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As we draw near the Paris climate circus, here are four quotes from the Working Group 1 contribution to IPCC AR5. They illustrate just a few of the uncertainties in climate physics – in case circus folk forget to mention it during the performance.
Uncertainty about the lack of warming
In summary, the observed recent warming hiatus, defined as the reduction in GMST trend during 1998–2012 as compared to the trend during 1951–2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgment, medium confidence). The forcing trend reduction is primarily due to a negative forcing trend from both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus, because of uncertainty in the magnitude of the volcanic forcing trend and low confidence in the aerosol forcing trend. Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus.
TS.4 Understanding the Climate System and Its Recent Changes
Uncertainty about clouds
Cloud formation processes span scales from the sub-micrometre scale of CCN, to cloud-system scales of up to thousands of kilometres. This range of scales is impossible to resolve with numerical simulations on computers, and this is not expected to change in the foreseeable future.
7.2.2 Cloud Process Modelling
Uncertainty about models
Although it is possible to write down the equations of fluid motion that determine the behaviour of the atmosphere and ocean, it is impossible to solve them without using numerical algorithms through computer model simulation, similarly to how aircraft engineering relies on numerical simulations of similar types of equations. Also, many small-scale physical, biological and chemical processes, such as cloud processes, cannot be described by those equations, either because we lack the computational ability to describe the system at a fine enough resolution to directly simulate these processes or because we still have a partial scientific understanding of the mechanisms driving these processes. Those need instead to be approximated by so-called parameterizations within the climate models, through which a mathematical relation between directly simulated and approximated quantities is established, often on the basis of observed behaviour.
FAQ 12.1 | Why Are So Many Models and Scenarios Used to Project Climate Change?
Uncertainty about uncertainty
In proposing that ‘the process of attribution requires the detection of a change in the observed variable or closely associated variables’ (Hegerl et al., 2010), the new guidance recognized that it may be possible, in some instances, to attribute a change in a particular variable to some external factor before that change could actually be detected in the variable itself, provided there is a strong body of knowledge that links a change in that variable to some other variable in which a change can be detected and attributed. For example, it is impossible in principle to detect a trend in the frequency of 1-in-100-year events in a 100-year record, yet if the probability of occurrence of these events is physically related to large-scale temperature changes, and we detect and attribute a large-scale warming, then the new guidance allows attribution of a change in probability of occurrence before such a change can be detected in observations of these events alone. This was introduced to draw on the strength of attribution statements from, for example, time-averaged temperatures, to attribute changes in closely related variables.
10.2.1 The Context of Detection and Attribution
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