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Abstract
The United Kingdom Fine-Resolution Antartic Model (FRAM project) it a community program to study the Southern Ocean. Central to this is an eddy-resolving three-dimensional primitive-equation ocean general circulation model. The open boundary condition at the northern boundary is described here. The boundary condition is based on that of Stevens (1990).
Abstract
The United Kingdom Fine-Resolution Antartic Model (FRAM project) it a community program to study the Southern Ocean. Central to this is an eddy-resolving three-dimensional primitive-equation ocean general circulation model. The open boundary condition at the northern boundary is described here. The boundary condition is based on that of Stevens (1990).
Abstract
The impact of initial condition uncertainty on short-range (0–48 h) simulations of explosive surface cyclogenesis is examined within the context of a perfect model environment. Eleven Monte Carlo simulations are performed on 10 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global spectral model. The perturbations used to represent the initial condition error have a magnitude and spatial decomposition that closely matches estimates of global analysis error.
Large variability characterizes the error growth rates, both among the individual Monte Carlo simulations and among the case-average values. Some individual simulations display error growth doubling times as fast as approximately 12 h during the 24-h period of most rapid intensification, while others exhibit virtually no error growth. The variability is also reflected in the wide 90% confidence bounds for many surface weather elements such as the cyclone position and central pressure. However, no statistically significant differences are found between the initial states leading to large simulation errors and those leading to negligible errors. These results attest to the importance of initial condition uncertainty as the major cause of forecast variability and indicate a strong sensitivity to subtle differences in initial perturbation location and structure.
The effect that simple ensemble averaging has on reducing uncertainty is discussed. Averaging a 16-member ensemble decreases the random component of the initial data error by 80%–90% and the 90% confidence bounds by 70%–80% for cyclone position, central pressure, and 12-h pressure change. It is hypothesized that ensemble forecasting could benefit the utility of short-range forecasts for many weather elements of operational interest and conclude that research efforts should be directed at examining its effectiveness in an operational setting.
Abstract
The impact of initial condition uncertainty on short-range (0–48 h) simulations of explosive surface cyclogenesis is examined within the context of a perfect model environment. Eleven Monte Carlo simulations are performed on 10 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global spectral model. The perturbations used to represent the initial condition error have a magnitude and spatial decomposition that closely matches estimates of global analysis error.
Large variability characterizes the error growth rates, both among the individual Monte Carlo simulations and among the case-average values. Some individual simulations display error growth doubling times as fast as approximately 12 h during the 24-h period of most rapid intensification, while others exhibit virtually no error growth. The variability is also reflected in the wide 90% confidence bounds for many surface weather elements such as the cyclone position and central pressure. However, no statistically significant differences are found between the initial states leading to large simulation errors and those leading to negligible errors. These results attest to the importance of initial condition uncertainty as the major cause of forecast variability and indicate a strong sensitivity to subtle differences in initial perturbation location and structure.
The effect that simple ensemble averaging has on reducing uncertainty is discussed. Averaging a 16-member ensemble decreases the random component of the initial data error by 80%–90% and the 90% confidence bounds by 70%–80% for cyclone position, central pressure, and 12-h pressure change. It is hypothesized that ensemble forecasting could benefit the utility of short-range forecasts for many weather elements of operational interest and conclude that research efforts should be directed at examining its effectiveness in an operational setting.
Abstract
The impact of initial condition uncertainty on short-range (up to 48 h) forecasts of large-scale explosive cyclogenesis is examined. Predictability experiments are conducted on 11 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global, high-resolution, spectral model. Results are derived from the 11-case ensemble average. The perturbation used to represent the initial condition error in this study has a magnitude and spatial distribution that closely matches estimates of global analysis error. Results from the predictability experiments are compared to a set of physics sensitivity experiments which are used to represent an estimate of a “typical” modeling, error.
Compared to the control simulations, the inclusion of initial error produces a composite cyclone with maximum deepening rate that is slightly reduced and a 24 h period of most rapid deepening that is somewhat delayed. The absolute position error in the surface cyclone is approximately 100 km the first +36 h of the forecast then abruptly increases to 300 km by +48 h. We estimate that, on the average, the forecast error due to initial condition uncertainty is as large as that due to the modeling error associated with today's best operational models, whereas five years ago modeling error was much more important.
The relative importance of initial condition uncertainty for explosive cyclogenesis is compared to that for the entire midlatitude flow in general. Error growth rates in an explosive cyclogenetic environment are 50% greater in the upper troposphere (500 mb and above) and two times faster near the surface (850 mb and below). The rapid growth rates indicate that short-range forecasts of explosive cyclogenesis are much wore sensitive to initial error than those for ordinary flows.
The case-to-case variability exhibited by the 11-member ensemble is examined. Noteworthy departure from the aggregate results are evident In individual cases, initial condition error can lead to short-range forecast differences which can be either greater than those due to a typical modeling error or much less. This variability implies a strong sensitivity to initial condition perturbation location and structure.
Abstract
The impact of initial condition uncertainty on short-range (up to 48 h) forecasts of large-scale explosive cyclogenesis is examined. Predictability experiments are conducted on 11 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global, high-resolution, spectral model. Results are derived from the 11-case ensemble average. The perturbation used to represent the initial condition error in this study has a magnitude and spatial distribution that closely matches estimates of global analysis error. Results from the predictability experiments are compared to a set of physics sensitivity experiments which are used to represent an estimate of a “typical” modeling, error.
Compared to the control simulations, the inclusion of initial error produces a composite cyclone with maximum deepening rate that is slightly reduced and a 24 h period of most rapid deepening that is somewhat delayed. The absolute position error in the surface cyclone is approximately 100 km the first +36 h of the forecast then abruptly increases to 300 km by +48 h. We estimate that, on the average, the forecast error due to initial condition uncertainty is as large as that due to the modeling error associated with today's best operational models, whereas five years ago modeling error was much more important.
The relative importance of initial condition uncertainty for explosive cyclogenesis is compared to that for the entire midlatitude flow in general. Error growth rates in an explosive cyclogenetic environment are 50% greater in the upper troposphere (500 mb and above) and two times faster near the surface (850 mb and below). The rapid growth rates indicate that short-range forecasts of explosive cyclogenesis are much wore sensitive to initial error than those for ordinary flows.
The case-to-case variability exhibited by the 11-member ensemble is examined. Noteworthy departure from the aggregate results are evident In individual cases, initial condition error can lead to short-range forecast differences which can be either greater than those due to a typical modeling error or much less. This variability implies a strong sensitivity to initial condition perturbation location and structure.
Abstract
The relative importance of different parameterized physical process and baroclinic dynamics in numerical simulations of explosive oceanic cyclogenesis is examined. The numerical simulations are derived from a global spectral model having nine vertical levels and a rhomboidal 31 truncation. Eleven cases of rapid cyclogenesis over the North Pacific Ocean that occurred in a 150-day simulation of perpetual January conditions are used as initial conditions for model sensitivity experiments. Statistical techniques based on predictability theory are employed to estimate the relative importance of the sensitivity experiments.
Results from the simulation comparisons indicate that the total diabatic heating accounts for about one-half of the cyclone's deepening rate, with baroclinic dynamics accounting for the remaining part. The absence of diabatic heating also leads to a systematic error in the position of the cyclone. Surface fluxes of sensible heat are responsible for about one-half of the deepening rate due to diabatic processes, while latent heating due to grid-scale resolvable precipitation in conjunction with surface latent heat flux accounts for most of the remaining half. An increase in the surface drag over the ocean to its larger land value was found to be of comparable importance to both surface sensible heat flux and latent beat release, but was only half as important as the total diabatic heating. These changes in the model were judged to produce highly significant response especially at low levels. The addition of radiative heating, the substitution of a Kuo-type cumulus parameterization scheme for the model's default moist convective adjustment scheme, and a factor of 4 increase in the horizontal diffusion did not produce significant responses.
The case-to-case variability exhibited by the 11-member ensemble is examined. The potential danger in attempting to generalize results from a single case of explosive cyclogenesis as being representative of those for the ensemble average is illustrated.
Abstract
The relative importance of different parameterized physical process and baroclinic dynamics in numerical simulations of explosive oceanic cyclogenesis is examined. The numerical simulations are derived from a global spectral model having nine vertical levels and a rhomboidal 31 truncation. Eleven cases of rapid cyclogenesis over the North Pacific Ocean that occurred in a 150-day simulation of perpetual January conditions are used as initial conditions for model sensitivity experiments. Statistical techniques based on predictability theory are employed to estimate the relative importance of the sensitivity experiments.
Results from the simulation comparisons indicate that the total diabatic heating accounts for about one-half of the cyclone's deepening rate, with baroclinic dynamics accounting for the remaining part. The absence of diabatic heating also leads to a systematic error in the position of the cyclone. Surface fluxes of sensible heat are responsible for about one-half of the deepening rate due to diabatic processes, while latent heating due to grid-scale resolvable precipitation in conjunction with surface latent heat flux accounts for most of the remaining half. An increase in the surface drag over the ocean to its larger land value was found to be of comparable importance to both surface sensible heat flux and latent beat release, but was only half as important as the total diabatic heating. These changes in the model were judged to produce highly significant response especially at low levels. The addition of radiative heating, the substitution of a Kuo-type cumulus parameterization scheme for the model's default moist convective adjustment scheme, and a factor of 4 increase in the horizontal diffusion did not produce significant responses.
The case-to-case variability exhibited by the 11-member ensemble is examined. The potential danger in attempting to generalize results from a single case of explosive cyclogenesis as being representative of those for the ensemble average is illustrated.
Abstract
In regions with large tracer gradients and/or velocities the advection scheme that is used in many ocean models leads to significant under- and overshoot of the tracer values. In the case of the U.K. Fine Resolution Antarctic Model (FRAM), this lead to unphysical negative surface temperatures in some regions and overheating in others. In this paper, a new advection scheme is proposed and tested in the ocean model context using a limited-area model centered on a region where problems occurred in FRAM.
Abstract
In regions with large tracer gradients and/or velocities the advection scheme that is used in many ocean models leads to significant under- and overshoot of the tracer values. In the case of the U.K. Fine Resolution Antarctic Model (FRAM), this lead to unphysical negative surface temperatures in some regions and overheating in others. In this paper, a new advection scheme is proposed and tested in the ocean model context using a limited-area model centered on a region where problems occurred in FRAM.
Abstract
A principal component analysis of the combined fields of sea surface temperature (SST) and surface zonal and meridional wind reveals that the dominant mode of intraseasonal (30 to 70 day) covariability during northern winter in the tropical Eastern Hemisphere is that of the Madden–Julian oscillation (MJO). Regression calculations show that the submonthly (30-day high-pass filtered) surface wind variability is significantly modulated during the MJO. Regions of increased (decreased) submonthly surface wind variability propagate eastward, approximately in phase with the intraseasonal surface westerly (easterly) anomalies of the MJO. Because of the dependence of the surface latent heat flux on the magnitude of the total wind speed, this systematic modulation of the submonthly surface wind variability produces a significant component in the intraseasonal latent heat flux anomalies, which partially cancels the latent heat flux anomalies due to the slowly varying intraseasonal wind anomalies, particularly south of 10°S.
A method is derived that demodulates the submonthly surface wind variability from the slowly varying intraseasonal wind anomalies. This method is applied to the wind forcing fields of a one-dimensional ocean model. The model response to this modified forcing produces larger intraseasonal SST anomalies than when the model is forced with the observed forcing over large areas of the southwest Pacific Ocean and southeast Indian Ocean during both phases of the MJO. This result has implications for accurate coupled modeling of the MJO. A similar calculation is applied to the surface shortwave flux, but intraseasonal modulation of submonthly surface shortwave flux variability does not appear to be important to the dynamics of the MJO.
Abstract
A principal component analysis of the combined fields of sea surface temperature (SST) and surface zonal and meridional wind reveals that the dominant mode of intraseasonal (30 to 70 day) covariability during northern winter in the tropical Eastern Hemisphere is that of the Madden–Julian oscillation (MJO). Regression calculations show that the submonthly (30-day high-pass filtered) surface wind variability is significantly modulated during the MJO. Regions of increased (decreased) submonthly surface wind variability propagate eastward, approximately in phase with the intraseasonal surface westerly (easterly) anomalies of the MJO. Because of the dependence of the surface latent heat flux on the magnitude of the total wind speed, this systematic modulation of the submonthly surface wind variability produces a significant component in the intraseasonal latent heat flux anomalies, which partially cancels the latent heat flux anomalies due to the slowly varying intraseasonal wind anomalies, particularly south of 10°S.
A method is derived that demodulates the submonthly surface wind variability from the slowly varying intraseasonal wind anomalies. This method is applied to the wind forcing fields of a one-dimensional ocean model. The model response to this modified forcing produces larger intraseasonal SST anomalies than when the model is forced with the observed forcing over large areas of the southwest Pacific Ocean and southeast Indian Ocean during both phases of the MJO. This result has implications for accurate coupled modeling of the MJO. A similar calculation is applied to the surface shortwave flux, but intraseasonal modulation of submonthly surface shortwave flux variability does not appear to be important to the dynamics of the MJO.
Abstract
Due in part to sparse conventional observation coverage in the Antarctic region, atmospheric studies in this part of the globe often rely more heavily on numerical models. Model representation of atmospheric processes in the Antarctic remains inferior to representation in the Northern Hemisphere midlatitudes. Poor representation may be related to inaccurate model analyses that do not optimally utilize the limited observation network. Here, the ensemble Kalman filter (EnKF) data assimilation (DA) technique is employed in lieu of variational DA techniques to investigate impacts on model analysis accuracy. This DA technique [provided by the Data Assimilation Research Testbed (DART)] is coupled with a polar-modified, mesoscale numerical model that together compose Antarctic-DART (A-DART). A-DART is cycled with DA and run over a 1-month period, assimilating only conventional observations. Results show relatively good agreement between A-DART and observations. Comparison with radiosonde temperature and geostationary satellite wind observations shows large differences between RMSE and ensemble spread in the upper troposphere. The analysis increment shows large values in the eastern Atlantic–western Indian Oceans associated with geostationary satellite wind observations. Further evaluation determines that geostationary satellite wind observations may be biased in this region. Overall, this baseline demonstration of ensemble-based modeling applied in the Antarctic produced short-term forecasts that were competitive with two operational modeling systems while assimilating on the O(106) fewer observations. A-DART is capable of assimilating additional observations for a variety of applications. This study highlights the capability of applying this ensemble-based DA technique for process and forecast studies in an observation-sparse region.
Abstract
Due in part to sparse conventional observation coverage in the Antarctic region, atmospheric studies in this part of the globe often rely more heavily on numerical models. Model representation of atmospheric processes in the Antarctic remains inferior to representation in the Northern Hemisphere midlatitudes. Poor representation may be related to inaccurate model analyses that do not optimally utilize the limited observation network. Here, the ensemble Kalman filter (EnKF) data assimilation (DA) technique is employed in lieu of variational DA techniques to investigate impacts on model analysis accuracy. This DA technique [provided by the Data Assimilation Research Testbed (DART)] is coupled with a polar-modified, mesoscale numerical model that together compose Antarctic-DART (A-DART). A-DART is cycled with DA and run over a 1-month period, assimilating only conventional observations. Results show relatively good agreement between A-DART and observations. Comparison with radiosonde temperature and geostationary satellite wind observations shows large differences between RMSE and ensemble spread in the upper troposphere. The analysis increment shows large values in the eastern Atlantic–western Indian Oceans associated with geostationary satellite wind observations. Further evaluation determines that geostationary satellite wind observations may be biased in this region. Overall, this baseline demonstration of ensemble-based modeling applied in the Antarctic produced short-term forecasts that were competitive with two operational modeling systems while assimilating on the O(106) fewer observations. A-DART is capable of assimilating additional observations for a variety of applications. This study highlights the capability of applying this ensemble-based DA technique for process and forecast studies in an observation-sparse region.
Abstract
The impact of initial condition uncertainty (ICU) on the onset and maintenance of eastern North Pacific blocking is examined within the framework of a general circulation model (GCM) and the perfect model assumption. Comparisons are made with the contrasting zonal flow regime. Twenty-member ensembles of perturbed simulations are run out to 15 days for the zonal flow, and for blocking at lead times of 8, 4, 2, and 0 days.
Blocking occurs in 95% of the 0-day lead simulations and declines monotonically to 65% for the 8-day lead simulations. The uncertainty in the exact time of onset among those simulations that form blocks also increases with lead time. The synoptic-scale features in both the blocking and zonal ensembles saturate, relative to climatological variance, and decorrelate (anomaly correlation coefficient < 0.5) by 6 days. The planetary-scale features, however, maintain skill relative to climatology beyond 10 days. The zonal simulations are generally the first to saturate and decorrelate, followed by simulations of blocking maintenance (0-day lead) and onset (2-, 4-, and 8-day lead), respectively. Thus, initial flows that project negatively (zonal flows) on the GCM’s Pacific–North American teleconnectivity pattern are more sensitive to ICU, and thus are less predictable than positive (blocking flows) projections.
While the results for this study demonstrate that error growth due to ICU ultimately imposes limits on the predictability of blocking, they also suggest that skillful ensemble predictions of transitions to a blocked state are possible at long lead times if the model error is held to a minimum. The majority of the perturbed simulations make the transition into a blocked state with an associated sustenance of skill even after the loss of skill in the synoptic-scale waves. The results are consistent with the hypothesis that the planetary-scale waves may need to be preconditioned toward the formation of blocking events. They also may, in part, help explain the poor performance of operational models in forecasts of blocking onset.
Abstract
The impact of initial condition uncertainty (ICU) on the onset and maintenance of eastern North Pacific blocking is examined within the framework of a general circulation model (GCM) and the perfect model assumption. Comparisons are made with the contrasting zonal flow regime. Twenty-member ensembles of perturbed simulations are run out to 15 days for the zonal flow, and for blocking at lead times of 8, 4, 2, and 0 days.
Blocking occurs in 95% of the 0-day lead simulations and declines monotonically to 65% for the 8-day lead simulations. The uncertainty in the exact time of onset among those simulations that form blocks also increases with lead time. The synoptic-scale features in both the blocking and zonal ensembles saturate, relative to climatological variance, and decorrelate (anomaly correlation coefficient < 0.5) by 6 days. The planetary-scale features, however, maintain skill relative to climatology beyond 10 days. The zonal simulations are generally the first to saturate and decorrelate, followed by simulations of blocking maintenance (0-day lead) and onset (2-, 4-, and 8-day lead), respectively. Thus, initial flows that project negatively (zonal flows) on the GCM’s Pacific–North American teleconnectivity pattern are more sensitive to ICU, and thus are less predictable than positive (blocking flows) projections.
While the results for this study demonstrate that error growth due to ICU ultimately imposes limits on the predictability of blocking, they also suggest that skillful ensemble predictions of transitions to a blocked state are possible at long lead times if the model error is held to a minimum. The majority of the perturbed simulations make the transition into a blocked state with an associated sustenance of skill even after the loss of skill in the synoptic-scale waves. The results are consistent with the hypothesis that the planetary-scale waves may need to be preconditioned toward the formation of blocking events. They also may, in part, help explain the poor performance of operational models in forecasts of blocking onset.
Abstract
This paper addresses the question of time changes in the cloud parcels comprising a cumulus cloud. Observations are analyzed which show how a cloud parcel begins its life on the upshear side of the cloud. As the cloud as a whole continues to develop upshear, the particular parcel is diluted by entrainment of dry air from above it, and the vertical cycling of cooled parcels formed in this way produces complex modifications of the cloud droplet spectra which can be traced in the observations. As this process continues, the parcel is found closer and closer to the downshear evaporating side of the cloud as the parcel is modified and ages. When sufficiently diluted, the whole cloud column subsides and evaporates.
The largest cloud drops, and the ice particles, are found in the older, diluted, decaying side of the cloud.
Abstract
This paper addresses the question of time changes in the cloud parcels comprising a cumulus cloud. Observations are analyzed which show how a cloud parcel begins its life on the upshear side of the cloud. As the cloud as a whole continues to develop upshear, the particular parcel is diluted by entrainment of dry air from above it, and the vertical cycling of cooled parcels formed in this way produces complex modifications of the cloud droplet spectra which can be traced in the observations. As this process continues, the parcel is found closer and closer to the downshear evaporating side of the cloud as the parcel is modified and ages. When sufficiently diluted, the whole cloud column subsides and evaporates.
The largest cloud drops, and the ice particles, are found in the older, diluted, decaying side of the cloud.
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