Thursday, July 14, 2022

Tim Palmer (Oxford): Status and Future of Climate Modeling — Manifold Podcast #16


Tim Palmer is Royal Society Research Professor in Climate Physics, and a Senior Fellow at the Oxford Martin Institute. He is interested in the predictability and dynamics of weather and climate, including extreme events. 

He was involved in the first five IPCC assessment reports and was co-chair of the international scientific steering group of the World Climate Research Programme project (CLIVAR) on climate variability and predictability. 

After completing his DPhil at Oxford in theoretical physics, Tim worked at the UK Meteorological Office and later the European Centre for Medium-Range Weather Forecasts. For a large part of his career, Tim has developed ensemble methods for predicting uncertainty in weather and climate forecasts. 

In 2020 Tim was elected to the US National Academy of Sciences. 

Steve, Corey Washington, and Tim first discuss his career path from physics to climate research and then explore the science of climate modeling and the main uncertainties in state-of-the-art models. 

In this episode, we discuss: 

00:00 Introduction 
1:48 Tim Palmer's background and transition from general relativity to climate modeling 
15:13 Climate modeling uncertainty 
46:41 Navier-Stokes equations in climate modeling 
53:37 Where climate change is an existential risk 
1:01:26 Investment in climate research 

Tim Palmer (Oxford University) 

The scientific challenge of understanding and estimating climate change (2019) 


Physicist Steve Koonin on climate change

Note added
: For some background on the importance of water vapor (cloud) distribution within the primitive cells used in these climate simulations, see:

Low clouds trap IR radiation near the Earth, while high clouds reflect solar energy back into space. The net effect on heating from the distribution of water vapor is crucial in these models. However, due to the complexity of the Navier-Stokes equations, current simulations cannot actually solve for this distribution from first principles. Rather, the modelers hand code assumptions about fine grained behavior within each cell. The resulting uncertainty in (e.g., long term) climate prediction from these approximations is unknown.

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