Decadal climate predictions improved by ocean ensemble dispersion filtering

Abstract

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere‐ocean interaction plays an important role for such climate forecasts. While short‐term weather forecasts represent an initial value problem and long‐term climate projections represent a boundary condition problem, the decadal climate prediction falls in‐between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

“…The ensemble resampling approach in EDF is close to that of jackknife resampling (Quenouille, ). The EDF was shown to lead to more accurate predictions than those from the MiKlip prediction system in terms of global mean and regional temperature, precipitation, and winter cyclones (Kadow et al, ).…”

Current and Emerging Developments in Subseasonal to Decadal Prediction

“…However, initialization using full-field observational values can lead to initial shocks affecting skill (Kröger et al 2018) and in such cases initialization combining observed anomalies with the model's own climatology can be beneficial until underlying model errors can be reduced (Volpi et al 2017). Basic initialization strategies continue to be an active research area particularly for decadal prediction (Brune et al 2018), and methods extending to forecast runs such as the ensemble dispersion filter, which replaces the ensemble members with the ensemble mean every 3 months (Kadow et al 2017) are also being explored. Comparisons that apply different initialization methods to the same model can yield valuable insights (Polkova et al 2019); further issues specific to the initialization of the land, ocean, and sea ice components are considered in the next section.…”

Estimates of Decadal Climate Predictability From an Interactive Ensemble Model

“…In general, the IE does not greatly change the mean state of SST, but slight shifts are found, for example, in the Gulf Stream/North Atlantic current region ( Figure S5). There are large regions like the subtropical North Atlantic showing relatively longer predictability with the IE, which could be simply explained by the noise reduction in the IE suggesting that the noise is an important factor limiting predictability (Kadow et al, 2017). Nevertheless, there are considerable regions showing shorter decadal predictability such as the subpolar North Atlantic.…”