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Climate models may perform equally well for simulating the present-day and 20th century climates, yet produce very different responses to likely changes in forcing (such as greenhouse gases and insolation) in the future. Therefore, it is important to compare current state-of-the-art climate model simulations of past climates against the benchmarks of paleo-observations. The Paleoclimate Modelling Intercomparison Project (PMIP) is a long-standing initiative endorsed by PAGES and the World Climate Research Programme JSC/ CLIVAR Working Group on Coupled Models (WGCM). It provides for coordination tion (here the 400 year early Holocene control climate before the perturbation; Fig. 1a) are statistically different, by comparing the variance and average. Subsequently, we separated cold and warm anomalies that are significant at the 99% level (Fig. 1b). For the grid-cells with significant cold and/or warm temperature anomalies, we calculated the following properties: the duration of the longest anomaly, the maximum 31-year mean temperature anomaly and the timing of the onset of the longest anomaly relative to the freshwater forcing (Fig. 1). Applying this method on the separate ensemble members generates a climate response that also includes anomalous data points resulting from natural climate variability. Since we are interested in the temperature anomaly that is forced by the lake drainage, we average the output properties (magnitude, timing and duration) of the 10 ensemble members and mask grid-cells that do not show a significant anomalous response in each ensemble member. Subsequently, we generate anomaly maps for each of the properties. In contrast to many studies that aim to derive the externally forced climate signal (e.g., Stott et al., 2000), we do not first average the ensemble members and then perform the statistical test. The reasoning behind this is that we are interested in the signal that could be registered in climate proxy archives. This signal is comparable to the climate signal of a single ensemble member and different from the artificially enhanced signal of the ensemble average. We then average the properties in the ensemble members and filter out grid-cells that do not show a climate response in each of the ensemble members to obtain the robust response. This step is reasonable because all ensemble members showed a similar climate evolution.

PMIP2 climate model-proxy data intercomparisons for the LGM