Search Results
You are looking at 1 - 4 of 4 items for
- Author or Editor: Andrew W. Colman x
- Refine by Access: All Content x
Abstract
The predictability of rainy season rainfall over northeast Brazil for the relatively long period 1912–98 is analyzed using dynamical and empirical techniques. The dynamical assessments are based on the HadAM2b atmospheric model forced with the Met Office Global Sea Ice and Sea Surface Temperature Dataset (GISST3). Ensembles of simulations and hindcasts starting from real initial conditions for 1982–93 made under the European Community Prediction of Climate Variations on Seasonal to Interannual Timescales (PROVOST) program are analyzed. The results demonstrate a relatively high degree of predictability. Its source lies mostly in tropical Atlantic and Pacific sea surface temperatures. The results confirm the less extensive evidence of other authors that northeast Brazil is a region where two separate ocean basins influence seasonal climate to a comparable extent. Overall, the sea surface temperature gradient between the northern and southern tropical Atlantic appears to be the more important influence, though El Niño can be dominant when it is strong. These assessments of predictability are consistent with the performance of over a decade of real-time long lead and updated forecasts, issued over the period 1987–98. Multiple regression and linear discriminant analysis prediction techniques, together with model forecasts in the last few years, were used to provide best estimate and probability real-time forecasts of rainy season rainfall. These forecasts had a level of skill that was close to the state of the art in seasonal forecasting
Abstract
The predictability of rainy season rainfall over northeast Brazil for the relatively long period 1912–98 is analyzed using dynamical and empirical techniques. The dynamical assessments are based on the HadAM2b atmospheric model forced with the Met Office Global Sea Ice and Sea Surface Temperature Dataset (GISST3). Ensembles of simulations and hindcasts starting from real initial conditions for 1982–93 made under the European Community Prediction of Climate Variations on Seasonal to Interannual Timescales (PROVOST) program are analyzed. The results demonstrate a relatively high degree of predictability. Its source lies mostly in tropical Atlantic and Pacific sea surface temperatures. The results confirm the less extensive evidence of other authors that northeast Brazil is a region where two separate ocean basins influence seasonal climate to a comparable extent. Overall, the sea surface temperature gradient between the northern and southern tropical Atlantic appears to be the more important influence, though El Niño can be dominant when it is strong. These assessments of predictability are consistent with the performance of over a decade of real-time long lead and updated forecasts, issued over the period 1987–98. Multiple regression and linear discriminant analysis prediction techniques, together with model forecasts in the last few years, were used to provide best estimate and probability real-time forecasts of rainy season rainfall. These forecasts had a level of skill that was close to the state of the art in seasonal forecasting
Abstract
The height of waves at North Sea oil and gas installations is an important factor governing the degree to which operational activities may be undertaken at those facilities. A link between the North Atlantic Oscillation (NAO) and winter (defined as December–February) wave heights at North Sea oil and gas installations has been established. A tool has been developed that uses a forecast NAO index to predict the proportions of wave heights in four categories that could be used to assess the operational downtime that will be experienced in the coming winter. The wave height forecasting system is shown to have useful skill in predicting the probability of occurrence of a stormy winter, and therefore probability forecasts provide a potentially useful guide to whether more or less disruption than the “climatological mean” might be experienced. The main limit on the skill of the wave forecasts is our very limited ability to accurately predict the NAO index on seasonal time scales.
Abstract
The height of waves at North Sea oil and gas installations is an important factor governing the degree to which operational activities may be undertaken at those facilities. A link between the North Atlantic Oscillation (NAO) and winter (defined as December–February) wave heights at North Sea oil and gas installations has been established. A tool has been developed that uses a forecast NAO index to predict the proportions of wave heights in four categories that could be used to assess the operational downtime that will be experienced in the coming winter. The wave height forecasting system is shown to have useful skill in predicting the probability of occurrence of a stormy winter, and therefore probability forecasts provide a potentially useful guide to whether more or less disruption than the “climatological mean” might be experienced. The main limit on the skill of the wave forecasts is our very limited ability to accurately predict the NAO index on seasonal time scales.
Abstract
Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.
Abstract
Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.
