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 127 (2001) 685-708
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.
MONTHLY WEATHER REVIEW 129 (2001) 2226-2248
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
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 127 (2001) 709-731
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
ATMOSPHERIC SCIENCE LETTERS 2 (2001) 72-80
Niederfrequente Variabilität großräumiger atmosphärischer Zirkulationsstrukturen in spektralen Modellen niederer Ordnung. Reports on Polar Research.
AWI (2000) 356
METEOROLOGICAL APPLICATIONS 7 (2000) 163-175
North Atlantic oscillation: Diagnosis and simulation of decadal variability and its long-period evolution
Izvestiya - Atmospheric and Ocean Physics 36 (2000) 555-565
Two 1000-year numerical experiments based on the IFA RAN global climate model, the first with completely interacting atmosphere and ocean and the second with a fixed climatic mean annual cycle of sea surface temperature, are analyzed. In both cases, a quasi-decadal cyclicity (QDC), but with substantially different amplitude-frequency characteristics, is detected for the North Atlantic Oscillation (NAO) in winter. Significant changes in the QDC regimes from one century to another are observed in the model. A comparison of the numerical results with empirical data and reconstructions reveal a fairly good agreement of the QDC amplitude and periods for winter NAO regimes in the model with completely interacting atmosphere and ocean for individual model subperiods on the order of a century. The model results suggest that interdecadal NAO variations of natural origin can be noticeably strengthened in the climate system without any influence of external, in particular, anthropogenic factors. In the case of a fixed annual cycle of SST, the QDC amplitudes are underestimated several times by the model, and no positive correlation is observed between the amplitudes and periods of the NAO QDC in contrast to the empirical data, reconstructions, and the model with completely interacting atmosphere and ocean.
Arctic and Antarctic ozone layer observations: Chemical and dynamical aspects of variability and long-term changes in the polar stratosphere
Polar Research 19 (2000) 193-204
The altitude dependent variability of ozone in the polar stratosphere is regularly observed by balloon-borne ozonesonde observations at Neumayer Station (70°S) in the Antarctic and at Koldewey Station (79°N) in the Arctic. The reasons for observed seasonal and interannual variability and long-term changes are discussed. Differences between the hemispheres are identified and discussed in light of differing dynamical and chemical conditions. Since the mid-1980s, rapid chemical ozone loss has been recorded in the lower Antarctic stratosphere during the spring season. Using coordinated ozone soundings in some Arctic winters, similar chemical ozone loss rates have been detected related to periods of low temperatures. The currently observed cooling trend of the stratosphere, potentially caused by the increase of anthropogenic greenhouse gases, may further strengthen chemical ozone removal in the Arctic. However, the role of internal climate oscillations in observed temperature trends is still uncertain. First results of a 10 000 year integration of a low order climate model indicate significant internal climate variability, on decadal time scales, that may alter the effect of increasing levels of greenhouse gases in the polar stratosphere.
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 52 (2000) 391-411
REPORTS ON PROGRESS IN PHYSICS 63 (2000) 71-116
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 126 (2000) 2035-2067
JOURNAL OF THE ATMOSPHERIC SCIENCES 57 (2000) 1327-1340