Usability of Climate Data in Climate Change Planning and Management

1. The usability of climate data in climate-change planning & management(Informally, for Faculty) Richard B. Rood rbrood@umich.edu October 27, 2015

2. Outline • A collection of slides for discussion

3. Talking about these papers • Lemos and Rood, Climate Projections and their Impact on Policy and Practice, WIRSEcc, 2010 • Useful vs Usability (not the only ones) • Uncertainty Fallacy • Rood and Edwards, Climate Informatics: Human Experts and the End-to-End System, Earthzine, 2014 • Improving the usability of data systems and data services • Barsugli et al., Practitioners Dilemma, EOS, 2013 • Briley et al., Overcoming barriers climate information for decision-making, Climate Risk Management, 2015 • Briley et al., Meteorological process and uncertainty in decision-making, Theoretical and Applied Meteorology, 2015

4. Useful and Usability • Scientists often talk about the usefulness of their data (observations or projections) • Practitioners talk about the usability of data, information and knowledge • Practitioners? • Urban planners • Public health • Ecosystem managers • Water managers • …

5. Knowledge from Predictions Motivates action Uncertainty of the Knowledge that is Predicted ACTION Science: Knowledge and Uncertainty • Uncertainty always exists • New uncertainties will be revealed • Uncertainty can always be used to keep from doing something

6. Science: Knowledge and Uncertainty Knowledge from Predictions Motivates action Uncertainty of the Knowledge that is Predicted ACTION • Uncertainty always exists • New uncertainties will be revealed • Uncertainty can always be used to keep from doing something What we are doing now is, largely, viewed as successful. We are reluctant to give up that which is successful. We are afraid that we will suffer loss.

7. The Uncertainty Fallacy • That the systematic reduction of scientific uncertainty will lead to action (development of policy, use in planning, etc.) is a fallacy. • Uncertainty can always be used to disrupt action. (Selective doubt.) • Climate is a political, economic, … • Practitioners more often want uncertainty descriptions and context-based descriptions rather than quantification. Usable Science? Tang and Dessai (2012)

8. CLIMATE 530 • Climate Change in Planning and Design

9. Provision of data, information and knowledge • Useful, usability • Uncertainty • Description • Context • Quantification • How uncertainty is used

10. How do we organize problem solving? Observations Predictions Data … Loading Dock Model

11. Knowledge System: Translation • Need to bring together disparate information and different points of view to develop strategies for applied problem solving • Key to development of successful strategies: iterative process or co-development with information providers and information users Cash et al: 2002 Lemos & Morehouse, 2005 Dilling & Lemos, 2011

12. Knowledge System, Science Focused Dilling & Lemos, 2011 • Information brokers • Collaborative group processes • Embedded capacity • Boundary Organizations • Knowledge Networks Applications Science & Research Cash et al: 2002 • Boundary Management • Dual Accountability • Boundary Objects Cash et al: 2002 • Legitimacy • Credibility • Salience

13. Credibility, Legitimacy, Salience • Credibility is an attribute of scientific adequacy. • Legitimacy is an attribute of objectivity, fairness, and a lack of political bias. • Salience requires that information be relevant to the problem to be addressed.

14. Salience is most Challenging • Usable Science? Tang and Dessai (2012) • U.K. Climate Projections 2009 (UKCP09) • Bayesian probabilistic projections – highly quantitative uncertainty descriptions • Increases credibility and legitimacy • Reduces salience and usability • Understanding and Interpretation • Information required • Strategy to increase salience • Tailoring to adaptation context or problem

15. Translation • The chain from useful to usable can be viewed as translation

16. Types of Translational Information Applications Global Regional Local Basic Data Digital Information Indices Downscaled GIS Formats Seasonality Assessments IPCC NCA Local Narratives What has happened? What will happen? What are the impacts? Guidance Judgment Model Output Fact Sheets Summaries Images Figures Observations Quality Assessment Homogeneity Uncertainty Descriptions Risk Assessments

17. Engagement with cities (and others) • Often the first question is what data are available and how do we get it? • After discussions of data quality, uncertainty, evaluation and data manipulation we move to three questions: • What has happened? • What will happen? • What are the impacts or consequences? • GLISA Climate Information Guide

18. Experience from Climate Change Problem(We are early in this process)http://www.glisaclimate.org/climate-information-guide Vulnerability Risk Benefit What Has Happened? What Will Happen?

19. Data consequences of questions • What has happened leads almost inevitably to weather station data • Trusted by locals and planners • What will happen leads to use of projections • Climate Model Intercomparison Project (CMIP) • Downscaled versions of CMIP • Other sources of projection information

20. Data consequences of questions • Linking what has happened (station data) to what will happen (model projections) requires evaluation of models relevant to the problem at hand – • In most cases that we work on, handing the climate projections or downscaling data to practitioner is of little value • What is desired is a context based narrative description

21. Evaluation / Salience / Tailoring • Evaluation of the data, information knowledge for the specific application is essential to usability. • The need to provide data to be used in evaluation rather than to be plotted and used is a challenge to how we design data systems. • Especially because of the data use in applications • Need for application relevant data / indices

22. Alignment of information • Here we see • Local observation or experience • Alignment with regional observations • Alignment with the narrative of the models • More precipitation in extreme events • Vulnerability • Likely success in integrating climate knowledge in policy and planning

23. Role of “processes” • If the climate model represents the weather processes of a region or locality with some fidelity, then there is a framework for the discussion of uncertainty. • Absence of process fidelity / absence of definitive “what has happened” undermines usability  strategies • Examples: • U.S. Gulf Coast – sea breeze precipitation / El Nino • U.S. Great Lakes – lake effects

24. Human experts • Human experts are an integral part of the information system. Rather than design the human out of the information system, effort should be focused on collecting the needed human expertise and improving the efficiency of the human expert.

25. Evaluation • An important part of the climate information system is the need to evaluate the suitability of data and knowledge for a particular application.  Therefore, information system design needs to facilitate the evaluation step.  The unmet need for evaluation stands as a barrier to delivering the most appropriate and readily usable data for particular purposes.