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Earth Syst. Dynam., 6, 719-730, 2015
https://doi.org/10.5194/esd-6-719-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
18 Nov 2015
Attribution in the presence of a long-memory climate response
K. Rypdal Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway
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.

Citation: Rypdal, K.: Attribution in the presence of a long-memory climate response, Earth Syst. Dynam., 6, 719-730, https://doi.org/10.5194/esd-6-719-2015, 2015.
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Short summary
Human and natural forces drive climate change. If we have a model for the climate response to forcing, we can identify distinct fingerprints for each force, and their footprint in the observed global temperature can be determined by statistical analysis. This process is called attribution. This work examines the effect delays (long-range memory) in the climate response have on the magnitude of the various footprints. The magnitude of the human footprint turns out to be only weakly affected.
Human and natural forces drive climate change. If we have a model for the climate response to...
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