Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues
of uncertainties in the observations and / or model parameterizations.
However, the ability to interpret some changes, particularly for non-temperature variables, is limited
by uncertainties in the observations, physical understanding of the climate system, climate models and external forcing estimates.
The large structural
uncertainties in observations hamper our ability to determine how well models simulate the tropospheric temperature changes that actually occurred over the satellite era.
In Sect. 2, we describe the model ensembles and the application of the rank histogram approach, including a description of the statistical method used to define the reliability of model ensembles from the rank histogram, and a method for
handling uncertainties in the observations.
In addition, patterns in the data may be modeled in a way that accounts for randomness and
uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics.
Ideally, studies account for model uncertainty, forcing uncertainty (for example, in aerosol forcing εaer or natural forcing εnat),
uncertainty in observations, εobs, and internal climate variability («noise»).
Additionally, it's important to make a good estimate of
the uncertainty in the observations.
Additionally,
the uncertainty in the observations in the earth's energy balance is significant.
«A review of sources of systematic errors and
uncertainties in observations and simulations at 183 GHz.»
Critcisms of the energy budget model approach are that it is sensitive to
uncertainties in observations and doesn't account for slow feedbacks between the atmosphere, deep oceans and ice sheets.
However natural variability and
uncertainties in the observations and forcings mean that the issue is far from clear - cut.