1. Uncertainty and Complexity: Thresholds in Climate Change Science? Brendan M. Hall CeAL, University of Gloucestershire, UK. Queens University, Kingston, Ontario, Canada. 19/6/08

2. Introduction PhD research – Perceptions of uncertainty + complexity in climate change science Conceptual framework = threshold concepts Today’s presentation: Framing uncertainty and complexity »Climate change »Complex systems and modelling »Human factors »Post-normal science Implications for teaching and learning »Troublesome knowledge Threshold concepts? Provisional findings

3. There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know. - Donald Rumsfeld (2002)

4. Uncertainty & Complexity in Climate Change Science Climate change is a ‘big’ issue Understanding Predicting Acting The climate system is complex – ordered forcing + chaos (Rind 1999) Understanding of individual components may be fairly good but composite effect is uncertain (Gautier and Solomon 2005) Models can be constructed but have limitations (Shackley et al. 1998) Complexity  Uncertainty

5. “The multiplicity of models is imposed by the contradictory demands of a complex, heterogeneous nature and a mind that can only cope with a few variables at a time; by the contradictory desiderata of generality, realism and precision; by the need to understand and also to control; even by the opposing esthetic [sic] standards which emphasise the simplicity and power of a general theorem as against the richness and the diversity of living nature. These conflicts are irreconcilable” (Levins 1966)

6. Human factors and ‘Post-Normal’ Science The climate change issue is socially-situated Human impact on climate – climate’s impact on humans Agenda and dispute Policy/Mitigation Climate change science = Post-Normal? (Saloranta 2001) ‘Normal’ science = puzzle solving within present paradigms (Kuhn 1996, Saloranta 2001) Post-Normal science = facts uncertain, values disputed, stakes high, decisions urgent (Funtowicz and Ravetz 2003) ‘Extended Peer Community’ – stakeholders brought into dialogue on scientific input to decision making Uncertainties are technical/methodological AND epistemological/ethical (Saloranta 2001) Quality

7. “Climate change science necessitates the ability to deal with uncertainty on several levels – not only uncertainty about the workings of the complex physical climate system, but also uncertainty with respect to social and cultural processes that mediate human response to changes within the system” - Rebich and Gautier (2005, p. 355 )

8. Implications for Teaching and Learning? Complexity – conceptually difficult Uncertainty – counter intuitive Post-Normal science – does not fit with students understanding of science (i.e. puzzle solving, hypothesis testing) Uncertainty/complexity addressed explicitly? Troublesome Knowledge? (Perkins 1999)

9. Thresholds? Threshold concepts (Meyer and Land 2003, 2005) Troublesome? Transformative? Integrative? Irreversible?

10. Provisional Findings GEES conference (Plymouth June 2006) Uncertainty and complexity identified as Threshold Concepts by participating academics (Knight 2006, Hall 2006) Interviews with academics in Geography departments in England and Wales (PhD research) Ongoing Uncertainty emerging as a key concept BUT more technical/methodological (i.e. stats) Complexity more implicit Context?

11. References Funtowicz S., Ravetz, J. 2003. Post-normal science. Report to International Society for Ecological Economics. In Internet Encyclopedia of Ecological Economics. February 2003. Gautier, C., Solomon, R. 2005. A preliminary study of students’ asking quantitative scientific questions for inquiry-based climate model experiments. Journal of Geoscience Education 53(4). 432-433 Hall, B. 2006. Teaching and learning uncertainty in science: the case of climate change. Planet 17. 48-49 Knight, Y. 2006. Knowledge, evidence, complexity and uncertainty: a summary. Planet. 17 24-25 Kuhn, T.S. 1996. The Structure of Scientific Revolutions. (Chicago: University of Chicago Press) Levins, R. 1966. The Strategy of model building in population biology. Amer. Sci. 54(4). 421-431 Meyer, J.H.F., Land, R. 2003. Threshold concepts and troublesome knowledge: Linkages to ways of thinking and practising within the disciplines. ETL Project Occasional Report 4, May 2003. From: http://www.ed.ac.uk/etl/docs/ETLreport4.pdf (20/10/06)http://www.ed.ac.uk/etl/docs/ETLreport4.pdf Meyer, J. H. F., Land, R. (2005) Threshold concepts and troublesome knowledge (2): Epistemological considerations and a conceptual framework for teaching and learning, Higher Education, 49. 273 – 288 Rebich, S., Gautier, C. 2005. Concept mapping to reveal prior knowledge and conceptual change in a mock summit course on global climate change. Journal of Geoscience Education. 53(4). 355- 365 Perkins, D. 1999. The many faces of constructivism. Educational Leadership. 57(3). 6-11 Rind, D. 1999. Complexity and Climate. Science. 284. 105-107 Saloranta, T.M. 2001. Post-normal science and the global climate change issue. Climatic Change. 50. 395–404 Shackley, S., Young, P., Parkinson, S., Wynne, B. 1998. Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Climatic Change. 38. 159- 205