With the impacts of climate change accelerating at a rapid pace, there is a critical urgency and responsibility for environmental sciences research to be more responsive to society’s scientific needs. There have been several calls for environmental sciences to be more engaged with potential decision-makers, local communities, and users of research. “Co-production” is emerging as a popular engaged-research model that has helped to improve the actionability, impact, as well as acceptance of environmental research. In co-production, scientists conduct their research collaboratively and iteratively with non-academic partners such as decision-makers, communities, or other knowledge-holders. While there are burgeoning examples of how co-production can be used for applied research and development of environmental solutions, there are fewer examinations of how this approach can be applied to research that examines fundamental or basic earth and environmental sciences.

Here, we will highlight the case-study of a US Department of Energy funded basic sciences project - HyperFACETS - that has used co-production for probing fundamental understanding of decision-relevant climate processes. Through 8 years of co-production engagements, we identify three ways in which collaborations can improve the impact of basic sciences: (1) By identifying fundamental research questions/directions that are both scientifically and societally impactful (2) By improving the rigor and credibility of key modelling choices, and (3) By developing research on the “Science of Actionable Knowledge”.

Specifically, we will detail our operational model for co-production, illustrating how we refined our approach to suit a fundamental sciences effort. We include details on the types of non-academic partners that we engaged, the role and expertise of our ‘boundary spanners’, and the mindset shift that was essential for effective co-production. We also provide longitudinal evidence on how the co-production helped to bring in additional social and ethical values (associated with climate-informed resource management) into the climate modeling process. Our operational model, and lessons learnt can be useful to other fundamental sciences projects that are interested in improving the rigor, actionability, and impact of their work.