Climate model uncertainty has changed little over the past few decades despite advances in model complexity and resolution, extensive observational datasets, and considerable resources dedicated to model intercomparison and evaluation projects. In this presentation we review how perturbed parameter ensembles (PPEs) are helping to address this long-term uncertainty challenge. There have been around a hundred PPE studies across climate, weather, atmospheric chemistry, clouds and aerosols using process-based high-resolution models through to global-scale models. Over the last few years, the number of PPE studies has grown rapidly, as has the range of challenges that they are being applied to. Building on the successful applications of PPEs that have emerged over the last few years, we define several research priorities that would accelerate our understanding of model uncertainty and ultimately help to reduce it. Opportunities include:Providing robust uncertainty estimates and more fully characterizing the plausible spread in climate projections, which is vital to better communicate current knowledge to downstream science, impacts and decisions. Defining model development priorities by efficiently identifying model structural model deficiencies. Diagnosing the causes of inter-model spread within MIPs and to enable statistically rigorous multi-model constraint, Identifying new observations and new ways of using existing observations to provide tighter constraints on model uncertainty. Contributing to the development of parameterizations by disentangling complex processes and sensitivities across a hierarchy of models. We also highlight how fully exploiting the potential of PPEs requires closer collaboration of the modelling and observational communities to address the particular challenges of using observations in model uncertainty quantification and constraint.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)