Influence of forcings and circulation patterns on mean temperatures at different scales: an analysis by neural network modeling

We present an analysis of the influence of various forcings and circulation patterns on annual and seasonal temperatures observed in the past, both at global and regional scales. In this framework, multilayer perceptrons show their ability to fully catch nonlinear relationships among these variables and allow us to "weight" the magnitude of different causes on the temperature behavior. In particular, our results show the necessity of including anthropogenic inputs for explaining the temperature behavior at global scale. Furthermore, we can assess the relative influences of global forcings and regional circulation patterns in determining regional temperature trends. Therefore, this activity can be very useful in order to identify the fundamental elements for a successful downscaling of Atmosphere-Ocean General Circulation Models, even on future scenarios.