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| Titel |
Predicting Climate Change using Response Theory: Global Averages and Spatial Patterns |
| VerfasserIn |
Valerio Lucarini, Frank Lunkeit, Francesco Ragone |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250125766
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| Publikation (Nr.) |
 EGU/EGU2016-5400.pdf |
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| Zusammenfassung |
| The provision of accurate methods for predicting the climate response to anthropogenic and
natural forcings is a key contemporary scientific challenge. Using a simplified and efficient
open-source climate model featuring O(105) degrees of freedom, we show how it is possible
to approach such a problem using nonequilibrium statistical mechanics. Using the theoretical
framework of the pullback attractor and the tools of response theory we propose a simple
yet efficient method for predicting - at any lead time and in an ensemble sense -
the change in climate properties resulting from increase in the concentration of
CO2 using test perturbation model runs. We assess strengths and limitations of
the response theory in predicting the changes in the globally averaged values of
surface temperature and of the yearly total precipitation, as well as their spatial
patterns. We also show how it is possible to define accurately concepts like the
the inertia of the climate system or to predict when climate change is detectable
given a scenario of forcing. Our analysis can be extended for dealing with more
complex portfolios of forcings and can be adapted to treat, in principle, any climate
observable. Our conclusion is that climate change is indeed a problem that can
be effectively seen through a statistical mechanical lens, and that there is great
potential for optimizing the current coordinated modelling exercises run for the
preparation of the subsequent reports of the Intergovernmental Panel for Climate Change. |
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