ICAS external seminar

Dan Lunt (University of Bristol)

"The state-dependence of climate sensitivity:  a paleoclimate perspective"


Climate sensitivity is a key metric used to assess the magnitude of global warming given increased CO2 concentrations. The geological past can provide insights into climate sensitivity, and this talk will begin with a review of some previous studies that have attempted to constrain climate sensitivity with paleo data and models.  However,on timescales of millions of years, factors other than CO2 can drive climate, including paleogeographic forcing and solar luminosity. Here, through an ensemble of climate model simulations covering the period 150–35 million years ago, we show that climate sensitivity to CO2 doubling varies between ∼3.5 and 5.5 ◦C through this time. These variations can be explained as a nonlinear response to solar luminosity, evolving surface albedo due to changes in ocean area, and changes in ocean circulation. The work shows that the modern climate sensitivity is relatively low in the context of the geological record, as a result of relatively weak feedbacks due to a relatively low CO2 baseline, and the presence of ice and relatively small ocean area in the modern continental configuration.


Dan studied Physics at the University of Oxford for his undergraduate degree, and then did a PhD in Meteorology at the University of Reading.  Following this he was a postdoc at the LSCE in Paris, before arriving at the University of Bristol in 2003.

His research centres on past climate change, with a focus on (i) understanding how and why climate has changed in the past and (ii) what we can learn about the future from the past.  My main tools are climate models, and much of my work is underpinned by model-data comparisons.

(i) The analysis and interpretation of past climate data has led to the formation of many hypotheses regarding the mechanisms affecting past climate change. Models are ideal tools to test these hypotheses. In addition, modelling can itself lead to hypotheses which are testable by the collection and interpretation of new data, and can indicate regions in which new data could usefully be collected.

(ii) Past climate data can also inform our predictions of the future, through providing analogues of future climate change under high carbon dioxide concentrations, and through the evaluation of models used to predict the future.

Both of these aspects are central to the Deep-Time Model Intercomparison Project (DeepMIP), which he leads. 

Much of his work focuses on characterising Climate Sensitivity, the globally averaged increase in temperature due to a doubling of carbon dioxide.  Associated with this he is a Lead Author on the forthcoming Intergovernmental Panel on Climate Change sixth assessment report (IPCC AR6).