QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 129 (2003) 2401-2423
FIRST CHAMP MISSION RESULTS FOR GRAVITY, MAGNETIC AND ATMOSPHERIC STUDIES (2003) 441-446
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 128 (2002) 1641-1670
Nature 415 (2002) 512-514
Increasing concentrations of atmospheric carbon dioxide will almost certainly lead to changes in global mean climate. But because--by definition--extreme events are rare, it is significantly more difficult to quantify the risk of extremes. Ensemble-based probabilistic predictions, as used in short- and medium-term forecasts of weather and climate, are more useful than deterministic forecasts using a 'best guess' scenario to address this sort of problem. Here we present a probabilistic analysis of 19 global climate model simulations with a generic binary decision model. We estimate that the probability of total boreal winter precipitation exceeding two standard deviations above normal will increase by a factor of five over parts of the UK over the next 100 years. We find similar increases in probability for the Asian monsoon region in boreal summer, with implications for flooding in Bangladesh. Further practical applications of our techniques would be helped by the use of larger ensembles (for a more complete sampling of model uncertainty) and a wider range of scenarios at a resolution adequate to analyse average-size river basins.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 128 (2002) 747-774
ATMOSPHERIC SCIENCE LETTERS 2 (2001) 72-80
Atmospheric Science Letters 2 (2001)
In order to investigate whether climate models of different complexity have the potential to simulate natural atmospheric circulation regimes, 1000-year-long integrations with constant external forcing have been analysed. Significant non-Gaussian uni-, bi-, and trimodal probability density functions have been found in 100-year segments. © 2001 Royal Meteorological Society.
JOURNAL OF CLIMATE 14 (2001) 3212-3226
MONTHLY WEATHER REVIEW 129 (2001) 2226-2248
Nonlinear Processes in Geophysics 8 (2001) 357-371
Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.
A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 127 (2001) 279-304
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 127 (2001) 709-731
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 127 (2001) 685-708
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 52 (2000) 391-411
REPORTS ON PROGRESS IN PHYSICS 63 (2000) 71-116
JOURNAL OF THE ATMOSPHERIC SCIENCES 57 (2000) 1327-1340
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 126 (2000) 2013-2033
METEOROLOGICAL APPLICATIONS 7 (2000) 163-175
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 126 (2000) 2035-2067