Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*

Abstract

Current methods for initializing coupled atmosphere–ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

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

“…However, the issue of drift may be tackled successfully by a posteriori bias correction (e.g., Magnusson et al 2012) and the issue of initial shocks is not exclusive to initialization from full-fields. A variety of reasons such as application of independent, uncoupled atmosphere and ocean reanalyses or application of a reanalysis product of poor quality in the assimilation procedure can lead to an imbalanced initial state in the coupled prediction system that triggers initial shocks in the forecasts (e.g., Balmaseda and Anderson 2009;Mulholland et al 2015;Pohlmann et al 2017). In this paper, we investigate how changing our decadal prediction system from anomaly nudging to full-field nudging in the ocean influences our prediction skill for sea surface temperature (SST) and ocean heat content (OHC) in the northern North Atlantic.…”

Forcing Single‐Column Models Using High‐Resolution Model Simulations

“…One problem encountered when initializing a model from a nonnative starting state is the possibility of an initial shock as the forecast model moves rapidly toward its own attractor (Klocke & Rodwell, ; Mulholland et al, ). We only see evidence suggesting this behavior in humidity forecasts in the SCM, with an initial rapid drying and associated anomalously high precipitation rate over the first hour, which decrease to a stable rate.…”

Different types of drifts in two seasonal forecast systems and their dependence on ENSO

“…Mullholland et al (2015), for example, found that imbalances between the atmosphere and ocean initial conditions could create drifts. Heat fluxes to the ocean component will almost certainly be erroneous in the initial state as during the assimilation phase ocean data increments can compensate for errors in the atmospheric forcing.…”

“…Our definition of drift includes changes caused by imperfect initialization, which are sometimes referred to as "shock" (Mullholland et al 2015). We consider the drift in the first four months of the 1 May and 1 November start dates and also the summer (June-August) and winter (December-February) biases.…”

“…Despite its importance, there are relatively few studies of the development of model biases, and the World Climate Research Group (WCRP) Working Group on Subseasonal to Interdecadal Prediction (WGSIP) has recently initiated a project to focus on this area. 1 Strong drifts away from an observed state have been reported in weather forecasts to result from an imbalance in fluxes between the atmosphere and ocean conditions due to insufficient communication between the two model components whilst creating the initial conditions (Mullholland et al 2015). This can be mitigated with coupled data Abstract Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions.…”