A global climatology of the ocean surface during the Last Glacial Maximum mapped on a regular grid (GLOMAP)

• Published in: Climate of the Past
• Main Authors: Paul, André, Mulitza, Stefan, Stein, Rüdiger, Werner, Martin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications (EGU) 2021
• Subjects: Sea ice
• Online Access: https://oceanrep.geomar.de/id/eprint/52429/; https://oceanrep.geomar.de/id/eprint/52429/1/cp-17-805-2021.pdf; https://cp.copernicus.org/articles/17/805/2021/; https://doi.org/10.5194/cp-17-805-2021

We present a climatology of the near-sea-surface temperature (NSST) anomaly and the sea-ice extent during the Last Glacial Maximum (LGM, 23 000–19 000 years before present) mapped on a global regular 1∘×1∘ grid. It is an extension of the Glacial Atlantic Ocean Mapping (GLAMAP) reconstruction of the Atlantic NSST based on the faunal and floral assemblage data of the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO) project and several recent estimates of the LGM sea-ice extent. Such a gridded climatology is highly useful for the visualization of the LGM climate, calculation of global and regional NSST averages, and estimation of the equilibrium climate sensitivity, as well as a boundary condition for atmospheric general circulation models. The gridding of the sparse NSST reconstruction was done in an optimal way using the Data-Interpolating Variational Analysis (DIVA) software, which takes into account the uncertainty in the reconstruction and includes the calculation of an error field. The resulting Glacial Ocean Map (GLOMAP) confirms the previous findings by the MARGO project regarding longitudinal and meridional NSST differences that were greater than today in all oceans. Taken at face value, the estimated global and tropical cooling would imply an equilibrium climate sensitivity at the lower end of the currently accepted range. However, because of anticipated changes in the seasonality and thermal structure of the upper ocean during the LGM as well as uneven spatial sampling, the estimated cooling and implied climate sensitivity are likely to be biased towards lower values.