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L. Bärring and M. Hulme


The rainfall anomaly index (RAI) has been widely used to study variations over time in Sahelian rainfall. Its interpretation is often complicated by excessive missing data and changes in station network, both of which prevent a precise quantification of the significance of any given RAI estimate. Also, unless the time series are filtered, high interannual variability often obscures important rainfall fluctuations. Here we apply a simple method for calculating approximate confidence limits of areal rainfall estimates to annual data from two constant network configurations in Sudan and West Africa. These cover, respectively, the periods 1920–88 (13 stations) and 1922–85 (12 stations) and contain only 3 and 5 missing annual totals out of 897 and 768. The resulting annual RAI estimates, and 95% confidence limits, were subjected to a 9-point binomial filter, and a 30-point retrospective uniform filter (i.e., an annually updated WMO reference period), also called a running mean. By combining the RAI and confidence-level estimates with different filters we develop a technique that should be useful for interpreting RAIs and assessing, the impact of climate on natural resources. This technique can be used to construct quantitative indicators of the terms climate anomalies, climate fluctuations, and climatic change. We illustrate this by applying tentative indicators to the two Sahel series, and also, by way of contrast, to an annual RAI for southern Sweden (15 stations covering the period 1861–1988 with 3 missing annual totals out of 1920). For example, recent individual anomalous years occurred in Sudan in 1978 and 1988 (wet) and 1984 (dry), and for both West Africa and Sudan a climatic change compared to century-mean rainfall had nearly occurred by the late 1980s. Southern Sweden has witnessed two recent climate fluctuations in the early 1970s (dry) and in the mid-1980s (wet). In conclusion, we hint at some refinements to the technique, but stress that for climate monitoring purposes the need for station networks of high quality and consistency over time will remain undiminished.

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T. J. Osborn and M. Hulme


The validation of climate model simulations creates substantial demands for comprehensive observed climate datasets. These datasets need not only to be historically and geographically extensive, but need also to be describing areally averaged climate, akin to that generated by climate models. This paper addresses one particular difficulty found when attempting to evaluate the daily precipitation characteristics of a global climate model, namely the problem of aggregating daily precipitation characteristics from station to area.

Methodologies are developed for estimating the standard deviation and rainday frequency of grid-box mean daily precipitation time series from relatively few individual station time series. Temporal statistics of such areal-mean time series depend on the number of stations used to construct the areal means and are shown to be biased (standard deviations too high, too few raindays) if insufficient stations are available. It is shown that these biases can be largely removed by using parameters that describe the spatial characteristics of daily precipitation anomalies. These spatial parameters (the mean interstation correlation between station time series and the mean interstation probability of coincident dry days) are calculated from a relatively small number of available station time series for Europe, China, and Zimbabwe. The relationships that use these parameters are able to successfully reproduce the statistics of grid-box means from the statistics of individual stations. They are then used to estimate the statistics of grid-box means as if constructed from an infinite number of stations (for standard deviations) or 15 stations (for rainday frequencies), even if substantially fewer stations are actually available. These estimated statistics can be used for the evaluation of daily precipitation characteristics in climate model simulations, and an example is given using a simulation by the Commonwealth Scientific and Industrial Research Organisation atmosphere general circulation model. Applying the authors’ aggregation methodology to observed station data is a more faithful form of model validation than using unadjusted station time series.

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