Tim Palmer - On seamless prediction and the reliability of climate forecasts
It is well understood that forecasts of weather and climate, whether 1 day ahead or 1 decade ahead, are
necessarily probabilistic - neither the initial state, nor the models used to integrate the equations of motion
are perfectly known. This itself of itself does not make decision making impossible or even, necessarily,
difficult. However, it is crucially important that if decision makers are not to be misled, forecast
probabilities must be reliable. A summary of the reliability of ECMWF System 4 seasonal forecasts is
described. For decadal timescales, an assessment of reliability poses a severe challenge because the
number of independent initial dates available to make decadal forecasts is very limited and on these
timescales models continue to show substantial biases against observations. Here it is proposed that the
reliability of coupled model seasonal forecasts can be a useful guide to longer timescale probabilistic
reliability. This is tested by studying idealised seasonal ensemble forecasts made at T95 atmospheric
resolution, and initialised and verified using mutlidecadal T959 integrations run in AMIP and in climatechange timeslice mode. Time permitting, some remarks will be made on some new proposals for using
inexact computing hardware in next generation cloud-resolved global climate models.