The natural variability over the North Pacific, where the influence of tropical El Nino-Southern Oscillation (ENSO) events is substantial, is examined to determine whether there is a large change owing to a difference in the ENSO forcing anomaly. The hindcast ensemble runs of the Seasonal Forecast Model of the National Centers for Environmental Prediction are analyzed for this assessment. Four sets of 10-member ensemble hindcasts out to 7 months with T42 horizontal resolution and another four sets with T62 resolution are examined in detail. The results consistently indicate that the natural variability, on both seasonal and monthly time scales, is significantly smaller during El Nino boreal winters than during La Nina boreal winters. The implication is that the predictability on both seasonal and monthly time scales over the North Pacific is potentially higher during El Nino winters than during La Nina winters.

Abstract Teleconnection patterns have been extensively investigated, mostly with linear analysis tools. The lesser-known asymmetric characteristics between positive and negative phases of prominent teleconnections are explored here. Substantial disparity between opposite phases can be found. The Pacific–North American (PNA) pattern exhibits a large difference in structure and statistical significance in its downstream action center, showing either a large impact over the U.S. southern third region or over the western North Atlantic Ocean. The North Atlantic–based patterns display significant impacts over the North Atlantic for large positive anomalies and even larger impacts over the European sector for large negative anomalies. The monthly variance is distributed nearly evenly over the entire North Atlantic basin. A teleconnection pattern based on different regions of the basin has been known to assume different structure and time variations. The extent of statistical significance is investigated for thr...

A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill.An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the model's response to SST forcing is assumed to be perfect, is computed. This perfect model predictability allows one to make some rough extrapolations about the skill that could be expected if one could greatly improve the mean response of the GCMs without significantly impacting the variance of the ensemble. These perfect model predictability skills are better than the statistical model simulations during the summer, but for the winter, present-day statistical forecasts already have skill, that is as high as the upper bound for the GCMs. Simultaneous improvements to the GCM mean response and reduction in the GCM ensemble variance would be required for these GCMs to do significantly better than the statistical model in winter. This does not preclude the possibility that, as is presently the case, a statistical blend of GCM and statistical predictions could produce a simulation better than either alone.Because of the primitive state of coupled ocean-atmosphere GCMs, the vast majority of seasonal predictions currently produced by GCMs are performed using a two-tiered approach in which SSTs are first predicted and then used to force an atmospheric model; this motivates the examination of the simulation problem. However, it is straightforward to use the statistical model to produce true forecasts by changing its predictors from simultaneous to precursor SSTs. An examination of the decrease in skill of the statistical model when changed from simulation to prediction mode is extrapolated to draw conclusions about the skill to be expected from good coupled GCM predictions.

Beyond the deterministic limit where the initial value sensitive predictability can hardly be found, the boundary condition dependent potential predictability is examined. The tropical anomalies of the opposite phases of El Nino/Southern Oscillation (ENSO) can significantly change the extratropical natural variability on a wide range of spatial and temporal scales. We address the impact on the low-frequency variability, such as the persistent blocking flows, and the high-frequency variability, represented primarily by the storm tracks. The NCEP/NCAR reanalyses are used for this investigation. Several diagnostics tools help to reveal the dynamical processes leading to the large change of the natural variability and the potential predictability in the extratropical latitudes between these two phases of the ENSO cycle. During El Nino winters, the principal storm tracks are steered more into the southern and Baja California region, by the much eastward extended subtropical jets. On the other hand, the storms are diverted more into the higher latitudes (Aleutians and Gulf of Alaska) during La Nina winters, when jetstreams are much weaker east of 160 degrees W. Although being passively steered to widely different regions, the high-frequency transients do feed back actively to strengthen and maintain the subtropical jet across the central North Pacific and also act to slow down the equatorward flank of the jet. The feedback by the transients is stronger during the El Nino than the La Nina winters, helping in maintaining stronger signals from the tropics for the El Nino winters. There is also a large change of low-frequency variability: much larger magnitude of kinetic energy and height variance during La Nina than El Nino winters. The local barotropic energy diagnosis reveals that, on average, the low-frequency components extract more energy from time-mean flows during La Nina than El Nino winters, helping in explaining the presence of much larger low-frequency variability during the La Nina winters. With stronger ENSO signals and weaker natural variability during El Nino winters, the potential predictability in the north Pacific sector is significantly higher, on these two counts, than during the La Nina winters.

A substantial asymmetric impact of tropical Pacific SST anomalies on the internal variability of the extratropical atmosphere is found. A variety of diagnoses is performed to help reveal the dynamical processes leading to the large impact. Thirty-five years of geopotential heights and 29 years of wind fields analyzed operationally at the National Centers for Environmental Prediction (NCEP), formerly the National Meteorological Center, and three sets of 10-yr-long perpetual January integrations run with a low-resolution NCEP global spectral model are investigated in detail for the impact of the SST anomalies on the blocking flows over the North Pacific. The impact on large-scale deep trough flows is also examined.Both the blocking and deep trough Bows develop twice as much over the North Pacific during La Nina as during El Nino winters. Consequently, the internal dynamics associated low-frequency variability (LFV), with timescales between 7 and 61 days examined in this study, display distinct characteristics: much larger magnitude for the La Nina than the El Nino winters over the eastern North Pacific, where the LFV is highest in general.The diagnosis of the localized Eliassen-Palm fluxes and their divergence reveals that the high-frequency transient eddies (1-7 days) at high latitudes are effective in forming and maintaining the large-scale blocking flows, while the midlatitude transients are less effective. The mean deformation field over the North Pacific is much more diffluent for the La Nina than the El Nino winters, resulting in more blocking flows being developed and maintained during La Nina by the high-frequency transients over the central North Pacific.In addition to the above dynamical process operating on the high-frequency end of the spectrum, the local barotropic energy conversion between the LFV components and the time-mean Bows is also operating and playing a crucial role. The kinetic energy conversion represented by the scalar product between the E vector of the low-frequency components and the deformation D vector of the time-mean flow reveals that, on average, the low-frequency components extract energy from the time-mean flow during La Nina winters while they lose energy to the time-mean Bow during El Nino winters. This local barotropic energy conversion on the low-frequency end of the spectrum, together with the forcing of the high-frequency transients on blocking flows on the high-frequency end, explain why there is a large difference in the magnitude of low-frequency variability between the La Nina and the El Nino winters.

The characteristics of extratropical low-frequency variability are examined using a comprehensive atmospheric general circulation model. A large experiment consisting of 13 45-yr-long integrations forced by prescribed sea surface temperature (SST) variations is analyzed. The predictability of timescales of seasonal to decadal averages is evaluated. The variability of a climate mean contains not only climate signal arising from external boundary forcing but also climate noise due to the internal dynamics of the climate system, resulting in various levels of predictability that are dependent on the forcing boundary conditions and averaging timescales. The focus of this study deviates from the classic predictability study of Lorenz, which is essentially initial condition sensitive. This study can be considered to be a model counterpart of Madden's ''potential'' predictability study.The tropical SST anomalies impact more on the predictability over the Pacific/North America sector than the Atlantic/Eurasia sector. In the former sector, more significant and positive impacts are found during El Nino and La Nina phases of the ENSO cycle than during the ENSO inactive period of time, Furthermore, the predictability is significantly higher during El Nino than La Nina phases of the ENSO cycle. The predictability of seasonal means exhibits large seasonality for both warm and cold phases of the ENSO cycle. During the warm phases, a high level of predictability is observed from December to April. During the cool phases, the predictability rapidly drops to below normal from November to March, The spring barrier in the atmospheric predictability is therefore a distinct phenomenon for the cold phase, not the warm phase, of the ENSO cycle. The cause of the barrier can be traced to the smaller climate signal and larger climate noise generated during cold events, which in turn can be traced back to the rapidly weakening negative SST anomalies in the tropical Pacific east of the date line.Due to the fact that the signal to noise ratio of this model climate system is very small, an upper bound in atmospheric predictability is present, even when a perfect model atmosphere is considered and large ensemble mean predictions are exploited. The outstanding issues of the dynamical short-term climate prediction employing an atmospheric general circulation model are examined, the current model deficiencies identified, and continuing efforts in model development addressed.

A low-resolution version of the National Meteorological Center's global spectral model was used to generate a 10-year set of simulated daily meteorological data. Wintertime low-frequency large-amplitude anomalies were examined and compared with those observed in the real atmosphere. The geographical distributions of the mean and variance of model and real atmosphere show some resemblance. However, careful comparisons reveal distinct regions where short-term climate anomalies prefer to develop. The model's low-frequency anomalies (LFAs) over the North Pacific (North Atlantic) tend to occur about 1500 miles east (southeast) of those observed, locating themselves much closer to the western continents. Because of the misplacement of the model's LFA centers, their associated circulation patterns deviate substantially from those observed.The frequency distributions of the LFAs for both the model and reality display large skewness. The positive and negative large LFAs were, therefore, examined separately, and four-way intercomparisons were conducted between the model, the observed, the positive, and the negative LFAs. The separate analyses resulted in distinguishable circulation patterns between the positive and negative large LFAs, which cannot possibly be identified if a linear analysis tool, such as an empirical orthogonal function analysis, were used to extract the most dominant mode of the circulations. Despite pronounced misplacement of large LFAs of both polarities and a general underestimation of their magnitudes, the model does have the capability of persisting its short-term climate anomaly at certain geographical locations. Over the North Pacific, the model's positive LFAs persist as long or longer than those found in reality, while its negative LFAs persist only one-fourth as long (10 versus 40 days).The principal storm tracks and mean zonal wind at 250 mb (U250) were also examined to supplement the low-frequency anomaly investigation. Contrasting with observations, the model's U250s display considerable eastward extension and its storm tracks near the jet exit show substantial equatorward displacement over both the North Pacific and the North Atlantic oceans. These model characteristics are consistent with the behavior that the model's large LFAs also prefer to develop over the regions far east and southeast of those observed in the real atmosphere.

A series of 90-day integrations by a low-resolution version (T40) of the National Meteorological Center's global spectral model was analyzed for its performance as well as its low-frequency variability behavior. In particular, 5-day mean 500-mb forecasts with leads up to 88 days were examined and compared with the observations. The forecast mean height decreased rapidly as forecast lead increased. A severe negative bias of the mean height in the Tropics was caused by a negative temperature bias and a drop of the surface pressure of about 2 mb. The forecast variance also dropped rapidly to a minimum of 75% of the atmospheric standard deviation before being stabilized at day 18. The model could not maintain large anomalous Bows from the atmospheric initial conditions. However, it is quite capable of generating and maintaining large anomalies after drifting to its own climatology and temporal variability.At extended ranges, the model showed better skill over the North Pacific than North Atlantic when the season advanced to the colder period of the DERF90 (dynamical extended-range forecasts 1990) experiments. The model also displayed dependence on circulation regimes, although the skill fluctuated widely from day to day in general. Blocking hows in the forecast were found to systematically retrogress to the Baffin Island area from the North Atlantic. Therefore, improvements of the model's systematic errors, including its drift, appear to be essential in order to achieve a higher level of forecast performance. However, no generalization can be made due to the usage of a low-resolution model and the experiments being carried out over a rather short time span, from only 3 May to 6 December 1990.

The internal dynamics associated low-frequency variabilities (LFVs) with timescales between 7 days and 31 days, relevant to the potential predictability of monthly means, are investigated. Using observed data and a set of general circulation model produced data, it is shown that the LFV characteristics are distinct for widely different basic flows: much smaller variability for north Pacific cyclonic basic flows than anti-cyclonic basic flows. Their preferred development locations are also distinct. The dynamical processes leading to this large difference is examined in the light of the local barotropic energy conversion between basic flow and low-frequency components. Over the eastern North Pacific where the LFVs are primarily located, the energy conversion decays more from the low-frequency disturbances into the cyclonic basic flows, while much more extraction of LFV energy From the anti-cyclonic basic flows takes place. The internal LFVs considered here are closely related to the climate noise pertaining to the prediction of monthly means. A dynamical link between the phase of El Nino/Southern Oscillation (ENSO) and the potential predictability of monthly means can therefore be established: during the warm phase of ENSO when the mean north Pacific flow is usually cyclonic, the potential predictability of the monthly means can be expected to be higher than during the cool phase, due to the substantially different magnitudes of unpredictable noise being generated through barotropic instability of these different zonally varying basic flows.

Experiments on dynamical extended-range forecasting (DERF) conducted at the National Meteorological Center (NMC) are analyzed for the relationships between skill of medium-extended-range forecasts over the Pacific-North America region and the fluctuations of the Pacific-North American (PNA) mode of low-frequency variability. To isolate the effects of the prominent El Nino-Southern Oscillation anomalies that prevailed during the DERF period, the performance of forecasts is evaluated separately for the North Pacific and the North Atlantic sectors. Distinct features are observed. Much better skill in both dynamical and persistence forecasts is found for the Pacific sector than the Atlantic sector.The relationships between the polarity and amplitude of the PNA mode and the predictability of the prediction model are also investigated. The PNA circulation regime in the initial conditions as a predictor of forecast skill is contrasted in detail with the PNA mode in the forecasts. The statistical significance of the relationships is examined. The results indicate that the PNA mode in the forecasts is a better predictor for forecasting the forecast skill than the PNA mode in the initial conditions.

Prediction of blocking flows by a comprehensive general circulation model is still not satisfactory. A large portion of the unskillful forecasts can be traced to the model's inability to predict the evolution of blocking beyond a few days into the forecast. Realizing the fact that blocking is often observed to form following a series of intensive cyclogenesis activities and that the model tends to underestimate the intensity of the synoptic-scale transient eddies, a series of 10-day forecasts were conducted to assess the impact of transient eddies on the establishment of blocking flows. When the fast-propagating synoptic-scale disturbances were suppressed in the initial conditions, the subsequent forecasts completely failed to predict a blocking anticyclone. However, when the transient eddies were enhanced in the initial conditions to compensate for the deficiency of the model, blocking flows were predicted and evolved in remarkable agreement with the observations.The dynamical processes during establishment of blocking flow were then examined by a series of daily isentropic potential vorticity charts. The role played by the transient eddies can be identified through these charts, which help to explain why the transient eddies are crucial in establishing the blocking flows.

Abstract Early results are presented of an experimental program in Dynamical Extended Range Forecasting at the National Meteorological Center. The primary objective of this program is to assess the feasibility of extending operational numerical weather prediction beyond the medium range to the monthly outlook problem. Additionally, the extended integrations provide greater insight into systematic errors and climate drift and thereby feedback to model development. In this paper the principal focus is upon assessment of a contiguous set of 108 thirty-day integrations generated with the then operational Medium Range Forecast model from initial conditions 24 hours apart between 14 December 1986 and 31 March 1987. Results indicate some serious model deficiencies such as the tendency for zonalization, i.e., systematically stronger midlatitude zonal flow than observed, and a stratospheric cold bias, which continues to grow through the 30--day integrations. In the 1–30 day mean Northern Hemisphere 500 mb height f...

Energy spectra of high–altitude atmospheric turbulence data were analyzed. Kolmogorov's similarity theory for the inertial subrange was applied to infer energy dissipation rates from the spectra. The dissipation rates increase as the third power of the truncated root-mean-square values of turbulence: thus, the former can be inferred directly from the later with relative case. In the stratosphere the dissipation rates can occasionally he of the same order as those in the surface layer; a single curve can hardly represent the dissipation state in the atmosphere.