Abstract

An analysis and verification of 15 years of Climate Prediction Center (CPC) operational seasonal surface temperature and precipitation climate outlooks over the United States is presented for the shortest and most commonly used lead time of 0.5 months. The analysis is intended to inform users of the characteristics and skill of the outlooks, and inform the forecast producers of specific biases or weaknesses to help guide development of improved forecast tools and procedures. The forecast assessments include both categorical and probabilistic verification diagnostics and their seasonalities, and encompass both temporal and spatial variations in forecast skill. A reliability analysis assesses the correspondence between the forecast probabilities and their corresponding observed relative frequencies. Attribution of skill to specific physical sources is discussed. ENSO and long-term trends are shown to be the two dominant sources of seasonal forecast skill. Higher average skill is found for temperature than for precipitation, largely because temperature benefits from trends to a much greater extent than precipitation, whose skill is more exclusively ENSO based. Skill over the United States is substantially dependent on season and location. The warming trend is shown to have been reproduced, but considerably underestimated, in the forecasts. Aside from this underestimation, and slight overconfidence in precipitation forecast probabilities, a fairly good correspondence between forecast probabilities and subsequent observed relative frequencies is found. This confirms that the usually weak forecast probability anomalies, while disappointing to some users, are justified by normally modest signal-to-noise ratios.

1. Introduction

Operational seasonal outlooks for surface mean temperature and total precipitation over the United States have been routinely made by the Climate Prediction Center (CPC) since December 1994 and now span a 15-yr period. The seasonal outlook information is disseminated to the user community in several formats, for example, the probabilities of tercile-based categories (Fig. 1) or probability of exceedance (POE) (Barnston et al. 2000).

Fig. 1.

An example of CPC’s seasonal climate outlook for (top) surface temperature and (bottom) precipitation. For surface temperature, regions in red (blue) indicate where the probability for warm (cold) category is the largest. For precipitation, regions in green (brown) indicate where the probability for wet (dry) category is the largest. The numbers indicate forecast probability. Regions marked EC are indicated when probabilities for all three categories are equal to the climatological probability of 33⅓%.

Fig. 1.

An example of CPC’s seasonal climate outlook for (top) surface temperature and (bottom) precipitation. For surface temperature, regions in red (blue) indicate where the probability for warm (cold) category is the largest. For precipitation, regions in green (brown) indicate where the probability for wet (dry) category is the largest. The numbers indicate forecast probability. Regions marked EC are indicated when probabilities for all three categories are equal to the climatological probability of 33⅓%.

The CPC seasonal outlooks for surface temperature and precipitation are released around the middle of the calendar month; the first target season being the next 3-month period. For example, the first target season for the seasonal outlook released in the middle of June is for the climate conditions averaged over the July–September (JAS) season. Beginning with the first target period, seasonal outlooks are issued for the upcoming 13 overlapping seasons, extending out to approximately 1 yr. This analysis and verification procedure is applied to the outlooks for the first (0.5 month) lead time. It is intended to help inform the user community about the past performance of the operational seasonal outlooks, providing guidance on the potential utility of the real-time forecast information for decision making processes. The skill assessments are also useful for the producers of the seasonal outlooks, informing them of potential systematic biases in the forecast tools in order that they may focus on improving such weaknesses.

The analyses are aimed at defining the spatial and temporal variations of performance, including seasonal dependence, spanning the assessment of categorical and probabilistic aspects of the forecasts (section 3). The analyses also attempt to provide insight into the sources of skill (section 4). The present analyses extend recent summaries of the CPC seasonal outlooks presented by O’Lenic et al. (2008) and Livezey and Timofeyeva (2008). In section 5 we discuss the use and interpretation of the probabilistic seasonal climate outlook information, and the potential utility for beneficial societal application of the real-time seasonal forecasts within the context of the verification findings.

2. Forecast format, data, verification methods, and sources of prediction skill

a. Seasonal outlook format

A comprehensive summary of seasonal prediction methods used for generating the operational seasonal outlooks at the CPC, and their historical evolution, appears in O’Lenic et al. (2008); a brief overview is presented here.

CPC’s seasonal outlooks rely on a combination of empirical and dynamical prediction tools (Van den Dool 2007; Trocolli et al. 2008). Empirical seasonal prediction methods depend on identifying relationships between a set of predictors (e.g., sea surface temperature) and the forecast variables (i.e., surface temperature and precipitation), based on historical observed data (e.g., Barnston 1994). Dynamical seasonal prediction methods rely on comprehensive general circulation models (GCMs) that are initialized from the observed states for the different components of the Earth system—ocean, land, atmosphere, etc.—and are integrated forward in time (Ji et al. 1994).

Seasonal outlooks for U.S. surface temperature and precipitation are probabilistic in nature, detailing shifts in the probabilities, where they exist, away from their climatological values (of ⅓) for three equiprobable tercile-based categories. The category boundaries are defined using observational data over the most recently completed 30-yr base period covering three decades (e.g., 1981–2010 at the time of this writing). The categories (often called simply terciles) are referred to as below, near, and above normal. An example of the graphical format of the seasonal outlook is shown in Fig. 1 for surface temperature (top) and precipitation (bottom). The seasonal outlooks for the three terciles are condensed into a single figure with the probability shown only for the category having the highest probability value. For example, for the surface temperature outlook (Fig. 1), regions in red (blue) are regions where the forecast probability for the seasonal mean surface temperature is largest in the above (below) normal category. For seasonal total precipitation, green (brown) denotes regions with the largest probability in the above (below) normal category. Seasonal outlook maps also have regions marked EC (short for equal chances), corresponding to the case when the probabilities for all three categories are assigned as the climatological probability of 33⅓%. A forecast of EC is made when there is no basis for altering the probabilities from their climatological values, and providing useful information to the users.

b. Data

Our analyses verify the shortest lead seasonal outlook for surface temperature and precipitation from January–March 1995 to December–February (DJF) 2009/10. The spatial domain of the analysis is the continental United States, despite the fact that CPC’s outlooks also include Hawaii and Alaska. Seasonal outlook probabilities, and the verification data, are interpolated onto a 2° × 2° latitude–longitude spatial grid. Corresponding observations for surface temperature and precipitation come from a CPC analysis. The tercile boundaries for seasonal means for forecasts issued prior to May 2001 are based on the observational data for the 1961–90 period, while for forecasts issued from May 2001 and later the 1971–2000 period are used. The same change in the climatological period is applied in computing the observed seasonal mean anomalies for verification, and for determining the category of the observed seasonal means.

c. Verification measures

Verification of the seasonal outlooks is done using both categorical and probabilistic measures. For a categorical verification of the outlook, the Heidke skill score (HSS; O’Lenic et al. 2008) is used. The HSS tallies the number of “hits” (locations in which the category having the highest forecast probability matches the later observed category), and compares this number to the number expected by chance in the absence of any skill. The HSS is a scaled measure of the percentage improvement in skill relative to a climatology (EC) forecast, and is defined as

 
formula

where c is the number of grid points with hits, t is the total number of grid points in the outlook, and e is number of grid points expected to be correct by chance (and equals t/3). In the case of more than one category having the highest forecast probability, a hit is divided if one of them is later observed. In CPC’s outlooks, two-way ties never occur;1 in the case of the EC forecast there is a three-way tie and thus one-third of a hit is always tallied, equaling the hit rate expected by chance and contributing to a zero HSS.

The HSS can be computed in two ways: 1) for all points, including those where the EC forecast is issued, and 2) for only points where the outlook differs from EC. The latter is referred to as HSS1, while the former is referred to as HSS2. By definition, |HSS1| ≥ |HSS2| and both are scaled to range from −50 (when the forecast categories at all of the points are incorrect) to 100 (when the forecast categories at all points are correct). Maximizing HSS2 encourages making non-EC forecasts wherever a forecast signal is believed to exist, even if weak. On the other hand, HSS1 values may be maximized by making non-EC forecasts over only a small region where the confidence in the forecast is highest. In the extreme case, a non-EC forecast may be issued at only the one point at which the forecasters are most highly confident, and if that forecast results in a hit (miss), the HSS1 score is 100% (−50%).

As a measure of categorical skill, the HSS disregards the magnitude of the outlook probabilities that indicate the forecast confidence level, which is of importance from the user’s decision making perspective. To verify the probabilistic information in the seasonal outlooks, the ranked probability skill score (RPSS) is used. The RPSS is a measure of the squared distance between the forecast and the observed cumulative probabilities; a detailed discussion is found in Kumar et al. (2001) and Wilks (2006). As a final assessment tool, we also use reliability diagrams (Wilks 2006) to compare the forecast probabilities against their corresponding frequencies of observed occurrence to estimate systematic biases in the assigned probabilities, the frequency distribution of the issued probabilities (i.e., sharpness), and other characteristics of the forecasts.

Skill measures are computed either as time series, or as spatial maps. For the time series analyses, the skill score is computed between the seasonal outlook and the verifying analysis averaged over the entire forecast spatial domain.2 Alternatively, at each grid point, forecast skill between the outlook and the verification time series is computed as averaged over the entire period of record, and the geographical distribution of local skill is displayed as a map. These two approaches provide complementary information. For example, while the time series of skill illustrates how skill depends on the temporal variation in the strength of predictors (e.g., interannual variations in the sea surface temperature in the east-central tropical Pacific), the spatial map provides information about which regions have higher or lower skill. Both dimensions of skill are expected to be seasonally dependent.

d. A summary of forecast tools

The main predictors on which CPC’s seasonal outlooks depend are the El Niño – Southern Oscillation (ENSO) related SST variability, as well as low-frequency trends (O’Lenic et al. 2008; Livezey and Timofeyeva 2008). In making seasonal outlooks, the state of tropical Pacific SSTs is first anticipated, using the corresponding teleconnection signals over the United States when appropriate. The future state of the SSTs is generally based on a combination of forecast guidance from empirical and dynamical coupled model prediction tools (O’Lenic et al. 2008; Barnston et al. 2012). Another important tool used as input to CPC’s seasonal climate outlooks is low-frequency trends that are obtained based on the optimum climate normal (OCN) methodology (Huang et al. 1996a). Further discussion about the skill contributions of these tools appears in section 4c.

Seasonal outlooks at the CPC also rely at times on initial soil moisture anomalies and on guidance that includes land surface conditions provided by dynamical prediction systems [e.g., the operational Climate Forecast System (CFS) coupled model; Saha et al. (2006)]. Dynamical forecasts have the potential to contribute to the skill of seasonal outlooks from the specification of atmospheric, oceanic, and land initial conditions (akin to that for medium-range weather prediction). However, the relative contribution from the initial conditions is difficult to quantify. Some analyses have shown that even for short lead-time seasonal prediction, the influence of the longer-lasting boundary conditions dominates (Kumar et al. 2011; Peng et al. 2011; Chen et al. 2010). In the preparation of the operational seasonal outlooks, the trend and the ENSO-based signals, together with other tools, are merged together to form the probability forecast.

3. Results

a. Coverage of seasonal outlooks

We first analyze the spatial and temporal variabilities of the coverage of seasonal outlooks, with coverage being defined as the percentage of grid points having non-EC forecasts. The temporal analysis is done for each season, consisting of a time series of the coverage. Alternatively, the spatial analysis results in a map showing the geographical distribution of the percentage of times the forecast at any location was non-EC. Time series and spatial maps of coverage provide complementary information. As expected for the forecast skills, it is also expected that the temporal and spatial variations in coverage should be attributable to known physical mechanisms that contribute to predictability on seasonal time scales (e.g., ENSO) and exhibit seasonal dependence.

Time series (Fig. 2, top) and spatial patterns (bottom) show that the mean coverage for the temperature outlooks was 50.6%, and for precipitation was 29.9%. If the coverage of non-EC forecasts is an indication of predictability (or the signals) associated with the respective variable, larger coverage for temperature is consistent with the fact that it has more sources of predictability than precipitation. Sources of predictability for temperature include ENSO, trends, and initial soil moisture anomalies. In contrast, most of the predictive skill for precipitation comes from ENSO (Quan et al. 2006), with signals from trend and soil moisture anomalies smaller than for temperature (Huang et al. 1996a; Huang et al. 1996b). Larger coverage for surface temperature is also consistent with the substantially higher skill of CPC’s forecasts for temperature than precipitation. Similar differences in the skill for temperature and precipitation have been reported in skill analyses of operational forecasts or predictability based on atmospheric GCM (AGCM) simulations (Barnston et al. 2010; Fawcett et al. 2004; Goddard et al. 2003; Peng et al. 2000).

Fig. 2.

Percentage of cases that a non-EC forecast was made over the 15-yr verification period. (top) Time series of percentage of grid points with non-EC for (left) surface temperature forecasts and (right) precipitation forecasts. (bottom) Spatial map of percentage of non-EC forecasts for (left) surface temperature at each grid point and (right) precipitation. The result is 0% when all data points have an EC forecast and is 100% when all data points have a non-EC forecast.

Fig. 2.

Percentage of cases that a non-EC forecast was made over the 15-yr verification period. (top) Time series of percentage of grid points with non-EC for (left) surface temperature forecasts and (right) precipitation forecasts. (bottom) Spatial map of percentage of non-EC forecasts for (left) surface temperature at each grid point and (right) precipitation. The result is 0% when all data points have an EC forecast and is 100% when all data points have a non-EC forecast.

Figure 2 (top) shows that the forecast coverage has large variability among neighboring (and overlapping) seasons, as well as more generally. The standard deviation of coverage for both the temperature and precipitation time series is approximately 20%. Large variability among neighboring seasons seems at odds with the slow evolution of the set of predictors upon which the seasonal outlooks depend (e.g., ENSO, trends). This discrepancy may occur because of the subjective nature of the seasonal outlooks (including the rotation of the lead forecaster), inconsistencies among the various forecast tools (that rely on different sources for prediction skill), and also the seasonality in the spatial extent and strength of the signals associated with the various predictors (particularly ENSO). The time spectrum of the variability in the coverage is better understood in terms of the autocorrelation for the time series of skill (to be shown as a somewhat related variable), to be discussed later.

The spatial pattern of the percentage coverage (Fig. 2, bottom left) for temperature has its largest values over the southwestern United States and a minimum over the northeast region. For precipitation (Fig. 2, bottom right), the largest coverage is along the southern tier of states and over the Northwest. These spatial patterns can be attributed to the spatial structure of the signals associated with known predictors. For precipitation, non-EC seasonal outlooks are made much more frequently in the southern and northwestern United States, regions that correspond to well-known signals related to ENSO (see Fig. 14 and Peng et al. 2000; Ropelewski and Halpert 1987). For temperature, the maximum of non-EC seasonal outlooks over the southwestern United States corresponds to the region of positive surface temperature trends in recent decades (Huang et al. 1996a).

The seasonalities of the percent of area forecast for surface temperature and precipitation are shown in Fig. 3. For precipitation (red curve), a clear pattern is shown, with largest spatial coverage during winter and a minimum in the summer. This is consistent with the seasonality of ENSO-related precipitation signals over the United States, and also with the seasonality of the tropical Pacific ENSO SST anomaly amplitude that peaks during boreal winter.

Fig. 3.

Seasonal cycle of percentage of non-EC forecasts over the 15-yr verification period for (black curve) surface temperature and (red curve) precipitation. The seasonal cycle is from the average, for each season, of the time series shown in the top row of Fig. 2.

Fig. 3.

Seasonal cycle of percentage of non-EC forecasts over the 15-yr verification period for (black curve) surface temperature and (red curve) precipitation. The seasonal cycle is from the average, for each season, of the time series shown in the top row of Fig. 2.

The seasonality of the coverage for surface temperature forecasts has a more complicated structure with the largest coverage during winter, a secondary peak during summer (e.g., for June–August, JJA), and minima during spring and especially fall. The winter maximum is consistent with the larger amplitude of the signal related to ENSO, and also with the larger trends in temperature during boreal winter (examples to be shown later in section 4c). Minima during the spring and fall seasons are expected on the basis of the greater difficulty, in the extratropics, in establishing persistent climate anomaly signals during the transitional seasons of climatological SST and the large-scale atmospheric circulation patterns and, hence, more random, short-lived anomaly patterns. A possible explanation for the greater coverage in spring than fall is the use of soil moisture conditions as one of the predictive tools in spring, which may contribute to the peak in coverage in subsequent summers as well.

b. Analysis of skill in the seasonal surface temperature outlook

Time series and spatial maps of HSS1 and HSS2 for the seasonal temperature forecasts are shown in Fig. 4. Time series of HSS1 and HSS2 (top left), and a smoother version based on an 11-point running mean (top right),3 are shown in Fig. 4, with an average for HSS1 (HSS2) of 22.6 (11.3). Temporal variations in HSS from one season to the next are considerable. The low-frequency variability in HSS is better depicted in the time-smoothed version that shows extended periods of high or low skill. Over the 15-yr history of CPC’s seasonal outlooks, the time series of HSS does not exhibit any discernible trend, in contrast with medium-range forecasts (i.e., 6–10 and 8–14 day) where improvements in models and data assimilation systems have resulted in a distinct upward trend in the various measures of skill (Saha et al. 2010). Possible reasons for the lack of apparent improvement in the seasonal predictions are 1) the generally lower level of skill, making a given modest percentage improvement more difficult to discern amid the large temporal fluctuations, and 2) the much smaller number of temporal degrees of freedom on the seasonal than medium-range weather time scale, such that the chronology of high-amplitude climate events (e.g., ENSO episodes, such as the one in the late 1990s) can more strongly determine when skill is highest than can the gradual improvement seen in the prediction models and procedures.

Fig. 4.

(top left) HSS1 (red curve) and HHS2 (black curve) for seasonal surface temperature forecasts. (top right) The same time series as in the top-left panel but with an 11-point smoother. (bottom) Spatial map of (left) HSS1 and (right) HSS2 averaged over the 15-yr verification period.

Fig. 4.

(top left) HSS1 (red curve) and HHS2 (black curve) for seasonal surface temperature forecasts. (top right) The same time series as in the top-left panel but with an 11-point smoother. (bottom) Spatial map of (left) HSS1 and (right) HSS2 averaged over the 15-yr verification period.

The spatial distribution of HSS1 and HSS2 for temperature over the 15 yr (Fig. 4, bottom) shows a preference for higher skill in the southwestern United States (with HSS1 approaching ~60–70), the northwest, and across the southern tier of states. The lowest skill is found in the central plains, part of the Ohio Valley and parts of the northeast.

To evaluate the probabilistic aspects of the surface temperature seasonal outlooks, we use RPSS, which is computed over all grid points, including those with EC forecasts. The time mean of the RPSS time series (Fig. 5, top) is 0.03 (or 3%), which is much lower than the corresponding HSS2 of 11.3%. This is consistent with the equivalence between HSS2 and RPSS for low to moderate skill values (Tippett et al. 2010; Kumar 2009). Indeed, a scatterplot between HSS2 and RPSS (Fig. 6) confirms this relationship, and also shows a high correlation between HSS2 and RPSS (r = 0.88). The spatial structure of the RPSS (Fig. 5, bottom) is similar to those for the HSS1 and HSS2 (Fig. 4, bottom), again suggesting a strong correspondence between categorical and probabilistic skill.

Fig. 5.

(top) Spatial average of RPSS for each of the surface temperature forecasts over the 15-yr period (black line). The red line is the 11-point moving average. (bottom) Spatial map of the time-mean RPSS over the 15-yr verification period.

Fig. 5.

(top) Spatial average of RPSS for each of the surface temperature forecasts over the 15-yr period (black line). The red line is the 11-point moving average. (bottom) Spatial map of the time-mean RPSS over the 15-yr verification period.

Fig. 6.

Scatterplot of RPSS (x axis) and HSS2 (y axis) for surface temperature over the 15-yr verification period. The scatterplot is based on the unsmoothed time series of RPSS and HSS2 shown in Figs. 5 and 4, respectively.

Fig. 6.

Scatterplot of RPSS (x axis) and HSS2 (y axis) for surface temperature over the 15-yr verification period. The scatterplot is based on the unsmoothed time series of RPSS and HSS2 shown in Figs. 5 and 4, respectively.

We next assess the reliability and sharpness of the probabilistic seasonal temperature outlooks. The reliability is a measure of the correspondence between the forecast probabilities and their subsequent observed relative frequency, spanning the full range of issued forecast probabilities. For example, if the above normal category is assigned a probability of 60% in 200 instances (over all grid points for all forecasts over the 15 yr), then the forecasts are perfectly reliable if the later observed seasonal mean anomalies are in the above normal category in exactly 120 of those instances.

The reliability levels of the temperature forecasts for the above and below normal categories are shown in Fig. 7 as the red and blue curves, respectively. For each category, forecasts are binned for a different probability range (x axis), and are compared to their corresponding observed relative frequency of occurrence (y axis). The plots in the insets show the percentage of cases in which each of the forecast probability bins was issued. The diagonal line shows perfectly reliable forecasts. Although the format of CPC’s forecast maps is such that only the dominant category is shown, the probabilities of the two other categories are defined according to rules provided by CPC. These rules stipulate that when one of the outer categories (above or below normal) is assigned a positive probability anomaly with respect to the climatological probability (⅓), the opposite extreme category has a negative anomaly of equal magnitude while the near-normal category retains the climatological probability. However, in those rare cases when the probability of the dominant extreme category exceeds 0.633, the probability of the opposite extreme category remains at 0.033 and the probability of the near-normal category declines by the amount that the dominant probability exceeds 0.633. The reliability analysis covers forecasts for both positive and negative probability anomalies of the above and below normal categories, as well as the cases of zero probability anomaly (the EC forecasts). Because enhanced probabilities for the near-normal category are rarely issued, and because the expected skill for this category is low (Van den Dool and Toth 1991), reliability analysis for this category is not included.

Fig. 7.

Reliability diagram for the above normal (red curve) and below normal (blue curve) categories for all seasons and all grid points of surface temperature forecasts over the 15-yr verification period. Least squares regression lines are shown, weighted by the sample sizes represented by each point. The y = x diagonal line represents perfect reliability. The probabilities are binned as 0.05-wide intervals, e.g., 0.20–0.25 (plotted at 0.225), except that the EC forecasts (0.333) occupy their own bin and the lower and upper adjacent bins span 0.25–0.332 and 0.334–0.40, respectively. The inset histograms are the frequency distributions for these probability bins for the (top left) above and (bottom right) below normal categories.

Fig. 7.

Reliability diagram for the above normal (red curve) and below normal (blue curve) categories for all seasons and all grid points of surface temperature forecasts over the 15-yr verification period. Least squares regression lines are shown, weighted by the sample sizes represented by each point. The y = x diagonal line represents perfect reliability. The probabilities are binned as 0.05-wide intervals, e.g., 0.20–0.25 (plotted at 0.225), except that the EC forecasts (0.333) occupy their own bin and the lower and upper adjacent bins span 0.25–0.332 and 0.334–0.40, respectively. The inset histograms are the frequency distributions for these probability bins for the (top left) above and (bottom right) below normal categories.

The reliability curve for the above normal temperature category indicates a tendency to assign lower probabilities than are seen in the observed outcomes. For example, the forecast probability for the above normal category of 0.37 has been observed with a frequency of ~0.5. This tendency is seen for nearly all of the forecast probability bins, and it is clear that the above normal category was generally probabilistically underforecast. As one might expect, the opposite forecast bias occurs for the below normal category, with the observed relative frequencies much lower than the forecast probabilities. Thus, the forecast probabilities for temperature were generally biased toward underforecasting (overforecasting) the above (below) normal. Despite the forecasters’ awareness of the tendency toward above normal temperature observations in recent decades, their mean forecast probabilities of 0.36 and 0.29 for above and below normal fall far short of the observed relative frequencies of 0.47 and 0.23, respectively. Such a cold bias, indicated by the general vertical offset of the reliability curves with respect to the 45° perfect reliability line, was found in a study of the first several years of the current period of CPC’s forecasts (Wilks 2000), and also appeared in global probabilistic temperature forecasts by International Research Institute for Climate Prediction (IRI; Barnston et al. 2010).

Despite the general cold forecast bias, the confidence level of CPC’s temperature forecasts, indicated by the slope of the reliability lines, appears reasonably good. Lines with a slope steeper than 45° indicate underconfidence, while lines with a slope shallower than 45° indicate overconfidence—a more frequently noted characteristic of uncorrected dynamical model forecasts. Overconfidence is characterized by greater differences in forecast probability than the corresponding differences in observed relative frequency.

The frequency distributions of the forecast probabilities themselves (Fig. 7, inset diagrams) show, for both the above and below normal categories, that the largest frequency is for forecast probability denoting the EC forecasts (0.333; shown as its own bar) and the two adjacent probability bins (0.25–0.332 and 0.334–0.40), and the frequency falls off rapidly for probability ranges farther away from 0.33. Lower forecast frequencies at the low or the high ends of the probability range are indications of the conservative nature of the temperature outlooks, with “confident” forecast probabilities seldom issued. This conservatism (low forecast sharpness) may have consequences for decision making, where actions are triggered based on probability thresholds determined by a cost–loss analysis (Vizard et al. 2005; Kumar 2010). For example, if a response is triggered at a 60% forecast probability for the above normal temperature category, then, given the forecast frequency distribution (Fig. 7), an action will only be triggered on very rare occasions.

The seasonality of the skill of surface temperature is shown in Fig. 8. The HSS1 and HSS2 scores for each season are averages of approximately 15 cases from 1995 to 2009 (Fig. 8, top). These scores are surprisingly constant, with the summertime values similar to winter scores. The largest HSS1 (red curve) value occurs during JAS. However, due to the small sample, this relative peak may not be significant, and is not seen as prominently in HSS2. One would have expected skill for wintertime temperature forecasts to be higher as ENSO teleconnections over the United States are strongest then. Further, as will be shown in section 4c, the trends as depicted by the OCN are also largest for winter. On the other hand, the atmospheric internal variability is also the largest during winter, and it might counteract the predictability implied in the larger signals associated with ENSO and trend. The analysis in Kumar and Hoerling (1998) demonstrated a similar seasonal cycle of simulation skill based on atmospheric general circulation model (AGCM) simulations forced with observed SSTs. In the present study, the lowest skill level occurred during the fall season. The seasonal cycles of HSS1 and HSS2 are somewhat similar to the seasonality of the percentage area of the forecast coverage (Fig. 3).

Fig. 8.

(top) Seasonal cycle of HSS1 (red curve) and HSS2 (black curve) for surface temperature forecasts over the 15-yr verification period. Seasonal cycle is the average, for each season, of the corresponding unsmoothed time series in Fig. 4. Spatial distributions of the (middle left) HSS1 and (middle right) HSS2 skill levels for surface temperature forecasts for DJF and (bottom row) JJA.

Fig. 8.

(top) Seasonal cycle of HSS1 (red curve) and HSS2 (black curve) for surface temperature forecasts over the 15-yr verification period. Seasonal cycle is the average, for each season, of the corresponding unsmoothed time series in Fig. 4. Spatial distributions of the (middle left) HSS1 and (middle right) HSS2 skill levels for surface temperature forecasts for DJF and (bottom row) JJA.

The spatial structures of HSS1 and HSS2 for the winter and the summer seasons (Fig. 8) show a maximum in prediction skill over the Southwest for both seasons. As will be shown below in section 4c, the spatial pattern of the summertime skill has a close resemblance to the forecast guidance provided by the OCN tool. Further, since the ENSO signal during summer will be shown to be weak, most of the prediction skill is likely to be from the trend-based OCN.

For the winter season, the combination of the predictive signal from the trend and ENSO creates a more complex spatial structure of temperature prediction skill. While the signal from the OCN prediction tool generally favors above-normal temperatures over the entire United States with the maximum over the northern tier of states (see section 4c), where the interannual variability of the seasonal mean is also the largest, the El Niño (La Niña) signal is for warmer (colder) temperatures over the northern United States and colder (warmer) conditions over the southern United States. Therefore, depending on the amplitude and the phase of ENSO, the associated signal could either be in or out of phase with the OCN surface temperature signal. When the two signals are of opposite (same) sign, a reduction (enhancement) of predictive skill would occur, leading to a spatial structure of skill that may not correspond to the signal associated with either of the individual predictors. Therefore, although the surface temperature signal is strongest over the northern United States, particularly during winter, prediction skill there is not the highest; this lack of correspondence may occur because of difficulties in merging the forecast information based on ENSO with that based on the trend, through the OCN.

c. Analysis of skill in the seasonal precipitation outlook

The smoothed time series of HSS1 and HSS2 (top), and the spatial pattern of HSS1 and HSS2 (middle and bottom) for precipitation are shown in Fig. 9. Averaged over the 15-yr period, the HSS1 (HSS2) score is 11.0 (2.5), which is smaller than the corresponding surface temperature scores, demonstrating the difficulties associated with the skillful prediction of seasonal total precipitation. Similar to surface temperature, the time series over the 15-yr period does not show a discernible upward trend. In fact, the longest stretch of high skill appears early in the record from ~1996 to 1999, undoubtedly reflecting a heightened predictability associated with the 1997/98 El Niño episode followed by a strong, prolonged La Niña event.

Fig. 9.

(top) HSS1 (red curve) and HSS2 (black curve) for seasonal precipitation forecasts, after application of the 11-point smoother of the original seasonal scores. Spatial map of time average of (middle) HSS1 and (bottom) HSS2 over the 15-yr verification period.

Fig. 9.

(top) HSS1 (red curve) and HSS2 (black curve) for seasonal precipitation forecasts, after application of the 11-point smoother of the original seasonal scores. Spatial map of time average of (middle) HSS1 and (bottom) HSS2 over the 15-yr verification period.

Shown in Fig. 10 is the seasonality of the time series of spatially averaged HSS1 and HSS2 precipitation skill levels (top), and the spatial structures of the HSS1 and HSS2 skill levels in winter and summer. For HSS2 (black curve) the average skill tends to be highest during boreal winter and early spring, and near zero during the remainder of the year. This seasonality is consistent with that of the ENSO-related precipitation signal. The seasonality of HSS1 is noisier, with jumps in average skill from one season to the next. This is likely an artifact of the low skill values and the fact that the area covered by the non-EC precipitation outlook is generally small but highly variable (Fig. 2, top right). Hence, estimates of HSS1 forecast skill tend to have large sampling errors (Kumar 2009). A seasonal comparison of the spatial structure of prediction skill supports the discussion above, with higher skill during DJF than JJA. Therefore, the spatial structure of skill for DJF (Fig. 10) is similar to that for the annual mean (Fig. 9), and is reminiscent of the winter precipitation signal related to ENSO (to be discussed in greater detail in section 4c). This spatial structure features higher skill in a band extending from the Northwest across the central Rockies, and southern tier of states to Florida, and along the East Coast. Although there is a distinct dry (wet) signal related to El Niño (La Niña) over the Ohio Valley, and a corresponding local maximum in the frequency of non-EC forecasts there (Fig. 2, bottom right), the seasonal precipitation outlooks in that region have not been skillful.

Fig. 10.

As in Fig. 8, but for the seasonal precipitation forecasts.

Fig. 10.

As in Fig. 8, but for the seasonal precipitation forecasts.

The reliability of the precipitation outlooks for the above and below normal categories is shown in Fig. 11. For forecast probabilities in the most frequently issued probability bins between 0.25 and 0.40, the precipitation outlook has fair reliability for both above and below normal categories. However, above normal has been slightly underforecast while below normal has been slightly overforecast. The observed frequencies of observations have been greater for above normal (0.39) than below normal (0.30), but the mean forecast probabilities did not recognize this climate drift toward wet and were 0.33 for both categories. The dry forecast bias is small in comparison with the cold forecast bias noted above for temperature.

Fig. 11.

As in Fig. 7 (reliability analysis), but for the precipitation forecasts. Above normal precipitation is shown in green, and below normal in orange.

Fig. 11.

As in Fig. 7 (reliability analysis), but for the precipitation forecasts. Above normal precipitation is shown in green, and below normal in orange.

The slopes of the reliability curves for both categories indicate some degree of overconfidence. Because probabilities of >40% and <25% were forecast infrequently, the reliability curves for those forecast probability bins are subject to higher sampling variability (and are weighted lightly in the regression lines). However, most of these more extreme forecast probability bins show the dry bias, and to a larger degree.

4. Further analysis of prediction skill

a. Relationship between frequency of issuance of a non-EC forecast and predictive skill

Is there a relationship between the area covered by non-EC forecasts and the skill of that individual forecast? Similarly, is there a relationship between the frequency with which a non-EC outlook is made at a grid point and the corresponding time-mean skill at that grid point? Intuitively, one would expect both to be true, since the shift in forecast probabilities from climatology is presumably warranted by signals associated with one or more predictors (e.g., ENSO and/or trend). It is also known that there is a positive relationship between the signal strength (which would often result also in a greater spatial extent) and the expected value of the forecast skill (Kumar 2009). These two facts together argue for a link between the frequency for the issuance of a non-EC forecast and skill. To confirm these relationships for CPC’s outlooks, we conduct the analysis in time by analyzing the relationship between the coverage of non-EC forecasts and average forecast skill of the given forecast map, and in space by analyzing the geographical distribution of the percentage of time a non-EC forecast was made at a grid point against the time-average skill at that point.

Shown in Fig. 12 are scatterplots for temperature (top) and precipitation (bottom) for the spatial (left) and temporal (right) analyses. In the temporal analysis, the ENSO status of the forecast target season is indicated by the colors (see caption). For the temperature outlook, the frequency with which non-EC outlooks are made at different locations does have a somewhat linear relationship with the HSS1, with correlation 0.60 (Fig. 12, top left). Presumably, knowledge of signal(s) related to some predictor(s) results in higher time-average skill. This relationship, however, is not perfect, as there are locations where the frequency of non-EC forecasts is high (>70%), but average skill is near zero. For precipitation (Fig. 12, bottom left), a weaker, somewhat linear, relationship is also observed.

Fig. 12.

(top row) Scatterplot of percentage of non-EC forecasts (abscissa) and the corresponding HSS1 skill (ordinate) for surface temperature. (top left) The non-EC percentage and HSS1 skill for surface temperature in space (corresponding to Fig. 2, bottom left, and Fig. 4, bottom left, respectively). (top right) The non-EC percentage and HSS1 skill for surface temperature in time (corresponding to Fig. 2, top left, and red line in Fig. 4, top left, respectively), with the blue, black, and red dots denoting times of occurrence of La Niña, neutral, and El Niño conditions, respectively. (bottom row) As in the top row, but for precipitation: (bottom left) non-EC percentage and HSS1 skill for precipitation in space (corresponding to Fig. 2, bottom right, and Fig. 9, middle, respectively). (bottom right) The non-EC percentage and HSS1 skill for precipitation in time [corresponding to Fig. 2, top right, and Fig. 9, top (red lines), respectively].

Fig. 12.

(top row) Scatterplot of percentage of non-EC forecasts (abscissa) and the corresponding HSS1 skill (ordinate) for surface temperature. (top left) The non-EC percentage and HSS1 skill for surface temperature in space (corresponding to Fig. 2, bottom left, and Fig. 4, bottom left, respectively). (top right) The non-EC percentage and HSS1 skill for surface temperature in time (corresponding to Fig. 2, top left, and red line in Fig. 4, top left, respectively), with the blue, black, and red dots denoting times of occurrence of La Niña, neutral, and El Niño conditions, respectively. (bottom row) As in the top row, but for precipitation: (bottom left) non-EC percentage and HSS1 skill for precipitation in space (corresponding to Fig. 2, bottom right, and Fig. 9, middle, respectively). (bottom right) The non-EC percentage and HSS1 skill for precipitation in time [corresponding to Fig. 2, top right, and Fig. 9, top (red lines), respectively].

On the other hand, the same analysis in the time domain (Fig. 12, right panels) shows no indication of a linear relationship between the coverage of non-EC forecasts and skill, for both temperature and precipitation. Larger coverage is shown during active ENSO periods, particularly for precipitation, but higher HSS1 is not clearly indicated for those cases. A possible explanation for this result is that the HSS1 score carries no skill penalty for small coverage of non-EC forecasts, and during times of little or no signal the forecasters may be adept in deciding when a map should consist of mostly EC forecasts, and at which grid points a very few non-EC forecasts should be assigned. Thus, there may be just as much opportunity for a high HSS1 for maps with a small area of non-EC as maps with abundant non-EC areas. One might expect, however, that in the latter case there would be higher confidence in the interior portions of the large signal areas, and that the presence of such higher probability shifts might result in higher skill levels. However, as shown in the reliability diagrams (Figs. 7 and 11), most of the non-EC forecasts are issued with probabilities differing only by 0.05–0.10 from 0.333, especially for precipitation forecasts. This implies that many of the large signal areas do not contain much higher confidence in their interiors, but rather similar probability anomalies to those closer to the periphery of those areas. Moreover, a policy at CPC is that non-EC forecast areas are never drawn unless the probability of the dominant category equals or exceeds 0.40, which ensures that even small areas contain one or more interior location of adequate confidence.

When HSS2 is used for the time domain analyses, a weak linear relationship between coverage and skill appears (not shown), with correlations of 0.23 for temperature and 0.21 for precipitation. While slightly more toward expectation than the zero correlations seen for HSS1, the weakness of the result begs explanation.

A possible explanation for the asymmetric behavior for the spatial versus temporal analyses is as follows. For the HSS time series, a high level of skill for an individual season requires that the observed categories be the same as the forecast categories over a sufficient fraction of the area over which the prediction was made. Given that seasonal mean variability comprises several different modes of variability (Barnston and Livezey 1987), some of which (e.g., the North Atlantic Oscillation) are not predictable (Quan et al. 2006), a spatial coherence between the observed and forecast categories may be difficult to achieve, and may be highly variable over time. On the other hand, it may be easier to obtain a sufficient frequency of a match between the forecast and the observed category at a fixed spatial location over a long period of record. For example, if at a particular location the basis for a high rate of issuance of a non-EC forecast is due to the trend signal, the observed category may also have a consistently higher likelihood to be the same as the forecast.

b. Time variation in skill

In this section we examine the consistency of forecast performance from one season to the next. This analysis is interesting since the forecasts are likely to depend on slowly evolving signals (e.g., trend or ENSO), but we have seen that there is a rapid variation in observed skill (Fig. 4). The autocorrelation for the HSS2 skill for surface temperature and precipitation (Fig. 13) is computed from the unsmoothed time series of HSS2 shown in the top panels of Figs. 4 and 9, respectively. For a 1-month lag, the autocorrelation for temperature (precipitation) is only ~0.58 (~0.52), despite that adjacent seasonal forecasts have two months in common. The autocorrelations fall to less than 0.1 for a 3-month lag, when there is no overlap of months in the seasonal average. (Autocorrelations for HSS1 are slightly lower than those for HSS2: 1.00, 0.53, 0.24, and 0.01 for temperature, and 1.00, 0.31, 0.13, and 0.01 for precipitation.) The autocorrelation expected for random time series, due purely to the structural overlap of 1 or 2 months, is 0.67 for seasons having 2 months in common and 0.33 for seasons having 1 month in common. For HSS2, the autocorrelations for the overlapping seasons are lower than these baselines. This may be due to the fact that since skill depends both on forecasts as well as observations, the autocorrelation in the time series of skill is affected by the interplay of autocorrelation in those respective time series (i.e., time series of forecasts and observed seasonal means). Therefore, the autocorrelation of a single random time series is likely not an appropriate baseline.

Fig. 13.

Autocorrelation of HSS2 skill time series as a function of lag time (months) for surface temperature (blue bar) and precipitation (red bar) forecasts.

Fig. 13.

Autocorrelation of HSS2 skill time series as a function of lag time (months) for surface temperature (blue bar) and precipitation (red bar) forecasts.

c. An attribution analysis of skill

Seasonal prediction skill is generally attributed to physical causes such as the local or remote influence of slowly varying boundary conditions (e.g., SST or soil moisture) and to the influence of low-frequency trends relative to the climatology to which seasonal outlooks are made (Kumar et al. 2007; Trenberth et al. 1998). The state of the various predictors for seasonal outlooks during the analysis period is next summarized—first for temperature and then for precipitation.

The U.S. surface temperature (Fig. 14, top) and precipitation (middle) patterns related to the warm phase of ENSO are shown for winter (left) and summer (right). (Details about the compositing procedure are described online: http://www.cpc.noaa.gov/products/precip/CWlink/ENSO/composites.) It is important to note that (a) there is a considerable variability in the spatial structure of the ENSO signal from summer to winter and (b) signals have both positive and negative anomalies. Although not shown, the signal for the cold phase of the ENSO tends to be opposite of that for El Niño (Hoerling et al. 1997). The time series of the Niño-3.4 (5°N–5°S, 170°–120°W) SST anomaly [typically used to monitor ENSO; Barnston et al. (1997)] is shown in the bottommost panel of Fig. 14. ENSO variability is marked by one of the largest El Niño episodes (1997/98) followed by an extended La Niña until early 2001. The period since 2001 has featured relatively short-lived El Niño and La Niña episodes.

Fig. 14.

(top left) DJF and (top right) JJA surface temperature composites for the warm phase of the ENSO (i.e., El Niño). (middle left) DJF and (middle right) JJA precipitation composites for warm phase of ENSO. (bottom) The Niño-3.4 SST index over the analysis period.

Fig. 14.

(top left) DJF and (top right) JJA surface temperature composites for the warm phase of the ENSO (i.e., El Niño). (middle left) DJF and (middle right) JJA precipitation composites for warm phase of ENSO. (bottom) The Niño-3.4 SST index over the analysis period.

Seasonal forecast guidance for trends, based on the OCN, is shown in Fig. 15. Averaged over the entire analysis period and all seasons, the OCN has a warming signal over the entire United States. However, seasonal variability exists. For DJF (Fig. 15, middle), the entire United States also has a warming trend signal, while for summer (bottom) there is a cooling trend over the central plains that has been referred to as the summer warming hole (Wang et al. 2009). As the OCN methodology depends on the difference in surface temperature (precipitation) averaged over the last 10 (15) yr relative to the 30-yr mean climatology for a given season, the OCN forecasts vary only gradually from one year to the next. This is in contrast to the ENSO-related signal that depends on the phase and amplitude of SST anomalies, which have considerable interannual variability (Fig. 14).

Fig. 15.

Surface temperature forecasts (°C ) based solely on the OCN averaged over all 12 seasons for (top) all seasons, (middle) DJF, and (bottom) JJA. The red (blue) colors denote positive (negative) surface temperature anomalies.

Fig. 15.

Surface temperature forecasts (°C ) based solely on the OCN averaged over all 12 seasons for (top) all seasons, (middle) DJF, and (bottom) JJA. The red (blue) colors denote positive (negative) surface temperature anomalies.

In the final analysis we attempt to relate skill to its possible sources discussed above, and estimate the seasonal prediction skill for the trend and the ENSO signal as individual forecast tools. For the trend we specify the OCN alone as the seasonal outlook for each season, as is used in the operational forecasts. For the ENSO-based seasonal outlook, we first construct a regression pattern for the respective variable related to the Niño-3.4 SST using historical data, and then construct the outlook using the regression pattern and the Niño-3.4 SST for the target season.4 In constructing both the OCN- and ENSO-based categorical forecasts, the forecast category for each season is assigned based on the amplitude of the forecast anomaly, and whichever category the forced anomaly falls into based on the historical distribution of the respective forecasts, and regions with EC do not exist. (In the official CPC outlooks, the near-normal category is used, but only occasionally.) HSS2 skill scores for the individual tools are then compared with the HSS2 for the CPC outlooks.

In Fig. 16 (left), the spatial distribution of HSS2 for the official temperature forecast (top) is compared with the HSS2 for OCN alone as the seasonal forecast (middle) and for the ENSO-related temperature signal (bottom), for all seasons. It is apparent that the HSS2 for the official outlook has a very good resemblance with the seasonal outlook based on the OCN. Further, the spatial average of HSS2 for the OCN-based outlooks (16.7) is better than the HSS2 from the ENSO-based forecasts (5.0), as well as that from the official outlooks (11.3). The analysis indicates that the dominant source for the seasonal surface temperature outlook skill averaged over all seasons is the trend-related information. The lack of a strong contribution of the ENSO signal to CPC’s temperature forecasts is consistent with findings in Goddard et al. (2003) for the global temperature forecasts issued by the IRI over a shorter period.

Fig. 16.

Spatial map of time-averaged HSS2 for (top) the CPC surface temperature forecasts, (middle) OCN alone, and (bottom) the ENSO-related surface temperature signal alone. (left) HSS2 computed over all seasons, (middle) HSS2 computed over the seasons when the tropical Pacific SST state was observed to be in a positive or negative ENSO state (i.e., El Niño or La Niña), and (right) for seasons when the tropical Pacific was in the ENSO-neutral state. The sum of the verifying seasons included in the middle and the right columns equals the seasons included in the left column.

Fig. 16.

Spatial map of time-averaged HSS2 for (top) the CPC surface temperature forecasts, (middle) OCN alone, and (bottom) the ENSO-related surface temperature signal alone. (left) HSS2 computed over all seasons, (middle) HSS2 computed over the seasons when the tropical Pacific SST state was observed to be in a positive or negative ENSO state (i.e., El Niño or La Niña), and (right) for seasons when the tropical Pacific was in the ENSO-neutral state. The sum of the verifying seasons included in the middle and the right columns equals the seasons included in the left column.

A likely cause of the lower HSS2 for the ENSO-based surface temperature forecast is that ENSO is an episodic phenomenon (see Fig. 14, bottom), and skillful forecasts related to ENSO are possible when ENSO-related SST anomalies are occurring. Furthermore, even during ENSO episodes, climate impacts over the United States are weak during the warm half of the year. On the other hand, OCN, by definition, evolves more slowly, and there is always a forecast signal associated with it that affects some parts of the United States—during all years and all seasons. To test this hypothesis, we compute the skill analysis for the ENSO and non-ENSO seasons. The seasons with ENSO are defined using the categorization developed at CPC (Kousky and Higgins 2007), as listed online (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml).

A comparison of HSS2 for ENSO and non-ENSO years is shown by the columns in Fig. 16. The HSS2 for ENSO years (middle column) indicates that even for ENSO seasons, the skill of the OCN exceeds that for the ENSO-based forecasts, and is closer to the skill of the CPC official outlooks. Similar results are also seen in non-ENSO years, except that the HSS2 results for ENSO-based forecasts are close to zero.

The fact that the OCN alone is found to have higher HSS2 skill than that of the official outlook indicates difficulties in merging the forecast information from multiple predictors acting on different time scales. A challenge occurs, for example, when signals from OCN and ENSO are of the opposite sign in a given region.

A similar skill source analysis is done for the seasonal precipitation outlooks (Fig. 17). During ENSO-affected seasons, precipitation skill related to ENSO dominates the OCN signal (Fig. 17, middle), but over all seasons the two skill sources appear to have approximately equal contributions. However, as seen for temperature, the skill of the official forecasts fails to equal or exceed that of the best individual tool.

Fig. 17.

As in Fig. 16 (HSS2 for official vs individual tool, for all forecasts and for forecasts stratified by ENSO status), but for precipitation.

Fig. 17.

As in Fig. 16 (HSS2 for official vs individual tool, for all forecasts and for forecasts stratified by ENSO status), but for precipitation.

In the skill source analyses for both temperature and precipitation, the HSS2 scores for the official forecasts may be unfairly hindered by the EC forecasts that were never assigned for the competing individual skill source forecasts. Because of this analysis design artifact, we cannot conclude that CPC’s official forecasts would be more skillful if they were based solely on OCN for temperature and solely ENSO for precipitation during ENSO seasons. Nonetheless, the exercise provides a rough approximation of the relative contribution of the two main skill sources to the net predictability of the two variables over the United States.

5. Summary and discussion

Seasonal climate outlooks at the Climate Prediction Center have been made since 1995, providing a 15-yr archive of operational forecasts. An assessment of the skill levels of the surface temperature and precipitation seasonal outlooks at 0.5-month lead time is made to document the spatial and temporal patterns of behavior of skill, and to understand the possible skill sources. The skill assessment is based on both categorical and probabilistic measures. The salient results from the analysis can be summarized as follows:

  • a higher level of prediction skill for surface temperature outlooks than for precipitation;

  • a lack of discernable upward trend in skill over the 15-yr history for either variable;

  • a dominant contribution for surface temperature skill from the slowly evolving warming trend component; and a partial, or incomplete, reproduction of this warming in the temperature forecasts;

  • a dominant contribution for precipitation from ENSO-related tropical Pacific SST variability, and slightly overconfident, but otherwise reasonably reliable, probabilistic precipitation outlooks;

  • large variability in the skill of seasonal outlooks from one month to the next (i.e., forecast performance in the past few months is not a good indicator of skill for the next few seasons);

  • a distinct spatial preference of regions with higher skill; and

  • forecast probabilities, in general, not deviating much from the climatological probability of 33.3%; general justification of these modest deviations (through reliability analysis) in view of the frequently small signal-to-noise ratios.

One of the purposes of the analysis was to establish a benchmark for the skill of seasonal outlooks that can be revisited at regular time intervals to assess the influence of advances in tools and procedures used to make seasonal predictions. General circulation models, data assimilation methods for improved model initialization, consolidation methods for merging multiple forecast tools, and improvements in observations related to the slowly evolving component of the Earth system (e.g., ocean and land) are all potential avenues toward improvement in the skill of seasonal outlooks. This analysis was also aimed at informing the user community about the characteristics of the CPC seasonal outlooks (as assessed from the point of view of skill measures), and to inform the producers of the forecasts about possible biases or specific weaknesses.

Apart from the assessment of the level of seasonal prediction skill, there are some other important aspects of seasonal prediction that should also be kept in perspective. Some of these are discussed below.

Application of climate forecast information in the decision-making process generally depends on threshold probabilities that trigger actionable responses to either mitigate adverse impacts, or to benefit from the anticipated climate conditions. Such decision thresholds depend on an analysis of the cost (of taking the action) and benefit (that ensues as a consequence from the action). Cost–benefit analysis leads to probability thresholds above which benefits from taking certain action, on average, outweigh the cost (Vizard et al. 2005; Kumar 2010). Such probability thresholds are generally well removed from the climatological probabilities. As probabilities of CPC’s seasonal climate outlooks usually do not deviate substantially from the climatological probabilities (Figs. 7 and 11), the utility of seasonal outlooks in the decision-making process may not be high.

The forecast probabilities for a reliable climate outlook implicitly contain their own verification. For example, if the probability for an above normal category forecast is 60%, and if the forecast system is reliable, observations will either be in the normal or the below normal category 40% of the time. One of the foremost requirements in the cost–benefit analysis is the reliability of the forecasts. Departures from the perceived reliability of the forecasts (on which decisions are based) can have severe detrimental impacts on the anticipated benefits derived from the long-term use of the forecast information in the decision-making process (Vizard et al. 2005). Therefore, within the context of the economic value of the forecast information, the influence of the typical reliability of the forecast as shown in Figs. 7 and 11 requires a careful assessment. Two ways of assessing the reliability of a forecast system are 1) to build up a long history of the real-time seasonal outlooks (as analyzed in this paper) and 2) to develop a set of retrospective forecasts (i.e., hindcasts) that mimic as closely as possible the real-time forecast setting. However, continued upgrades to the forecast tools, particularly the dynamical prediction systems, and the subjectivity inherent in CPC’s official forecast, make the second possibility impractical.

From the perspective of the user of real-time climate outlooks, the use of the skill information based on the historical forecasts may still not be a straightforward issue, with several factors posing challenging problems. Skill information is an assessment of forecasts over a long history that combines climate outlooks made during many different climate conditions—for example, the presence or absence of an ENSO event. Individual real-time outlooks for a particular season and year, on the other hand, are context dependent, and given the climate conditions (e.g., the ENSO status) have their own levels of skill. Stratification into such broad conditional categories, which has been attempted here for ENSO, can be informative, but carries sample size reductions that increase sampling uncertainty. If probabilistic forecasts have reasonably good reliability, the anticipated level of skill is implicitly reflected in the forecast probabilities. Thus, during high signal conditions (e.g., a strong El Niño), skill levels can be anticipated to be higher than their average level for the given season at some locations, with probabilities deviating from 33% by greater amounts. How two sets of information (i.e., historical skill and skill implicit in the forecast probabilities5) are to be used in the decision-making context requires careful attention.

The analysis clearly points to the importance of recent warming trends to the prediction skill for surface temperature anomalies. With warming trends projected to continue (Solomon et al. 2007), it is expected that trends will remain an important source of temperature prediction skill on seasonal time scales. It is important to recognize, however, that because the seasonal climate outlooks are currently made relative to the mean of a prior 30-yr period ending at the completion of the most recent regular decade, the contribution of trends on prediction skill may remain near the level documented here.

Acknowledgments

Comments by Dr. Dave Unger and an anonymous reviewer helped improve the final version of the manuscript.

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Footnotes

1

Except for the case of the EC forecast, the near-normal category is never given the same probability as the highest of the two outer categories; furthermore, the two outer categories are never tied in having the highest probability.

2

Because most of the grid points lie in a latitude band limited to 30°–48°N, latitude-dependent area weighting is not conducted.

3

Note that the 11-month smoothed HSS2 is not a linear function of the 11-month smoothed HSS1, due to the varying coverage, so that occasional sign discrepancies between them may occur.

4

Here, we ignore the fact that we may occasionally, but infrequently, incorrectly predict the phase of ENSO for the target period in an actual short-lead forecast and, therefore, expect that the ENSO skill results may be slightly overestimated.

5

We should point out that these two sets of skill information are sometimes referred to as conditional (skill score specific to a climate condition) and unconditional (skill score aggregated over the full spectrum of climate conditions) skill scores (Kumar 2007).