Transcript Uncertainty and Complexity: Thresholds in Climate Change

Uncertainty and Complexity:
Thresholds in Climate Change
Science?
Brendan M. Hall
CeAL, University of Gloucestershire, UK.
Queens University, Kingston, Ontario, Canada. 19/6/08
Introduction
• PhD research – Perceptions of uncertainty +
complexity in climate change science
• Conceptual framework = threshold concepts
• Today’s presentation:
• Framing uncertainty and complexity
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Climate change
Complex systems and modelling
Human factors
Post-normal science
• Implications for teaching and learning
» Troublesome knowledge
• Threshold concepts?
• Provisional findings
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)
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
“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)
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
“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 )
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)
Thresholds?
• Threshold concepts (Meyer and Land
2003, 2005)
• Troublesome?
• Transformative?
• Integrative?
• Irreversible?
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?
References
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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)
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). 355365
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. 159205