Greenhouse gas emissions are warming the climate. A highly uncertain consequence is the crossing of climate tipping points, where parts of the Earth system undergo rapid, self-amplifying changes that are irreversible on societal timescales. Here we investigate tipping behavior in the North Atlantic subpolar gyre, a circulation feature south of Greenland that redistributes heat, freshwater, and nutrients. A sustained weakening would alter ocean stratification and deep-water formation, with impacts for marine ecosystems and European climate. A central bottleneck in quantifying tipping timescales, impacts and uncertainty is data scarcity: transitions are rare in observations and under-sampled in conventional climate-model ensembles. This limits data-hungry AI/ML approaches, while long high-resolution simulations needed to capture processes that trigger transitions are prohibitively expensive. We therefore employ rare-event algorithms, which oversample low-probability transitions by adaptively reweighting and branching ensemble members, generating statistically meaningful samples of rare outcomes at lower cost than direct simulation. We are planning a programme of rare-event sampling experiments in UKESM, designed for execution on ARCHER2. Because the relevant transitions are rare, the approach requires many parallel multi-year coupled simulations with branching and extensive analysis, making it exceptionally demanding computationally. ARCHER2’s national-scale performance and ability to sustain large ensembles are central to the project, enabling us to move beyond under-sampled ensembles toward meaningful estimates of SPG transition likelihoods and pathways. We will describe the proposed simulation plan and report initial implementation and performance tests.
Mascolo et al. (Thu,) studied this question.