EGU2020-20133
https://doi.org/10.5194/egusphere-egu2020-20133
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Assessing the quality of climate information for adaptation.

Marina Baldissera Pacchetti1, Suraje Dessai1, Seamus Bradley1, and David A Stainforth2
Marina Baldissera Pacchetti et al.
  • 1University of Leeds, Sustainability Research Institute, Earth and Environment
  • 2London School of Economics and Political Science, Grantham Research Institute on Climate Change and the Environment

There are now a plethora of data, models and approaches available to produce climate information intended to inform adaptation to a changing climate. There is, however, no analytical framework to assess the epistemic issues concerning the quality of these data, models and approaches. An evaluation of the quality of climate information is a fundamental requirement for its appropriate application in societal decision-making. By integrating insights from the philosophy of science, environmental social science and physical climate science, we construct an analytical framework for “science-based statements about future climate” that allows for an assessment of their quality for adaptation planning. We target statements about local and regional climate with a lead time of one to one hundred years. Our framework clarifies how standard quality descriptors in the literature, such as “robustness”, “adequacy”, “completeness” and “transparency”, rely on both the type of evidence and the relationship between the evidence and the statement. This clarification not only provides a more precise framework for quality, but also allows us to show how certain evidential standards may change as a function of the purpose of a statement. We argue that the most essential metrics to assess quality are: Robustness, Theory, Completeness, Adequacy for purpose, Transparency. Our framework goes further by providing guidelines on when quantitative statements about future climate are warranted and potentially decision-relevant, when these statements would be more valuable taking other forms (e.g. qualitative statements), and when statements about future climate are not warranted at all.

How to cite: Baldissera Pacchetti, M., Dessai, S., Bradley, S., and Stainforth, D. A.: Assessing the quality of climate information for adaptation., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20133, https://doi.org/10.5194/egusphere-egu2020-20133, 2020.

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  • CC1: Comment on EGU2020-20133, John Clarke, 05 May 2020

    Hi Marina,

    Thank you for the abstract, presentation and your (brief) session on the chat. I can't claim to fully understand it, but it's pleasing to see that your field(s) can help inform better delivery/communication of projections for adaptation. Regarding your comment on 'false precision', it is a constant struggle to balance users' desire for information that is easy to understand and apply with the complexity that is inherent to climate projections. In my experience (10+ years in Australia and the Pacific Islands) assisting (and training) users make meaningful use of projections data, I have frequently seen the false precision problem manifested when climate projections information is presented in over-simplified ways (e.g. the eye-catching stuff that the media - and some IT specialists - love). It's a shame you didn't get to give the full presentation.

    Best wishes,

       John

    CSIRO Melbourne, Australia

    • AC1: Reply to CC1, Marina Baldissera Pacchetti, 05 May 2020

      Dear John,

      Thank you for your comment.

      With our framework we do indeed hope to draw attention to the point you make: when information is generated, the assumptions need to be clearly stated (which is what we would call "transparency") and their consequences and limitations also need to be explained (in order to clarify whether the information is "adequate for purpose X", where X, in our case, is informing climate adaptation). I would agree that some ways of conveying information and the uncertainty related to that information can obscure these assumptions and their consequences, especially when this information is intended for non expert audiences.

      Hopefully I will be able to share the published version of the paper soon!

      All best,

      Marina

      • CC2: Reply to AC1, John Clarke, 07 May 2020

        Hi Marina,

        Many thanks - I look forward to seeing the paper.

        Best wishes,

           John