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- Author or Editor: T. N. Palmer x
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Abstract
Stimulated by the results of a simple SST anomaly experiment with the ECMWF forecast model, a study was carried out to examine the model parameterization of evaporation from the tropica] oceans. In earlier versions of the model, these fluxes were parameterized with neutral transfer coefficients in accordance with the Charnock relation with equal coefficients for momentum, heat, and moisture. Stability correction was applied using Monin-Obukhov theory. This parameterization resulted in an extremely weak coupling between atmosphere and ocean at wind speeds below 5 m s−1. The transfer coefficients for heat and moisture have now been modified for low wind speeds to bring them in accordance with the empirical scaling law for free convedion. It is shown that these revisions to the transfer coefficients at very low wind speeds (<5 m s1) have a dramatic positive impact on almost all aspects of the model's simulation of the tropics. These include much improved seasonal rainfall distributions (with the virtual elimination of a tendency to generate a double ITCZ in both winter and summer), a much improved Indian monsoon circulation, and substantially reduced tropical systematic errors. The model previously had an eagerly bias in the zonal-mean upper tropical tropospheric flow with a corresponding cold bias in the deep tropics; it is shown that the flux revision substantially reduces this. Furthermore, the revision to the fluxes greatly enhances the model's ability to represent interannual and intraseasonal variability (see also the companion paper by Palmer et al.).
Abstract
Stimulated by the results of a simple SST anomaly experiment with the ECMWF forecast model, a study was carried out to examine the model parameterization of evaporation from the tropica] oceans. In earlier versions of the model, these fluxes were parameterized with neutral transfer coefficients in accordance with the Charnock relation with equal coefficients for momentum, heat, and moisture. Stability correction was applied using Monin-Obukhov theory. This parameterization resulted in an extremely weak coupling between atmosphere and ocean at wind speeds below 5 m s−1. The transfer coefficients for heat and moisture have now been modified for low wind speeds to bring them in accordance with the empirical scaling law for free convedion. It is shown that these revisions to the transfer coefficients at very low wind speeds (<5 m s1) have a dramatic positive impact on almost all aspects of the model's simulation of the tropics. These include much improved seasonal rainfall distributions (with the virtual elimination of a tendency to generate a double ITCZ in both winter and summer), a much improved Indian monsoon circulation, and substantially reduced tropical systematic errors. The model previously had an eagerly bias in the zonal-mean upper tropical tropospheric flow with a corresponding cold bias in the deep tropics; it is shown that the flux revision substantially reduces this. Furthermore, the revision to the fluxes greatly enhances the model's ability to represent interannual and intraseasonal variability (see also the companion paper by Palmer et al.).
Abstract
Stochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the stochastically perturbed parameterization tendency (SPPT) scheme, which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely, the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good grounds for implementing stochastic schemes in ocean models, the results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.
Abstract
Stochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the stochastically perturbed parameterization tendency (SPPT) scheme, which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely, the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good grounds for implementing stochastic schemes in ocean models, the results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.
Abstract
The fastest perturbation growth (optimal growth) in forecasts of El Niño–Southern Oscillation (ENSO) with the Zebiak and Cane model is analyzed by singular value decomposition of forward tangent models along forecast trajectories in a reduced EOF space. The authors study optimal growth in forecast runs using two different initialization procedures and discuss the relationship between optimal growth and forecast skill.
Consistent with Part I of this work, one dominant growing singular vector is found. Most of the variation of optimal growth, measured by the largest singular value, for warm events and mean condition is seasonal, attributable to the seasonal variations in the background states. For cold events the seasonal optimal growth is substantially suppressed. The first singular vector is approximately white in EOF space, while its final pattern after a 6-month evolution is dominated by the first EOF. The energy norm amplifies between 5- and 24-fold in 6 months. This indicates that small-scale disturbances are able to draw energy efficiently from the mean seasonal background states and evolve into large scales, characteristic of ENSO, in several months.
The difference fields between the initial conditions generated with the standard initialization procedure and the more recent one of Chen et al. (referred to as old and new ICs) are often so large that the optimal growth for the two sets of forecasts is very different. In such situations, linear growth is not an adequate measure of predictability of ENSO. That the present ZC forecast skill is significantly improved by the new initialization procedure indicates that the inherent ENSO predictability is only a secondary factor controlling current forecast skill; the imbalances between the model and data discussed by Chen et al. are the primary factor.
Optimal growth describes dominant initial error growth only when initial error covariance is white under a choice of norm. If the difference fields between the old and new ICs are considered representative of the error fields of the old ICs, the initial error covariance is red under the energy norm. So a new norm that makes the initial error covariance white is used. The first singular vectors under the new norm are insensitive to initial time and optimization time, and are dominated by the first few EOFs. When the first singular vector components of the initial error fields are removed from the old ICs, the forecast skill is improved significantly. Thus the suppression of a single initial error structure accounts for most of the new scheme’s improvement in forecast skill.
Abstract
The fastest perturbation growth (optimal growth) in forecasts of El Niño–Southern Oscillation (ENSO) with the Zebiak and Cane model is analyzed by singular value decomposition of forward tangent models along forecast trajectories in a reduced EOF space. The authors study optimal growth in forecast runs using two different initialization procedures and discuss the relationship between optimal growth and forecast skill.
Consistent with Part I of this work, one dominant growing singular vector is found. Most of the variation of optimal growth, measured by the largest singular value, for warm events and mean condition is seasonal, attributable to the seasonal variations in the background states. For cold events the seasonal optimal growth is substantially suppressed. The first singular vector is approximately white in EOF space, while its final pattern after a 6-month evolution is dominated by the first EOF. The energy norm amplifies between 5- and 24-fold in 6 months. This indicates that small-scale disturbances are able to draw energy efficiently from the mean seasonal background states and evolve into large scales, characteristic of ENSO, in several months.
The difference fields between the initial conditions generated with the standard initialization procedure and the more recent one of Chen et al. (referred to as old and new ICs) are often so large that the optimal growth for the two sets of forecasts is very different. In such situations, linear growth is not an adequate measure of predictability of ENSO. That the present ZC forecast skill is significantly improved by the new initialization procedure indicates that the inherent ENSO predictability is only a secondary factor controlling current forecast skill; the imbalances between the model and data discussed by Chen et al. are the primary factor.
Optimal growth describes dominant initial error growth only when initial error covariance is white under a choice of norm. If the difference fields between the old and new ICs are considered representative of the error fields of the old ICs, the initial error covariance is red under the energy norm. So a new norm that makes the initial error covariance white is used. The first singular vectors under the new norm are insensitive to initial time and optimization time, and are dominated by the first few EOFs. When the first singular vector components of the initial error fields are removed from the old ICs, the forecast skill is improved significantly. Thus the suppression of a single initial error structure accounts for most of the new scheme’s improvement in forecast skill.
Abstract
The authors examine the sensitivity of the Battisti coupled atmosphere–ocean model—considered as a forecast model for the El Niño–Southern Oscillation (ENSO)—to perturbations in the sea surface temperature (SST) field applied at the beginning of a model integration. The spatial structures of the fastest growing SST perturbations are determined by singular vector analysis of an approximation to the propagator for the linearized system. Perturbation growth about the following four reference trajectories is considered: (i) the annual cycle, (ii) a freely evolving model ENSO cycle with an annual cycle in the basic state, (iii) the annual mean basic state, and (iv) a freely evolving model ENSO cycle with an annual mean basic state. Singular vectors with optimal growth over periods of 3, 6, and 9 months are computed.
The magnitude of maximum perturbation growth is highly dependent on both the phase of the seasonal cycle and the phase of the ENSO cycle at which the perturbation is applied and on the duration over which perturbations are allowed to evolve. However, the spatial structure of the optimal perturbation is remarkably insensitive to these factors. The structure of the optimal perturbation consists of an east–west dipole spanning the entire tropical Pacific basin superimposed on a north–south dipole in the eastern tropical Pacific. A simple physical interpretation for the optimal pattern is provided. In most cases investigated, there is only one structure that exhibits growth.
Maximum perturbation growth takes place for integrations that include the period June–August, and the minimum growth for integrations that include the period January–April. Maxima in potential growth also occur for forecasts of ENSO onset and decay, while minima occur for forecasts initialized during the beginning of a warm event, after the transition from a warm to a cold event, and continuing through the cold event. The physical processes responsible for the large variation in the amplitude of the optimal perturbation growth are identified. The implications of these results for the predictability of short-term climate in the tropical Pacific are discussed.
Abstract
The authors examine the sensitivity of the Battisti coupled atmosphere–ocean model—considered as a forecast model for the El Niño–Southern Oscillation (ENSO)—to perturbations in the sea surface temperature (SST) field applied at the beginning of a model integration. The spatial structures of the fastest growing SST perturbations are determined by singular vector analysis of an approximation to the propagator for the linearized system. Perturbation growth about the following four reference trajectories is considered: (i) the annual cycle, (ii) a freely evolving model ENSO cycle with an annual cycle in the basic state, (iii) the annual mean basic state, and (iv) a freely evolving model ENSO cycle with an annual mean basic state. Singular vectors with optimal growth over periods of 3, 6, and 9 months are computed.
The magnitude of maximum perturbation growth is highly dependent on both the phase of the seasonal cycle and the phase of the ENSO cycle at which the perturbation is applied and on the duration over which perturbations are allowed to evolve. However, the spatial structure of the optimal perturbation is remarkably insensitive to these factors. The structure of the optimal perturbation consists of an east–west dipole spanning the entire tropical Pacific basin superimposed on a north–south dipole in the eastern tropical Pacific. A simple physical interpretation for the optimal pattern is provided. In most cases investigated, there is only one structure that exhibits growth.
Maximum perturbation growth takes place for integrations that include the period June–August, and the minimum growth for integrations that include the period January–April. Maxima in potential growth also occur for forecasts of ENSO onset and decay, while minima occur for forecasts initialized during the beginning of a warm event, after the transition from a warm to a cold event, and continuing through the cold event. The physical processes responsible for the large variation in the amplitude of the optimal perturbation growth are identified. The implications of these results for the predictability of short-term climate in the tropical Pacific are discussed.
Abstract
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.
Abstract
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.
Abstract
No Abstract available.
Abstract
No Abstract available.
Trustworthy probabilistic projections of regional climate are essential for society to plan for future climate change, and yet, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with complete accuracy. How can we determine whether specific regional climate projections may be untrustworthy in the light of such generic deficiencies? A calibration method is proposed whose basis lies in the emerging notion of seamless prediction. Specifically, calibrations of ensemblebased climate change probabilities are derived from analyses of the statistical reliability of ensemblebased forecast probabilities on seasonal time scales. The method is demonstrated by calibrating probabilistic projections from the multimodel ensembles used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), based on reliability analyses from the seasonal forecast Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset. The focus in this paper is on climate change projections of regional precipitation, though the method is more general.
Trustworthy probabilistic projections of regional climate are essential for society to plan for future climate change, and yet, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with complete accuracy. How can we determine whether specific regional climate projections may be untrustworthy in the light of such generic deficiencies? A calibration method is proposed whose basis lies in the emerging notion of seamless prediction. Specifically, calibrations of ensemblebased climate change probabilities are derived from analyses of the statistical reliability of ensemblebased forecast probabilities on seasonal time scales. The method is demonstrated by calibrating probabilistic projections from the multimodel ensembles used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), based on reliability analyses from the seasonal forecast Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset. The focus in this paper is on climate change projections of regional precipitation, though the method is more general.
Results from a 3 1/2-yr experimental program of extended-range integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model are summarized. The topics discussed include
Our results are broadly consistent with those from other major centers evaluating the feasibility of dynamical extended-range prediction. We believe that operational extended-range forecasting using the ECMWF model may be viable to day 20—and possibly beyond—following further research on techniques for Monte Carlo forecasting, and when model systematic error in the tropics has been reduced significantly.
Results from a 3 1/2-yr experimental program of extended-range integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model are summarized. The topics discussed include
Our results are broadly consistent with those from other major centers evaluating the feasibility of dynamical extended-range prediction. We believe that operational extended-range forecasting using the ECMWF model may be viable to day 20—and possibly beyond—following further research on techniques for Monte Carlo forecasting, and when model systematic error in the tropics has been reduced significantly.
Abstract
No Abstract available.
Abstract
No Abstract available.
The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requirements to predict its behavior reliably are so enormous that the nations of the world should create a small number of multinational high-performance computing facilities dedicated to the grand challenges of developing the capabilities to predict climate variability and change on both global and regional scales over the coming decades. Such facilities will play a key role in the development of next-generation climate models, build global capacity in climate research, nurture a highly trained workforce, and engage the global user community, policymakers, and stakeholders. We recommend the creation of a small number of multinational facilities with computer capability at each facility of about 20 petaflops in the near term, about 200 petaflops within five years, and 1 exaflop by the end of the next decade. Each facility should have sufficient scientific workforce to develop and maintain the software and data analysis infrastructure. Such facilities will enable questions of what resolution, both horizontal and vertical, in atmospheric and ocean models, is necessary for more confident predictions at the regional and local level. Current limitations in computing power have placed severe limitations on such an investigation, which is now badly needed. These facilities will also provide the world's scientists with the computational laboratories for fundamental research on weather–climate interactions using 1-km resolution models and on atmospheric, terrestrial, cryospheric, and oceanic processes at even finer scales. Each facility should have enabling infrastructure including hardware, software, and data analysis support, and scientific capacity to interact with the national centers and other visitors. This will accelerate our understanding of how the climate system works and how to model it. It will ultimately enable the climate community to provide society with climate predictions, which are based on our best knowledge of science and the most advanced technology.
The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requirements to predict its behavior reliably are so enormous that the nations of the world should create a small number of multinational high-performance computing facilities dedicated to the grand challenges of developing the capabilities to predict climate variability and change on both global and regional scales over the coming decades. Such facilities will play a key role in the development of next-generation climate models, build global capacity in climate research, nurture a highly trained workforce, and engage the global user community, policymakers, and stakeholders. We recommend the creation of a small number of multinational facilities with computer capability at each facility of about 20 petaflops in the near term, about 200 petaflops within five years, and 1 exaflop by the end of the next decade. Each facility should have sufficient scientific workforce to develop and maintain the software and data analysis infrastructure. Such facilities will enable questions of what resolution, both horizontal and vertical, in atmospheric and ocean models, is necessary for more confident predictions at the regional and local level. Current limitations in computing power have placed severe limitations on such an investigation, which is now badly needed. These facilities will also provide the world's scientists with the computational laboratories for fundamental research on weather–climate interactions using 1-km resolution models and on atmospheric, terrestrial, cryospheric, and oceanic processes at even finer scales. Each facility should have enabling infrastructure including hardware, software, and data analysis support, and scientific capacity to interact with the national centers and other visitors. This will accelerate our understanding of how the climate system works and how to model it. It will ultimately enable the climate community to provide society with climate predictions, which are based on our best knowledge of science and the most advanced technology.
