Professor Tim Palmer
Tim Palmer is a 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 the mid 1970s, 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.
The theoretical side of his work explores questions around where climatic processes on different space and time scales interact. On the practical side, he has developed and worked on the application of weather and climate forecasts systems for malaria prediction, flood forecasting, crop yield estimation, and more. Most recently his research has focused on simulating climate at extremely high resolution.
- Weather forecasting
- Weather forecasting
- Extreme weather
- Climate modelling
- Computer modelling
- Resilience in the developing world benefits everyone (2020)
- Short-term tests validate long-term estimates of climate change (2020)
- Human creativity and consciousness: unintended consequences of the brain's extraordinary energy efficiency? (2020)
- The physics of numerical analysis: a climate modelling case study (2020)
- The scientific challenge of understanding and estimating climate change (2019)
Professor Palmer has extensive media experience including national print and broadcast.
Recent media work
- Climate change: Science failed to predict flood and heat intensity (BBC News, 2021)
- Can we fix climate models to better predict record-shattering weather? (New Scientist, 2021)
- What COVID forecasters can learn from climate models (Nature article, 2020)
- Climate worst-case scenarios may not go far enough, cloud data shows (The Guardian, 2020)
- Weird weather: Can computers solve UK puzzle? (BBC News, 2020)