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Tim Palmer

Emeritus

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Predictability of weather and climate
Tim.Palmer@physics.ox.ac.uk
Telephone: 01865 (2)72897
Robert Hooke Building, room S43
  • About
  • Publications

$p$-adic Distance, Finite Precision and Emergent Superdeterminism: A Number-Theoretic Consistent-Histories Approach to Local Quantum Realism

ArXiv 1609.08148 (2016)
Details from ArXiV

Calibrating climate change time-slice projections with estimates of seasonal forecast reliability

Journal of Climate American Meteorological Society 29:10 (2016) 3831-3840

Authors:

Mio Matsueda, Antje Weisheimer, Timothy Palmer

Abstract:

In earlier work, it was proposed that the reliability of climate change projections, particularly of regional rainfall, could be improved if such projections were calibrated using quantitative measures of reliability obtained by running the same model in seasonal forecast mode. This proposal is tested for fast atmospheric processes (such as clouds and convection) by considering output from versions of the same atmospheric general circulation model run at two different resolutions and forced with prescribed sea surface temperatures and sea ice. Here output from the high-resolution version of the model is treated as a proxy for truth. The reason for using this approach is simply that the twenty-first-century climate change signal is not yet known and, hence, no climate change projections can be verified using observations. Quantitative assessments of reliability of the low-resolution model, run in seasonal hindcast mode, are used to calibrate climate change time-slice projections made with the same low-resolution model. Results show that the calibrated climate change probabilities are closer to the proxy truth than the uncalibrated probabilities. Given that seasonal forecasts are performed operationally already at several centers around the world, in a seamless forecast system they provide a resource that can be used without cost to help calibrate climate change projections and make them more reliable for users.
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Invariant Set Theory

ArXiv 1605.01051 (2016)
Details from ArXiV

Oceanic stochastic parametrizations in a seasonal forecast system

Monthly Weather Review American Meteorological Society 144:5 (2016) 1867-1875

Authors:

M Andrejczuk, FC Cooper, S Juricke, TN Palmer, Antje Weisheimer, L Zanna

Abstract:

Stochastic parametrization provides a methodology for representing model uncertainty in ensemble forecasts. Here we study the impact of three existing stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.
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A personal perspective on modelling the climate system

Animal Behaviour Royal Society 472:2188 (2016)

Abstract:

Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1km global climate system model to run on the first exascale supercomputers in the early 2020s.
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