A new high-resolution global surface temperature dataset – combining recent measurements with global mean surface temperatures through the Holocene epoch

Accurately characterizing Earth's geothermal gradient is critical for a variety of geophysical and environmental studies. Earth’s surface temperature can affect the geothermal gradient to significant depths over long time periods and, in turn, affect calculations of, for example, the brittle-ductile transition depth. It is expected that the geotherm has stabilised with respect to the long-term average surface temperatures, not current or relatively current averaged measurements. Therefore, when using the surface temperature, it is important to account for long-term behaviour without losing spatial variability.

Datasets containing predicted global mean surface temperatures (GMST) throughout the Holocene epoch, spanning the last 12,000 years, offer valuable insights into historic temperature conditions but often lack in spatial resolution. Meanwhile, modern surface temperature data, derived from advanced sensors and satellite observations, provides high-resolution snapshots of global temperatures over relatively short time periods. By combining these sources, we can create a dataset that not only retains the current spatial distribution but also integrates historical thermal data into a single dataset. This high-resolution global mean surface temperature dataset (HRGMST) would more closely match the current stabilised geothermal gradient.

This hybrid dataset would help to refine models which use Earth's surface temperature distribution, providing a more accurate representation of the subsurface thermal state. The improved dataset can offer significant insights for geophysical research, including better assessments of subsurface heat flow, energy resources, and tectonic processes.

This work will outline the development of a new HRGMST dataset that integrates predicted Holocene averages with contemporary direct temperature measurements to more accurately represent Earth's long-term average surface temperature. It will outline the methodology behind the dataset creation and discuss challenges in merging paleoclimate data with contemporary measurements. The enhanced dataset promises to improve the understanding of Earth's internal temperature structure and support more precise calculations in geothermal energy exploration and geophysical modelling. We will explore several methods of combining long-term GMST data with high-resolution data, testing the effect of each method using an existing global model and our software package ShellSet, discussing the changes which arise due to the new surface temperature dataset along with seismotectonic implications.