Attributing correlation skill of dynamical global precipitation forecasts to statistical ENSO teleconnection using a set theory based approach

Abstract. Climate teleconnections are essential for the verification of valuable precipitation forecasts generated by global climate models (GCMs). This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Nino-Southern Oscillation (ENSO) teleconnection by using the coefficient of determination (R2). Specifically, observed precipitation is respectively regressed against GCM forecasts, Nino3.4 and both of them and then the intersection operation is implemented to quantify the overlapping R2 for GCM forecasts and Nino3.4. The significance of overlapping R2 and the sign of ENSO teleconnection facilitate three cases of attribution, i.e., significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts of global precipitation. For grid cells around the world, the ratio of significantly positive anomaly correlation attributable to positive (negative) ENSO teleconnection is respectively 10.8 % (11.7 %) in December-January-February (DJF), 7.1 % (7.3 %) in March-April-May (MAM), 6.3 % (7.4 %) in June-July-August (JJA) and 7.0 % (14.3 %) in September-October-November (SON). The results not only confirm the prominent contributions of ENSO teleconnection to GCM forecasts, but also present spatial plots of regions where significantly positive anomaly correlation is subject to positive ENSO teleconnection, negative ENSO teleconnection and teleconnections other than ENSO. Overall, the proposed attribution approach can serve as an effective tool to investigate the source of predictability for GCM seasonal forecasts of global precipitation.