Attribution in the presence of a long-memory climate response - K. Rypdal (Earth System Dynamics, 2015)
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.