Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
There are two main approaches for dealing with model biases in forecasts made with initialized climate models. In full-field initialization, model biases are removed during the assimilation process by constraining the model to be close to observations. Forecasts drift back towards the model’s preferred state, thereby re-establishing biases which are then removed with an a posterior lead-time dependent correction diagnosed from a set of historical tests (hindcasts). In anomaly initialization, the model is constrained by observed anomalies and deviates from its preferred climatology only by the observed variability. In theory, the forecasts do not drift, and biases may be removed based on the difference between observations and independent model simulations of a given period. Both approaches are currently in use, but their relative merits are unclear. Here we compare the skill of each approach in comprehensive decadal hindcasts starting each year from 1960 to 2009, made using the Met Office decadal prediction system. Both approaches are more skilful than climatology in most regions for temperature and some regions for precipitation. On seasonal timescales, full-field initialized hindcasts of regional temperature and precipitation are significantly more skilful on average than anomaly initialized hindcasts. Teleconnections associated with the El Niño Southern Oscillation are stronger with the full-field approach, providing a physical basis for the improved precipitation skill. Differences in skill on multi-year timescales are generally not significant. However, anomaly initialization provides a better estimate of forecast skill from a limited hindcast set.