Guidance on Using Climate Data in Decision-Making

This page and the sub-pages Using Climate Projections in the AE and Census Tracts offer Analytics Engine data users a set of guiding materials on the most appropriate ways to use climate data for decision making. It includes an explanation of why such guidance is important, provides answers to specific data-related questions that users commonly have, and includes a set of guiding principles (or recommended practices) for climate data use. These guidelines can be utilized by any technical or semi-technical data user to better understand the Do’s and Don’t of working with climate data in the Analytics Engine. By understanding how to effectively integrate climate data into sectors such as energy, municipal planning, natural resource management, and public health, data users can better anticipate and mitigate the impacts of climate change.

How can climate data inform decision-making?

Characterizing changing climate conditions is vital for planning and adaptation. Many sectors and communities are grappling with a growing need to use climate data to anticipate and plan for near- and long-term changes. By assessing the range of possible future conditions under a changing climate, communities can proactively plan for actions that can reduce the impacts of climate change and safeguard lives, livelihoods, and landscapes for decades to come.

The historical and future climate data available on the Analytics Engine can be used to analyze a variety of questions related to climate risks for an agency, community, or region. For such analyses, climate data is often used in combination with other qualitative and quantitative data, such as socioeconomic data, information on institutional capacities, local knowledge, etc. For instance, investor-owned electricity and natural gas utilities in California are required to undertake Climate Adaptation and Vulnerability Assessments. These assessments rely on climate as well as other types of data to identify the populations, infrastructure, and natural systems that are at risk of climate impacts, and to evaluate the utilities’ ability to adapt to such risks.

For an introductory primer on using climate information in risk and impact assessments, users can refer to this Guidance published by the White House. Users can also peruse this comprehensive Climate Data Users Guide developed by the Electric Power Research Institute (EPRI) which provides a fundamental understanding of the various aspects of using climate data. EPRI’s guide includes details about key concepts such as climate models, downscaling, choosing projections, and sources of historical and future climate data. Other valuable sources are also listed in the Analytics Engine website’s References page. Definitions of key terms are available on the Analytics Engine Glossary page.

How should guidance on climate data be used?

Using projections of future climate to inform decision making is an inherently complex process. The science on climate change is continuously being improved and updated, and new datasets and tools for analyzing climate data are actively being developed. Policy and regulation are also evolving to better integrate future climate information, but are faced with difficult questions about how to do this in effective and rigorous ways. Developing a suitable approach for using climate projections depends on several factors, including: the climate-related question that a user wants to answer, the decision that is being made with the climate data, the type of data that is available for the region and decision-context, regulations or mandates for data use, users’ technical capacities to work with climate data, users’ limitations for handling large datasets, users’ risk tolerance, planning timeframes, compatibility of the climate data with other data or frameworks that the user is working with, etc.

Due to this highly contextual nature of climate data use, there can be no one-size-fits-all guidance for how climate models and data should be used to answer specific planning or management-related questions. To this end, the Analytics Engine has undertaken a co-produced and deliberative process that is rooted in practice, regulation, and science, to develop a more flexible set of guiding materials. The following sections will offer context-specific answers for how to use the data available in the Analytics Engine (Guidelines section) as well as general scientific principles on appropriate uses of climate projections (Guiding Principles section [Upcoming]). However, the onus will ultimately always be on the user to identify the best approach for data use within their specific context. It is critical that climate data users incorporate guidance that is based in scientific best practices, such as what is outlined below, to ensure the integrity and appropriate interpretation of their analyses. As climate data and related policies are updated, guidance on data use will also necessarily evolve. Therefore users’ approaches to climate data should be flexible and adaptable to improvements over time. For further specific questions, please reach out to analytics@cal-adapt.org.

For detailed guidelines on appropriate use of climate projections in the Analytics Engine, please see the Using Climate Projections in the Analytics Engine page.

Acknowledgements and Citation

Guidance materials in this section were developed through many, often painstaking, deliberations and discussions with many people within and outside the Analytics Engine project team. Within the Analytics Engine team, Nancy Freitas led and managed the conceptualization and operationalization of all of the guidance-related materials that appear on the Analytics Engine website, with support from Kripa Jagannathan, Justine Bui, and Mark Koenig. Victoria Ford helped to develop the case examples and analytics that are referred to in this document. Justine Bui supported the refinement of the content. Eric Lehmer supported the development of the web-based version of the document. The rest of the Cal-Adapt: Analytics Engine team provided valuable feedback on various versions of this document, and were integral thought leaders for the design of all of the AE’s guidance materials.

Outside of the core Analytics Engine team, the project team members of the EPC-20-006 project "Development of Climate Projections for California and Identification of Priority Projections" helped to edit and refine this document. Specifically, Dan Cayan (Scripps, UC San Diego), David Pierce (Scripps, UC San Diego), Stefan Rahimi (UCLA and University of Wyoming), Julie Kalansky (Scripps, UC San Diego), and Alex Hall (UCLA) provided extensive feedback on several sections of the document. Alexander Pusch (SCE), Alexandria Chwierut (SCE), Eric Kuhle (PG&E), Michael Mak (Pathways Climate), Lindsay Luchinsky (Pathways Climate), as well as other members of the Cal-Adapt: Analytics Engine user engagement team provided critical feedback on the first version of this document. Alan Rhoades (LBNL) also supported the development of a few draft answers.

Finally, Kripa Jagannathan led the content development for the Guidelines Q&A, with support from Nancy Freitas. Owen Doherty, Andrew Jones and Noami Goldenson provided technical expertise in drafting the answers. This content should be cited as: Jagannathan, K., Freitas, N., Doherty, O., Jones, A., and Goldenson, N. (2024). Guidelines on appropriate use of climate projections in the Analytics Engine (v1.0). Cal-Adapt: Analytics Engine. https://analytics.cal-adapt.org/guidance/using_in_decision_making