Uncertainties

Overview | Examples | Development

Overview

It is well known, that the weather is instrinsically chaotic. Although much of this variability cancels when considering long-term average climate change, nevertheless over such timescales other uncertainties arise, concerning slow ocean mixing and ice-melt processes, biogeochemical cycles and biospheric feedbacks, and also changes in human society.

So climate prediction will never be an exact science, and any decision based on climate predictions must be a risk judgement. Since the risks depend on a complex combination of uncertainties from many interacting processes, this leads to conflicting advice from "experts" each focusing on specific parts of the whole system. People may then get the impression that everthing is so uncertain, so why bother to draw any conclusions at all?

Actually, a clearer picture can emerge, so long as we think carefully about how the various uncertainties fit together. Are they intercorrelated or independent - is the impact of an uncertain factor damped by negative feedbacks with other processes, or amplified by positive feedbacks? Are we more concerned about the uncertainty in the overall climate impact, or the uncertainty in the change in impact due to a particular mitigation or adaptation policy measure? The net benefit of that particular measure may be clear, regardless of the overall uncertainty.

Mixing Uncertanties

So we should be wary of summarising mixed uncertainties in overall probability statistics or error bands on plots. In particular, it can be misleading to combine very different kinds of uncertainties, for example:
  • Calculating a probability estimate for sea-level rise by mixing uncertainties from low probability - high impact processes such as the collapse of the West Antarctic ice-sheet, with high probability lower impact processes, such as thermal expansion. The result would be an in-between number which does not give meaningful information about either type of risk.
  • Quoting an uncertainty range in in future temperature predictions derived by combining uncertainties in natural climate system processes (as predicted by various global climate models), with uncertainty regarding emissions scenarios (considering various future human "world-views"). This combination may be useful when considering local adaptation policies, but is rather fatalistic in the context of global mitigation policy (the UNFCCC process) which aims to control emissions.
  • (see also Different approaches to the climate problem)

    Presentation in Java Climate Model

    So how can we present complex interacting uncertainties, avoiding mixed statistics? This interactive web model offers people a unique opportunity to experiment by adjusting parameters themselves. The instant response illustrates cause and effect -how much difference does each parameter make, and how does this depend on the settings of other parameters? By viewing several plots together, you can also look for feedback processes which may be dampening or amplifying the effect.

  • See also cause-effect relationships.

    Moreover, the mitigation options to stabilise greenhouse gas concentration and temperature (including emissions from all gases and aerosols) help to show the importance of scientific uncertainties in the context of inverse calculations -i.e. given a specific climate target, what are the range of pathways towards this.

  • See Mitigation /Stabilisation

    Of course, the presentation of uncertainty in this model could always be improved, see

  • future development.

    Specific Examples

    Discussion of specific uncertainties for each component of the system, is now contained in the documentation for each plot. See:
  • Carbon Cycle:
  • Other gases:, F-gases:
  • Radiative Forcing:
  • Temperature:
  • Sea-level:
  • Regional Climate:

    Some factors, however, are more uncertain than others. The table below makes some comparisons:
    Component Better understood Less well understood
    Emissions CO2, F-gases CH4, N2O, other gases (especially from soils)
    Carbon Cycle Ocean sink (physical and chemical) Biosphere sink (climate feedback effects)
    Atmospheric Chemistry F-gases, CH4, N2O Ozone and OH feedbacks
    Radiative Forcing Well-mixed greenhouse gases Solar Variability and Aerosols
    Temperature Ocean warming (except surprise circulation changes) Cloud processes and feedbacks ("climate sensitivity")
    Sea-level Thermal Expansion Polar icecaps
    Regional Climate Average Temperature Precipitation and Winds

    Naturally there are many different views regarding relative ucnertainties: Please tell me your opinions!
    Note also, that emissions scenarios are a different kind of uncertainty, not considered here. See also "different approaches to the climate problem"

    Further Development

  • It is acknowledged that an uncertainty range should be shown for some individual parameters, this feature will be developed soon, but suggestions for how best to portray it are welcome! It may be necessary to adjust this range dynamically where uncertainties are not independent.

  • Noting that "Climate change decision making is essentially a sequential process under general uncertainty" (IPCC Synthesis report Q1), how can we try to reach a particular goal such as that specified in UNFCCC Article Two? One approach may be to investigate "Fuzzy control" strategies incorporating deliberate climate-emissions feedbacks which are more robust against uncertainties. The structure of this model was designed to enable investigation of such feedbacks.

  • It is also important to note that many types of uncertainty cannot be represented in "deterministic" simple climate models such as this one. A long-term aim is to investigate the possibility to develop interactive versions of intermediate complexity models incorporating more non-linear feedbacks, and intrinsic and regional variability.