Impact of transient eddies on extratropical seasonal-mean predictability in DEMETER models

Transcription

1 Clim Dyn DOI /s Impact of transient eddies on extratropical seasonal-mean predictability in DEMETER models In-Sik Kang Jong-Seong Kug Mi-Jung Lim Da-Hee Choi Received: 28 December 2009 / Accepted: 18 June 2010 Ó Springer-Verlag 2010 Abstract The impact of transient eddies on extratropical seasonal-mean prediction and predictability was examined using DEMETER seasonal prediction data. Two distinct groups were found among the seven DEMETER models based on the simulated properties of their climatological state: (1) models of a strong jet stream and strong transient activity (strong transient models), which is close to the observed intensity, and (2) models of a weak jet stream and weak transient activity (weak transient models). In addition to climatology, the strong transient models tend to predict strong Pacific North American (PNA) patterns, whereas the weak transient models predict weak PNA patterns. Here we demonstrate that these differences mainly result from differences in the eddy feedback intensity. Due to synoptic eddy feedback, the strong transient models exhibit not only strong signal variance but also strong noise variance compared with those of the weak transient models. Interestingly two groups of models show the potential predictability of deterministic forecast, measured by the signal to noise ratio, which is similar to each other. However, the strong transient models produce the error to spread ratio smaller than that of the weak transient models, implying that the former models produce a more reliable spread for I.-S. Kang (&) M.-J. Lim School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea kang@climate.snu.ac.kr J.-S. Kug (&) Korea Ocean Research and Development Institute, Ansan, Korea jskug@kordi.re.kr D.-H. Choi National Institute of Meteorological Research, Seoul, Korea the probabilistic forecast. This study implies that a better representation of transient statistics is needed to improve the extratropical predictability of the dynamical seasonal prediction. Keywords Synoptic eddies Seasonal predictability Synoptic eddy feedback 1 Introduction Seasonal-mean atmospheric anomalies are generated both by slowly varying surface anomalies (Knox and Lawford 1990; Trenberth and Branstator 1992; Lyon and Dole 1995), which provide the major signals in the prediction, and by nonlinear atmospheric processes mainly associated with synoptic transients (Hoskins et al. 1983; Chang and Wallace 1987; Held et al. 1989; Ting and Held 1990; Limpasuvan and Hartmann 1999), which produce unpredictable noise in the prediction. There is still a debate regarding the role of tropical Sea Surface temperature (SST) forcing in triggering or amplifying internal modes of variability, such as the Pacific North American (PNA) pattern (Trenberth et al. 1998; Straus and Shukla 2002). Despite this, there is no doubt that transient statistics are strongly affected by tropical SST anomalies, such as ENSO (Lau and Nath 1991) and that the transient anomalies play an important role in extratropical circulation anomalies, such as the PNA pattern (Held et al. 1989; Hoerling and Ting 1994; Jin et al. 2006a; Pan et al. 2006). Thereby, transient anomalies associated with ENSO also contribute to a certain portion of seasonal-mean circulation anomalies in the PNA region and provide a large portion of the predictive signal in that region. Therefore, the prediction skill of seasonal-mean anomalies in the PNA region is affected

2 by the prediction skill not only of the ENSO SST anomalies but also of the extratropical transients. It is well known that the changes in transients during El Nino result from an intensification of the jet stream, particularly at the jet exit region. The intensified transient forcing in the jet exit region, in turn, enhances circulation anomalies in the downstream PNA region. Thus, this transient contribution to the PNA pattern can be a part of the signal that is directly related to the tropical Pacific SST anomalies. Recently, Jin et al. (2006a, b) developed a linear framework with a stochastic basic state that can dynamically derive ensemble-mean transient eddy feedback onto low-frequency flow. Based on the linear framework, they showed that the PNA response to a tropical forcing is significantly enhanced when the transient eddy feedback is turned on. This indicates that transient eddy feedback plays a critical role in enhancing the PNA response to tropical forcing. Therefore, the transient eddy contribution to the PNA pattern can be regarded as a signal that is predictable. It is also noted that the seasonalmean transient activities are fluctuating interannually with the same external forcing, and those internal changes of transient forcing produce an unpredictable circulation noise in the PNA region. In recent years, many operational centers have produced dynamical seasonal predictions using the ensemble method, where a number of predictions are made with slightly different initial conditions. In an ensemble prediction, the predictable signal is defined as the amplitude of the ensemble-mean, measured by its interannual variance, and the unpredictable noise as the variance of the deviations from the ensemble-mean. The potential predictability is defined by the signal-to-noise ratio. There have been a number of studies which have examined the potential predictability of various models (Zwiers 1996; Sugi et al. 1997; Rowell 1998; Kang et al. 2004). It is now well known that tropical precipitation, which can be considered as a forcing function to the atmospheric circulation, has a relatively high prediction skill, but that the extratropics have a low skill (Wang et al. 2008, 2009). The poor predictability in the extratropics is due to the large contribution of the noise related to the transients on the seasonal-mean anomalies. However, large regional differences appear in the prediction skill of extratropical circulation anomalies. The prediction skill over the PNA region is higher than those for the other extratropical regions. This relatively large skill in the PNA region is due to the influence that tropical Pacific SST and precipitation anomalies, which have relatively high prediction skills, impart on PNA circulation anomalies through the teleconnection mechanism (Hoskins and Karoly 1981; Wallace and Gutzler 1981). The potential predictability depends on the model: Kang and Shukla (2006) showed that the models they used had very different magnitudes of signal and noise. Interestingly, the models with larger signals tended to have larger noise, and the potential predictabilities of various models had relatively small differences, indicating that the signal and noise were somewhat related to each other. Differences in the predictability of extratropical circulation anomalies among ocean atmosphere coupled models result from differences in tropical Pacific SST anomalies and/or extratropical forcing anomalies, such as extratropical SST and those related to transient anomalies. The present study shows that transients play a major role in determining the differences in seasonal-mean anomalies in seven different DEMETER (Development of a European Multimodel Ensemble system for seasonal to interannual prediction) models, particularly in the PNA region. In Sect. 2, a brief description of the data and models is provided. Climatological transient statistics are discussed in Sect. 3. In Sect. 4, the ENSO-related interannual variability is described. Section 5 discusses potential seasonal predictability of the models. The summary and discussion are given in Sect Data The data used in this study were the seasonal prediction hindcast data of seven European ocean atmosphere coupled models under the Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER) project. The data period is 22 years, from 1980 to 2001 based on CGCM predictions. In this study, we focused on the seasonal-mean prediction for boreal winter (DJF) with initial conditions starting on 1 November. Each prediction consists of nine ensemble members, produced with slightly different initial conditions. Details of the model descriptions and prediction procedures for the seven models are found in Palmer et al. (2004). The ensemble-mean estimates the deterministic part of the prediction and the deviations from the ensemble-mean determine the spread of the prediction. Since the predicted SST anomalies of nine ensemble members are not much different from the ensemble-mean, the ensemblemean of atmospheric variables can be considered the signal component and the deviations from the ensemblemean as the noise. The variables used here are SST and precipitation for the description of tropical forcing anomalies and the geopotential height at 200-hPa for the description of circulation anomalies associated with the tropical forcing, particularly with ENSO. In order to validate model performance, the reanalysis data from National Centers for Environmental Prediction/National Center for

3 Atmospheric Research (NCEP/NCAR) are used (Kalnay et al. 1996). 3 Climatological-mean statistics The DEMETER models produce different mean climatological features because of differences in their underlying model physics. The present study does not examine how differences in model physics produce different climatological-mean patterns but focuses on how different models with different statistics on transient eddies produce different seasonal-mean circulation anomalies in the extratropics. Before examining the climatological transient statistics, model differences in upper-level zonal wind, which controls the transient activity (Charney 1948; Lau 1988), are presented. Figure 1 shows climatological-mean 200-hPa zonal-mean zonal winds for boreal winter (DJF) produced by various DEMETER models. Although the meridional structures of the zonal wind are similar, the wind intensity and particularly the jet intensity are quite different among the models. Interestingly, the models can be classified into two groups: one group of models (UKMO, INGV, MAXP) with a relatively stronger jet than observed one (black line) and another group of models (ECMW, LODY, METF, CERF) with a weaker jet. The acronyms of the models are use here as in Palmer et al. (2004). Note that the models with a stronger jet produce zonal winds relatively closer to the observations, though they overestimate the intensity. Examination of climatologicalmean transient statistics of individual models indicates that the models can also be classified into these two groups. The transient activity is estimated by the root-mean-square (RMS) of the high-frequency component of 200-hPa geopotential height with a time scale of 2 8 days. Figure 2 shows transient activities simulated by individual models along N, where transients activity exhibits at maximum. As expected, the climatological-mean transient activity depends on the upper-level zonal wind. Therefore, it is quite clear that models with stronger jet simulate stronger transient activity, suggesting that classification into two groups will be valid. In addition, strong group produces similar magnitude of the observed transient activity. Figure 3 shows multi-model composite for climatological-mean distribution of transient eddy activity. The models with stronger zonal winds produce a transient intensity 50 60% larger than that of the models with weaker zonal winds. Hereafter, we refer to the models having a strong (weak) jet and transient activity as strong (weak) transient models. We now examine the relationship between the climatological-mean states and the interannual variations. Figure 4 shows the RMS of the interannual variation in winter-mean at the 200-hPa geopotential height. The interannual variations are estimated from each ensemble member of seasonal predictions. The spatial patterns for the strong and weak groups are similar to each other, showing strong variability over the Pacific and Atlantic stormtrack regions and the polar region. However, their amplitudes are distinctively different from each other, particularly in the eastern north Pacific and North American regions, where downstream regions of storm tracks are located. The strong transient models produce interannual variations with amplitudes much larger than those by the weak transient models. We also checked individual models, and individual models also show quite consistent results with the composite of their group (not shown). This result indicates that the interannual variations, particularly the intensity, are strongly related to the climatological-mean properties, such as the zonal wind and transients. Fig. 1 Climatological-mean 200-hPa zonal-mean zonal wind for boreal winter (DJF). The meridional distributions of various DEME- TER models and observations are shown with different colors Fig. 2 Climatological zonal distribution of transient eddy activity averaged over 40 N 60 N. The transient activity is estimated by the RMS of the high-frequency component of the 200-hPa geopotential height with a time scale of 2 8 days

4 (c) (c) Fig. 3 Climatological distribution of transient eddy activity for a observation, b the average of three models with strong zonal winds, c the average of the four models with weak zonal winds. The contour interval is 10 m, with the shading indicating an ascent of 30 m Fig. 4 RMS of the interannual variation of the winter-mean 200-hPa geopotential height, a for the observation, b for the composite of three strong transient models, and c for the weak transient models. The contour interval is 20 m 4 ENSO-related interannual variability In this section, we investigate how the interannual variations of upper-level circulation anomalies are related to the transient activities, particularly during ENSO periods. For this purpose, we applied the singular value decomposition (SVD) analysis to the winter-mean of the 200-hPa geopotential height and to the RMS of the transient component of the 200-hPa geopotential height for each winter. Prior to applying the SVD analysis, the two variables are multimodel-averaged for the seven DEMETER models. The first singular vectors of the two variables are shown in Fig. 5a and b and the associated time series are shown in Fig. 5c. This shows that the seasonal-mean geopotential height anomalies over the Pacific and North America (Fig. 5a), which coincide with strong zonal wind in the subtropics and cyclonic circulation anomalies in the extratropics, are very closely related to the interannual modulation of the transient eddy activity in the eastern Pacific, whose pattern is characterized by the north south seesaw pattern of the weakened transients in the North and their enhanced activities to the South. This is quite consistent with the results of Lau (1988). In addition, the time series of the SVD are quite consistent with the observed counterpart and NINO3 SST, suggesting that these coupled modes are distinctly related to ENSO variation. It is noted that the circulation anomaly pattern represented by the geopotential height, shown in Fig. 5a, is the combination of the

5 (c) Fig. 5 First principal mode of the singular value decomposition (SVD) of 7 model composites of winter-mean anomaly and transient activity of 200-hPa geopotential height. a, b The first singular vectors of the winter-mean anomaly and transient activity, respectively, and zonal-mean zonal wind (jet stream) and PNA circulation anomalies. Next we examine how the transients and circulation anomalies during ENSO are different for the two different model groups. Figure 6 shows ENSO anomalies in the 200- hpa geopotential height in the observations and in the model groups. The ENSO anomalies are defined by the El Nino composite (1982/1983, 1991/1992 and 1997/1998) minus the La Nina composite (1988/1989, 1998/1999 and 1999/2000). The transient forcing shown in this figure is the 200-hPa geopotential height tendency due to the anomalous transient vorticity flux divergence, which is defined as. o / a ot ¼ f tr g r 2 rðv~ 0 f 0a Þ ð1þ where the transient components (f 0 and v~ 0 ) are obtained by applying a time-filter with a time scale of 2 8 days. The overbar denotes a seasonal-mean. As seen in the Fig. 6, the geopotential height anomaly is closely related to the transient forcing during ENSO, indicating a positive feedback. In particular, the strong transient models produce larger anomalous transient forcing and larger upper-level anomalies in the PNA region. Therefore, this indicates that the model differences of the anomalous geopotential height are related to the differences in the anomalous transient forcing during ENSO. In order to clearly show that different groups c the associated time series. Y-axis denotes the magnitude of normalized PC time series for the seasonal-mean geopotential height and transient activity simulate distinct magnitude of PNA pattern, Fig. 7 shows geopotential height anomalies simulated by individual models over North Pacific region where the center of the cyclonic flow is located. It is remarkable that the strong transient models always produce larger anomalies than those of the weak transient models, indicating robust distinction between two groups. However, differences in tropical forcing anomalies can also be a possible source of the differences in the transients and extratropical circulation anomalies. However, as seen in Fig. 8, the two groups of models produce similar magnitudes of precipitation anomalies in the tropics. Similar results can be obtained with the SST anomalies and Rossby wave source over the tropics (not shown). In fact, the models with weak transients (Fig. 8c) produce slightly larger rainfall anomalies in the tropical Pacific compared with those of Fig. 8b but produce significantly smaller extratropical anomalies (Fig. 6c, f), indicating that the tropical forcings can not explain the extratropical differences between the two groups. Therefore, the model differences in the extratropical circulation anomalies are controlled mainly by the differences in the transients, which are related to the differences in climatological-mean properties of the models, such as the intensity of baroclinic instability associated with the climatological-mean zonal wind. It is noted that the tropical Pacific SST anomaly is an ultimate source of extratropical circulation anomalies

6 (c) (d) (e) (f) Fig. 6 ENSO anomalies of the 200-hPa geopotential height, a the observations, b the composite of strong transient models, and c of the weak transient models. d, e, f are as a, b, c except for transient eddy forcing. ENSO anomaly is defined by the El Nino composite minus the La Nina composite. The contour intervals are 30 m for 200-hPa geopotential height and ms -1 for transient eddy forcing Fig. 7 Magnitude of 200-hPa geopotential height anomalies associatd with ENSO averaged over 25 N 60 N, 150 E 120 W. Red (Blue) bar represents strong(weak) transient models during ENSO; however, a part of the extratropical circulation anomalies are excited directly by tropical forcing through the teleconnection mechanism (Horel and Wallace 1981; Hoskins and Karoly 1981) and transient anomalies associated with the jet stream changes also play an important role in the PNA circulation anomalies (Held et al. 1989; Jin et al. 2006a). The intensity of transient forcing to the seasonal-mean circulation can be roughly estimated by the parameter c in Eq. (2) as below. R a /reg ðx; yþ o/ a ot ðx; y; zþ dx dy tr cðtþ ¼ R h i a 2 ð2þ /reg ðx,yþ dx dy where / reg a ðx; yþ is geopotential height pattern regressed with PNA index over the domain of 150 E 60 W and (c) Fig. 8 As in Fig. 6 except for precipitation anomaly. The contour interval is 4 mm day -1 and the shading denotes values greater than -6 mm day -1 and less than 6 mm day N 70 N. The parameter c denotes the strength of transient forcing, which is estimated with the model data. Figure 9 shows the distribution of c (y-axes) for different

7 Fig. 9 Scatter diagrams of the relationship between the winter-mean PNA index (x-axes) and the geopotential height tendency due to the transient eddy forcing averaged over the domain of 150 E 60 W and 30 N 70 N for a the strong transient models and b the weak transient models values of the winter-mean PNA index (x-axes). It is clearly shown that the forcing of the transients for the same anomaly of PNA index is different between the two model groups; the strong transient models have a transient feedback intensity that is about two times larger than that of the weak transient models. It is interesting that the transient feedback intensity for the PNA anomalies is very sensitive to the climatological-mean property of the model, particularly related to the mean statistics of transient eddies. 5 Potential seasonal predictability The previous section examined the ensemble-mean seasonal-mean circulation anomalies that appeared in the two different groups of models and showed the dependency of circulation anomalies on the climatological-mean property of the model, particularly on the transients. For the case of seasonal prediction, the ensemble-mean of an atmospheric variable can be treated approximately as a forced part (signal), in response to the SST, because the spread of SST anomalies of ensemble members is small over a few months. The ensemble deviations can be considered as an internal part (noise) in the seasonal prediction. The signal and noise variances and the signal-to-noise ratio of 200-hPa geopotential height are estimated for each model. Figure 10 shows multi-model averaged signal and noise variance, computed separately for the models of strong and weak transients. The signal variance of strong transient models shows relatively large values. Although the signal variance of the weak transient models is relatively small in the extratropics, the subtropical variability has a similar magnitude of signal variance in both the strong and weak models. The subtropical circulation variability is directly related to the tropical precipitation variability. This relationship can be confirmed by the similarity of the signal variances of tropical precipitation for the two groups of models. However, the direct teleconnection response is not a major mechanism for extratropical and high-latitude circulation variability; instead, the transientmean flow feedback mechanism seems to be more important. Figure 10c and d clearly show the role of transients in generating the noise in the seasonal-mean prediction. The noise variance of strong transient models is about two times larger than that of small transient models in the extratropics. This suggests that the two-way interaction between low-frequency flow and synoptic eddies play a role in enhancing not only signal part but also noise part of low-frequency flows. The signal-to-noise ratio of each model, which represents the potential predictability of the model (Rowell 1998; Kang and Shukla 2006) is shown in Fig. 10e and f. Since the transients amplify not only the signal but also the noise, the signal-to-noise ratio appears to be similar for the two groups of models. However, the regional differences are also seen in the figures, particularly in the subtropical Pacific and PNA region. The small transient models produce a large signal and relatively little noise in the subtropical Pacific south of 30 N and as a result, the signal-to-noise ratios of the small transient models are larger in the subtropical Pacific compared with those of the large transient models. It is worth noting that to a large extent the signalto-noise ratio is less than one, indicating that the potential predictability is low in the extratropics. However, in the PNA region, although both signal and noise are large in the strong transient models, the signal differences are more significant than those of noise. As a result, the signal-tonoise ratios of the strong transient models are somewhat larger than those of the weak transient models in North Pacific and North Canada, which are main centers of the PNA pattern. However, the circulation anomalies seem to be strongly affected by the transient noise in the polar

8 Fig. 10 Variance of the seasonal-mean prediction anomaly, averaged for the strong and weak transient models. a, b are for the signal, c, d are for the noise and e, f are for the signal-to-noise ratio. The contour interval is m 2 for signal and noise variances (c) (d) (e) (f) Fig. 11 Correlation skills of seasonal-mean 200-hPa geopotential height anomalies of a the strong transient models and b the weak transient models. The contour interval is 0.2 region, where the teleconnection signal is small, indicating no predictability. It is noted that the potential predictability is different from the actual predictability of the ensemble-mean prediction, when considering the prediction skill of deterministic forecasts, as measured by the correlation between the ensemble-mean (signal) and observation anomalies. Interestingly, the prediction skill of both model groups is fairly similar in terms of correlation skill (Fig. 11). It is noted that the correlation skill measures the similarity of phase of the two anomaly patterns but not the amplitudes. However, as seen in Fig. 6, the strong transient models produce relatively large anomalies whose amplitudes are closer to the observations than those of the small transient models. Therefore, the strong transient models shows smaller RMS error compared to that of the weak transient models as shown in Fig. 12. In particular, the RMS errors are significantly small over North Pacific region, where the magnitude of ENSO anomalies are distinctively different as shown in Figs 6 and 7. In addition, two models groups may have distinctively different predictive skill in terms of probabilistic forecast, because ensemble spreads of the predictions are quite

9 Fig. 12 The same as Fig. 11 except for RMS error different between two groups. The spread can be measured by the amplitude of the noise as shown in Fig. 10c and d. Although it is quite difficult to measure the spread in the observation, it is expected that the spread is better represented by the strong transient models by considering the interannual variations of the seasonal-mean anomalies shown in Fig. 4. To check whether the model spread represents the uncertainty of the ensemble-mean prediction properly, we calculate the error-to-noise ratio. The error is calculated from the error variance of anomalous geopotential height at 200-hPa. The noise is estimated from the ensemble spread as shown in Fig. 10c and d. Figure 13 shows the errorto-noise ratio for the strong and weak models. If the ratio is greater than one, the ensemble spread does not represent the uncertainty of the prediction appropriately. Namely, the true state is located out of range in the forecast ensemble. In general, the current seasonal prediction exhibits a ratio greater than one over extratropical regions, indicating that the ensemble spread is not adequate to represent the prediction uncertainty. An optimal perturbation may be required to increase ensemble spread (Yang et al. 2006, 2008; Ham 2009; Kug et al. 2010). As shown in Fig. 10, however, it is clear that the ratio is larger overall in the weak models than that in the strong models because the noise variance is smaller in the weak models (Fig. 8c, d). Because the synoptic eddy feedback is weak, the weak models tend to underestimate the ensemble spread. The underestimated spread in the weak models can degrade the skill of the probabilistic forecast. Therefore, strong Fig. 13 The error-to-noise ratio of a the strong transient models and b the weak transient models transient models would produce a probabilistic forecast with a more reliable spread. 6 Summary and concluding remarks The potential predictability depends on the model characteristics. In other words, different models produce different signal and noise combinations and, therefore, a different potential predictability that is represented by the signalto-noise ratio. The present study investigates how these differences are related to tropical forcing and extratropical synoptic transients by using the seven DEMETER models. It is found that both the signal and noise parts of the seasonal-mean prediction of PNA circulation anomalies are strongly related to the intensity of the transient feedback driven by the seasonal-mean anomalies. The models with strong transients produce PNA anomalies bigger than those of weak transient models. Also the spread of the prediction, represented by the noise, is determined mainly by the transient variations and much less by the internal variations of tropical forcing. Comparison of these model statistics to observed counterparts shows that the strong transient models mimic the observation better than the weak transient models do. The transient differences result from differences in the climatological-mean zonal wind. Therefore, the climatological-mean flow is an important factor to better simulate and predict the seasonal-mean circulation anomalies.

10 The details of transient amplitude and phase are not possible to predict about 2 weeks later (Lorenz 1960). However, the statistical behavior of transients for a month or a season may not be chaotic and part of such statistics can be possibly predicted if we understand the relationship among the mean flow, transients, and slowly varying boundary conditions. For example, El Nino influences the storm track by modifying the Western Pacific jet stream, which in turn leads to mean flow anomalies in the downstream region over the Pacific-North American region (Held et al. 1989). The relationship between the high-frequency transients and time-mean flow during warm evens was found by Straus and Shukla (1997). Kang and Lau (1986) also demonstrated that the atmospheric internal processes can generate one of the leading modes of stationary wave anomalies by modifying the time-mean zonal flow. For better seasonal prediction, particularly for the extratropical circulation anomalies, it is essential to understand the processes in the model that are determining the transient intensity and its feedback to the mean flow. Until now, however, that understating is very limited because not many efforts have been made with various models. As shown in Figs. 1 and 2, climatological-mean transients are strongly related to the jet intensity, which is related to the meridional temperature gradient. The intensity of tropical precipitation would be a strong candidate for generating a different meridional temperature gradient, but the two groups of models do not show many differences in climatologicalmean precipitation in the tropics. Alternative mechanisms in the models accounting for the differences in seasonal prediction are their different cloud distributions, which affect the radiation budget and ultimately, the temperature, and their different feedback mechanisms of transients on the mean flow. Further research is required to understand the mechanisms that determine the climatological-mean transient statistics of the models and their transient feedback on the seasonal-means, and in particular for determining the model dependency of the transient intensity. Acknowledgments This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF C1AAA ). References Chang F, Wallace J (1987) Meteorological conditions during heat waves and droughts in the United States Great Plains. Mon Weather Rev 115: Charney J (1948) On the scale of atmospheric motions. 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