Conclusions

In this work we have used a multiple linear regression model, to see how different predictors are correlated to each other, and how they are correlated to temperature and precipitation in the southern part of Sweden.

The correlation between the large and small indices vary over the year, but both cases show an increase in correlation during the winter. The MoVa index and the ViGö index are much better correlated than what the NAO and the KaUp indices are. One possible explanation for this can be the much shorter distace between the lines MoVa and ViGö in the latitudinal direction, compared to the distance in the longitudinal direction between NAO and KaUp.

The correlation between the predictors and the predictands, temperature and precipitation, vary between different stations. The south-north predictors, NAO and KaUp, show different signs concerning the temperature in the summer. This is remarkable, but one should have in mind, that the correlation coefficient between the two is very low during the summer months.

To recieve a good approximation concerning the precipitation amount by using this multiple regression model, it’s almost enough to use the mean pressure predictor, because the other predictors are very low or not even significant, except for some stations. The NAO predictor is only significant for a few months concerning precipitation. It should also be mentioned, that the model shows a lower amount of precipitation than what is observed when we are talking about great amounts of precipitation. 

Finally, this regression model is based on pressure differences or just the mean pressure. This means, that the model doesn’t take into account such phenomena as convective clouds, local rain or thunder storms, subsidence inversions, sea breeze effects, etc.