Multiyear climate predictions using two initialization strategies

Abstract

[1] Multiyear climate predictions with two initialization strategies are systematically assessed in the EC-Earth V2.3 climate model. In one ensemble, an estimate of the observed climate state is used to initialize the model. The other uses estimates of observed ocean and sea ice anomalies on top of the model climatology. The ensembles show similar spatial characteristics of drift related to the biases in control simulations. As expected, the drift is less with anomaly initialization. The full field initialization overshoots to a colder state which is related to cold biases in the tropics and North Atlantic, associated with oceanic processes. Despite different amplitude of the drift, both ensembles show similar skill in multiyear global temperature predictions, but regionally differences are found. On multiyear time scales, initialization with observations enhances both deterministic and probabilistic skill scores in the North Atlantic. The probabilistic verification shows skill over the European continent.

Decadal climate prediction with a refined anomaly initialisation approach

“…FFI has the strongest drift because its initial state corresponds to the observed state and it is the furthest from the model climate. These results are consistent with Hazeleger et al (2013). The AI method imposes the bias of the model from the initial state of the system.…”

“…4a) in NOINI is negligible because the initial state of NOINI is a random state within the range of the possible states of the model climate and therefore it is the most balanced with the model climate. Its bias is negative along the whole forecast time, consistent with the strong cold tropical bias of the model (Hazeleger et al 2013). …”

“…When implementing anomaly initialisation or anomaly nudging, density is often not directly assimilated. This is the case for DePreSys (Smith et al 2007), the CNRM-CM5.1 , the MPI-OM (Matei et al 2012) and the ECEarth (Hazeleger et al 2013) forecast systems. Instead, it is computed by the model from the assimilated temperature and salinity fields through a non-linear relation.…”

“…Previous studies (Smith et al 2013;Hazeleger et al 2013;Bellucci et al 2014) showed that the differences in skill of the two techniques at interannual time scales are small and limited to specific regions. Volpi et al (2016) showed that the AI allows for reducing the drift but some residual drift is still present.…”

Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic

“…Both of our baseline systems use anomaly initialization in the ocean and full-field initialization in the atmosphere. In the prototype system, we change to full-field initialization in the ocean, which has been discussed as an alternative to anomaly initialization in recent studies on decadal prediction, although with no clear general recommendation for one or the other (Magnusson et al 2012;Smith et al 2013a;Hazeleger et al 2013;Polkova et al 2014;Carrassi et al 2014;Bellucci et al 2015;Volpi et al 2016). In addition to the ocean state estimate from ORAS4 (Balmaseda et al 2013), which was already used in baseline1, another state estimate, GECCO2 (Köhl 2015), was introduced to calculate initial ocean fields in the prototype system.…”