Extended Abstract

such a mo del. Second, we ha ve o bserved that the tim e evolution of a sp ecific k ey sea surfacetemperature forcing region, r elevant to season scal es, can ofte n b e ef ficiently de scribed in a low -dimensional phase space. T he possibility for constructing a l ow-dimensional phas e space fro m thepredictor fiel ds all ow a closer examination of their i mpact on t he seasonal climate over a spe cificclimate zone leading to the possi bility of nonlinear and more dynamically based st atistical predictionin contrast to pure statistical approaches. Our preliminary application of this method to the predictionof the winter and spring surface air te mperature (SAT) over Europe showed significant improvementin the sk ill score (Pokrovsky, 2009b). Third, the geographical distribution of the pre dictive skills, aswell as their ti me behavior, varies fr om one key region of sea surface temperature forcing to another.Therefore, the final optimal prediction might be achieved through a linear or nonlinear combination ofthe predicted results derived from different key forcing regions.Figure 1. Fuzzy-neural model scheme2. Forecasting methodologyThe use o f a neur al n etwork (NN) is a powerful nonlin ear scheme based on b lack boxstatistics, where one can tune the model parameters to arrive at a good prediction, but can seeneither th e phase r elation between t he pred ictand and predictors, nor the origi n of skills .Therefore, we assume that th e pr edictability of seasonal c limate is c onnected with forcingfields such as the sea surface temperature or others. The key to a truly successful applicationof a neural network model lies in the understanding of the underlying physical mechanism forthe relati on be tween predict or and p redictand fields (P okrovsky, 2000).We used acomprehensive neural n etwork model, which is based on a combination of fuzzy l ogicmodules an d neural netw ork p rinciple structur es. The g eneral sc heme of our self-l earningfuzzy-neural model is presen ted at f ig. 1. Let us consider it from the left to r ight direction.The lef t module d efines the i nitial f ield assim ilation as the input inform ation. Further, thisdataset should b e classified in several fuzzy logic modules. It is n ecessary to e mphasize thateach input meteorological field is linked to several fuzzy sets. The classification procedure isrelated to diurnal or seasonal cycles or to various type of spatial distribution of meteorologicalparameters, e.g., t ype of at mospheric ci rculation. That m eans t hat each fi eld s hould beevaluated by means of a co mplex proce dure and t hen should be attributed t o o ne of theclusters (fig.1). Nonetheless, metric distances of each field to cluster cente rs are taken intoaccount in the next model layer, which is called a hidden layer. A hidden layer is designed toperform n on-linear transfor mation of distance variab les into outpu t variab les. A very-275-