Relations between erythemal UV dose, global solar radiation, total ozone column and aerosol optical depth at Uccle, Belgium

At Uccle, Belgium, a long time series (1991-2013) of simultaneous measurements of erythemal ultraviolet (UV) dose (S-ery), global solar radiation (S-g), total ozone column (Q(O3)) and aerosol optical depth (tau(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(O3) all increase by respectively 7, 4 and 3% per decade. tau(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(O3), was a significant change point present in the time series around February 1998 and March 1998, respectively. The change point in Q(O3) 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(O3) and tau(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(O3) and a decrease in tau(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 tau(aer) to S-ery is very low, and for this reason it seems unnecessary to include tau(aer) in the MLR analysis. Including Q(O3), however, is justified to increase the adjusted R-2 and to decrease the MABE of the model