Actionable climate science entails the provision of local-to-regional scale climate information to support decision-making regarding climate impacts, adaptation and mitigation. Despite advances in theories, observations, climate modeling techniques, and high performance computing, large uncertainties in model projections of climate change remain that challenge our understanding of the controlling processes, and hampers the use and communication of climate information for societal benefits.
The FACETS project aims to develop a hierarchical model evaluation framework informed by different uses of climate models and climate model outputs by climate scientists and stakeholders for planning and managing resources. The framework features a cascade of metrics including standard descriptive metrics, new climatological and ingredients-based quality metrics, new phenomena-based metrics for specific event types, and integrated regional metrics that combine the above metrics for specific regions. Metrics specifically tailored for the Energy-Water-Land nexus will inform strategies for modeling human-Earth system dynamics at regional scales.
The metrics will take advantage of multi-scale, multi-source observational data for evaluation of model fidelity and exploration of the mechanisms and sources of model errors and uncertainty. Representative approaches from a suite of dynamical, empirical, and hybrid downscaling models are selected and structured under hierarchical experiments that feature benchmarking simulations across a range of spatial resolutions and modeling approaches. Some numerical experiments will focus on the impacts of future land use and land cover changes associated with food and bioenergy crop production and urbanization, and expansion of wind turbine deployment, which highlight specific challenges for modeling the E-W-L nexus.
Key outcomes of the project are: (1) A set of methodologies, algorithms, and software functions and scripts for individual and integrated metrics based on standard and high risk / high benefit approaches; (2) Determination of the relative value of the metrics, differentiation of model skill based on the metrics and hierarchical experiments, and understanding of the spatial scale dependence of the results; (3) Elucidation of local human impacts related to the E-W-L nexus on climate and the requirements for coupled modeling of the human-Earth systems at regional scales; (4) A rich dataset produced by the hierarchical modeling experiments archived and made available for use by the broader climate science community and stakeholders; and (5) A rigorously tested and community-vetted model evaluation framework and tools that form the basis for future development of a computationally enabled user-friendly model evaluation system for community use.