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In previous model-data comparisons, the centennial to millennial scale variance of local climate (e.g., SST) reconstructed from proxies was significantly higher than that simulated by climate models. One possible explanation is the lack of long-term feedback mechanisms, e.g. from the representation of changes in ice-sheets in models. Additionally, proxy records are short, and sparse, and the climate signal is significantly modified during the processes of encoding, archiving, and recovery.Here we introduce new methods to infer the climate variability of the past from proxy data and compare them to new transient model simulations of the last deglaciation. This will allow us to estimate the amplitude of climate variability and to evaluate whether climate models are capable of capturing changes in climate variability between different climate states (e.g. glacial vs. interglacial periods), which is also relevant for the accuracy of future projections. We compare the variability of marine d18O reconstructed from proxies with that simulated by a state-of-the-art Earth System Model.From the proxy side, our analysis is based on a new dataset of marine oxygen isotope data from planktonic foraminifera compiled for the PALMOD project. We use new methods to first calculate power-spectra for the LGM, transition and Holocene and then to correct these spectra by fitting a Bayesian model describing the effects of bioturbation and measurement error on the reconstructed climate signal. From the model side we use marine d18O variability calculated using temperature and salinity from transient model simulations of the last deglaciation, performed within the PALMOD project, that include changes in the ice sheets.This combination of new data and methods will allow us to investigate the effect of different ice-sheet configurations and physical parametrizations in the model on their ability to characterise long-timescale climate variability and its dependence on climate state.

Heinrich events are one of the prominent signals of glacial climate variability. They are characterised as abrupt, quasi-periodic episodes of ice-sheet instabilities during which large numbers of icebergs are released from the Laurentide ice sheet. These events affect the evolution of the global climate by modifying the ocean circulation through the addition of freshwater and the atmospheric circulation through changes in ice-sheet height. However, the mechanisms controlling the timing and occurrence of Heinrich events remain enigmatic to this day. Here, we present simulations with a coupled ice-sheet solid Earth model that aim to quantify the importance of different boundary forcings for the timing of Heinrich events. We focus the analysis on two prominent ice streams of the Laurentide ice sheet with the land-terminating Mackenzie ice stream and the marine-terminating Hudson ice stream. Our simulations identify different surge characteristics for the Mackenzie ice stream and the Hudson ice stream. Despite their different glaciological and climatic settings, both ice streams exhibit responses of similar magnitude to perturbations to the surface mass balance and the geothermal heat flux. However, Mackenzie ice stream is more sensitive to changes in the surface temperature. Changes to the ocean temperature and the global sea level have a negligible effect on the timing of Heinrich events in our simulations for both ice streams. We also show that Heinrich events for both ice streams only occur in a certain parameter space. Transitioning from an oscillatory Heinrich event state to a persistent streaming state can lead to an ice volume loss of up to 30%. Mackenzie ice stream is situated in a climate that is particularly close to this transition point, underlining the potential of the ice stream to have contributed to prominent abrupt climate events during glacial-interglacial transitions.

An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations’ reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital- as well as millennial-scale temperature variations. The use of proxy forward modeling avoids the need to reconstruct gridded or regional mean temperatures from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model-data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales. We attribute this diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model-data agreement.

Jet streams control hydroclimate variability in the mid-latitudes with important impacts on water availability and human societies. According to future projections, global warming will change jet stream characteristics, including its mean position. Variability of these characteristics on hourly-to-daily timescales is key to understanding the mid-latitudes circulation. Therefore, most analysis methods of present-day jet streams are designed for 6-hourly data. By modelling the climate since the Last Glacial Maximum, we can investigate the long-term drivers of jet stream characteristics. However, for transient simulations of the last deglaciation, 3d wind fields are only archived with a monthly resolution due to storage limitations. Hence, jet variability at shorter timescales cannot be identified, and established methods can’t be used.Here, we study to what extent changes of jet stream characteristics can be inferred from monthly wind fields. Therefore, we compare latitudinal jet stream positions, strength, tilt and their variability from daily and monthly wind fields in reanalysis data and for LGM and PI simulations. We test three different methods to construct jet stream typologies and metrics. This comparison identifies to which extend these jet stream characteristics can be robustly studied from monthly wind fields. In addition, our analysis assesses the added value of archived daily data for future research. Once the limitations of monthly wind output are known, jet stream characteristics in transient simulations of the last deglaciation can be analysed. This analysis provides new insights on jet stream changes on decadal-to-orbital timescales and identifies the factors controlling these changes.

The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.

The increasing number of Earth system model simulations that try to simulate the climate during the last deglaciation (ca 20 to 10 thousand years ago) creates a demand for benchmarking against environmental proxy records synthesized for the same time period. Comparing these two data sources over a period with changing background conditions requires new methods for model-data comparison that incorporate multiple types and sources of uncertainty. Natural archives of past reality are distributed sparsely and non-uniformly in space and time. Signals that can be obtained are in addition perturbed by uncertainties related to dating, the relationship between the proxy sensor and environmental fields, the archive build-up, and measurement. On the other hand, paleoclimate simulations are four-dimensional, complete, and physically consistent representations of the climate. However, they are subject to errors due to model inadequacies and sensitivity to the forcing protocol, and will not reproduce any particular history of unforced variability. We present a method for probabilistic, multivariate quantification of the deviation between paleo-data and paleoclimate simulations that draws on the strengths of both sources of information and accounts for the aforementioned uncertainties. We compare the shape and magnitude of orbital- and millennial-scale temperature fluctuations during the last deglaciation and compute metrics of regional and global model-data mismatches. We test our algorithm with an ensemble of published simulations of the deglaciation and simulations from the ongoing PalMod project, which aims at the simulation of the last glacial cycle with comprehensive Earth system models. These are evaluated against a compilation of temperature reconstructions from multiple archives. Our work aims for a standardized model-data comparison workflow that will be used in PalMod. This workflow can be extended subsequently with additional proxy data, new simulations, and improved representations of proxy uncertainties.