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  • View in gallery

    The climatological-average 500-hPa geopotential height field (contour interval: 10 dam) and associated horizontal velocity vectors for 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. (c) The difference between the CCM2 and the NCEP–NCAR reanalyses (contour interval: 50 m). A scale vector is provided for the velocity field.

  • View in gallery

    Standard deviation of the low-pass-filtered 500-hPa geopotential height field derived from 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. Contour interval is 10 m.

  • View in gallery

    As in Fig. 2 except for the difference between the CCM2 and NCEP–NCAR reanalyses (contour interval: 5 m). Light (dark) shading indicates regions where the model exhibits positive (negative) biases that are significant at the 5% confidence level.

  • View in gallery

    The summed frequency of positive and negative cases of persistent flow anomalies for 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. Contour interval is 3.

  • View in gallery

    As in Fig. 4 except for the difference between the CCM2 and NCEP–NCAR reanalyes (contour interval: 3). Light (dark) shading indicates regions where the model exhibits positive (negative) frequency biases that are significant at the 5% confidence level.

  • View in gallery

    The frequency of (a) positive and (b) negative cases of persistent flow anomalies for 15 winters of the NCEP–NCAR reanalyses. Contour interval is 2.

  • View in gallery

    As in 6 except for the CCM2 simulation.

  • View in gallery

    The 500-hPa geopotential height anomalies (contour interval: 25 m) and associated wave activity flux vectors for persistent cyclonic flow anomaly cases occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases. A scale vector is provided for the wave activity flux field.

  • View in gallery

    The 500-hPa geopotential height field (contour interval: 10 dam) and associated horizontal velocity vectors for persistent cyclonic flow anomaly cases occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases.

  • View in gallery

    As in Fig. 8 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

  • View in gallery

    As in Fig. 9 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

  • View in gallery

    As in Fig. 8 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

  • View in gallery

    As in Fig. 9 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

  • View in gallery

    As in Fig. 8 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

  • View in gallery

    As in Fig. 9 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

  • View in gallery

    Climatological-average 500-hPa zonal wind field (contour interval: 6 m s−1) superimposed on barotropic E vectors calculated for persistent cyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases. A scale vector is provided for the E-vector field.

  • View in gallery

    As in Fig. 16 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

  • View in gallery

    As in Fig. 16 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

  • View in gallery

    As in Fig. 16 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

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The Statistics and Horizontal Structure of Anomalous Weather Regimes in the Community Climate Model

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  • 1 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia
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Abstract

The statistics, horizontal structure, and linear barotropic dynamics of anomalous weather regimes are evaluated in a 15-winter integration of the NCAR Community Climate Model (CCM2). Statistical and ensemble analyses of simulated regimes are contrasted with parallel analyses derived from NCEP–NCAR reanalyses. The CCM2 replicates much of the structure of observed frequency distributions for anomalous weather regimes over the North Pacific and North Atlantic regions. The main differences are a northward shift and longitudinal broadening of the North Pacific frequency maximum and a weakening and southward shift of the North Atlantic maximum.

Ensemble analyses reveal that simulated North Pacific regimes attain a more isotropic horizontal anomaly structure than observed cases, which are zonally elongated. The E-vector diagnoses indicate that North Pacific cases in the CCM2 are also associated with much weaker local barotropic energy conversions from the climatological-mean flow. This is partly due to the relatively weak climatological-mean diffluence simulated by the CCM2 in the jet exit region over the eastern North Pacific. The model’s North Atlantic regimes have horizontal anomaly patterns quite similar to observed cases, except for a southwestward shift relative to observations. Both simulated and observed North Atlantic cases exhibit robust local barotropic interactions with the climatological-mean flow, with the strongest conversions shifted southwestward in the model.

The results suggest a larger role for mechanisms besides barotropic instability in maintaining anomalous weather regimes over the North Pacific in the CCM2. The model’s North Atlantic events occur southwest of observed cases apparently in order to more efficiently utilize the available “barotropic energy reservoir” in the model climatology. The authors conclude that for GCMs to properly represent important dynamical characteristics of anomalous weather regimes, it is paramount that the model accurately depict the climatological-mean stationary wave field.

Corresponding author address: Dr. Robert X. Black, School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332-0340.

Email: rob.black@eas.gatech.edu

Abstract

The statistics, horizontal structure, and linear barotropic dynamics of anomalous weather regimes are evaluated in a 15-winter integration of the NCAR Community Climate Model (CCM2). Statistical and ensemble analyses of simulated regimes are contrasted with parallel analyses derived from NCEP–NCAR reanalyses. The CCM2 replicates much of the structure of observed frequency distributions for anomalous weather regimes over the North Pacific and North Atlantic regions. The main differences are a northward shift and longitudinal broadening of the North Pacific frequency maximum and a weakening and southward shift of the North Atlantic maximum.

Ensemble analyses reveal that simulated North Pacific regimes attain a more isotropic horizontal anomaly structure than observed cases, which are zonally elongated. The E-vector diagnoses indicate that North Pacific cases in the CCM2 are also associated with much weaker local barotropic energy conversions from the climatological-mean flow. This is partly due to the relatively weak climatological-mean diffluence simulated by the CCM2 in the jet exit region over the eastern North Pacific. The model’s North Atlantic regimes have horizontal anomaly patterns quite similar to observed cases, except for a southwestward shift relative to observations. Both simulated and observed North Atlantic cases exhibit robust local barotropic interactions with the climatological-mean flow, with the strongest conversions shifted southwestward in the model.

The results suggest a larger role for mechanisms besides barotropic instability in maintaining anomalous weather regimes over the North Pacific in the CCM2. The model’s North Atlantic events occur southwest of observed cases apparently in order to more efficiently utilize the available “barotropic energy reservoir” in the model climatology. The authors conclude that for GCMs to properly represent important dynamical characteristics of anomalous weather regimes, it is paramount that the model accurately depict the climatological-mean stationary wave field.

Corresponding author address: Dr. Robert X. Black, School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332-0340.

Email: rob.black@eas.gatech.edu

1. Introduction

An essential ingredient of accurate extended-range weather forecasts and climate simulations is an adequate representation of intraseasonal variations having timescales of a week or longer. During Northern Hemisphere winter, such low-frequency variability (LFV) is linked to recurrent large-scale anomalous circulation regimes (Dole 1986a; Cheng and Wallace 1993) occurring in the exit regions of the major climatological jet streams (Blackmon 1976; Dole and Gordon 1983). Anomalous circulation (or weather) regimes represent an integral component of climate, as they are closely associated with prolonged abnormal surface weather (Dole 1986b; Hansen et al. 1993) and significant variations in the primary midlatitude storm tracks (Lau 1988). Recent studies of operational forecasts suggest that the skill of medium and extended-range weather forecasts is partlyrelated to the occurrence of particular anomalous weather regimes (Palmer 1988; Branstator et al. 1993; Renwick and Wallace 1995). In particular, certain regime transitions are more accurately forecast than others (Tibaldi and Molteni 1990; Kimoto et al. 1992; Chen 1992).

Persistent flow anomalies (PFAs) are long-lived, large-amplitude departures of the upper-tropospheric flow field from climatological-mean values (Dole and Gordon 1983). During boreal winter PFAs are most frequent in the “key” regions over the North Pacific, North Atlantic, and northern Russia. The structure and time evolution of PFAs have been documented in papers by Dole and collaborators (Dole 1986a; Dole 1989; Dole and Black 1990). PFA patterns project strongly upon the leading atmospheric teleconnection patterns [e.g., the Pacific–North American and eastern Atlantic patterns of Wallace and Gutzler (1981)]. Consequently, PFAs are robust realizations of large-scale anomalous weather regimes. The PFA methodology represents a case-oriented approach toward studying atmospheric LFV. Virtually identical anomalous flow patterns are identified in observational studies based on clustering methods (Cheng and Wallace 1993; Kimoto and Ghil 1993). Potential source mechanisms for anomalous weather regimes include large-scale transient development (Farrell 1989; Borges and Hartmann 1992; Molteni and Palmer 1993), instabilities of the climatological flow (Simmons et al. 1983; Frederiksen 1983), anomalous topographic or diabatic forcing (Hoskins and Karoly 1981; Navarra 1990), and anomalous nonlinear forcing by synoptic-scale transient eddies (Shutts 1986; Nakamura and Wallace 1993).

The diagnostic analyses of Black (1997) indicate that the development and breakdown of PFAs over the North Pacific and North Atlantic regions are primarily associated with local midlatitude wave sources. Observational and modeling studies have found that the maintenance of anomalous flow regimes is associated with both linear interactions with the zonally asymmetric climatological-mean flow (Branstator 1992; Schubert et al. 1993) and anomalous forcing by synoptic-scale transients (Mullen 1987; Lau and Nath 1991). There has been much less work performed on determining the specific mechanisms responsible for regime evolution. The observational studies of Dole and Black (1990) and Black and Dole (1993) indicate that the onset of cyclonic PFAs over the North Pacific primarily results from a large-scale transient development. The model-based analyses of Higgins and Schubert (1994) suggest that anomalous forcing by synoptic-scale eddies also contributes to the onset of North Pacific PFAs. Intraseasonal variations in tropical heating may play either a synergistic (Kiladis and Weickmann 1992) or catalytic (Higgins and Schubert 1996) role in the time evolution of extratropical weather regimes.

Given the impact of anomalous weather regimes upon surface weather and short-term climate, the success of both regional climate simulations and extended-range forecasts is dependent upon how accurately such regimes are represented in numerical models used for such purposes. In this paper we provide an evaluation of the statistics, horizontal structure, and linear barotropic dynamics of anomalous weather regimes as simulated by the NCAR Community Climate Model (CCM), an important tool used in many ongoing studies of climate and extended-range weather prediction. Cases are identified from a 15-winter seasonal-cycle integration of the CCM forced by observed sea surface temperatures. The model results are directly contrasted with parallel analyses derived from NCEP–NCAR reanalyses for the same 15-winter period, providing for a rather “clean” validation of the model characteristics. Section 2 describes the datasets and methods used. Salient aspects of the models winter climate are overviewed in section 3. In section 4 regional frequency distributions are presented. Horizontal structural characteristics are examined in section 5, while barotropic interactions with the winter climatology are studied in section 6. Discussion and conclusions are provided in section 7.

2. Data and methods

a. Community Climate Model

The model data are derived from an extended integration of version 2.1 of the NCAR CCM (hereafter, CCM2). The CCM2 is a multilevel spectral general circulation model, described by Hack et al. (1993), that utilizes a hybrid vertical coordinate scheme having 18 levels, reducing to pressure coordinates above 100 hPa. The model incorporates a diurnal solar cycle, the radiative effects of stratospheric ozone, and a mass flux convective parameterization (Hack 1994). The transport of moisture and trace constituents is accomplished using a semi-Lagrangian scheme.

The simulation examined consists of 15 partial seasonal-cycle runs of the CCM2 at a horizontal resolution of T42. Each run extends from 1 October through 15 March of the following year and is forced by observed sea surface temperatures analyzed for the 15 winters extending from 1979/80 through 1993/94. Initial conditions were derived from existing AMIP CCM2 integrations archived at NCAR. The 1 October initialization provides a 2-month time period prior to winter during which the model simulation can adjust to the specified boundary conditions. In most existing extended integrations of the CCM2, the history files consist of daily averaged data. In contrast, instantaneous data values are archived at twice-daily intervals during the simulation described above. This provides a unique model dataset that is suitable for studies of wintertime phenomena having relatively short timescales, such as regime transition and cyclogenesis. The history tapes are archived on the mass storage data system at NCAR and are publicly available to all users. Further details of the CCM2 simulation are provided in the appendix for interested readers.

b. NCEP–NCAR reanalyses

The observational dataset used for model validation is the reanalysis produced in a joint project between the National Centers for Environmental Prediction (NCEP) and NCAR. As described by Kalnay et al. (1996), the NCEP–NCAR reanalysis combines a fixed state-of-the-art global data assimilation system with existing historical data archives to construct a continuous multidecadal set of observational analyses. The primary objective of the reanalysis project is to provide a detailed and accurate dataset for research and climate monitoring purposes emphasizing data completeness, time coherence, and quality control.

There are several major advantages of the NCEP–NCAR reanalyses. First, the input data source types are rather broad and include data that are not normally available in real time for operational analyses. Also, a fixed data assimilation system is used over the entire reanalysis time period providing a continuous climate record that is not affected by periodic operational changes inthe analysis system. The final reanalysis output is carefully monitored to minimize spurious data values and data gaps. Lastly, we note that the NCEP–NCAR dataset provides quantitative estimates of fields that are normally difficult to obtain, such as vertical motion, diabatic heating, and vertical diffusion. Further details of the NCEP–NCAR reanalyses can be found in Kalnay et al. (1996).

The reanalyses presently extend from 1958 through 1996, encompassing the time period of the CCM2 integration. For the purposes of the present study, a subset of the reanalyses archived on NCAR’s mass storage data system is extracted. Specifically, we use the pressure coordinate output of dataset ds090.0 archived on a 2.5° latitude by 2.5° longitude horizontal grid. Twice-daily (0000 and 1200 UTC) instantaneous records of 500-hPa geopotential height were compiled for the Northern Hemisphere for the 15 consecutive winters from 1979/80 through 1993/94. The integrity of the data processing routines were tested by contrasting the reanalysis results with parallel results derived from historical NMC operational analyses. As expected, the two independent analyses of 500-hPa height (not shown) have very similar structure—particularly over traditionally data-rich regions such as the interior of the continental United States.

c. Methodology

Cases are selected by applying the threshold crossing procedure described in Dole and Gordon (1983) to the 500-hPa geopotential height time series of both the model and observational datasets. Anomalies are defined as deviations from local seasonal trend values. The anomaly time series are low-pass filtered using the 31-point time filter of Blackmon (1976), which isolates periods longer than 10 days. Cases are then identified by applying the selection criteria at specific individual grid points. Local maxima in PFA occurrence are referred to here as “key” points and the regions surrounding them are called “key” regions. The 500-hPa height anomaly threshold and duration criteria used were ±100 m and 7.5 days, respectively. In order to increase the sample sizes for the given model dataset, the duration criteria was shortened compared to past studies of PFAs. The basic results are insensitive to such variations in the identification criteria (Dole and Gordon 1983). Prior to case identification, the reanalysis data were linearly interpolated to the model grid to allow for consistent spatial sampling of the two datasets. This also permits direct quantitative comparisons between the model and observations. For the observed cases the resulting key points are located at 40°N, 160°W over the North Pacific and at 52°N, 17°W over the North Atlantic. For the model cases the key points are located at 46°N, 152°W over the North Pacific and at 46°N, 25°W over the North Atlantic.

Like-signed cases identified at the four key pointsform the basis of our composite analyses. For each PFA type, case means are calculated as time averages of the 500-hPa height over the lifetime of each case. Composite mean fields are then constructed by averaging together all of the case means for each PFA type. Composite anomaly fields consist of the composite mean fields minus the respective winter climatologies of the model and observations. Winter is taken to be the 90-day period from 1 December through 28 February. The PFA types analyzed in this study are Pacific cyclonic (PACCYC), Pacific anticyclonic (PACANT), Atlantic cyclonic (ATLCYC), and Atlantic anticyclonic (ATLANT).

3. Wintertime climatology

We begin with a brief overview of a few key aspects of the model’s wintertime climate for the 15-yr period. The wintertime average 500-hPa geopotential heights and associated wind fields are displayed in Fig. 1 for both the CCM2 and the NCEP–NCAR reanalyses. Although the model captures many of the basic characteristics of the observed winter flow, the model circulation is more zonal than found in the observations with a general negative height bias at higher latitudes. This is a common deficiency of GCMs and is consistent with earlier analyses of the CCM2 by Hack et al. (1994). There are also important regional differences, particularly over the Pacific–North American region. The East Asian subtropical jet stream is significantly retracted toward the west in the CCM2, with a relatively narrow upper level trough located over the East Asian coast. In association with this feature, there is a considerable positive model bias (of more than 100 m in some locations) in the 500-hPa height field over the central North Pacific. Also, the climatological stationary wave pattern located over North America is noticeably weaker in the model than in the reanalyses. Farther downstream, the model exhibits anomalously low heights over the northeastern North Atlantic. We note that, in association with these model biases, much weaker patterns of zonal diffluence are observed over the eastern sectors of the North Pacific and North Atlantic Oceans in the CCM2 compared to observations. As will be discussed later in the paper, this may have important consequences for the dynamical forcing of anomalous weather regimes.

Anomalous weather regimes represent a subset of intraseasonal low-frequency variability in the atmosphere. To obtain an overall sense of the regional levels of low-frequency variability in the model, Fig. 2 shows the standard deviation of the daily low-pass filtered 500-hPa geopotential height fields for both the CCM and reanalyses. The observed standard deviation field (Fig. 2b) is characterized by two prominent maxima—one over the Gulf of Alaska and a second to the south of Iceland. There are also ridges of weaker variability extending away from these two maxima. The two primary maxima are shifted slightly from the positions found inBlackmon’s (1976) landmark observational analysis. Also, the present analyses do not replicate the distinct third maximum found by Blackmon over northern Russia. These differences may simply reflect long-term (decadal timescale) natural variability in the climate system acting to modulate the characteristics of shorter-term variability. Another possibility is that the differences may partly represent changes that have occurred in the observing network and data assimilation systems over the years. In any case, observational analyses based upon the 15 winters of 1979/80 through 1993/94 are themost appropriate for the present purposes as, in principle, both the observed and simulated systems experience the same lower boundary forcing.

The corresponding standard deviation field for the model integration is displayed in Fig. 2a. To first order, the model does a good job of replicating the observed low-frequency variability. Although there are differences in the regional patterns, the overall level of variability is quite comparable to observations. This is a significant improvement over the behavior of earlier GCMs, for which the simulated level of intraseasonallow-frequency variability was often found to be relatively weak (Lau and Nath 1987; Ferranti et al. 1994; Chen and van den Dool 1995). The higher level of LFV in the CCM2 is also indicated by the statistical analyses of Hack et al. (1994).

The model captures the basic location and magnitude of the two primary LFV maxima over the North Pacific and North Atlantic and simulates some of the observed ridges of variance extending away from these maxima.There are also important differences to note, however. In particular, the CCM2’s North Pacific maximum is zonally too broad and is shifted northward compared to observations. Over the North Atlantic, the model maximum is relatively weak and shifted southward. The ridges extending away from the North Atlantic maximum appear more distinct in the model. Interestingly, the model has a more robust “third” maximum over northern Russia compared to observations. This may simply reflect a lack of background variability over the North Atlantic sector, however

The differences between the model and observed standard deviations are quantified in Fig. 3. In order to test the significance of any differences found, we apply a statistical test that accounts for interannual variability in climate parameters within multiwinter climate ensembles. Following the methods of Chervin (1981), we apply the first moment test variate:
i1520-0493-126-4-841-e1
where X is a seasonally averaged climate variable, 〈X〉 is an ensemble average of X over many seasons, σ is the ensemble standard deviation of X, and the subscripts m and o indicate model and observed parameters, respectively. The denominator represents a composite estimate of interannual variability. The absolute value of r1 is used to test the null hypothesis that there is no significant difference between simulated and observed climate ensembles at individual grid points.

The test variates in Chervin’s paper are designed tocontrast equal-numbered ensembles. In the present study we contrast 15 model winters against 15 winters of reanalyses. As discussed before, the reanalysis data are interpolated to the T42 Gaussian grid of the CCM2. In addition, the model output and observational data are both sampled twice daily. Thus, each ensemble average is derived from samples that are consistent in space and time (Randel and Williamson 1990).

The shading in Fig. 3 represents regions where there are significant biases in the model’s representation of the low-pass-filtered 500-hPa height variance. Over the North Pacific, the model’s low-frequency variability is anomalously strong both downstream and upstream of the Gulf of Alaska, consistent with the zonal elongations noted in the Pacific maximum in Fig. 2a. Also, there is a negative model bias to the south of the Gulf of Alaska, consistent with the observed northward shift in the model’s local maximum. In the immediate vicinity of the maximum observed variability over the Gulf of Alaska (Fig. 2b), however, there is no significant difference between the model and observations. In the Atlantic sector, there is a significant negative model bias over and to the east of Iceland, consistent with the noted southward shift in the model maximum relative to observations. Although there are also significant model biases away from the two primary centers of action, these are regions of generally weak low-frequency variability and are not considered further in this paper.

4. Frequency distribution of persistent flow anomalies

The regional frequency distributions of persistent flow anomalies are overviewed within this section. Persistent flow anomalies can be considered as an important subset of intraseasonal LFV that includes the strongest and most persistent low-frequency events. The summed frequency of positive and negative cases are displayed in Fig. 4 for the CCM2 and the reanalyses. In both datasets, we see the familiar regional maxima located over the eastern North Pacific and the central North Atlantic. The local frequency maxima in the model data are comparable to and, in some cases, even exceed their reanalysis counterparts. Once again, this is a significant result that differs from past studies indicating generally lower numbers of low-frequency events in GCM simulations (Blackmon et al. 1986; Dugas and Derome 1992). As for the low-pass standard deviation field, the Pacific maximum in the model is zonally distended and shifted northward from observations while the Atlantic maximum is shifted slightly southwestward. The difference field for the summed distributions of positive and negative cases is displayed in Fig. 5. The patterns have much in common with Fig. 3, with significant positive model biases located to the east and west of the Gulf of Alaska and negative biases to the south. No sigificant bias is observed over the Gulf of Alaska, itself, however. Over the North Atlantic there is a low-over-high couplet consistent with the noted southwestward displacement of the Atlantic maximum in Fig. 4a.

In Figs. 6 and 7 the simulated and observed distributions are broken down into the separate frequency of positive and negative events. Although there are some differences in detail, overall both positive and negative cases tend to occur in the same location at about the same frequency. The observed distributions tend to be somewhat more broad meridionally and, over the NorthAtlantic, positive cases tend to be slightly more frequent than negative cases. For both observed and simulated distributions, the regional maxima indicate that, on average, there are typically 1–2 positive and 1–2 negative events that occur during individual winters in each region. To summarize, although the PFA frequency distribution of the CCM2 is comparable to observations for the same time period, there are significant regional structural differences in the distributions. The next section examines the horizontal anomaly structure associated with the different classes of PFA events identified at the Pacific and Atlantic key points.

5. Horizontal structure

As discussed in section 2, the typical flow characteristics associated with PFAs are obtained by constructing composite averages over all like-signed cases identified at individual regional key points. This isolates PFA flow attributes that are common among many cases. Composite 500-hPa analyses are presented in Figs. 8–15 to contrast the upper-tropospheric structure of simulated and observed events. For a succinct depiction of both the horizontal structure and propagation characteristics of the associated large-scale flow anomalies, for each class of PFA events we first overlay 500-hPa geopotential height anomalies and vectors of the horizontal wave activity flux derived from the height anomalies. The Rossby wave propagation characteristics of large-scale stationary waves are readily deduced using the stationary wave activity flux of Plumb (1985). Forslowly varying, almost-plane waves Plumb’s wave activity flux is parallel to group velocity. Additionally, for steady waves the horizontal flux divergence is balanced by the vertical flux convergence or local wave sources, which includes the barotropic interaction to be discussed in the following section. This diagnostic was applied by Black and Dole (1993) and Black (1997) to deduce regional sources and sinks of wave activity during the life cycles of observed PFAs. Second, for each class ofevents we also overlay the total composite (climatology plus anomaly) 500-hPa height field with vectors of the associated wind field to examine the manifestation of PFAs in the total flow field.

The results for the PACCYC cases are displayed in Figs. 8 and 9. These cases are related to the positive phase of the Pacific–North American (PNA) teleconnection pattern (Wallace and Gutzler 1981) and are associated with an eastward extension of the East Asian jet stream, an enhanced upper-level ridge over the west coast of North America, and a deep trough over easternNorth America (contrast Figs. 1b and 9b). From the anomaly analyses (Fig. 8) we note that both the CCM2 and NCEP cases are characterized by a distinct wave train emanating from a large-amplitude negative height anomaly located over the North Pacific key region. The wave activity fluxes are directed along the wave train and indicate an eastward propagation of Rossby waveenergy downstream away from the primary anomaly over the North Pacific. Although there is a general correspondence between the model and observed cases, there are important differences to recognize too. First, we note that for the model events the primary cyclonic circulation anomaly over the North Pacific is less zonally elongated with a more isotropic shape than foundin observations. Also, the downstream wave train and associated wave activity flux pattern is notably stronger for the model cases. Finally, we note some evidence of a weak northward wave activity flux within the southwest quadrant of the primary flow anomaly in the model cases (Fig. 8a). This signature is not evident for observed cases (Fig. 8b) as found by Black (1997).

The total 500-hPa flow field for PACCYC cases is shown in Fig. 9. Overall, we note that the total flow field is similar in both cases, except that the amplitude of the total wave pattern is notably stronger for the observed cases. This is true even over North America where Fig. 8 indicated that the model had a stonger anomaly field. This is due to the earlier noted biases in the model’s winter climatology. Specifically, the climatological wave pattern over North America is considerably stronger in the NCEP analyses than for the CCM2 (Fig. 1). Also, we recall that the model climatology has a significant positive bias over the key region, leading to the weaker North Pacific trough in Fig. 9a. The end result is that the total stationary wave pattern over the PNA region during PACCYC events is weaker in the CCM2 than for observations.

The PACANT cases (Figs. 10 and 11) represent the negative phase of the PNA teleconnection pattern and are typically associated with a North Pacific ridge and a relatively zonal flow over North America (Fig. 11b). The composite anomalies are contrasted in Fig. 10 for the model and observed cases. As for PACCYC cases, these cases are characterized by a wave train extending away from the key region eastward toward the North Atlantic. In addition, PACANT cases exhibit many of the same structural differences that were noted for PACCYC cases. In particular, the primary anomaly over the North Pacific in the model cases has a more isotropic shape without the zonal elongation found in observations. Also, as for PACCYC cases the downstream wave pattern is somewhat stronger in the model events. Both simulated and observed PACANT cases exhibit a very weak northward wave activity flux pattern on the southward flank of the primary flow anomaly. Considering the total flow field (Fig. 11) the model cases display a more pronounced ridge over the Gulf of Alaska than the observed cases. This is consistent with the CCM2’s positive climatological height bias over this region (Fig. 1). Over North America, on the other hand, the total flow is characterized by very similar patterns in both the CCM2 and observations. This is due to the opposing effects in this region between the model’s 1) relatively weak climatological wave pattern and 2) relatively strong anomalous wave train pattern.

The results for the ATLCYC cases are presented in Figs. 12 and 13. These cases project strongly upon the eastern Atlantic (EA) teleconnection pattern of Wallace and Gutzler (1981) and are synoptically associated with an eastward extension of the climatological jet stream located over east coast of North America (Fig. 13b). For both the model and observed cases, the 500-hPaanomalous flow field is characterized by a large-scale negative height anomaly over the key region that exhibits a combined zonal elongation and a southwest to northeast horizontal tilt. This anomaly pattern is shifted to the southwest in the model, consistent with the different key points used to identify cases. We note that, as is generally true for North Atlantic PFAs (Dole 1986a), only a weak downstream wave train is evident for these cases. Although the structure of the primaryflow anomaly appears quite similar for both, the wave activity flux pattern identifies an important distinction for the model cases, as there is a substantial flux of wave activity directed toward the primary flow anomaly from an anomalous ridge located to its northwest (Fig. 12a). This pattern is virtually absent in the observed cases, which exhibit only weak signs of remote forcing, and indicates that there may be important differences between the dynamical forcing of simulated and observed ATLCYC cases. Specifically, for the model events there is a distinct anomalous wave source located to the northwest of the primary flow anomaly that is absent in observations. This difference will explored in the next section of the paper. The total 500-hPa field is quite similar over the North Atlantic for both cases (Fig. 13). In addition to the previously noted climatological differences upstream over the North Pacific, the maindifference nearby the Atlantic key region is the strength of the trough located upstream over Hudson Bay.

Figures 14 and 15 display the results for ATLANT cases, which are often manifested by blocking flows over the eastern North Atlantic (Fig. 15b). The anomalous flow patterns associated with ATLANT cases are quite similar between the CCM2 and observations. The primary signature is a zonally elongated positive height anomaly centered over the key region with a very weakdownstream low over the northern Mediterranean. The primary difference is that the primary flow anomaly associated with the model cases is shifted southwestward in relation to that of the observed cases. For both the model and observed cases, the wave activity flux patterns indicate only weak signs of remote forcing. We note, however, that similar to the CCM2 ATLCYC cases there is some evidence of an anomalous wave source associated with an oppositely signed height anomalylocated to the northwest of the primary flow anomaly. This pattern is considerably weaker for the ATLANT cases, however, and we consider the agreement between the simulated and observed anomaly fields associated with ATLANT cases to be much better than for the ATLCYC cases. Finally, we note that in the total flow field (Fig. 15) the observed cases exhibit a much stronger ridge over the eastern North Atlantic (almost a blocking pattern) compared to that of the CCM2. Thisis primarily due to the negative height bias that the model climatology exhibits over the eastern North Atlantic (Fig. 1) as the height anomaly fields themselves (Fig. 14), are actually very similar. This result has important implications for whether or not GCMs are able to adequately replicate blocking flows. Since many definitions of blocking are based upon a measure of the total flow field, the model’s representation of the climatological stationary waves plays a crucial role in the deduced blocking statistics of the model. Thus, even though a model may have an adequate representation of low-frequency perturbations, it may seriously misrepresent blocking flows.

6. Barotropic interactions

In our comparison of the CCM2 to NCEP–NCAR reanalyes we have found two key results that would directly impact the barotropic dynamics of low-frequency anomalies such as PFAs. First, we noted that the model climatology exhibits much weaker zonal diffluence in the key PFA regions over the North Atlantic and Pacific Oceans. Second, we found that the simulated PFAs have notably different horizontal structures than observed cases, particularly over the North Pacific region where the model cases exhibit much less zonal elongation. One of the key proposed mechanisms for initiating and maintaining PFAs is the barotropic interaction of large-scale flow anomalies with the horizontal deformation of the climatological-mean background flow (Farrell 1989; Simmons et al. 1983). Specifically, a zonally elongated perturbation will tend to experience linear barotropic development within a region of large-scale diffluence. These ideas warrant further investigation within the context of our present analyses.

A succinct method for assessing the barotropic interactions of a perturbation with the climatological-mean flow is to use the perturbation flow to calculate the barotropic E vectors of Hoskins et al. (1983) and examine their relationship with the climatological-mean zonal wind field (U). The E vectors are given by E = [′)2(u′)2, (u′)(υ′)], where an overbar indicates the time-average perturbation structure, and are determined by the horizontal perturbation structure. Zonally elongated eddies are generally associated with westward-pointing E vectors, whereas meridionally elongated eddies will have eastward-pointing E vectors. The meridional component of the E vector will be nonzero for eddies with meridional phase tilts.1 To a good approximation, the sign and magnitude of local barotropic energy conversions from the climatological-mean flow into the perturbation field are given by E·U (Simmons et al. 1983). Therefore, positive conversions into theeddies will occur in regions where E vectors point toward higher values of U. This diagnostic was used by Dole and Black (1990) in their observational study of the formation of PACCYC events and further details of the application and interpretation of the E-vector diagnostic can be found in their paper. In the context of the previous wave activity flux analyses, the barotropic energy conversions represent a local source of wave activity

In Figs. 16–19 we overlay E vectors derived from the PFA composite anomaly fields with contours of the climatological-mean zonal wind U for the CCM2 and reanalyses, respectively. Before discussing the individual classes of events, it is worthwhile to comment on the structural differences in the U field between the CCM2 and reanalyses. Focusing upon the North Atlantic and North Pacific key regions, it is evident that for both regions the east–west gradient of U is considerably weaker in the CCM2 than for the reanalyses. This is consistent with the earlier noted deficiencies in zonal diffluence over the eastern ocean regions (Fig. 1) and suggests that the available energy “reservoir” for barotropic amplification of zonally elongated perturbations is weaker in these regions for the model. There remain nonnegligible variations in U near both of the key regions in the model climatology, however, particularly north and south of the jet exit regions. Thus, it is possible that suitable alternate perturbation structures may be able to efficiently extract energy barotropically from the climatological-mean flow in the model.

The results for PACCYC cases are presented in Fig. 16. The model climatology exhibits a relatively weak southeast-to-northwest–oriented horizontal gradient in U southwest of the key region (Fig. 16a). Comparing the two patterns of E vectors, we see that for the observed PACCYC cases there is a robust pattern of E vectors pointing up the gradient of U in the East Asian jet exit region (Fig. 16b). This is consistent with the perturbation structure in Fig. 8b and indicates robust local barotropic energy conversions from the climatological-mean flow into the large-scale flow anomalies. For the model cases, not only is the zonal gradient in U less pronounced, but the associated E-vector pattern is simply not as well organized within the jet exit region. As a result, the local barotropic energy conversions for the model cases are accordingly much weaker. Nonetheless, the model cases do appear to take advantage of the available gradient in U found to the southwest of the key region. For both model and observations, there is a weaker pattern of eastward E vectors located just off the west coast of Canada. These are not oriented across significant horizontal gradients in U and therefore are barotropically inert.

The E-vector patterns for PACANT cases are shown in Fig. 17. Qualitatively, the results are strikingly similar to those for PACCYC. The observed events have an associated E-vector pattern that is even more favorably aligned than PACCYC with the climatological east–westgradient in U (Fig. 17b), indicating strong barotropic energy conversions in this region. The model cases, on the other hand, exhibit an E-vector pattern that is nearly identical to that of the PACCYC cases and is associated with much weaker barotropic energy conversions. As for the PACCYC cases, there are also eastward-pointing E vectors within the eastern edge of the primary flowanomaly that are not favorably aligned with the climatological U field. The analyses for the North Pacific PFAs suggest that in the CCM2 there is a smaller barotropic energy reservoir (related to east–west variations in U) from which PAC PFAs are able to draw from. As a consequence, the model cases do not take on a zonally elongated structure to extract from such a reservoir. Nonetheless, our earlier results suggest that both the model and observations exhibit the same high level oflow-frequency variability over the Gulf of Alaska (e.g., Fig. 2). Thus, it is possible that other dynamical processes may play a more significant role in maintaining the model’s low-frequency variability within this region.

The results for ATLCYC are displayed in Fig. 18. Along the axis of the North Atlantic jet exit, zonal variations in U are stronger in the observations than for the model (Fig. 18). As a result, the strongest zonal gradients in U are shifted southwestward in the model compared to observations. This leads to a net southwestward shift in the primary “barotropic energy reservoir” of the model compared to observations. This appears to be reflected in the associated E-vector patterns, both of which exhibit eastward-pointing E vectors within the southern portion of the primary flow anomaly (Fig. 12), indicating positive barotropic energy conversions. For the model cases, however, the upgradient E-vector pattern is displaced southwestward in relation to the core of the climatological jet exit, consistent with the observed southwestward shift in the primary flow anomaly (Fig. 12a).

For both model and observed cases there are also southwestward-pointing E vectors located to the northwest of the key region. These are associated with a southwest-to-northeast horizontal perturbation tilt earlier noted to exist in the northwest quadrant of the primary flow anomaly for ATLANT cases (Fig. 12). This pattern is more robust for the model cases, however, and is favorably located within a region of strong north–south variations in U (Fig. 18a). The E-vector pattern for the observed ATLCYC cases (Fig. 18b) in this region is weaker and less favorably aligned with the existing gradients in U. Thus, the model cases appear to have a significant additional barotropic energy source located northwest of the key region associated with a southwest to northeast perturbation structure embedded within a strong southward gradient of U. This distinction is entirely consistent with the wave activity flux analyses of Fig. 12, which indicate that model cases have a distinct anomalous wave source located northwest of the primary flow anomaly that is absent in observations.

The results for ATLANT cases are presented in Fig. 19. Consistent with the flow anomaly structures displayed in Fig. 14, the E-vector patterns for the simulated and observed cases are quite similar. The key distinction is the southwestward pattern shift for the model events in relation to observations. As noted for the ATLCYC cases, this shift is consistent with an apparent southwestward shift in the barotropic energy reservoir associated with horizontal variations in U. Unlike for the ATLCYC cases, the E-vector patterns located northwest of the key region for the ATLANT are unfavorably aligned with the existing gradients in U, suggesting that only weak barotropic conversions occur in this region. This is consistent with wave activity analyses that reveal that the anomalous wave source located northwest of the key region in the ATLANT cases is much weaker in the ATLCYC cases (compare Figs. 12a and 14a). Of the four classes of PFA’s studied in our paper, the synoptic and dynamic analyses of the ATLANT anomalies appear to have the best correspondence to observed cases. The primary difference is the southwestward shift of the primary flow anomaly compared to observed cases and the manifestation of the cases in the total height field (Fig. 15).

Our results may help to explain the southwestward shift of the local maxima in PFA frequency observedfor the model over the North Atlantic region (Fig. 4). Although the model cases have a very similar structure to the observed cases, as noted earlier the primary flow anomalies are shifted southwestward relative to that of observed cases. This is consistent with a parallel southwestward shift in the primary zonal gradients in U, which provide a barotropic energy reservoir for zonally elongated perturbations. What appears to happen is that the model cases simply adjust their location to moreefficiently extract energy from the climatological-mean flow. This is analogous to what might happen to regional cyclone frequency given a spatial shift in the low-level baroclinic zone within a storm track. In addition, the ATLCYC cases take advantage of an additional barotropic energy source located to the northwest of the key region. For the North Pacific cases, the model’s zonal gradients in U appear insufficient to support the growth and maintenance of zonally elongated perturbations. Therefore, it is likely that additional mechanisms are necessary to maintain low-frequency variability over the North Pacific region. Thus, even though the CCM2 replicates the observed level of LFV over the North Pacific, it may not be adequately replicating the proper sources for this LFV. In other words, the model may be right for the wrong reasons.

7. Discussion and conclusions

We have evaluated the statistics, horizontal structure, and linear barotropic dynamics of anomalous weather regimes as represented in a 15-winter simulation of version 2.1 of the NCAR Community Climate Model forced by observed sea surface temperatures. Statistical and ensemble analyses of simulated regimes are directly contrasted with parallel analyses derived from the NCEP–NCAR reanalyses for the same 15-winter time period, providing a suitable validation of model behavior. As noted in earlier studies (Hack et al. 1994), the model’s wintertime climatological-mean upper-tropospheric height field has a significant bias, particularly over the Pacific–North American (PNA) region, with anomalously high heights over the North Pacific and a relatively weak stationary wave pattern over North America. Associated with these biases, the model simulation also exhibits weaker zonal diffluence compared to observations in the exit regions of the midlatitude storm tracks over the North Pacific and North Atlantic Oceans. This lack of diffluence is related to relatively weak zonal variations in the climatological-mean zonal wind (U), particularly over the North Pacific jet exit region. Over the North Atlantic there is an associated effective southwestward shift in the strongest east–west gradients of U. These regional differences in the climatological-mean flow indicate that, at the very least, there is a restructuring of the “barotropic energy reservoir” that is available for perturbation amplification in the model (e.g., Farrell 1989; Simmons et al. 1983).

The differences between simulated and observed low-frequency variations are tested using the first-moment test variate of Chervin (1981). The CCM2 does a rather good job in replicating observed levels of low-frequency variability (LFV) and persistent flow anomalies (PFAs) over the North Pacific and North Atlantic key regions, in some locations actually exceeding observed values. The main differences are a significant longitudinal broadening and northward shift of the North Pacific maximum and a weakening and southward shift of theNorth Atlantic maximum. Interestingly, the model is able to represent a third local maxima over northern Russia, which many earlier GCM’s have been unable to capture. Although regional differences in the frequency of PFA’s exist between the CCM2 and observations, most of these differences are located away from the primary centers of LFV indicating a first-order correspondence between the model and observations. There is no assurance, however, that this statistical behavior carries over to the model’s representation of the structure and dynamics of PFAs.

PFAs occurring over the North Pacific region are related to the PNA teleconnection pattern of Wallace and Gutzler (1981). Composite structural analyses of these cases indicate that there are substantial differences between the simulated and observed anomaly fields. For both cyclonic and anticyclonic cases, the primary flow anomaly over the Pacific key region attains a distinctly more isotropic horizontal structure in the model than in the reanalyses, for which zonally elongated structures are found. Noting the relatively weak zonal diffluence of the model climatology in this region, this suggests a potentially weaker role for barotropic interactions in maintaining model events. This behavior is confirmed by E-vector analyses which indicate that observed PFA cases over the North Pacific are associated with much stronger barotropic energy conversions from the climatological-mean flow than their model counterparts. The model events exhibit relatively weak E-vector patterns that are not favorably aligned in the jet exit region to produce strong barotropic conversions. These results suggest that other dynamical (or physical) processes may play a more significant role in maintaining the model’s low-frequency variability over the North Pacific.

An additional distinction of the simulated North Pacific cases is that they exhibit a stronger downstream anomaly pattern extending over North America. This behavior may also be related to the mean-flow structure in the jet exit region. Since the model climatology exhibits a weaker eastward diminuation of the midlatitude jet, Rossby wave energy may more readily propagate downstream along the wave guide. Analyses of the total (climatology plus anomaly) flow field associated with simulated PACCYC and PACANT cases indicate that neither closely resemble their observed counterparts. This is due to deviations both in the PFA anomaly structure and in the model climatology within the PNA region. Lastly, we note that anomalous wave sources are primarily local for both simulated and observed PFAs over the North Pacific.

North Atlantic PFAs are related to the eastern Atlantic teleconnection pattern. Unlike North Pacific cases, the horizontal anomaly structures associated with North Atlantic PFAs are similar between the model and reanalyses, especially for ATLANT cases. The main difference is that the model anomaly patterns are shifted southwestward relative to observations. The primary flow anomaly pattern associated with ATLCYC cases displays both zonal elongation and a southwest-to-northeast horizontal phase tilt. Wave activity flux analyses indicate that the model events have a remote anomalous wave source located northwest of the primary flow anomaly that is not evident in observations. The E-vector analyses reveal that this remote wave source is associated with robust barotropic energy conversions from the climatological-mean flow occurring northwest of the key region. This conversion signature is considerably weaker in the observed ATLCYC cases. On the other hand, both the model and observed cases exhibit robust patterns of upgradient E vectors in the southern portion of the primary flow anomaly, indicating strong positive barotropic conversions. The primary distinction is a southwestward shift in the E-vector pattern associated with the model cases, which parallels the southwestard shift of the strongest east–west variations in U over the North Atlantic. To the extent that PFAs result from barotropic interactions with the climatological-mean flow, these results are consistent with the notion that the location and/or structure of PFAs is dictated by spatial gradients in U. It is likely that the model cases occur southwest of the observed cases simply because of the earlier noted shift in the barotropic energy reservoir over the North Atlantic in the model. In addition, the model cases take advantage of an additional barotropic energy source located to the northwest of the key region. In terms of the total flow field, ATLCYC cases very much resemble their observed counterparts.

ATLANT cases, often associated with blocking flows over the North Atlantic, have very similar anomaly structures in the model and observations. Besides a southwestward shift in the primary flow anomaly, of all the PFA types studied here the simulated ATLANT cases exhibit the best correspondence to observed anomaly structures. Both model and observed cases have strong associated barotropic energy conversions located south of the key region and neither exhibit strong evidence of remote forcing. As for ATLCYC cases, the model events appear to be shifted southwestward in order to more efficiently extract energy from the climatological-mean flow. In terms of the total flow field, observed ATLANT cases exhibit a much more robust“blocking” signature than the model cases. This is largely due to a negative height bias in the model climatology over the eastern North Atlantic. This result has important implications for whether or not GCM’s are able to adequately replicate blocking flows. Since many definitions of blocking are based upon a measure of the total flow field, the model’s representation of the climatological stationary waves plays an important role in the deduced blocking statistics of the model. Thus, even though a model may have an adequate representation of low-frequency anomalies, it may seriously misrepresent blocking flows. An additional consideration is the phase relationship between low-frequency anomalies and the climatological stationary waves. Blockingis favored when positive low-frequency height anomalies occur within a climatological-mean ridge.

To summarize, although the CCM2 does a very good job representing the regional statistics of intraseasonal low-frequency variability, a closer examination reveals important differences in both the structure and dynamics of certain classes of events. Many of these differences appear tied to variations in the configuration of the climatological-mean background flow. Over the North Pacific region there appears to be an insufficient barotropic energy reservoir in the jet exit region to support zonally elongated low-frequency anomalies, whereas over the North Atlantic there is a southwestward shift in the primary energy reservoir. We speculate that additional dynamical and/or physical processes are necessary to account for the high levels of low-frequency variability found over the North Pacific in the model simulation. For the North Atlantic region, both model and observed cases exhibit significant barotropic interactions with the climatological-mean flow. These interactions are simply shifted horizontally in space in order to better take advantage of the existing spatial variations in U.

To the extent that low-frequency variability results from a barotropic interaction with the zonally varying climatological-mean flow, in order for a GCM to be able to properly represent the dynamics of low-frequency variations it is likely necessary that the model provide an accurate representation of the climatological-mean stationary wave pattern. GCMs with a flow that is too“zonal” may be predisposed to large errors in their representation of intraseasonal low-frequency variability. To examine this hypothesis, we plan to pursue parallel analyses of anomalous weather regimes in an extended integration of the CCM3, which is characterized by a considerably better representation of the climatological-mean stationary waves, particularly over the Pacific–North American region (J. Hack 1996, personal communication).

Acknowledgments

We thank David Baumhefner and Thomas Mayer of the National Center for Atmospheric Research (NCAR) for their help in performing the model integrations. We also appreciate the thoughtful comments of the anonymous reviewers. Many of the computational analyses were performed at NCAR. We would like to acknowledge the Scientific Computing Division of NCAR for computer time. NCAR is sponsored by the National Science Foundation. This study is based on work supported by the National Science Foundation under Grants ATM-9411188 and ATM-9634667.

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APPENDIX

CCM2 Simulation Datasets

The model dataset consists of 15 partial seasonal-cycle simulations of version 2.1 of the NCAR Community Climate Model (CCM2). The simulations extend from 0000 UTC 1 October to 0000 UTC 2 April of the following year for the winters 1979/80 through 1993/94.2 The standard T42 horizontal truncation with 18 vertical levels is used. The model is forced by analyzed sea surface temperatures (SSTs), the first nine winters consisting of the AMIP boundary conditions. The model dates exactly correspond to the time of the imposed SST forcing. The standard climatological ozone cycle and time-invariant boundary datasets are used and initial conditions are derived from existing CCM2 integrations (the first 10 winters from AMIP case 422 and the final 5 winters from AMIP case 389A). The 1 October initialization allows a 2-month time period prior to winter during which time the model can adjust to the specified boundary conditions. Standard values for CO2 concentration and solar constant (330 ppm and 1370 W m−2, respectively) are used.

The unique aspect of the dataset is the method of data archival. Unlike most CCM2 datasets, which consist of daily averages, instantaneous data values are written to history files once every 12 h for these simulations. This makes the datasets more suitable for studying phenomena at short timescales, such as cyclogenesis and weather regime transitions. The history files are archived on NCAR’s mass storage data system (MSS) in the paths/BLACKRX/ccm2/xxyy, where xxyy varies from 7980 through 9394. Each history file (except h0023 of the first 14 winters and h0021 of the final winter) contains 16 data samples packed 3 to 1. In addition to the default master fields, the quantities “ETADOT,” “DUV,” and“DVV” are saved to the history files. Further details of the integrations are available from the corresponding author upon request.

Fig. 1.
Fig. 1.

The climatological-average 500-hPa geopotential height field (contour interval: 10 dam) and associated horizontal velocity vectors for 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. (c) The difference between the CCM2 and the NCEP–NCAR reanalyses (contour interval: 50 m). A scale vector is provided for the velocity field.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 2.
Fig. 2.

Standard deviation of the low-pass-filtered 500-hPa geopotential height field derived from 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. Contour interval is 10 m.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 3.
Fig. 3.

As in Fig. 2 except for the difference between the CCM2 and NCEP–NCAR reanalyses (contour interval: 5 m). Light (dark) shading indicates regions where the model exhibits positive (negative) biases that are significant at the 5% confidence level.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 4.
Fig. 4.

The summed frequency of positive and negative cases of persistent flow anomalies for 15 winters of (a) the CCM2 simulation and (b) the NCEP–NCAR reanalyses. Contour interval is 3.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 5.
Fig. 5.

As in Fig. 4 except for the difference between the CCM2 and NCEP–NCAR reanalyes (contour interval: 3). Light (dark) shading indicates regions where the model exhibits positive (negative) frequency biases that are significant at the 5% confidence level.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 6.
Fig. 6.

The frequency of (a) positive and (b) negative cases of persistent flow anomalies for 15 winters of the NCEP–NCAR reanalyses. Contour interval is 2.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 7.
Fig. 7.

As in 6 except for the CCM2 simulation.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 8.
Fig. 8.

The 500-hPa geopotential height anomalies (contour interval: 25 m) and associated wave activity flux vectors for persistent cyclonic flow anomaly cases occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases. A scale vector is provided for the wave activity flux field.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 9.
Fig. 9.

The 500-hPa geopotential height field (contour interval: 10 dam) and associated horizontal velocity vectors for persistent cyclonic flow anomaly cases occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 10.
Fig. 10.

As in Fig. 8 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 11.
Fig. 11.

As in Fig. 9 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 12.
Fig. 12.

As in Fig. 8 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 13.
Fig. 13.

As in Fig. 9 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 14.
Fig. 14.

As in Fig. 8 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 15.
Fig. 15.

As in Fig. 9 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 16.
Fig. 16.

Climatological-average 500-hPa zonal wind field (contour interval: 6 m s−1) superimposed on barotropic E vectors calculated for persistent cyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 23 CCM2 cases and (b) 23 observed cases. A scale vector is provided for the E-vector field.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 17.
Fig. 17.

As in Fig. 16 except for anticyclonic flow anomalies occurring over the North Pacific. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 19 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 18.
Fig. 18.

As in Fig. 16 except for cyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 22 CCM2 cases and (b) 21 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

Fig. 19.
Fig. 19.

As in Fig. 16 except for anticyclonic flow anomalies occurring over the North Atlantic. Ensemble-averaged fields are derived from (a) 20 CCM2 cases and (b) 18 observed cases.

Citation: Monthly Weather Review 126, 4; 10.1175/1520-0493(1998)126<0841:TSAHSO>2.0.CO;2

1

We note that the relationships to eddy structure discussed here are phase-averaged relationships that may not apply at individual points within the perturbation field.

2

The simulation of the winter of 1993/94 extends forward to 0000 UTC 15 March 1995.

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