The production of actionable earth system science relies on effective communication of regional data and its associated uncertainties across sectors. To be of value beyond academic circles, data must be sufficiently credible (i.e., physically grounded), understandable (communicated in the vocabulary of the decision-makers), and useful for the particular decisions that need to be made. Comprehensive assessment of both dynamical and statistical earth system models adds substantial value to their outputs, particularly when the evaluation criteria are the product of a two-way dialogue between scientists and end-users. Substantial progress has now been made on developing comprehensive frameworks for data assessment that incorporate process-oriented, feature-specific, and use-inspired metrics. These efforts have been particularly advanced over the continental U.S. (CONUS) under both the Department of Energy Hyperion and FACETS projects. This project continues these efforts so as to further (1) advance our understanding of processes at the earth system-water-energy-land-decision interface, and (2) fundamentally improve our ability to perform credible modeling of particular regions. Here a multi-pronged approach has been developed to achieve these goals, by:

First, engaging scientists and stakeholders within working groups to build common understanding and strategy for achieving these goals. This project will further refine the engagement process from earlier work and understand how datasets are being used for specific decision applications.

Second, developing a storyline context to frame our assessment activities and provide a means to effectively interface our scientific pursuits with stakeholder interests. Formally, a storyline is a physically self-consistent unfolding of a past event, or of a plausible future event or pathway. These storylines are chosen to be representative of major climatic events that have impacted, or would impact, the policy or decision context.

Third, developing new metrics and leveraging existing metrics to evaluate and understand modeled processes, and subsequently inform credibility and uncertainty in light of a non-stationary earth system. We will perform deep dives into weather extremes, snowpack, low-flows and flooding regimes in rivers, water quality, and wind extremes. A key outcome will be the identification of process biases and errors that are most responsible for uncertainties in available products.

Fourth, using differential credibility analysis (DCA) to understand what aspects of predictions and projections of earth system change are credible given our understanding of the processes and errors in these models. This process involves a broad assessment of model performance, and will target the validity of datasets for decision making.

Fifth, developing a deeper understanding of multi-sector interactions (those at the earth system-water-energy-land-decision interface), the interplay between global and regional forcings and implications for key aspects of energy supply using earth system simulations conducted at a range of spatial scales for scenarios of changing land use, irrigation and energy mix.