Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 3: Practical considerations, relaxed assumptions, and using tree-ring data to address the amplitude of solar forcing

Abstract

Abstract.A statistical framework for evaluation of climate model simulations by comparison with climate observations from instrumental and proxy data (part 1 in this series) is improved by the relaxation of two assumptions. This allows autocorrelation in the statistical model for simulated internal climate variability and enables direct comparison of two alternative forced simulations to test whether one fits the observations significantly better than the other. The extended framework is applied to a set of simulations driven with forcings for the pre-industrial period 1000-1849 CE and 15 tree-ring-based temperature proxy series. Simulations run with only one external forcing (land use, volcanic, smallamplitude solar, or large-amplitude solar) do not significantly capture the variability in the tree-ring data -although the simulation with volcanic forcing does so for some experiment settings. When all forcings are combined (using either the small-or large-amplitude solar forcing), including also orbital, greenhouse-gas and non-volcanic aerosol forcing, and additionally used to produce small simulation ensembles starting from slightly different initial ocean conditions, the resulting simulations are highly capable of capturing some observed variability. Nevertheless, for some choices in the experiment design, they are not significantly closer to the observations than when unforced simulations are used, due to highly variable results between regions. It is also not possible to tell whether the small-amplitude or large-amplitude solar forcing causes the multiple-forcing simulations to be closer to the reconstructed temperature variability. Proxy data from more regions and of more types, or representing larger regions and complementary seasons, are apparently needed for more conclusive results from model-data comparisons in the last millennium.

Testing the consistency between changes in simulated climate and Alpine glacier length over the past millennium

“…Nevertheless, the spatio-temporal structure of the temperature changes is complex, with warm and cold periods being generally not synchronous between different regions (PAGES 2k Consortium, 2013). Those conclusions are in overall agreement with the results derived from global climate models driven by estimates of natural and anthropogenic forcings, although mod-H. Goosse et al: Simulated Alpine glacier length over the past millennium els tend to underestimate the magnitude of the changes in some regions and to simulate more homogenous changes than in the reconstructions (Goosse et al, 2005;Raibble et al, 2006;Gonzalez-Rouco et al, 2006;Jungclaus et al, 2010;Fernández-Donado et al, 2013;Landrum et al, 2013;Neukom et al, 2014;Moberg et al, 2015;PAGES2k-PMIP, 2015;Otto-Bliesner et al, 2016).…”

The PMIP4 contribution to CMIP6 – Part 3: The last millennium, scientific objective, and experimental design for the PMIP4 <i>past1000</i> simulations

“…mostly pre-PMIP3) or low (PMIP3) estimates of solar variations, several studies have investigated which of these provide a better fit to temperature reconstructions, but the results have so far been mixed. Whereas simulations with larger TSI variability give a somewhat better representation of the size of the MCA-LIA transition for Northern Hemisphere temperatures (Fernández-Donado et al, 2013), statistical assessment (Hind and Moberg, 2013;Moberg et al, 2015;PAGES2k-PMIP3 group, 2015) and more detailed regional analyses (e.g. Luterbacher et al, 2016) were inconclusive.…”

“…Collaborative work has focused on reconstruction-model intercomparison (e.g. Bothe et al, 2013;Moberg et al, 2015;PAGES2k-PMIP3 Group, 2015) and assessment of modes of variability (e.g. Raible et al, 2014).…”

Continental-scale temperature variability in PMIP3 simulations and PAGES 2k regional temperature reconstructions over the past millennium

“…The issue may be addressed by analysing an ensemble of simulations, which provides information on the range that can be simulated by one single model (e.g. Goosse et al, 2005;Yoshimori et al, 2005;Jungclaus et al, 2010;Moberg et al, 2015) or a set of models (e.g. Jansen et al, 2007;Lehner et al, 2012;Fernández-Donado et al, 2013, Bothe et al, 2013b.…”