Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene
- 1RIGC/JAMSTEC, Yokohama Institute for Earth Sciences, Yokohama, Japan
- 2University of Bremen, Bremen, Germany
- 3AORI, University of Tokyo, Tokyo, Japan
Abstract. Paleoclimate simulations provide us with an opportunity to critically confront and evaluate the performance of climate models in simulating the response of the climate system to changes in radiative forcing and other boundary conditions. Hargreaves et al. (2011) analysed the reliability of the Paleoclimate Modelling Intercomparison Project, PMIP2 model ensemble with respect to the MARGO sea surface temperature data synthesis (MARGO Project Members, 2009) for the Last Glacial Maximum (LGM, 21 ka BP). Here we extend that work to include a new comprehensive collection of land surface data (Bartlein et al., 2011), and introduce a novel analysis of the predictive skill of the models. We include output from the PMIP3 experiments, from the two models for which suitable data are currently available. We also perform the same analyses for the PMIP2 mid-Holocene (6 ka BP) ensembles and available proxy data sets.
Our results are predominantly positive for the LGM, suggesting that as well as the global mean change, the models can reproduce the observed pattern of change on the broadest scales, such as the overall land–sea contrast and polar amplification, although the more detailed sub-continental scale patterns of change remains elusive. In contrast, our results for the mid-Holocene are substantially negative, with the models failing to reproduce the observed changes with any degree of skill. One cause of this problem could be that the globally- and annually-averaged forcing anomaly is very weak at the mid-Holocene, and so the results are dominated by the more localised regional patterns in the parts of globe for which data are available. The root cause of the model-data mismatch at these scales is unclear. If the proxy calibration is itself reliable, then representativity error in the data-model comparison, and missing climate feedbacks in the models are other possible sources of error.