Earth System Initialization for Decadal Prediction (EaSyInit)
Designing mitigation and adaptation strategies to respond to the consequences of global climate are some of the world’s greatest challenges. The decision support that is essential to meet this challenge requires high-resolution regional climate predictions over the next 10 to 30 years. In the next decades, climate changes due to natural causes and anthropogenic activities are expected to have similar amplitude.
There is a commitment to climate change due to the increase emissions of greenhouse gasses and the related radiative forcing that we have already made and that needs to be quantified with initialized climate change projections. Natural variability may strengthen or damp the climate response due to anthropogenic activities these potential impacts need to be accurately quantified. Aspects of the natural climate variability may be predictable on decadal time scales. The advance of the earth system observation network, the ARGO in situ ocean measurements in particular, the increased knowledge on decadal modes of variability and the improvement of earth system models has led to numerical experimentation on decadal predictions. Recent studies have shown that predictability can be obtained in complex numerical climate models, based on the long-term memory that resides in the ocean.
The World Climate Research Program has realized the potential of decadal predictions. In the next Coordinated Model Intercomparison Project (CMIP5), which will feed into the IPCC 5th Assessment, a protocol has been set up to provide decadal predictions. It is expected that the ensemble of decadal predictions will be used in the process to develop adaptation strategies, as many investments on infrastructure and in industry are paid of in decades.
Ocean Synthesis Directory
To facilitate the usage of modern ocean synthesis products for the initialization of coupled earth system models for decadal predictions the Ocean Synthesis Directory at ICDC provides information on and access to data from most of the available global ocean syntheses.