Sebastian Illing

The impact of stratospheric volcanic aerosol on decadal‐scale climate predictions

To understand the impact of volcanic aerosol on multiyear seasonal and decadal climate predictions, we performed Coupled Model Intercomparison Project Phase 5-type hindcasts without volcanic aerosol using the German Mittelfristige Klimaprognosen prediction system and compared them to the corresponding simulations including aerosols. Our results show that volcanic aerosol significantly affects the prediction skill for global mean surface air temperature in the first five years after strong volcanic eruptions. Also, on the regional scale a volcanic imprint on decadal-scale variability is detectable. Neglecting volcanic aerosol leads to a reduced prediction skill over the tropical and subtropical Atlantic, Indic, and west Pacific but to an improvement over the tropical east Pacific, where the model has in general no skill. Multiseasonal differences in the skill for seasonal mean temperatures are evident over Continental Europe with significant skill loss due to neglection of volcanic aerosol in boreal winter over central Europe, Scandinavia and over southeastern Europe, and the East Mediterranean in boreal summer.

Decadal climate predictions improved by ocean ensemble dispersion filtering

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 timescales. 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 towards 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 towards the ensemble mean.