3. Assess the claim that, following the recent publication of

the Working Group I contribution (2013) to the IPCC Fifth

Assessment Report,’ there is now no scientific basis left for

disputing the central role of human influence in generating

contemporary global warming’, even though both explaining and

predicting the variable pace of warming continues to prove

surprisingly difficult

Global warming (GW) refers to the unequivocal increase in the

average global surface temperature (IPCC, 2013). The ability

to attribute this to humans is crucial for motivating

international action to mitigate its effects. In their most

recent Assessment Report (AR5) the Intergovernmental Panel on

Climate Change (IPCC) have stated with 95% certainty that,

since the mid-20th century, anthropogenic influence has been

the dominant cause of GW (ibid). This conclusion is said to be

the received opinion of the great majority of governments and its’

populations (Henderson, 2009). Nevertheless there remain

‘climate sceptics’ who argue that this claim is undermined by

the inability of climatologists to explain and predict

variations in the pace of such warming.

Firstly a brief explanation of GW and the anthropogenic

contribution is required. Since the 20th century it is

virtually certain (>99%) that the troposphere has warmed

(IPCC, 2013). This warming has been driven by alternations to

the Earth’s energy budget, more specifically an increase in

the net irradiation at the tropopause (radiative forcing)

(IPCC, 2013b, pp.9). In brief, the theory that human influence

is central to GW derives from the fact that anthropogenic

radiative forcing (mainly due to greenhouse gas (GHG)

emissions) is significantly larger than natural radiative

forcing, which have been more or less neutral during the 20th

century (Fig.1).

The ‘greenhouse effect’ driving GW (an aspect of climate

change (CC)) operates on long timescales, creating multi-

decadal trends, and is therefore distinct from climate

variation (the variable pace of warming). This distinction is

not to say that the climate variation is irrelevant to GW,

simply to clarify that they are different. This relationship

is illustrated by explaining the non-linearity between GHG

emissions and temperature. While the concentration of GHGs in

the atmosphere has been increasing steadily since the

Industrial Revolution, average global

Figure 1: The trends of radiative forcing, for the indicated time periods (IPCC, 2013, pp.695)

temperature has varied significantly (Karl et al., 2009). The

argument that this non-linear relationship undermines the

warming effect of GHGs would only hold if we falsely assumed

that they were the only determinant of temperature. Climate

variation occurs mostly within the bounds of CC and is mostly

due to natural internal variations (IPCC, 2013). However the

climate system is a complex web of interacting components and

climate change and variation do not operate exclusively. Both

are driven by natural variation (IPCC, 2007, Glossary). To

attribute an amount of GW to humans demands an understanding

of the climate system of a whole. The uncertainties limiting

our ability to explain climate variation will also limit our

ability to attribute GW to human influences, even though they

are different processes.

This essay will not discuss the inability to predict future

warming. To say that humans have a central role in

contemporary GW does not require any predictions of the

future, as this is made difficult by aleatory uncertainty

(randomness) and uncertainty over human actions. Given that

the IPCC’s focus on attribution has been within the context of

past warming (IPCC, 2013, 875-952), and abiding by the literal

meaning of contemporary, the inability to predict future

warming is not relevant in this context.

To establish the extent to which GW is attributable to humans

requires the use of models, a highly contestable area of

climate science (NIPCC, 2013). Climate science is a type of

experimental science, and since we have only one Earth

experiments must be done using computer models of the climate

called general circulation models (GCMs) (Swart et al., 2009);

the IPCC itself does not create GCMs but aggregates the data

of multiple climate models created by external scientific

bodies. For the purposes of attribution, GCMs are created with

and without anthropogenic forcing. Since the GCMs used by the

IPCC cannot simulate contemporary GW without anthropogenic

forcing, this suggests that humans are a central cause of that

warming trend (IPCC, 2013). This conclusion relies on our

faith in models. Some of the main points of contention and

reasons for concurrence are assessed below.

Incomplete knowledge of the inputs in climate models creates

uncertainty. Since the IPCC’s First Assessment Report in 1992,

general understanding of the climate has greatly improved, yet

there remain significant gaps in our knowledge (NIPCC, 2013).

The uncertainty regarding the effect of aerosols is one of the

largest. Knowledge of aerosols is vital as they have a central

role in anthropogenic radiative forcing, however their total

effect in the atmosphere is poorly understood (the IPCC (2013)

stating with only medium confidence that it is between -1.9

and -0.1 Wm-2 (see Fig.1 for these numbers in context)).

Uncertainty within models is cumulative; meaning uncertainty

over one input will increase uncertainty over another due to

the interactions within the climate system, such as the

aerosol-cloud interaction (Maslin, 2013).

To incorporate small-scale processes models use a process of

parameterisation; in this respect the IPCC’s use of multiple

models has proven useful. To parameterise a variable is to

solve its equations outside the model and insert the results

afterwards, a process that grossly simplifies reality, but is

necessary for small scale processes such as the aerosol-cloud

interaction and the behaviour of convection currents over

oceans, for which the resolution of climate models is too

large to include and subject to computational limitations

(Anasthwamy, 2011). The choice of parameters has a massive

effect on the emergent behaviour produced by climate models;

climatologist Steve Woolnough (ibid) suggests that most of the

uncertainty range of climate sensitivity in the IPCC’s Fourth

Assessment Report is “probably attributable to the differences

in parameterisations”. Given their significant influence it

may be disconcerting that the choice of parameterisation is an

unavoidably subjective choice based on the best judgements of

experts. The use of multiple models (an ensemble approach) by

the IPCC balances the biases of different models against each

other to reduce uncertainty (Recihler & Kim, 2008).

Nevertheless the simplification is an undeniable flaw and to

achieve precision via aggregation of a reasonably small sample

(Taylor et al., 2012) is not ideal.

Incomplete data of the climate contributes to model

uncertainty. For example, data on deep ocean temperatures is

scarce, even though its effects on global temperature are

thought to be significant (Keigvin et al., 1994); from depths

of 700m -2000m there are no annual global temperature or heat

content estimates prior to 2005, and data for depths below

2000m remains very poor (IPCC, 2013, pp.114). The

incompleteness of data sets contributes to statistical

uncertainty by limiting our knowledge of inputs (above), and

allowed introduces the possibility of ignorance (where we

don’t know what we don’t know). It is difficult to comment on

the importance of a factor that we are ignorant of. Swart et

al. (2009) claim that the IPCC take into account “all other

forms of ignorance about a system for which we don’t have well

founded precise probabilities” (pp.5), however the conflation

of of the terms ‘recognised ignorance’ and ‘ignorance’ (see

for example Table 1, pp.4) means it is unclear whether the

authors are referring to, ‘unknown unknowns’ or ‘known

unknowns’.

When uncertainties are captured by the IPCC, they are

integrated into their conclusions so that they result in

imprecision and do not necessarily form a scientific basis for

dispute. How this relates to the question of attribution is

illustrated by the error bars in Figure 1 (note this shows

radiative forcing which is not the total contribution of

factors). The error bars represent a 5-95% range of

confidence, meaning the IPCC have are 90% confident that the

real value of radiative forcing will be within this range.

Figure 1 shows the likelihood that anthropogenic radiative

forcing is less than the upper range of natural forcing, is

less than 5% (extremely unlikely). While basing science on

likelihood may not seem completely sound, one has to accept

the remarks of theoretical physicist Carlo Rovelli (2011),

that science is not about certainty, and that science relies

instead on the overwhelming unlikeliness that a conclusion is

wrong.

The selection of data between available sources has been

suggested to be a bias process. A recent report from the

National Science Academy (NSA) (2013) has suggested that even

small variations in solar output was have significant effect

of the Earth’s net energy budget. However, while these

findings were acknowledged by NASA (2013), they were ignored

by the IPCC (2013), plausibly because it would refute their

conclusions that solar variations will not influence 21st

century GW. In a Review of the Processes and Procedures of the IPCC (2013)

the InterAcademy Council criticised the insufficient

documentation of the selection of technical and scientific

information, and the complete absence of criteria by which

participants in the assessment process are chosen. The level

of subjectivity allowed for in the selection process is an

area for concern. It allows for the IPCC potentially to

artificially sculpt their confidence levels by choosing only

information that agrees with their conclusions.

We have already mentioned the benefits of the ensemble

approach for improving models, however this does not address

the issue of why we trust the models to reflect the actual

climate system of Earth. The main justification of derives

from their ability to backtest/hindcast. Backtesting refers to

the ability to map the past, for example if you input data

into a model that you believe reflects the conditions of a

past event/date, would it recreate the climate of that time?

If the results of the model are consistently closely related

to those observed in real life, then we have reason to believe

that the model works well (Houghton, 2009). For a non-

scientist it can be extremely difficult to know how successful

the models of the IPCC have been in this respect due to the

polarised interpretations of their results. While the IPCC

naturally proclaim their success, the Nongovernmental

International Panel on Climate Change (NIPCC) (2013) argue

that there remains large differences between model results and

observations.

When assessing the claims of the IPCC we must drop the

assumption that they are purely based on the results of models

and observed data; expert judgement introduces another level

of subjectivity. It is useful to explore this point through

the treatment of uncertainty by the IPCC. There are two ways

in which uncertainty is measured in AR5, quantitatively by

‘likelihood’ or qualitatively by ‘confidence’. The

quantitative format of statements of likelihood can be

misleading in that it suggest objectivity, however expert

judgement is involved in both terms; IPCC publications

(particularly from Working Group 1) often fail to make this

explicit (Swart et al., 2009), in spite of the transparency

that the lead authors are recommended to display in this area

(Mastrandrea et al., 2010).The subjectivity involved at this

stage may invalidate the conclusions if we do not trust the

judgements of experts.

There exists no quantitative study, to my knowledge that

summates the effects of expert judgement of likelihood

estimates, which would suggest in what light we should

interpret the IPCC’s claim of 95% certainty. Using the Third

Assessment Report as an example of practice, while the results

of models and observed data stated with >90% likelihood, the

existence of a significant anthropogenic climate signal,

expert judgement lowered this level of certainty to >66%

(Petersen, 2006). Equally it is general practice to downweight

likelihood assessments to account for residual uncertainty.

These two provide only speculative evidence that the expert

judgement involved may in fact make the 95% a modest and thus

more trustworthy figure.

Whether or not there is a scientific basis for disputing the

conclusions of the IPCC depends very much on the level of

trust held in the experts. While the complexity of climate

science creates a vast number of uncertainties, these are not

themselves an indication of poor science. Furthermore the

degree of certainty with which GW can be attributed to human

influence suggest that we do not require a high degree of

precision. However the rather hidden subjectivity behind the

science means that we can trust the science to the extent that

we can trust the scientists.

Word Count: 1992

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