Constraining uncertainty of multi decadal climate projections
CONSTRAIN will focus research on three climate science knowledge gaps and a policy-facing knowledge gap that can be resolved over the next 4-5 years to significantly improve our understanding of how natural and human factors affect multi-decadal regional climate change. This will cement EU science as the world-leader in understanding climate sensitivity and climate variability, deliver significantly improved capability to make climate projections for the next 20-50 years, and provide up-to-date scientific evidence for international climate policy in two phases: Phase 1 will deliver a timely characterisation of physical science uncertainty and how it affects projections and committed levels of warming to the 2021 IPCC sixth assessment report; Phase 2 will deliver constrained surface temperature projections for the 2023 UNFCCC Global Stocktake. CONSTRAIN will take full advantage of climate model integrations from the sixth Climate Model Intercomparison Project (CMIP6) and will leverage existing H2020 and ERC projects. Novel CMIP6 analyses will be combined with dedicated high resolution simulations and new observations to address identified knowledge gaps on radiative forcing, cloud feedbacks and the relationship between ocean variability and atmospheric change. A fourth identified knowledge gap is the effective translation of new physical science understanding into an improved evidence base for policy decisions. CONSTRAIN will address this by developing climate model emulators that integrate and operationalise learning from across the consortium to provide new capability to assess impacts of climate change under a broad range of emission scenarios. We will focus on the expected spatially resolved decadal changes until mid-century providing robust evidence on climate sensitivity, and regional temperature, precipitation and circulation changes, thereby enabling evidence-based policy decisions that will directly benefit the EU's adaptation and mitigation strategy.