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We analyze the impact of a disintegrated Greenland Ice Sheet (GrIS) on the global climate through steady-state simulations with the MPI-ESM (Max Planck Institute for Meteorology Earth System Model). This advances our understanding of the intricate feedbacks between the GrIS and the full climate system. Sensitivity experiments enable the quantification of the individual contributions of altered Greenland surface elevation and properties (e.g., land cover) to the atmospheric and oceanic climate response. Removing the GrIS results in reduced mechanical atmospheric blocking, warmer air temperatures over Greenland and thereby changes in the atmospheric circulation. The latter alters the wind stress on the ocean, which controls the ocean-mass transport through the Arctic Gateways. Without the GrIS, the upper Nordic Seas are fresher, attenuating deep-water formation. In the Labrador Sea, deep-water formation is weaker despite a higher upper-ocean salinity, as the inflow of dense overflow from the Denmark Strait is reduced. Our sensitivity experiments show that the atmospheric response is primarily driven by the lower surface elevation, whereas altered Greenland surface properties mostly amplify but also counteract few of the changes. The lower Greenland elevation dominates the ocean response through wind-stress changes. Only in the Labrador Sea, altered Greenland surface properties dominate the ocean response, as this region stores excessive heat from the Greenland warming. The main drivers vary vertically: The elevation effect controls upper-ocean densities, while surface properties are important for the intermediate and deep ocean. Despite the confinement of most responses to the Arctic, a disintegrated GrIS also influences remote climates. The altered climate in response to a GrIS disintegration also constrains a potential ice-sheet regrowth to high-bedrock eastern Greenland. [ABSTRACT FROM AUTHOR]

During the last 20,000 years the climate of the earth has changed from a state much colder than today with large ice sheets in North America and Northwest Eurasia to its present state. The fully-interactive simulation of this transition represents a hitherto unsolved challenge for state-of-the-art climate models. We use a novel coupled comprehensive atmosphere–ocean–vegetation– ice sheet–solid earth model to simulate the transient climate evolution from the last glacial maximum to preindustrial times. The model considers dynamical changes of the glacier mask, land–sea mask and river routing. An ensemble of transient model simulations successfully captures the main features of the last deglaciation, as depicted by proxy estimates. In addition, our model simulates a series of abrupt climate changes, which can be attributed to different drivers. Abrupt cooling events during the glacial and the first half of the deglaciation are caused by Heinrich-event like ice-sheet surges, which are part of the model generated internal variability. We show that the timing of these surges depends on the initial state and the model parameters. Abrupt events during the second half of the deglaciation are caused by a long-term shift in the sign of the Arctic freshwater budget, changes in river routing and/or the opening of ocean passages. [ABSTRACT FROM AUTHOR]

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 and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series 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, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the 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. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception. [ABSTRACT FROM AUTHOR]

The last deglaciation (∼20 –11 ka BP) is a period of a major, long-term climate transition from a glacial to interglacial state that features multiple centennial- to decadal-scale abrupt climate variations whose root cause is still not fully understood. To better understand this time period, the Paleoclimate Modelling Intercomparison Project (PMIP) has provided a framework for an internationally coordinated endeavour in simulating the last deglaciation whilst encompassing a broad range of models. Here, we present a multi-model intercomparison of 17 transient simulations of the early part of the last deglaciation (∼20 –15 ka BP) from nine different climate models spanning a range of model complexities and uncertain boundary conditions and forcings. The numerous simulations available provide the opportunity to better understand the chain of events and mechanisms of climate changes between 20 and 15 ka BP and our collective ability to simulate them. We conclude that the amount of freshwater forcing and whether it follows the ice sheet reconstruction or induces an inferred Atlantic meridional overturning circulation (AMOC) history, heavily impacts the deglacial climate evolution for each simulation rather than differences in the model physics. The course of the deglaciation is consistent between simulations except when the freshwater forcing is above 0.1 Sv – at least 70 % of the simulations agree that there is warming by 15 ka BP in most places excluding the location of meltwater input. For simulations with freshwater forcings that exceed 0.1 Sv from 18 ka BP, warming is delayed in the North Atlantic and surface air temperature correlations with AMOC strength are much higher. However, we find that the state of the AMOC coming out of the Last Glacial Maximum (LGM) also plays a key role in the AMOC sensitivity to model forcings. In addition, we show that the response of each model to the chosen meltwater scenario depends largely on the sensitivity of the model to the freshwater forcing and other aspects of the experimental design (e.g. CO2 forcing or ice sheet reconstruction). The results provide insight into the ability of our models to simulate the first part of the deglaciation and how choices between uncertain boundary conditions and forcings, with a focus on freshwater fluxes, can impact model outputs. We can use these findings as helpful insight in the design of future simulations of this time period. [ABSTRACT FROM AUTHOR]

We present results of simulations with a physical-biogeochemical ocean model of the Mediterranean Sea for the last glacial maximum (LGM) and analyse the difference in physical and biochemical states between the present day and the past. Long-term simulations with an Earth system model based on ice sheet reconstructions provide the necessary atmospheric forcing data, oceanic boundary conditions at the entrance to the Mediterranean Sea, and river discharge to the entire basin. Our regional model accounts for changes in bathymetry due to ice-sheet volume changes, reduction in atmospheric carbon content, and an adjusted aeolian dust and iron deposition. The physical ocean state of the Mediterranean during the LGM shows a reduced baroclinic water exchange at the Strait of Gibraltar, a more sluggish zonal overturning circulation, and the relocation of intermediate and deep water formation areas – all in line with estimates from paleo sediment records or previous modelling efforts. Most striking features of the biogeochemical realm are a reduction of net primary production, an accumulation of nutrients below the euphotic zone, and an increase of organic matter deposition at the sea floor. This seeming contradiction of increased organic matter deposition and decreased net primary production challenges our view of possible changes in surface biological processes during the LGM. We attribute the origin of a reduced net primary production to the interplay of increased stability of the upper water column, changed zonal water transport at intermediate depths, and colder water temperatures, which slow down all biological processes during the LGM. The cold water temperatures also affect the remineralisation rates of organic material which explains the simulated increase of organic matter deposition, in good agreement with sediment proxy records. In addition, we discuss changes of an artificial tracer which captures the surface ocean temperature signal during organic matter production. A shifted seasonality of biological production in the LGM leads to a difference in the recording of the climate signal by this artificial tracer of up to 1 K. This could be of relevance for the interpretation of proxy records like e.g. alkenones. Our study does not only provide the first consistent insights into the biogeochemistry of the glacial Mediterranean Sea, it will also serve as the starting point for long-term simulations over the entire last deglaciation. [ABSTRACT FROM AUTHOR]

The quaternary climate is characterised by glacial-interglacial cycles, with the most recent transition from the last glacial maximum to the present interglacial (the last deglaciation) occurring between ~ 21 and 9 ka. While the deglacial warming at southern high latitudes is mostly in phase with atmospheric CO2 concentrations, some proxy records have suggested that the onset of the warming occurred before the CO2 increase. In addition, southern high latitudes exhibit a cooling event in the middle of the deglaciation (15–13 ka) known as the Antarctic Cold Reversal (ACR). In this study, we analyse transient simulations of the last deglaciation performed by six different climate models as part of the 4th phase of the Paleoclimate Modelling Intercomparison Project (PMIP4) to understand the processes driving southern high latitude surface temperature changes. While proxy records from West Antarctica and the Pacific sector of the Southern Ocean suggest the presence of an early warming before 18 ka, only half the models show a significant warming (~1 °C or ~10 % of the total deglacial warming). All models simulate a major warming during Heinrich stadial 1 (HS1, 18–15 ka), greater than the early warming, in response to the CO2 increase. Moreover, simulations in which the AMOC weakens show a more significant warming during HS1 as a result. During the ACR, simulations with an abrupt increase in the AMOC exhibit a cooling in southern high latitudes, while those with a reduction in the AMOC in response to rapid meltwater exhibit warming. We find that all climate models simulate a southern high latitude cooling in response to an AMOC increase with a response timescale of several hundred years, suggesting the model’s sensitivity of AMOC to meltwater, and the meltwater forcing in the North Atlantic and Southern Ocean affect southern high latitudes temperature changes. Thus, further work needs to be carried out to understand the deglacial AMOC evolution with the uncertainties in meltwater history. Finally, we do not find substantial changes in simulated Southern Hemisphere westerlies nor in the Southern Ocean meridional circulation during deglaciation, suggesting the need to better understand the processes leading to changes in southern high latitude atmospheric and oceanic circulation as well as the processes leading to the deglacial atmospheric CO2 increase. [ABSTRACT FROM AUTHOR]

Transient simulations of the last deglaciation have been increasingly performed to better understand the processes leading to both the overall deglacial climate trajectory as well as the centennial- to decadal- scale climate variations prevalent during deglaciations. The Paleoclimate Modelling Intercomparison Project (PMIP) has provided a framework for an internationally coordinated effort in simulating the last deglaciation (~20 – 11 ka BP) whilst encompassing a broad range of models. Here, we present a multi-model intercomparison of 17 simulations of the early part of the last deglaciation (~20 – 15 ka BP) from nine different climate models spanning a range of model complexities and uncertain boundary conditions/forcings. A main contrasting element between the simulations is the method by which groups implement freshwater fluxes from the melting ice sheets and how this forcing then impacts ocean circulation and surface climate. We find that the choice of meltwater scenario heavily impacts the deglacial climate evolution, but the response of each model depends largely on the sensitivity of the model to the freshwater forcing as well as to other aspects of the experimental design (e.g., CO2 forcing or ice sheet reconstruction). There is agreement throughout the ensemble that warming begins in the high latitudes associated with increasing insolation and delayed warming in the tropics aligned with the later increases in atmospheric CO2 concentration. The delay in this warming in the tropics is dependent on the timescale of the CO2 reconstruction used by the modelling group. Simulations with freshwater forcings greater than 0.1 Sverdrup (Sv) after 18 ka BP experience delayed warming in the North Atlantic, whereas simulations with smaller freshwater forcings begin deglaciating sooner. All simulations show a strong correlation between North Atlantic temperatures, atmospheric CO2 concentrations, and the AMOC. In simulations with a freshwater forcing greater than 0.1 Sv, North Atlantic temperatures correlate strongly with changes in the AMOC. Simulations with a smaller freshwater forcing show stronger correlations with atmospheric CO2. This indicates that the amount of meltwater strongly controls the climate trajectory of the deglaciation. Comparing multiple simulations run by the same model demonstrate model biases by showing similar surface climate spatial patterns despite the use of different ice sheet reconstructions and/or meltwater flux scenarios. Simulations run with different models, but similar boundary conditions, have provided insight into the sensitivity of individual models to particular forcings, such as the amount freshwater forcing, which has been highly debated in previous studies. This debate has stemmed from the so-called 'meltwater paradox' that exists in choosing how much meltwater to input into simulations of the last deglaciation (i.e., large and geologically inconsistent meltwater forcings that successfully produce abrupt climate events versus glaciologically realistic meltwater fluxes that do not). The results of this research highlight how important this decision is. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]