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| Titel |
Linearity, climate sensitivity and climate changes in the surface temperature field |
| VerfasserIn |
D. A. Stainforth, L. A. Smith |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250065590
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| Zusammenfassung |
| The relationship between equilibrium global mean temperature, Te, and applied radiative
forcing, Rf, is commonly expressed by the linear equation (Gregory et al., Roe and
Baker):
ΔTe = λ Rf
where λ is a constant known as the feedback parameter. Here we address the question of
whether the relationship between Te and Rf is, in fact, well quantified by such a linear
equation. Our analysis is based on the output of a global climate model. If the relationship
breaks down when moving from a single equation model to a complicated global circulation
model it is unlikely to reassert itself in the real world with all its additional complexity. Thus
nonlinearity in the model can be taken as a strong indication of nonlinearity in reality,
while linearity in a model might only be taken as a weak indication of linearity in
reality.
Thanks to the support of the climateprediction.net team and participants, we were able to
run large (>500 members) initial condition ensembles with a global climate model
(HadSM3) at seven values of Rf. Results will be presented demonstrating the nonlinear
nature of the Te, Rf relationship.
The term “linear” is used with a number of different meanings, leading to confusion in
discussions between the many disciplines involved in climate science research. Furthermore,
in the high-dimensional space of climate model output, linearity with Rf can be achieved in a
variety of ways: for instance, linearity in magnitude of change (as in the equation above),
linearity in degree of rotation within the high-dimensional model state space, linear change in
variance in each dimension. These will be illustrated. Analysis of the above ensembles will be
used to show that not only is the model nonlinear in the scalar global mean temperature, but
also in the pattern of change.
These results imply key messages for ensemble design. Most crucial is consideration of
larger initial condition ensembles than is typical in climate model experiments. Such
ensembles allow the reduction of sampling errors to levels sufficient to identify nonlinear
responses within the model. The ensembles used in this research can provide guidance on the
minimum ensemble size necessary to answer a variety of policy relevant questions. This will
be illustrated in terms of the minimum ensemble size necessary to identify the nonlinear
response in equilibrium global mean temperature.
References:
- Roe, G.H. and M.B. Baker, Why is climate sensitivity so unpredictable? Science, 2007.
318 629-632
- Gregory, J.M. et al. A new method for diagnosing radiative forcing and climate
sensitivity, Geophysical Research Letters, VOL. 31, L03205, 2004. |
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