Atmosphere, Oceans and Climate

Theme 3: Atmosphere, Oceans and Climate: the fluid Earth

Theme 3 is led by Dr. Finlo Cottier, Head of Physics and Technology Department, Scottish Association for Marine Science and Dr. Sian Henley NERC Research Fellow, University of Edinbugh.

SAGES Theme 3 is exploring four overarching questions:

(i) What are the mechanisms, drivers and effects of abrupt climate change?

(ii) How well can we predict and prepare for future extreme events: floods, droughts, storms and heatwaves?

(iii) How will pathways of environmental pollution change in the decades ahead, how do we prepare for it?

(iv) How does changing oceanic circulation influence climate and cryosphere of the Northern hemisphere?

This theme brings together Scotland’s leading scientists who study palaeoclimate reconstruction with those who study and model the dynamics of the present ocean and atmosphere. Our aim is to help predict future climate change driven by changes in the atmosphere-ocean system. The Earth’s sensitivity to change is recorded in past climates, observed primarily from ice-sheet and ocean sediment cores across the globe. We will develop high-resolution models simulating past climate changes as a means of determining the nature, mechanisms and drivers of rapid climate change.

The research focuses on the regional implications of climate change, for example flood risk and atmospheric dynamics. A major part of the research addresses the North Atlantic ocean currents and their importance in the global climate system. For example, the mechanisms and environmental significance of changes in the transport of warm saline Atlantic Water to North West Europe is effectively evaluated by reconstructing periods of variability and complete shutdown which occurred in the past.

The bringing together of ocean-atmosphere modellers and palaeo-climatologists will open up new horizons. The modellers will pose new questions for the palaeo-climatologists while, in return, records of climate change from the past will improve the models and their predictive capacity.