust concept. It is defined as the departure of the actual coefficient of variation from the value that could be expected ‘on average’, conditioned on the total annual amount of precipitation. A brief discussion of the so-called Jackknife error is included. The analysis revealed diverse areas of larger-than-normal, smaller-than-normal and close-to-normal variability. Negative anomalies occur more often but have, on an average, lower values than do positive anomalies. Large areas of slightly negative anomalies were found inland for all continents except Australia. A zonal pattern in the distribution of the anomalies was clearly seen at subtropical latitudes, which generally showed positive anomalies. This general picture is modified by various local factors, such as cold ocean currents, monsoon activity and cyclone formation areas. Global modes of climate variability, such as the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), affect the variability of precipitation either directly or by modifying other relevant atmospheric and oceanic processes. Their influence is seen in many areas with higher-than-normal variability and is especially true if the high variability is accompanied by large amounts of mean annual precipitation. The authors believe that the present methodology may be useful in assessing the quality of future global data sets. It is, however, very desirable that such data sets include interpolation error estimates. Copyright © 2012 Royal Meteorological Society
If you can't find a tool you're looking for, please click the link at the top of the page to "Go to old article view". Alternatively, view our Knowledge Base articles for additional help. Your feedback is important to us, so please let us know if you have comments or ideas for improvement.