In the first part we pointed out that there is a need to apply not only monetary measures to estimate the gains and losses from climate change. Daily climate is a strong determinant of human well-being. Slow and minor changes might be adapted easily but abrupt and bigger changes are difficult to adapt to and affect well-being. We introduced the concept and two measures of SWB as a non-income based welfare measure and pointed to the advantages and shortcomings in terms of reliability of this measure. Our empirical analysis applies life satisfaction as a measure of SWB. We control for income, age and family as well as employment status and find a significant positive effect from temperature on life satisfaction. Therefore, life satisfaction would increase with higher temperature in cold months; meanwhile the results on hot months were insignificant. Precipitation rates showed a diminishingly small negative and effect on life satisfaction. A
rise in the percentage of cloud covered days leads to a strong negative effect on life satisfaction. Generally our results go in line with Rehdanz and Maddison (2005). Further, we analyzed the effect of the variance of climate on life satisfaction but found so far only preliminary results