Detection and attribution of anthropogenic climate change is astatistical signal-in-noise problem which is produced from the presence of ournatural climate variability. Detection is a word commonly used as a referenceto identifying significant changes in climate such as an upward trend inglobal-mean temperature. The changes must be significantly different and thatcan be explained by natural internal variability. However, detection does notnecessarily give a definite explanation for the cause of change, we must alsoinclude the attribution. For example, when looking at enhanced greenhouseeffect – after statistical methods suggesting that a change has occurred – wemust attribute at least part of such a change to the enhanced greenhouseeffect.  There are two components for natural climate variability: internal andexternal.

Internal components interact within the coupledatmosphere-ocean-ice-land-biosphere system (IPCC, 1995). Whereas, externalcomponents are triggered by natural changes in the Sun’s output or in thevolcanic aerosol loading of the atmosphere. This tells us that due to thenatural internal and external processes, the climate is always changing evenwithout human interaction. Only when the changes are relatively unusual to thepredicted results from natural variability, do we state those changes inclimate are significant.  Detecting a significant change is considered as a statistical problem interms of nature. By using the standard approach, scientists are able to negatethat the observed change in climate can be explained by natural variability,this more commonly referred to as the statistical “null” hypothesis. If thenull hypothesis is rejected, it implies that a detection of a climate change ata specifically significant level has occurred. However, this does not identifythe cause of the change, therefore, we must apply attribution.

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To understandthe “cause and effect”, an investigation must be conducted which involves aseries of experiments. The investigation also involves using the real climatesystem which systematically studies different causes. The experimentationprocess comprises of the following:·        No systematicmethod ·        Varying numerouscauses simultaneously e.g. changing land surface properties, concentrations ofatmospheric greenhouse gases and anthropogenic aerosols The purpose of using the different methods mentioned above instead ofchanging an individual cause is that it’s more efficient, by eliminating theneed 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 comparisonbetween model simulations and the observed changes is required. The detectionof a significant climate change is referred to as a “unique attribution” whichinvolves human activities and both the consideration and elimination ofplausible non-anthropogenic mechanisms.

Though, in a statistical sense, thisdoes not mean results will be certain. The reason for this uncertainty is thatit’s a difficult task to define all probable “natural” climate change signals.Numerical models cannot be ruled out completely, even though it can only statewhether available observations are consistent or inconsistent. This is due toits claim on statistical detection of an anthropogenic signal. Furthermore, accordingto the Intergovernmental Panel on Climate Change, 1995 there is a distinctionbetween accomplishing “practically meaningful” and “statistically unambiguous”attribution. This distinction is based on different perceptions of risk betweenpolicymakers and scientists.  To summarise, in terms of statistics, detection of change is the processof demonstrating that an observed change in climate is highly unusual withoutproviding a reason for the change. On the other hand, attribution processes canestablish the cause and effect which provide us with reasons for the observedchange in climate.

Both cannot give a simple yes or no answer. Also, theresults produced from successfully conducting detection processes will not be adiscrete value, it will be given in a range instead.