Abstract. At Uccle, Belgium, a long time series of simultaneous measurements of erythemal ultraviolet (UV) dose (S ery ), global solar radiation (S g ), total ozone column (Q O 3 ) and aerosol optical depth (τ aer ) (at 320.1 nm) is available, which allows for an extensive study of the changes in the variables over time. Linear trends were determined for the different monthly anomalies time series. S ery , S g and Q O 3 all increase by respectively 7, 4 and 3 % per decade. τ aer shows an insignificant negative trend of −8 % per decade. These trends agree with results found in the literature for sites with comparable latitudes. A change-point analysis, which determines whether there is a significant change in the mean of the time series, is applied to the monthly anomalies time series of the variables. Only for S ery and Q O 3 , was a significant change point present in the time series around February 1998 and March 1998, respectively. The change point in Q O 3 corresponds with results found in the literature, where the change in ozone levels around 1997 is attributed to the recovery of ozone. A multiple linear regression (MLR) analysis is applied to the data in order to study the influence of S g , Q O 3 and τ aer on S ery . Together these parameters are able to explain 94 % of the variation in S ery . Most of the variation (56 %) in S ery is explained by S g . The regression model performs well, with a slight tendency to underestimate the measured S ery values and with a mean absolute bias error (MABE) of 18 %. However, in winter, negative S ery are modeled. Applying the MLR to the individual seasons solves this issue. The seasonal models have an adjusted R 2 value higher than 0.8 and the correlation between modeled and measured S ery values is higher than 0.9 for each season. The summer model gives the best performance, with an absolute mean error of only 6 %. However, the seasonal regression models do not always represent reality, where an increase in S ery is accompanied with an increase in Q O 3 and a decrease in τ aer . In all seasonal models, S g is the factor that contributes the most to the variation in S ery , so there is no doubt about the necessity to include this factor in the regression models. The individual contribution of τ aer to S ery is very low, and for this reason it seems unnecessary to include τ aer in the MLR analysis. Including Q O 3 , however, is justified to increase the adjusted R 2 and to decrease the MABE of the model.