Temperature
'The potential to narrow uncertainty in regional climate
predictions', 2009, BAMS
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Abstract: Faced by the realities of a
changing climate, decision makers in a wide variety of organisations
are increasingly seeking quantitative predictions of regional and
local climate. An important issue for these decision makers, and for
organisations that fund climate research, is what is the potential for
climate science to deliver improvements - especially reductions in
uncertainty - in such predictions? Uncertainty in climate predictions
arises from three distinct sources: internal variability, model
uncertainty and scenario uncertainty. Using data from a suite of
climate models we separate and quantify these sources. For predictions
of changes in surface air temperature on decadal timescales and
regional spatial scales, we show that uncertainty for the next few
decades is dominated by sources (model uncertainty and internal
variability) that are potentially reducible through progress in
climate science. Furthermore, we find that model uncertainty is of
greater importance than internal variability. Our findings have
implications for managing adaptation to a changing climate. Because
the costs of adaptation are very large, and greater uncertainty about
future climate is likely to be associated with more expensive
adaptation, reducing uncertainty in climate predictions is potentially
of enormous economic value. We highlight the need for much more work
to compare: a) the cost of various degrees of adaptation given current
levels of uncertainty; and b) the cost of new investments in climate
science to reduce current levels of uncertainty. Our study also
highlights the importance of targeting climate science investments on
the most promising opportunities to reduce prediction
uncertainty. doi: 10.1175/2009BAMS2607.1 |
Precipitation
'The potential to narrow uncertainty in projections of regional
precipitation change', 2010, Climate Dynamics
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Abstract:
We separate and quantify the sources of uncertainty in projections of
regional precipitation changes for the 21st century using the CMIP3
multi-model ensemble, allowing a direct comparison with a similar
analysis for regional temperature changes. For decadal means of
seasonal precipitation, internal variability is the dominant
uncertainty for predictions of the first decade everywhere, and for
many regions until the third decade ahead. Model uncertainty is
generally the dominant source of uncertainty for longer lead
times. Scenario uncertainty is found to be small or negligible for all
regions and lead times, apart from close to the poles at the end of
the century. For the global mean, model uncertainty dominates at all
lead times. The signal-to-noise ratio (S/N) of the precipitation
projections is highest at the poles but less than 1 almost everywhere
else, and is far lower than for temperature projections. In
particular, the tropics have the highest S/N for temperature, but the
lowest for precipitation. We also estimate a `potential S/N' by
assuming that model uncertainty could be reduced to zero, and show
that, for regional precipitation, the gains in S/N are fairly modest,
especially for predictions of the next few decades. This finding
suggests that adaptation decisions will need to be made in the context
of high uncertainty concerning regional changes in precipitation. For
regional temperature projections we find a far greater potential to
narrow uncertainty. doi: 10.1007/s00382-010-0810-6 |