A common strategy of sceptics is to intentionally confuse and conflate
different types of uncertainty.
One is that the two systems produce
different types of uncertainty.
«Novice readers were unable to identify the two
different types of uncertainties in this graph without substantial guidance,» the Zurich researchers wrote.
Viewing the statistical analysis from a more fundamental level will help to clarify some of the methodologies used in surface temperature reconstruction and highlight
the different types of uncertainties associated with these various methods.
A different type of uncertainty arises in systems that are either chaotic or not fully deterministic in nature and this also limits our ability to project all aspects of climate change.
Not exact matches
It can be difficult to integrate
different types of data with a weighting that properly accounts for degrees
of uncertainty.
Their culture is very
different to that
of the western man, and the
uncertainty of what
type of future they might have can be very daunting to them.
These
uncertainties are reflected in the model simulations
of aerosol concentrations which all show similar total amounts, but have very
different partitions among the
different types.
The first simulates the true temperatures, the second treats the measurement errors that would arise from this series from three
different sources
of uncertainty: i) usual auto - regressive (AR)-
type short range errors, ii) missing data, iii) the «scale reduction factor».
Looking at other decision making frameworks that are more suitable under conditions
of deep
uncertainty motivates a
different type of analysis and emphasizes assessment
of uncertainty and areas
of ignorance.
Apart from the first
type of uncertainty, there is sigmoid
uncertainty: the TCR will be
different by region and by timespan and so will have slower and faster rates
of change associated with it, sometimes stringing together as massive spikes, and always less predictable than we would desire.
In UKCIP08, for example, we are handling this problem by combining results from two
different types of ensemble data: One is a systematic sampling
of the
uncertainties in a single model, obtained by changing uncertain parameters that control the climate system; the other is a multi-model ensemble obtained by pooling results from alternative models developed at
different international centers.
I have also learned how
different types of decision makers make use
of forecast
uncertainty and confidence information.