Assessment of natural climate variability using a weather generator

It is common practice to use a 30-year period to derive climatological values, as recommended by the World Meteorological Organization. However this convention relies on important assumptions, of which the validity can be examined by deriving the uncertainty inherent to using a limited time-period for deriving climatological values. In this study a new method, aiming at deriving this uncertainty, has been developed with an application to precipitation for a station in Europe (Westdorpe) and one in Africa (Gulu). The weather generator framework is used to produce synthetic daily precipitation time-series that can also be regarded as alternative climate realizations. The framework consists of an improved Markov model, which shows good performance in reproducing the 5-day precipitation variability. The sub-seasonal, seasonal and the inter-annual signals are introduced in the weather generator framework by including covariates. These covariates are derived from an empirical mode decomposition analysis with an improved stability and significance assessment. Introducing covariates was found to substantially improve the monthly precipitation variability for Gulu. From the weather generator, 1,000 synthetic time-series were produced. The divergence between these time-series demonstrates an uncertainty, inherent to using a 30-year period for mean precipitation, of 11 % for Westdorpe and 15 % for Gulu. The uncertainty for precipitation 10-year return levels was found to be 37 % for both sites.