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- Author or Editor: Frederic Vitart x
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Abstract
A monthly forecasting system based on 32-day coupled ocean–atmosphere integrations has been set up at ECMWF. This system has run routinely since March 2002 every 2 weeks, and 45 cases from March 2002 to December 2003 have been verified. Results of this validation suggest that the model displays some skill in predicting weekly averaged 2-m temperature, precipitation, and mean sea level pressure anomalies relative to the climate of the past 12 years. For days 12–18, probabilistic scores indicate that the monthly forecasting system performs generally better than both climatology and the persistence of the previous weekly probabilities, suggesting that forecasts at that time range could be useful. After about 20 days of forecast, the model displays some skill in predicting events with a large threshold. At that time range, the performance of the system depends strongly on the geographical location, with Europe being a particularly difficult region. However, the model displays some useful skill after 20 days over North America, Asia, and the southern extratropics.
In order to calibrate the monthly forecasting system, a 5-member hindcast over the 12 years preceding the real-time forecast has been produced. Probabilistic scores computed with the hindcast confirm the main results obtained with the real-time forecast. The scores display strong seasonal variability, with the model being particularly skillful in winter.
Abstract
A monthly forecasting system based on 32-day coupled ocean–atmosphere integrations has been set up at ECMWF. This system has run routinely since March 2002 every 2 weeks, and 45 cases from March 2002 to December 2003 have been verified. Results of this validation suggest that the model displays some skill in predicting weekly averaged 2-m temperature, precipitation, and mean sea level pressure anomalies relative to the climate of the past 12 years. For days 12–18, probabilistic scores indicate that the monthly forecasting system performs generally better than both climatology and the persistence of the previous weekly probabilities, suggesting that forecasts at that time range could be useful. After about 20 days of forecast, the model displays some skill in predicting events with a large threshold. At that time range, the performance of the system depends strongly on the geographical location, with Europe being a particularly difficult region. However, the model displays some useful skill after 20 days over North America, Asia, and the southern extratropics.
In order to calibrate the monthly forecasting system, a 5-member hindcast over the 12 years preceding the real-time forecast has been produced. Probabilistic scores computed with the hindcast confirm the main results obtained with the real-time forecast. The scores display strong seasonal variability, with the model being particularly skillful in winter.
Abstract
The ECMWF monthly forecasting system is used to investigate the impact that an interactive ocean has on short-range and medium-range weather predictions in the Northern Hemisphere extratropics during wintertime. On a hemispheric scale the predictive skill for mean sea level pressure (MSLP) with and without an interactive ocean is comparable. This can be explained by the relatively small impact that coupling has on MSLP forecasts. In fact, deterministic and ensemble integrations reveal that the magnitude of forecast error and the perturbation growth due to analysis uncertainties, respectively, by far outweigh MSLP differences between coupled and uncoupled integrations. Furthermore, no significant difference of the ensemble spread between the uncoupled and coupled system is found. The authors’ conclusions apply equally for a number of cases of rapidly intensifying extratropical cyclones in the North Atlantic region. Further experimentation with different atmospheric model versions, different horizontal atmospheric resolutions, and different ocean model formulation reveals the robustness of the findings. The results suggest that (for the cases, resolutions, and model complexities considered is this study) the benefit of using coupled atmosphere–ocean models to carry out 1–10-day MSLP forecasts is relatively small, at least in the Northern Hemisphere extratropics during wintertime.
Abstract
The ECMWF monthly forecasting system is used to investigate the impact that an interactive ocean has on short-range and medium-range weather predictions in the Northern Hemisphere extratropics during wintertime. On a hemispheric scale the predictive skill for mean sea level pressure (MSLP) with and without an interactive ocean is comparable. This can be explained by the relatively small impact that coupling has on MSLP forecasts. In fact, deterministic and ensemble integrations reveal that the magnitude of forecast error and the perturbation growth due to analysis uncertainties, respectively, by far outweigh MSLP differences between coupled and uncoupled integrations. Furthermore, no significant difference of the ensemble spread between the uncoupled and coupled system is found. The authors’ conclusions apply equally for a number of cases of rapidly intensifying extratropical cyclones in the North Atlantic region. Further experimentation with different atmospheric model versions, different horizontal atmospheric resolutions, and different ocean model formulation reveals the robustness of the findings. The results suggest that (for the cases, resolutions, and model complexities considered is this study) the benefit of using coupled atmosphere–ocean models to carry out 1–10-day MSLP forecasts is relatively small, at least in the Northern Hemisphere extratropics during wintertime.
Abstract
The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.
Abstract
The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.
Abstract
The fact that aerosols are important players in Earth’s radiation balance is well accepted by the scientific community. Several studies have shown the importance of characterizing aerosols in order to constrain surface radiative fluxes and temperature in climate runs. In numerical weather prediction, however, there has not been definite proof that interactive aerosol schemes are needed to improve the forecast. Climatologies are instead used that allow for computational efficiency and reasonable accuracy. At the monthly to subseasonal range, it is still worth investigating whether aerosol variability could afford some predictability, considering that it is likely that persisting aerosol biases might manifest themselves more over time scales of weeks to months and create a nonnegligible forcing. This paper explores this hypothesis using the ECMWF’s Ensemble Prediction System for subseasonal prediction with interactive prognostic aerosols. Four experiments are conducted with the aim of comparing the monthly prediction by the default system, which uses aerosol climatologies, with the prediction using radiatively interactive aerosols. Only the direct aerosol effect is considered. Twelve years of reforecasts with 50 ensemble members are analyzed on the monthly scale. Results indicate that the interactive aerosols have the capability of improving the subseasonal prediction at the monthly scales for the spring/summer season. It is hypothesized that this is due to the aerosol variability connected to the different phases of the Madden–Julian oscillation, particularly that of dust and carbonaceous aerosols. The degree of improvement depends crucially on the aerosol initialization. More work is required to fully assess the potential of interactive aerosols to increase predictability at the subseasonal scales.
Abstract
The fact that aerosols are important players in Earth’s radiation balance is well accepted by the scientific community. Several studies have shown the importance of characterizing aerosols in order to constrain surface radiative fluxes and temperature in climate runs. In numerical weather prediction, however, there has not been definite proof that interactive aerosol schemes are needed to improve the forecast. Climatologies are instead used that allow for computational efficiency and reasonable accuracy. At the monthly to subseasonal range, it is still worth investigating whether aerosol variability could afford some predictability, considering that it is likely that persisting aerosol biases might manifest themselves more over time scales of weeks to months and create a nonnegligible forcing. This paper explores this hypothesis using the ECMWF’s Ensemble Prediction System for subseasonal prediction with interactive prognostic aerosols. Four experiments are conducted with the aim of comparing the monthly prediction by the default system, which uses aerosol climatologies, with the prediction using radiatively interactive aerosols. Only the direct aerosol effect is considered. Twelve years of reforecasts with 50 ensemble members are analyzed on the monthly scale. Results indicate that the interactive aerosols have the capability of improving the subseasonal prediction at the monthly scales for the spring/summer season. It is hypothesized that this is due to the aerosol variability connected to the different phases of the Madden–Julian oscillation, particularly that of dust and carbonaceous aerosols. The degree of improvement depends crucially on the aerosol initialization. More work is required to fully assess the potential of interactive aerosols to increase predictability at the subseasonal scales.
Abstract
The ECMWF Seasonal Forecasting System, based on ensembles of 200-day coupled GCM integrations, contains tropical disturbances that are referred to as model tropical storms in the present paper. Model tropical storms display a genesis location and a seasonal cycle generally consistent with observations, though the frequency of model tropical storms is significantly lower than observed, particularly over the North Atlantic and the eastern North Pacific. Several possible causes for the low number of model tropical storms are discussed.
The ECMWF Seasonal Forecasting System produces realistic forecasts of the interannual variability of tropical storm frequency over the North Atlantic and the western North Pacific, with strong linear correlations and low rms error obtained when comparing the forecasts to observations. The skill of the seasonal forecasting system in predicting the frequency of tropical storms is likely to be related to its skill in predicting sea surface temperatures. In particular, the model seems successful in predicting the occurrence and development of El Niño and La Niña events, and their impact on the large-scale circulation over the Atlantic. For the period 1991–99, a comparison with the statistical forecasts issued by the Colorado State Hurricane Forecast Team suggests that the ECMWF seasonal forecasting system produces a better June forecast of the total number of tropical storms over the North Atlantic. These results establish the feasibility of real-time forecasting of tropical storm statistics by dynamical methods.
Abstract
The ECMWF Seasonal Forecasting System, based on ensembles of 200-day coupled GCM integrations, contains tropical disturbances that are referred to as model tropical storms in the present paper. Model tropical storms display a genesis location and a seasonal cycle generally consistent with observations, though the frequency of model tropical storms is significantly lower than observed, particularly over the North Atlantic and the eastern North Pacific. Several possible causes for the low number of model tropical storms are discussed.
The ECMWF Seasonal Forecasting System produces realistic forecasts of the interannual variability of tropical storm frequency over the North Atlantic and the western North Pacific, with strong linear correlations and low rms error obtained when comparing the forecasts to observations. The skill of the seasonal forecasting system in predicting the frequency of tropical storms is likely to be related to its skill in predicting sea surface temperatures. In particular, the model seems successful in predicting the occurrence and development of El Niño and La Niña events, and their impact on the large-scale circulation over the Atlantic. For the period 1991–99, a comparison with the statistical forecasts issued by the Colorado State Hurricane Forecast Team suggests that the ECMWF seasonal forecasting system produces a better June forecast of the total number of tropical storms over the North Atlantic. These results establish the feasibility of real-time forecasting of tropical storm statistics by dynamical methods.
Abstract
The 2000 tropical cyclone season over the South Indian Ocean (SIO) was exceptional in terms of tropical cyclone landfall over Mozambique. Observed data suggest that SIO tropical cyclones have a track significantly more zonal during a La Niña event and tend to be more frequent when local SSTs are warmer. The combination of both conditions happened during the 2000 SIO tropical cyclone season and may explain the exceptional number of tropical cyclone landfalls over Mozambique during that season. A set of experiments using an atmospheric model of fairly high resolution (T L 159, with a Gaussian grid spacing of 1.125°) forced by prescribed SSTs confirms the role of La Niña conditions and warmer local SSTs on the frequency of tropical cyclone landfalls over Mozambique. This also suggests that a numerical model can simulate the mechanisms responsible for the exceptional 2000 tropical cyclone season, and therefore could be used to explicitly predict the risk of landfall over Mozambique.
A coupled model with a T L 159 atmospheric component has been integrated for 3 months starting on 1 January of each year 1987–2001 to test this hypothesis. The hindcast produces significantly more tropical cyclone landfalls in 2000 than in any other year, and years with a predicted high risk of landfall generally coincide with years of observed tropical cyclone landfall.
Abstract
The 2000 tropical cyclone season over the South Indian Ocean (SIO) was exceptional in terms of tropical cyclone landfall over Mozambique. Observed data suggest that SIO tropical cyclones have a track significantly more zonal during a La Niña event and tend to be more frequent when local SSTs are warmer. The combination of both conditions happened during the 2000 SIO tropical cyclone season and may explain the exceptional number of tropical cyclone landfalls over Mozambique during that season. A set of experiments using an atmospheric model of fairly high resolution (T L 159, with a Gaussian grid spacing of 1.125°) forced by prescribed SSTs confirms the role of La Niña conditions and warmer local SSTs on the frequency of tropical cyclone landfalls over Mozambique. This also suggests that a numerical model can simulate the mechanisms responsible for the exceptional 2000 tropical cyclone season, and therefore could be used to explicitly predict the risk of landfall over Mozambique.
A coupled model with a T L 159 atmospheric component has been integrated for 3 months starting on 1 January of each year 1987–2001 to test this hypothesis. The hindcast produces significantly more tropical cyclone landfalls in 2000 than in any other year, and years with a predicted high risk of landfall generally coincide with years of observed tropical cyclone landfall.
Abstract
It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.
Abstract
It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.
Abstract
The skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast system to predict the occurrence of tropical cyclones (TCs) over the Southern Hemisphere during weekly periods has been evaluated and compared to the skill of a state-of-the-art statistical model. Probabilistic skill scores have been applied to a common series of hindcasts produced with the dynamical and statistical models. The ECMWF hindcasts have higher relative operating characteristic (ROC) scores than the statistical model for the first three weeks of integrations. The dynamical model also has skill over the Indian Ocean in week 4.
The ECMWF hindcasts have lower Brier skill scores than the statistical model after week 2, which is likely because this version of the ECMWF model creates about 30% more TCs than observations and therefore generates a large number of false alarms. A simple calibration has been applied to the ECMWF probabilistic forecasts that significantly improves their reliability, but at the expense of the sharpness. The calibrated dynamical model has higher Brier skill scores than the statistical model during the first three weeks, although the statistical model remains more reliable.
The multimodel combination of the calibrated dynamical forecasts with the statistical forecasts helps to improve the reliability of the ECMWF forecasts. The Brier skill score of the multimodel exceeds the Brier skill scores of the individual models, but with less sharpness than the calibrated dynamical model. This result suggests that the statistical model can be useful as a benchmark for dynamical models and as a component of a multimodel combination to improve the skill of the dynamical model. Potential economic value diagrams confirm that the multimodel forecasts are useful up to week 3 over the Southern Hemisphere.
Abstract
The skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast system to predict the occurrence of tropical cyclones (TCs) over the Southern Hemisphere during weekly periods has been evaluated and compared to the skill of a state-of-the-art statistical model. Probabilistic skill scores have been applied to a common series of hindcasts produced with the dynamical and statistical models. The ECMWF hindcasts have higher relative operating characteristic (ROC) scores than the statistical model for the first three weeks of integrations. The dynamical model also has skill over the Indian Ocean in week 4.
The ECMWF hindcasts have lower Brier skill scores than the statistical model after week 2, which is likely because this version of the ECMWF model creates about 30% more TCs than observations and therefore generates a large number of false alarms. A simple calibration has been applied to the ECMWF probabilistic forecasts that significantly improves their reliability, but at the expense of the sharpness. The calibrated dynamical model has higher Brier skill scores than the statistical model during the first three weeks, although the statistical model remains more reliable.
The multimodel combination of the calibrated dynamical forecasts with the statistical forecasts helps to improve the reliability of the ECMWF forecasts. The Brier skill score of the multimodel exceeds the Brier skill scores of the individual models, but with less sharpness than the calibrated dynamical model. This result suggests that the statistical model can be useful as a benchmark for dynamical models and as a component of a multimodel combination to improve the skill of the dynamical model. Potential economic value diagrams confirm that the multimodel forecasts are useful up to week 3 over the Southern Hemisphere.
Abstract
There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.
Abstract
There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.
Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
