Detection and attribution of anthropogenic climate change is a
statistical signal-in-noise problem which is produced from the presence of our
natural climate variability. Detection is a word commonly used as a reference
to identifying significant changes in climate such as an upward trend in
global-mean temperature. The changes must be significantly different and that
can be explained by natural internal variability. However, detection does not
necessarily give a definite explanation for the cause of change, we must also
include the attribution. For example, when looking at enhanced greenhouse
effect – after statistical methods suggesting that a change has occurred – we
must attribute at least part of such a change to the enhanced greenhouse


There are two components for natural climate variability: internal and
external. Internal components interact within the coupled
atmosphere-ocean-ice-land-biosphere system (IPCC, 1995). Whereas, external
components are triggered by natural changes in the Sun’s output or in the
volcanic aerosol loading of the atmosphere. This tells us that due to the
natural internal and external processes, the climate is always changing even
without human interaction. Only when the changes are relatively unusual to the
predicted results from natural variability, do we state those changes in
climate are significant.

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Detecting a significant change is considered as a statistical problem in
terms of nature. By using the standard approach, scientists are able to negate
that the observed change in climate can be explained by natural variability,
this more commonly referred to as the statistical “null” hypothesis. If the
null hypothesis is rejected, it implies that a detection of a climate change at
a specifically significant level has occurred. However, this does not identify
the cause of the change, therefore, we must apply attribution. To understand
the “cause and effect”, an investigation must be conducted which involves a
series of experiments. The investigation also involves using the real climate
system which systematically studies different causes. The experimentation
process comprises of the following:

No systematic

Varying numerous
causes simultaneously e.g. changing land surface properties, concentrations of
atmospheric greenhouse gases and anthropogenic aerosols


The purpose of using the different methods mentioned above instead of
changing an individual cause is that it’s more efficient, by eliminating the
need to observe climate responses, and then vary the next cause.


In addition, the use of numerical models is needed for the experiment.
To determine the signals for different hypothesised causes, a comparison
between model simulations and the observed changes is required. The detection
of a significant climate change is referred to as a “unique attribution” which
involves human activities and both the consideration and elimination of
plausible non-anthropogenic mechanisms. Though, in a statistical sense, this
does not mean results will be certain. The reason for this uncertainty is that
it’s a difficult task to define all probable “natural” climate change signals.
Numerical models cannot be ruled out completely, even though it can only state
whether available observations are consistent or inconsistent. This is due to
its claim on statistical detection of an anthropogenic signal. Furthermore, according
to the Intergovernmental Panel on Climate Change, 1995 there is a distinction
between accomplishing “practically meaningful” and “statistically unambiguous”
attribution. This distinction is based on different perceptions of risk between
policymakers and scientists.


To summarise, in terms of statistics, detection of change is the process
of demonstrating that an observed change in climate is highly unusual without
providing a reason for the change. On the other hand, attribution processes can
establish the cause and effect which provide us with reasons for the observed
change in climate. Both cannot give a simple yes or no answer. Also, the
results produced from successfully conducting detection processes will not be a
discrete value, it will be given in a range instead.