Using ocean surface paleo-density to evaluate PMIP3 and PMIP4 Last Glacial Maximum climate simulations

Quantitative reconstruction of ocean surface density during the Last Glacial Maximum (LGM) offers valuable insights into the ability of climate models to simulate past climate conditions, when global temperatures were about 4.5 to 6°C colder than today. We assess the performance of the LGM climate simulations, as part of the 3rd and 4th phase of the Paleoclimate Modeling Intercomparisons Project, using a recent ocean surface density reconstruction based on the δ 18 O of foraminiferal calcite ( δ 18 O c ). We consider the differences between the LGM and the preindustrial climates and each period separately, at both global and regional scales. Because surface density reflects the combined effects of temperature and salinity, we also examined sea surface temperature (SST) to better identify the processes underlying model–data differences. On a global scale, surface density reconstructions generally exhibit greater spatial variability than simulated surface density anomalies (LGM − PI), although part of this difference is reduced when reconstruction uncertainties are taken into account. Model simulations tend to underestimate the magnitude of reconstructed density anomalies and substantial differences between cumulative distribution persist. Part of the mismatch may arise from the uneven spatial distribution of reconstructions, which are mostly located near coastal areas. Density anomaly (LGM − PI) differences between data and models are largely controlled by sea surface salinity (SSS), with SST contributing to a lesser extent. This influence of SSS is directly linked to the reduction in tropical precipitation during the LGM: model simulations that best match the large-scale density anomalies also simulate the strongest reductions in reconstructed low-latitude precipitation during the LGM, highlighting the key role of hydrological cycle changes in shaping surface density. All simulations capture key features of the reconstructed surface densities when the LGM and PI periods are analysed separately at the global scale, with Taylor diagrams and complementary performance metrics indicating moderate to good overall agreement despite differences among simulations. Regional analyses show that most simulations reproduce reconstructed Indian Ocean surface density reasonably well, although model simulations performance is systematically lower in the North Indian Ocean than in the South Indian Ocean. Focusing on the Indo-Pacific Warm Pool, proxy reconstructions indicate a weakened West-East tropical Indian surface density gradient during the LGM, but only 7 out of 14 model simulations (50 %) reproduce this feature. These results highlight the need to improve and better constrain regional hydrological cycle changes in models, as improving their representation is crucial to reduce uncertainties in both paleoclimate simulations and future climate projections.

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Journal
Climate of the past
Published
2026-10-06
DOI
https://doi.org/10.5194/cp-22-1803-2026
Primary Topic
Geology and Paleoclimatology Research
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article
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article

Using ocean surface paleo-density to evaluate PMIP3 and PMIP4 Last Glacial Maximum climate simulations

Masa Kageyama, T. Caley, Didier Swingedouw, Pascale Braconnot et al.
Climate of the past
Geology and Paleoclimatology Research
article

Using ocean surface paleo-density to evaluate PMIP3 and PMIP4 Last Glacial Maximum climate simulations

Masa Kageyama, T. Caley, Didier Swingedouw, Pascale Braconnot, Héloïse Barathieu
article en

Abstract

Quantitative reconstruction of ocean surface density during the Last Glacial Maximum (LGM) offers valuable insights into the ability of climate models to simulate past climate conditions, when global temperatures were about 4.5 to 6°C colder than today. We assess the performance of the LGM climate simulations, as part of the 3rd and 4th phase of the Paleoclimate Modeling Intercomparisons Project, using a recent ocean surface density reconstruction based on the δ 18 O of foraminiferal calcite ( δ 18 O c ). We consider the differences between the LGM and the preindustrial climates and each period separately, at both global and regional scales. Because surface density reflects the combined effects of temperature and salinity, we also examined sea surface temperature (SST) to better identify the processes underlying model–data differences. On a global scale, surface density reconstructions generally exhibit greater spatial variability than simulated surface density anomalies (LGM − PI), although part of this difference is reduced when reconstruction uncertainties are taken into account. Model simulations tend to underestimate the magnitude of reconstructed density anomalies and substantial differences between cumulative distribution persist. Part of the mismatch may arise from the uneven spatial distribution of reconstructions, which are mostly located near coastal areas. Density anomaly (LGM − PI) differences between data and models are largely controlled by sea surface salinity (SSS), with SST contributing to a lesser extent. This influence of SSS is directly linked to the reduction in tropical precipitation during the LGM: model simulations that best match the large-scale density anomalies also simulate the strongest reductions in reconstructed low-latitude precipitation during the LGM, highlighting the key role of hydrological cycle changes in shaping surface density. All simulations capture key features of the reconstructed surface densities when the LGM and PI periods are analysed separately at the global scale, with Taylor diagrams and complementary performance metrics indicating moderate to good overall agreement despite differences among simulations. Regional analyses show that most simulations reproduce reconstructed Indian Ocean surface density reasonably well, although model simulations performance is systematically lower in the North Indian Ocean than in the South Indian Ocean. Focusing on the Indo-Pacific Warm Pool, proxy reconstructions indicate a weakened West-East tropical Indian surface density gradient during the LGM, but only 7 out of 14 model simulations (50 %) reproduce this feature. These results highlight the need to improve and better constrain regional hydrological cycle changes in models, as improving their representation is crucial to reduce uncertainties in both paleoclimate simulations and future climate projections.

Climate of the pastVol. 22(10)
Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Environnements et Paléoenvironnements Océaniques et Continentaux (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Institut Polytechnique de Bordeaux (FR)
Openalex Percentile: Top 18%
Geology and Paleoclimatology Research
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