Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jd007535, 2007 Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model Steven Cocke, 1 T. E. LaRow, 2 and D. W. Shin 2 Received 19 May 2006; revised 7 August 2006; accepted 4 October 2006; published 22 February [1] Seasonal rainfall predictions over the southeast United States using the recently developed Florida State University (FSU) nested regional spectral model are presented. The regional model is nested within the FSU coupled model, which includes a version of the Max Plank Institute Hamburg Ocean Primitive Equation model. The southeast U.S. winter has a rather strong climatic signal due to teleconnections with tropical Pacific sea surface temperatures and thus provides a good test case scenario for a modeling study. Simulations were done for 12 boreal winter seasons, from 1986 to Both the regional and global models captured the basic large-scale patterns of precipitation reasonably well when compared to observed station data. The regional model was able to predict the anomaly pattern somewhat better than the global model. The regional model was particularly more skillful at predicting the frequency of significant rainfall events, in part because of the ability to produce heavier rainfall events. Citation: Cocke, S., T. E. LaRow, and D. W. Shin (2007), Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model, J. Geophys. Res., 112,, doi: /2006jd Introduction [2] The Florida State University Nested Regional Spectral Model (FSUNRSM) was developed by Cocke [1998] and originally used for studies in tropical weather. The model has since been modified for seasonal climate simulations [Cocke and LaRow, 2000], and now forms a core part of our regional climate modeling system. Regional climate model studies have found the dynamical downscaling approach successful [see, e.g., Georgi, 1990; Ji and Vernekar, 1997; Tanajura, 1996; Hong and Leetma, 1999; Fennessy and Shukla, 2000; Misra et al., 2003]. It has become an important tool for downscaling studies and applications for seasonal weather prediction. One important application is crop modeling. Crop models generally require weather data at 10 to 20 km spatial scale, and at daily temporal resolution. Currently, crop models typically utilize a weather generator to statistically downscale coarse resolution global model predictions. It is natural to ask, then, can a regional model provide sufficiently detailed and reasonably more accurate input for downscale models than what can be obtained from statistical downscaling of coarser model results? While we do not attempt to answer that question in this paper, we try to lay the foundation for further study by doing a simple evaluation of the FSUNRSM for seasonal rainfall prediction. [3] We have selected the southeast United States for our domain of interest, as our future downscaling applications 1 Department of Meteorology, Florida State University, Tallahassee, Florida, USA. 2 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA. Copyright 2007 by the American Geophysical Union /07/2006JD will focus on this region, and the southeast United States has potential predictability due to strong teleconnections to tropical Pacific sea surface temperatures. Early studies such as Ropelewski and Halpert [1986, 1987] established a teleconnection between the El Niño/Southern Oscillation (ENSO) and North American precipitation patterns. Later Studies such as Montroy [1997] and Markowski and North [2003] focused on tropical Pacific sea surface temperatures (SSTs) and their connection to U.S. precipitation. Higgins et al. [2000] examined the teleconnection with tropical Pacific rainfall rather than SSTs, and looked at interannual as well as decadal timescales and factors influencing predictability. A common result of these and other studies is that the southeastern United States has a very strong wintertime signal that could lead to predictability. Because of this strong wintertime signal, we have chosen for our first study to do wintertime simulations (November-February). [4] We briefly describe the regional climate model in section 2. The experimental setup is described in section 3 and results are provided in section 4. Future work and conclusions are given in section Model [5] The regional climate model used in this study consists of the FSU Global Spectral Model (FSUGSM), which is coupled to a version of the Hamburg Ocean Primitive Equation (HOPE) model developed at Max Planck Institute, and the FSUNRSM. Details of the climate model are given by Cocke and LaRow [2000], and additional details on the regional model are provided by Cocke [1998]. [6] The FSUGSM is a spectral model, which in this study is run at T63 resolution (approximately ) and 1of14

2 27 vertical levels. The FSUGSM has a variety of physical parameterization schemes available to it, including the original FSU physics package and most of the National Center for Atmospheric Research Community Climate model (NCAR CCM) version 3.6 atmospheric physics package. There are six convection schemes that can be selected. For this experiment, we use the FSU physics package and the relaxed Arakawa-Schubert scheme developed at the Naval Research Laboratory and originally used in the NOGAPS model [Rosmond, 1992]. There are two land surface schemes that have been implemented: the simple FSU land surface scheme and the Community Land Model version 2 (CLM2) [Shin et al., 2005]. As the CLM2 was not implemented when this study was started, we used the simple FSU land model. This model has 3 soil temperature layers and prescribed soil moisture, albedo and surface roughness based on climatology for 24 United States Geological Survey (USGS) land use categories. The choice of our physical parameterizations, including convection scheme, used in this study is based on our earlier successful results described by Cocke and LaRow [2000]. [7] The HOPE version G (HOPE-G) ocean model is a z- coordinate model with 17 vertical layers and variable horizontal resolution. The resolution is 0.5 in the tropics, and around 2.5 at the upper latitudes. The model is active to 70 north and south, and is relaxed toward climatology elsewhere. Ocean assimilation and additional details are described by LaRow and Krishnamurti [1998] and LaRow et al. [2005]. [8] The FSUNRSM is a spectral perturbation model, which in many aspects is similar to the NCEP regional spectral model [Juang and Kanamitsu, 1994]. The regional model evolves perturbations (deviations) from the driving global model solution. These perturbations, which need not be small, are represented by double Fourier functions in both north-south and east-west direction. The basic time step algorithm is as follows: The prognostic base fields from the global model (wind, temperature, humidity and surface pressure), essentially the boundary conditions, are spectrally transformed, via a Fourier-Legendre transformation, from the global model output directly to the regional grid. The regional spectral perturbations from the previous time step or initial condition are then spectrally transformed via a double Fourier transform and added to the global values on the regional grid to obtain the full regional field. Once the full regional field is obtained, the physical and dynamical time tendencies are then computed. Regional perturbation time tendencies are then derived by subtracting the full regional model time tendencies from the global model time tendencies. The global model time tendencies are computed via a reverse semi-implicit algorithm of the transformed output, which have been linearly interpolated in time to the time step of the regional model. The regional perturbation time tendencies are then transformed to spectral space in order to solve the semi-implicit algorithm to obtain perturbations for the next time step. The nesting interval is every 3 hours, and currently is only one way; that is, the regional solution is not fed back to the global model. The FSUNRSM has the same options for physical parameterizations as the global model, and we used the same parameterizations for both models in this experiment. A mathematical description of the model is given by Cocke [1998]. 3. Experiment [9] We performed 12 seasonal integrations, starting from 1 November of each year from 1986 to The integrations were four months in length. The initial conditions were provided by European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, which were available at T106 resolution. The global model was run at T63 resolution and 27 vertical levels. The regional model was run at about 50 km resolution and the same number of vertical levels. The regional model used a Mercator projection over the domain ( N, E). [10] The verification data that we use is cooperative station daily precipitation data provided by National Climatic Data Center (NCDC). This is the Data Set 3200, or Surface Land Daily Cooperative Summary of the Day, and is available, along with documentation, at NCDC s web site: There are approximately 8,000 stations active over the whole United States, roughly 100 to 200 stations per state. We only use station data for the southeastern U.S. states of Florida, Georgia, Alabama, Mississippi, Louisiana, Arkansas, Kentucky, Tennessee, Virginia, North Carolina, South Carolina and West Virginia. The station data was objectively analyzed using a Cressman analysis [Cressman, 1959] to a 0.5 linear lat-lon grid over the domain ( E, N). Model results were interpolated to this analysis grid, except where stated otherwise. [11] In the results that follow, unless otherwise stated, we will discard the results for the first month of the integration (November). We will examine the seasonal anomalies for December-January-February (DJF) and well as the frequency of significant precipitation. The anomalies will be defined with respect to the time period means (3 months 12 years = 36 months) for both modeled and observed results, as this is the limit of the time period for which we have a model climatology. As the climatology may not be stationary, it is most appropriate to use the same time period for both model and observed climatology. We are most interested in interannual variability in any case. Also, some skill measures that we will use, such as temporal correlation, are insensitive to time-invariant means. 4. Results [12] In the studies of Markowski and North [2003] and Montroy [1997], principal component analysis was used to establish the teleconnection between tropical Pacific SSTs and regional distributions of precipitation in the United States, including the eastern United States. A common result is that the first principal component (PC), the ENSO mode, explained most of the variance throughout the United States, but especially in the southeast United States. The first PC, depending on rotation, has strong loading amplitude in the central or east tropical Pacific. Thus various SST indices, such as the Japan Meteorological Agency (JMA) index or Niño 3 and 4 indices, characterize this mode rather well. The JMA index is based on the monthly SST anomaly in the region 4 S to4 N, and from 150 W to90 W. The 2of14

3 Figure 1. JMA monthly SST anomaly for These anomalies are not filtered. anomalies are filtered via a 5-month running mean. ENSO phase based on this index is as follows: if the JMA index is above 0.5 C for six consecutive months, including the months of October, November and December, then the phase is categorized as El Niño; if the index is below 0.5 C the phase is La Niña, and Neutral in all other cases. The determination of phase based on this index has been shown to give strong agreement with the Southern Oscillation Index (SOI) [Hanley et al., 2003]. While the JMA index is properly defined as being filtered with a 5-month running mean, for the purposes of this paper we will use the unfiltered anomaly index. We do this for two reasons: (1) As a predictor, the JMA index would potentially contain future data if using a filter (for example, the November index depends on December and January anomalies, which are not known in November), and (2) when defining the JMA index based on model SSTs, we only have 4 month integrations. [13] In Figure 1 we show the unfiltered JMA index for The years 1986, 1987, 1991 and 1997 are classified as El Niño events, and 1988 and 1998 are La Niña events. The results of Montroy [1997] established a linear connection between the tropical Pacific SSTs and U.S. precipitation. Hoerling et al. [1995] describes some nonlinear effects of the teleconnection, and Montroy et al. [1998] expands prior work to the nonlinear regime. Nevertheless, in the southeast United States, the linear component predominates, with warmer (colder) SSTs leading to wetter (drier) winters in the coastal southeast states, and the opposite signal more inland. We illustrate this in Figure 2a. Here we show the temporal correlation between the observed DJF precipitation and the November JMA index for the years The shaded regions indicate statistical significance at the 5% level, though the presence of the strong 1997 El Niño has a large impact on the correlation. Removing the 1997 season tends to lower the correlations by about 0.1 over much of the southeast. Nevertheless, it can be clearly seen that there is a strong teleconnection between the tropical SSTs and the wintertime precipitation over the coastal southeast states, consistent with the results we discussed earlier. The precipitation appears to be negatively correlated in the interior states. This result compares very well with the more accurate and detailed analysis of Montroy [1997] and Markowski and North [2003]. By using the November index, we have done a lag correlation to demonstrate the potential predictability, as the DJF SST anomalies would not be known at time of seasonal integration in forecast mode. In the works by Montroy [1997] and Markowski and North [2003], contemporaneous (0 lag) SSTs were used. [14] In Figure 2b we show the correlation between the global model wintertime precipitation and the model JMA index. Thus, over the southeast coastal states the global model exhibits a similar response to the tropical Pacific SST forcing, which is encouraging. The interior states are positively correlated, in contrast with the observed, and this needs to be further investigated. We note that over the southeast United States during the winter, the precipitation generated by the models is mostly due to large-scale precipitation and not convection. For the November-February time period, the precipitation over land was more than 90% large scale in the models. [15] We show in Figure 3 the DJF precipitation anomalies for the observed station data (Figure 3, top) and the regional model simulations (Figure 3, bottom). The anomalies are with respect to the 12 year mean for For the 1986 El Niño and 1988 La Niña years the regional model simulated the coastal southeast precipitation rather well, though rather poorly for the interior states, consistent with the correlations shown in Figure 2b. The 1997 event was particularly well simulated by the model. The distributions of the intense rain as well as the dryness in the interior states were well reproduced. [16] The years of 1987 and 1995 invite additional inquiry. The 1987 El Niño event would normally lead one to expect above normal precipitation in the southeast, but in fact the observed precipitation was below normal, and the model indicated large areas of below normal precipitation in the southeast as well. By examining the JMA index in Figure 1, it can be seen that by the boreal wintertime, early 1988, the tropical Pacific SSTs were rapidly cooling, leading to the strong La Niña event of The JMA anomaly 3of14

4 Figure 2. Temporal correlation of the November JMA index and (a) the observed station data and (b) the FSU Global Model for was already 0.0 C by February, This suggests that perhaps the southeast was beginning to experience La Niña or neutral conditions by wintertime. Similarly, the neutral year 1995 was experiencing cool SSTs in the winter of , and may partially explain the relative dryness of that winter. The JMA anomalies for October, November and December were 0.7 C, and the year narrowly misses the criteria for a La Niña year. The 12 year record is too short to draw any firm conclusions, but there is a hint that the contemporaneous tropical Pacific SSTs may be a stronger indicator of the southeast U.S. precipitation signal than simply the classification of the ENSO phase. This is illustrated in Figure 4, where we show the area-averaged temporal lag correlation squared (explained variance) of the wintertime precipitation and the JMA index. The lag goes from 9 months prior to the November of the season to 9 months after. It can be seen that the correlation is highest in February. In other words, the February SSTs are most strongly correlated to the precipitation for the DJF season. Again, the record that we used here is too short to assign any statistical significance, but there is a suggestion that the tropical teleconnection is fairly rapid, and that better prediction of SSTs, particularly the timing of the transition of the tropical Pacific anomalies could lead to better seasonal rainfall prediction in the southeast. [17] The wintertime precipitation in the southeast is primarily driven by large-scale flow, the passage of frontal systems, and much of the southeast is not affected by steep orography, so one might ask whether a regional model would improve predictions in this region. We show the temporal correlation between the observed seasonal anomaly DJF precipitation and the global model in Figure 5a, and for the regional model in Figure 5b. It can be seen that the skill in both models are along the coastal states, the areas of highest expected predictability. The regional model does appear to give higher correlations, and larger regions that are statistically significant at the 5% level (shaded regions). Correlations are generally very sensitive to extrema, in our case the 1997 event, so one must be cautious about drawing any strong conclusions, despite passing statistical tests. When 1997 is removed, the pattern of correlations are quite similar, though reduced somewhat. Note that we consider only local significance here, as the verification period is rather short, and we expect skill only in rather localized regions. Thus single indicators of skill should be viewed with caution. [18] As another indication of skill, we use the anomaly equitable threat score (AETS). This score is just the well known equitable threat score [Schaefer, 1990], where the event is defined as precipitation above an anomaly threshold. The equitable threat score is not susceptible to bias toward extrema as with correlation. In Table 1 we show the AETS and bias for the entire domain for threats of 1.5, 1.0, 0.5, 0.0, 0.5, 1.0, and 1.5 mm/day above normal precipitation. The bias is defined as the number of forecast threats divided by the number observed. The last two columns in Table 1, labeled GG AETS and GG Bias, represent the scores computed on the native Gaussian grid (approximately resolution) of the global model. We did not compute the regional results on the native regional Mercator grid as it is very similar in resolution to the analysis grid. We see that the regional model scores higher overall for each threat. One reason for this improvement is that the regional model is better able to produce more intense precipitation. The regional model is capable of producing sharper frontal systems, resulting in heavier rainfall. We shall see more evidence of this below. For the global results, the scores and bias were not substantially 4of14

5 Figure 3. DJF precipitation anomalies (mm/day) for for (top) observed station data and (bottom) the regional spectral model. 5of14

6 Figure 3. (continued) 6of14

7 Figure 3. (continued) 7of14

8 Table 1. Anomaly Equitable Threat Score a Threat Regional AETS Regional Bias Global AETS Global Bias GG AETS GG Bias a All scores computed on a common 0.5 grid except GG AETS and GG Bias which represent scores of the global model computed on the native (Gaussian) grid of the global model. Figure 4. Area averaged lagged explained variance of the JMA index and DJF precipitation. The month of November is the month immediately preceding the winter DJF season. changed whether the analysis was done on the Gaussian grid or the higher-resolution 0.5 analysis grid. Several methods were used to grid the station data to the global grid, but we found that simple box averaging (where the global grid points were the centers of adjacent boxes) yielded the highest AETS, and those are the results presented in Tables 1 and 2. Thus interpolation did not degrade the global model results too much, though there was some noticeable impact at higher threats. [19] Because of the severity of the bias, in terms of the number of threats, in the global model results, we sought to determine whether this could be improved by a correction. First, we determine the cumulative distribution functions (CDFs) of the forecast and observed anomalies, CDF f and CDF o. Then we used the corrected forecast anomaly, fa bias = CDF f 1 (CDF o ( fa)), where fa is the forecast anomaly and fa bias is the bias corrected anomaly. This method ensures that the bias is very close to 1 for all nonzero threats. The correction does not change the sign of the anomaly; hence there is no correction in the case of the threat value being zero. The CDFs are defined separately for positive and negative threats. The results of this correction are shown in Table 2. The results for the regional model did not change much, as little correction was needed. For the global model results, there was general improvement for most scores, particularly for the more extreme threats. On the whole, the regional model still has somewhat higher skills for all threats, though the bias corrected results narrow the gap. However, Figure 5. Temporal correlation between observed station data and (a) global model DJF precipitation and (b) regional model DJF precipitation. Shaded regions indicate statistical significance at approximately 5% level. 8of14

9 Table 2. Bias Corrected AETS Threat Regional AETS Global AETS GG AETS when we look at the spatial distribution of the scores, we begin to see more significant differences between the global and regional results, as we shall see next. [20] In Figure 6 we show the AETS for each grid point where the threshold event is defined as 0.5 mm/day or greater above normal precipitation for the global and regional models. We chose the global model results for the case of no bias correction and computed on the global grid ( GG AETS ) because of this case having the highest skill for the 0.5 mm/day threat. The regional results are also not bias corrected. The pattern of skill is similar to that given by the correlation, with the regional model having a larger area of higher skill. These scores are quite noisy, because of only 12 years of data. The event of greater the 0.5 mm/day above normal occurs approximately 30% of the time, so there is a relatively small number of events per grid point. To assess statistical significance of the AETS, we used a Monte Carlo approach. Using the observed and modeled frequency distribution of the 0.5 mm/day above normal threat, we find that the scores needed for 10%, 5% and 2% level of significance to be approximately 0.24, 0.30 and 0.44 respectively. The consistency of the AETS and the correlations suggests that the models have some significant skill in the coastal southeast region, with the regional model being somewhat more skillful in the coastal regions. [21] In addition to seasonal means and anomalies, we are interested in higher-frequency events produced by the models. One statistic in particular is the number of significant rainfall events in a season. For some applications, such as agricultural crop models, the frequency of rainfall (or length of drought) may be more important than total seasonal rainfall amount. In Figure 7 we show the count of occurrences of greater than 12 mm/day daily rainfall events for the and winter seasons for the models and observations. The regional model was clearly better able to simulate the number of events than the global model, with a wider range of counts and better contrast between the wetter and drier regions, for both winter seasons. [22] One reason for the greater fidelity in reproducing the number of occurrences is that the regional model is able to capture heavier rainfall events overall. In Figure 8 we show the average number of rainfall events per grid point over the whole domain by threshold amount for NDJF For small rainfall events, the regional model tends to produce too many events, but for larger rainfall events, especially about 16 mm/day and above, the regional model produced a number reasonably close to the observed. The global model, however, underpredicted the number of events at all thresholds, and only about half the correct number at large thresholds. As noted above, the regional model tends to produce sharper fronts, leading to higher rainfall amounts. In Figure 9 we show a passage of a frontal system on 9 February 1996, in the simulation for both the regional and global model. Here the regional model produces more intense and localized rainfall, due to the more sharply defined front. [23] A study by Gershunov and Barnett [1998, hereinafter referred to as GB98] examined the teleconnection between ENSO and extreme precipitation, and found that Figure 6. Anomaly equitable threat scores for threat greater than 0.5 mm/day above normal precipitation for (a) global model DJF precipitation and (b) regional model precipitation. Critical scores for the 10%, 5% and 2% level of significance are 0.24, 0.30 and 0.44, respectively. 9of14

10 Figure 7. DJF occurrences of rainfall greater than 12 mm/day for the (top) season and (bottom) Figure 8. Average count of precipitation events per grid point location over whole regional domain by threshold value of precipitation for observed, global and regional models. 10 of 14

11 Figure 9. Snapshot of precipitation (in mm) for a day in February 1996 of the simulations for (a) regional model and (b) global model. in El Niño years, there was generally an increase in heavy precipitation events in the southeast United States, and a decrease for La Niña years. We will define heavy precipitation as being greater than 15 mm/day. This is roughly the top quartile of precipitation events greater than 2 mm/day. This definition is very similar to that used in GB98. We examine the difference in heavy precipitation, compared to a neutral year composite, for 4 ENSO years: 1986, 1988, 1991 and We chose the neutral year composite to be the average of 1989, 1990, 1992 and The year 1987, while an El Niño by the JMA index, was considered transitional, since by the wintertime the tropical Pacific SSTs were rapidly cooling. Similarly, 1996 was experiencing rapid warming during the wintertime. Both 1994 and 1995 experienced brief but somewhat strong SST anomalies during the winter. Both of these winters experienced somewhat above normal (1994) or below normal (1995) precipitation consistent with the wintertime SST anomaly (see Figure 3). The differences are shown in Figure 10. The regional model reasonably predicted an increase in heavy precipitation events along the coastal southeast states in the El Niño years (1986, 1991 and 1997), and a decrease in the La Niña year (1988). The spatial distribution and number of events are in good agreement with the observed along the coastal states. The results are not so good for the inland states, as was the case with the results for the seasonal anomalies discussed above. The results in Figure 10 do not take into account that there are more rainy days in El Niño years (and fewer in La Niña years) relative to the composite neutral years. To determine whether there is a shift in the rainfall distribution, we define a percent anomaly as in GB98: P anom =(N i ( f b /f i ) N b )/N b where P anom is the percent change in anomalous extreme events (defined as exceeding 15 mm/day) relative to the base years (the neutral year composite defined above), N i and N b are the number of extreme events in year i and the neutral year composite, respectively, and f i and f b are the number of rainy days in year i and the neutral year composite, respectively. The results are shown in Figure 11. Overall there appears to be a shift toward heavier precipitation in the coastal states during El Niño years, more markedly so for For the La Niña year 1988 there is a decrease in most coastal areas, though some regions show an increase, contrary to observed. The results are noisy because of the limited number of years in the study, but seem to be reasonably consistent with GB98. It is interesting to note that 1988 was poorly forecast in the interior states, failing to capture the increase in heavy precipitation events. This appears similar to the results for the Max Planck Institute ECHAM3 model presented in GB98 for La Niña years, suggesting perhaps a common model problem. 5. Conclusion [24] The southeast United States has long been known to have potential predictability during winter because of its strong teleconnection to tropical Pacific SSTs. This region is therefore a good candidate for testing model predictions, and identifying deficiencies. The global model performs reasonably in simulating the proper rainfall anomalies in the southeast, particularly in the coastal states. The regions of predictability are consistent with what would be expected from tropical SST forcing, indicating that the model is capable of reproducing the teleconnection response. This response is strongest for strong ENSO events, as expected, though there appear to be some response to weaker events as well. We suggest that improving the SST prediction in the coupled model will lead to better simulations of the winter season, as there is some evidence that the atmospheric boreal wintertime signal responds rather quickly to the change in tropical SSTs. [25] The regional model appeared to improve the skill of the wintertime rainfall predictions of the global model. Overall, the skill was highest in strong ENSO years, with 1997 being very well simulated. The regional model offered improved skill in the seasonal anomaly prediction as well as 11 of 14

12 Figure 10. Difference in heavy precipitation events for ENSO years (1986, 1988, 1991, and 1997) from the neutral year composite of 1989, 1990, 1992, and (top) Observed and (bottom) regional model. 12 of 14

13 Figure 11. Percent anomaly change in heavy precipitation events relative to the neutral year composite for ENSO years 1986, 1988, 1991 and (top) Observed and (bottom) regional model results. 13 of 14

14 the frequency of precipitation events. Some of this skill appears to be due to the regional model being better able to produce heavier precipitation events, afforded by its higher resolution. Other local forcing factors, such as steep orography or cumulus convection did not appear to be a factor here. [26] The twelve years of simulation is too short to draw any firm conclusions about the skill of the model. We have recently upgraded the global and regional models significantly, and plan to enlarge this experiment. Furthermore, a larger number of ensembles for each season should be done to assess the internal variability of the model and to provide more confidence in the level of skill in the model. [27] Acknowledgment. This work was funded by grants from the National Science Foundation (grants NSF ATM and ATM ) and NOAA (grant NA16GP1365). References Cocke, S. (1998), Case study of Erin using the FSU Regional Spectral Model, Mon. Weather Rev., 126, Cocke, S., and T. E. LaRow (2000), Seasonal predictions using a nested regional spectral model embedded in a coupled ocean-atmosphere model, Mon. Weather Rev., 128, Cressman, G. P. (1959), An operational objective analysis system, Mon. Weather Rev., 87, Fennessy, M. J., and J. Shukla (2000), Seasonal prediction over North America with a regional model nested in a global model, J. Clim., 13, Georgi, F. (1990), Simulation of regional climate using a limited area model nested in a general circulation model, J. Clim., 3, Gershunov, A., and T. P. Barnett (1998), ENSO influence on intraseasonal extreme rainfall and temperature frequencies in the contiguous United States: Observations and model results, J. Clim., 11, Hanley, D. E., M. A. Bourassa, J. J. O Brien, S. R. Smith, and E. R. Spade (2003), A quantitative evaluation of ENSO indices, J. Clim., 16, Higgins, R. W., A. Leetma, Y. Xue, and A. Barnston (2000), Dominant factors influencing the seasonal predictability of U.S. precipitation and surface air temperature, J. Clim., 13, Hoerling, M. P., M. Ting, and A. Kumar (1995), Zonal flow-stationary wave relationship during El Niño: Implications for seasonal forecasting, J. Clim., 8, Hong, S.-Y., and A. Leetma (1999), An evaluation of the NCEP RSM for regional climate modeling, J. Clim., 12, Ji, Y., and A. D. Vernekar (1997), Simulation of the Asian summer monsoons of 1987 and 1988 with a regional model nested in a global GCM, J. Clim., 10, Juang, H. M., and M. Kanamitsu (1994), The NMC nested regional spectral model, Mon. Weather Rev., 122, LaRow, T. E., and T. N. Krishnamurti (1998), Initial conditions and ENSO prediction using a coupled ocean-atmosphere model, Tellus, Ser. A, 50, LaRow, T. E., S. D. Cocke, and D. W. Shin (2005), Multi-model proxy for seasonal climate studies, J. Clim., 18, Markowski, G. R., and G. R. North (2003), Climatic influence of sea surface temperature: Evidence of substantial precipitation correlation and predictability, J. Hydrometeorol., 4, Misra, V., P. A. Dirmeyer, and B. P. Kirtman (2003), Dynamical downscaling of seasonal simulations over South America, J. Clim., 16, Montroy, D. L. (1997), Linear relation of central and eastern North American precipitation to tropical Pacific sea surface temperature anomalies, J. Clim., 10, Montroy, D. L., M. B. Richman, and P. J. Lamb (1998), Observed nonlinearities of monthly teleconnections between tropical Pacific sea surface temperature anomalies and central and eastern North American precipitation, J. Clim., 11, Ropelewski, C. F., and M. S. Halpert (1986), North American precipitation and temperature patterns associated with El Niño/Southern Oscillation (ENSO), Mon. Weather Rev., 114, Ropelewski, C. F., and M. S. Halpert (1987), Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation, Mon. Weather Rev., 115, Rosmond, T. E. (1992), The design and testing of the Navy operational global atmospheric system, Weather Forecasting, 7, Schaefer, J. T. (1990), The critical success index as an indicator of skill, Weather Forecasting, 5, Shin, D. W., S. Cocke, T. E. LaRow, and J. J. O Brien (2005), Seasonal surface air temperature and precipitation in the FSU climate model coupled to the CLM2, J. Clim., 18, Tanajura, C. (1996), Modeling and analysis of the South American summer climate, Ph.D. dissertation, 164 pp., Univ. of Md., College Park. S. Cocke, Department of Meteorology, Florida State University, Room 411, Love Building, Tallahassee, FL 32306, USA. (scocke@fsu.edu) T. E. LaRow and D. W. Shin, Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA. 14 of 14