attribution in the presence of a long-memory climate response
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2015
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Abstract
Multiple, linear regression is employed to
attribute variability in the global surface temperature to various forcing
components and prominent internal climatic modes. The purpose of the study is
to asses how sensitive attribution is to long-range memory (LRM) in the model
for the temperature response. The model response to a given forcing component
is its fingerprint and is different for a zero response time (ZRT) model and
one with an LRM response. The fingerprints are used as predictors in the
regression scheme to express the response as a linear combination of
footprints. For the instrumental period 1880–2010 CE (Common Era) the LRM
response model explains 89 % of the total variance and is also favoured
by information-theoretic model selection criteria. The anthropogenic
footprint is relatively insensitive to LRM scaling in the response and
explains almost all global warming after 1970 CE. The solar footprint is
weakly enhanced by the LRM response, while the volcanic footprint is reduced
by a factor of 2. The natural climate variability on multidecadal timescales
has no systematic trend and is dominated by the footprint of the Atlantic
Multidecadal Oscillation. The 2000–2010 CE hiatus is explained as a natural
variation. A corresponding analysis for the last millennium is performed,
using a Northern Hemisphere temperature reconstruction. The Little Ice Age
(LIA) is explained as mainly due to volcanic cooling or as a long-memory
response to a strong radiative disequilibrium during the Medieval Warm
Anomaly, and it is not attributed to the low solar activity during the
Maunder Minimum.
| Reference Key |
rypdal2015earthattribution
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|---|---|
| Authors | ;K. Rypdal |
| Journal | itinéraires |
| Year | 2015 |
| DOI |
10.5194/esd-6-719-2015
|
| URL | |
| Keywords |
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