Background:

Both Internal Climate Variability (ICV) and anthropogenically induced climate change combinedly drive the evolution of future global and regional climate. The recently released Sixth Assessment Report of the IPCC (AR6) summarizes the report for policy makers called Summary for Policy Makers – henceforth referred to as SPM. Some of the relevant points about the role of humans in the global warming trend quoted in SPM are as follows:

                    "Human-induced climate change is already affecting many weather and climate extremes in every region across the globe. Evidence of observed changes in extremes such as heatwaves, heavy precipitation, droughts, and tropical cyclones, and, in particular, their attribution to human influence, has strengthened since AR5."

Variations in ICV in near-term are large enough to amplify or attenuate the human induced warming trend as reported in SPM is as follows:

                        "Internal variability has largely been responsible for the amplification and attenuation of the observed human-caused decadal to multi-decadal mean precipitation changes in many land regions. At global and regional scales, near-term changes in monsoons will be dominated by the effects of internal variability."

This makes ICV an important driver of trend in warming at multi-year to decadal timescales. Most important ICV affecting almost all the regional climate across the globe are Pacific Decadal Oscillation (PDO) / Inter-decadal Pacific Oscillation (IPO) in the Pacific Ocean and Atlantic Multi-decadal Oscillation (AMO) in the Atlantic Ocean. These variability are also believed to be the modulator of Global Mean Surface Temperature (GMST) in near-term, hence affecting many regional climate systems across the globe, as reported by many studies in recent past.

Skillful prediction of these ICV modes can help us predict the future evolution of climate at near-term scales. Decadal Climate Prediction Project (DCPP) within Coupled Model Inter-comparison Project Phase 6 (CMIP6) aims to produce skillful real-time forecast at multi-year scales. Experiments under DCPP are divided into three components with different deliverables. These components and their deliverables are as listed here.