A Last Glacial Maximum forcing dataset for ocean modelling

• Main Authors: Morée, Anne L., Schwinger, Jörg
• Format: Text
• Language: English
• Published: 2019
• Subjects: Sea ice
• Online Access: https://doi.org/10.5194/essd-2019-79; https://essd.copernicus.org/preprints/essd-2019-79/

Model simulations of the Last Glacial Maximum (LGM, ~ 21 000 years before present) can aid the interpretation of proxy records, help to gain an improved mechanistic understanding of the LGM climate system and are valuable for the evaluation of model performance in a different climate state. Ocean-ice only model configurations forced by prescribed atmospheric data (referred to as “forced ocean models”) drastically reduce the computational cost of paleoclimate modelling as compared to fully coupled model frameworks. While feedbacks between the atmosphere and ocean-sea-ice compartments of the Earth system are not present in such model configurations, many scientific questions can be addressed with models of this type. The data presented here are derived from fully coupled paleoclimate simulations of the Palaeoclimate Modelling Intercomparison Project (PMIP3). The data are publicly accessible at the NIRD Research Data Archive at https://doi.org/10.11582/2019.00011 (Morée and Schwinger, 2019). They consist of 2-D anomaly forcing fields suitable for use in ocean models that employ a bulk forcing approach. The data include specific humidity, downwelling longwave and shortwave radiation, precipitation, wind (v and u components), temperature and sea surface salinity (SSS). All fields are provided as climatological mean anomalies between LGM and pre-industrial times. These anomaly data can therefore be added to any pre-industrial ocean forcing data set in order to obtain forcing fields representative of LGM conditions as simulated by PMIP3 models. These forcing data provide a means to simulate the LGM in a computationally efficient way, while still taking advantage of the complexity of fully coupled model set-ups. Furthermore, the dataset can be easily updated to reflect results from upcoming and future paleo model intercomparison activities.