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| Titel |
Why is Climate sensitivity not doomed to be unpredictable? |
| VerfasserIn |
A. Hannart, J.-P. Boulanger, J.-L. Dufresne, P. Naveau |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250028978
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| Zusammenfassung |
| Uncertainties in model projections of future climate change are high, and have not decreased
substantially over the last 30 years: IPCC AR4 range of climate sensitivity - the increase in
globally averaged surface temperature expected for a doubling of CO2 - is [1.5-C, 4.5-C].
The causes and nature of this uncertainty, more specifically whether or not it will be
possible to reduce it, is subject to debate. Before digging into this problem, we start by
focusing on a basic, somewhat epistemological question: what is uncertainty in the
context of climate science, and how should it be quantified? While this question may
at first appear trivial, we claim that it has occasionally proved to be a source of
confusion in the aforementioned debate. We illustrate this point by revisiting one
among the most significant contributions to this debate in the recent past (Roe and
Baker, 2007) and highlight the fact that its conclusions are dramatically affected
by the chosen definition of uncertainty. To resolve this issue, we propose widely
used standard deviation as a unique, broadly applicable definition of uncertainty.
We then recall a general result on the propagation of uncertainty available from
Probability theory under this definition, and analyse its implication on a simple
stochastic Climate toy model. This analysis suggests that high uncertainty is not
an inevitable and general consequence of the nature of the climate system, and
hence is not doomed to remain high. More specifically, it suggests that reducing
uncertainty on feedbacks in GCMs, through an improved understanding of involved
physical processes, does lead to a reduction of uncertainty on climate sensitivity
projected by models. Finally, we elaborate on foreseeable advances in Climate
research and modeling that may lead to a decrease of feedback uncertainty, and
subsequently to a reduction of uncertainty on climate sensitivity in the near future. |
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