Predictions of Nino3.4 SST in CFSv1 and CFSv2: a diagnostic comparison
Abstract
Diagnostic evaluations of the relative performances of CFSv1 and CFSv2 in prediction of monthly anomalies of the ENSO-related Nino3.4 SST index are conducted using the common hindcast period of 1982–2009 for lead times of up to 9 months. CFSv2 outperforms CFSv1 in temporal correlation skill for predictions at moderate to long lead times that traverse the northern spring ENSO predictability barrier (e.g., a forecast for July made in February). However, for predictions during less challenging times of the year (e.g., a forecast for January made in August), CFSv1 has higher correlations than CFSv2. This seeming retrogression is caused by a cold bias in CFSv2 predictions for Nino3.4 SST during 1982–1998, and a warm bias during 1999–2009. Work by others has related this time-conditional bias to changes in the observing system in late 1998 that affected the ocean reanalysis serving as initial conditions for CFSv2. A posteriori correction of these differing biases, and of a similar (but lesser) situation affecting CFSv1, allows for a more realistic evaluation of the relative performances of the two CFS versions. After the dual bias corrections, CFSv2 has slightly better correlation skill than CFSv1 for most months and lead times, with approximately equal skills for forecasts not traversing the ENSO predictability barrier and better skills for most (particularly long-lead) predictions traversing the barrier. The overall difference in correlation skill is not statistically field significant. However, CFSv2 has statistically significantly improved amplitude bias, and visibly better probabilistic reliability, and lacks target month slippage as compared with CFSv1. Together, all of the above improvements result in a highly significantly reduced overall RMSE—the metric most indicative of final accuracy.
Keywords
Coupled ocean–atmosphere models NOAA CFSv1 and CFSv2 ENSO prediction Skill diagnosis Model hindcasts Nino3.4 SST index Target month slippage Statistical field significance1 Introduction
Some basic specifications for CFSv1 and CFSv2
CFSv1 | CFSv2 | |
---|---|---|
Horizontal resolution | T62 (~2°) | T126 (~1°) |
Vertical resolution | 64 levels | 64 levels |
Atmospheric model | GFS from 2003 | GFS from 2009 |
Ocean model | MOM3 | MOM4 |
No. ensemble members/month | 15 | 24 |
Initial conditions for 0.5 month outlook (example shown is for a seasonal mean forecast for DJF) | R2 and GODAS: five initial conditions each from near the 1st and 11th of Nov., and 21st of Oct. | CFSR: four initial conditions each from the 17th, 12th, 7th, 2nd of Nov., and 27th of Oct. |
Climatological base period | 1982–2004 | 1982–2004 |
Maximum forecast lead time | 9 months | 9 months |
Source of initial condition data (horizontal resolution) | NCEP/DOE reanalysis (T62) | Climate Forecast System Reanalysis, or CFSR (T382) |
Sea ice | Climatology | Predicted |
Carbon dioxide concentration setting | Fixed at 1988 level | Evolving with time |
Given the improvements in CFSv2 compared with CFSv1, one would expect relatively better predictive skill in CFSv2 in most fields and over many regions of the globe. However, a discontinuity at year 1999 in the CFSR, related to a change in the atmospheric observing system, induced a change in the characteristics of the SST used for the initial conditions for the CFSv2 integrations beginning that year—especially those in the tropical Pacific (Xue et al. 2011; Kumar et al. 2012; Xue et al. 2013). Here we compare the skill of predictions of Nino3.4 SST in the tropical Pacific by CFSv2 to those of CFSv1, and examine which features of the skill differences may be related to CFS model improvement, and/or to the 1999 discontinuity in the initial conditions due to the CFSR. More background about the 1999 discontinuity will be provided in the context of the initial presentation of results below, in Sect. 3.1.
The Nino3.4 region is selected as the focus of this study because it is closely associated with the ENSO state (Barnston et al. 1997), which influences seasonal climate through well known teleconnections (e.g., Ropelewski and Halpert 1987; Mason and Goddard 2001; Hoerling and Kumar 2002; among many others). We focus on prediction of the Nino3.4 index, and examine the significant performance differences between CFSv1 and CFSv2, and between each model version with and without corrections for their discontinuous climatologies.^{2} The ultimate interest is in model version comparisons following the corrections. The significance of the discontinuities themselves is assessed, given the 28-year hindcast records. The data and methods are described in Sect. 2, followed by results in Sect. 3 and a discussion and some conclusions in Sect. 4.
2 Data and methods
The retrospective forecasts (i.e., hindcasts) of CFSv1 and CFSv2 were initialized from their respective Reanalysis data, producing ensembles run on a time-staggered schedule within each month (e.g., 4 members at 5-day intervals for CFSv2). The hindcasts of both versions begin in 1982, and are run 9 months into the future (Table 1). Here, for simplicity the lead time is defined by the lead month order of the hindcast, ranging from 1 to 9, despite that lead time is often defined to be one less. The CFSR Reanalysis from which CFSv2 runs are initialized is at T382 (~38 km) horizontal resolution, while that for CFSv1, the NCEP/DOE Reanalysis (Kanamitsu et al. 2002), is at T62 (~2°).
The observed SST data against which the CFS hindcasts are verified are the monthly mean of the optimum interpolation version 2 (OIv2; Reynolds et al. 2002), at 1° resolution. Here we use the mean SST over the Nino3.4 region (5°N–5°S, 120°–170°W), and use 1-month averages for both predictions and observations.
For the deterministic verifications, only the ensemble mean of the model predictions is used, and treated as a single best guess forecast. For the probabilistic reliability analysis the distribution of the individual ensemble members are used to define the model forecast probabilities for the tercile-based categories. Those categories are defined with respect to the model’s climatological distribution, using individual members, which varies as a function of the start time and lead time. Tercile-based categories are also defined for the observations.
The verification measures include basic performance diagnostics for deterministic forecasts: temporal correlation with observations for a given season and lead time, root mean squared error (RMSE), ratio of interannual standard deviation of model predictions to those observed, and a lesser known measure called target month slippage. The latter is an indication of biases in the timing of the predictions, such as that in which predictions verify better on target months occurring earlier than the intended month (Tippett et al. 2012; Barnston et al. 2012). A final deterministic diagnostic is a comparison of linear trends in the model predictions to that observed. Prediction bias is not examined in the usual manner, because the bias in the Nino3.4 SST predictions of both models changes abruptly around a specific year within the hindcast history, and corrective measures are taken that largely eliminate model bias. Specifically, the differing biases observed over two portions of the hindcast period are removed individually using the two sub-period climatologies, so that each portion becomes bias-free.
A probabilistic verification analysis—reliability analysis (Murphy 1973; Wilks 2006)—is used to detect probabilistic confidence levels as described by the distribution of the models’ ensemble members associated with each prediction, and probabilistic biases. Reliability is a measure of the correspondence between the forecast probabilities and their subsequent observed relative frequencies, spanning the full range of issued forecast probabilities. Model probability forecasts are defined on the basis of the proportion of ensemble members falling into each of the three defined climatologically equiprobable categories. Perfect reliability would be achieved if, for example, the above normal Nino3.4 SST category were assigned a probability of 40 % in 20 instances over all of the issued forecasts, and the later observed seasonal mean anomalies were in the above normal category in exactly 8 (i.e., 40 %) of those instances. Here we analyze reliability for the 6-month lead forecasts, representing a moderate to long lead time. Because our sample size of predictions is small, we combine all target months, and form eleven 10 %-wide forecast probability bins centered on 0, 10, …, 90 and 100 % probability. Then there are (28) (12) = 336 predictions, resulting in an average of about 31 predictions per probability bin. However, as will be discussed in Sect. 3.1, the 336 predictions are not independent cases, because the ENSO state changes slowly so that forecasts of adjacent start times or adjacent lead times are strongly mutually correlated. The reliability analysis will be described further in the context of its application, in Sect. 3.6.
To help provide statistical support for the reality of the two separate bias periods for each CFS model version, t-tests for the mean difference are applied. Additionally, differences in skill between Nino3.4 predictions of CFSv1 and CFSv2, stratified by season and lead time, are assessed statistically under several arrangements of bias correction status. Comparisons when both model versions are corrected are of greatest interest. Given that there are 12 seasons and 9 lead times, collective (or field) significance tests (Livezey and Chen 1983) for the overall skill difference for the entire matrix of 108 skill differences are conducted to determine the likelihood that the individually significant cells in the matrix are significant by chance. Determining field significance requires accounting for the effective number of statistically independent cases in the data set. Here we estimate this number—the statistical degrees of freedom—based on the autocorrelation structure in the forecast and observed SST data.
3 Results
The comparative performance diagnostics for CFSv1 and CFSv2 are given first using deterministic verification measures in Sects. 3.1–3.5, followed by a probabilistic diagnostic measure (reliability analysis) in Sect. 3.6.
3.1 Anomaly correlation
Significance category for discontinuity in CFSv1 bias (for 1982–1990 vs. 1991–2009) as a function of target month and lead time
Lead (months) | Target month—CFSv1 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
9 | 1 | 1 | 5 | |||||||||
8 | 1 | 1 | ||||||||||
7 | 5 | 1 | 5 | |||||||||
6 | 1 | 5 | ||||||||||
5 | 5 | |||||||||||
4 | ||||||||||||
3 | ||||||||||||
2 | ||||||||||||
1 |
Significance category for discontinuity in CFSv2 bias (for 1982–1998 vs. 1999–2009) as a function of target month and lead time
Lead (months) | Target month—CFSv2 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
9 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | ||
8 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 1 | ||
7 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 1 | 5 | ||
6 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 1 | 5 | |||
5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | ||||
4 | 5 | 5 | 5 | |||||||||
3 | 5 | 5 | ||||||||||
2 | – | 1 | 1 | 5 | ||||||||
1 | – | – | – | 5 | 1 | 5 |
Applying (1) to the observed and predicted Nino3.4 SST data, we first note that the autocorrelation in both model and observed data at 1 year lag is near zero. This result, independently confirmed elsewhere for many ENSO-related variables, implies that for any single month/lead-time combination, the full 28 years can be used as the effective sample size. For lags smaller than 12 months, autocorrelations for the monthly observations during 1982–2009 are roughly 0.95, 0.87, 0.76, 0.65, 0.53, 0.40 and 0.29 for lags of 1 through 7 months, respectively. These autocorrelations are influenced equally by all times of the year, including times of relatively low or high autocorrelation. Autocorrelations in CFSv1 and CFSv2 for fixed lead times, while not identical to those of the observations, are approximately equivalent when aggregated over all lead times. Application of (1) to these autocorrelations for either model version versus observations, or a model version versus itself before and after correction, yields 1 temporal degree of freedom per 7.4 months, resulting in 1.60 degrees of freedom per year. Application of the same approach to the lead dimension pertains to autocorrelations between model predictions for fixed targets, where lag now represents differences in lead time. The result is identical for autocorrelations for the observations, but slightly stronger model autocorrelations in the lead dimension for fixed target than in the time dimension for fixed lead. The outcome is 1 degree of freedom per 9.8 months, and consequently the 9 lead times yield 1.82 degrees of freedom per forecast integration. Because the months within a year and the leads for forecasts for a given targeted month represent two separate dimensions, the entire matrix of 108 months/lead-time cells produces (1.60) (1.82) = 2.91 degrees of freedom per year of predictions over the 12 months and 9 lead times. Thus, while a single cell in the matrix provides 28 degrees of freedom over the 28 year period, the matrix of 108 time series, with 28 forecast-observation pairs each, supplies about 81 (2.91 times 28).
With an estimate of 81 effective degrees of freedom for all target months and leads over the 28 year period, a field significance test for the model bias discontinuities is applied to the average t-statistic across over the 108 target-month/lead combinations for CFSv1 (Table 2) and CFSv2 (Table 3). Although the physical underpinnings of both discontinuities have been identified (an XBT issue for CFSv1, and an ATOVS impact for CFSv2), and these causes may provide expectations of the directions of discontinuity in the case of each model version, we use a 2-sided test to be cautious. The result is a significance p value of 0.05 for CFSv1, and 0.002 for CFSv2. Although the discontinuity in the bias of CFSv1 is significant, it has not been a major issue in CFSv1 research. For example, while the performance of CFSv1 is examined in detail in Jin and Kinter (2009), the discontinuity is not discussed. In the case of CFSv2 the discontinuity is more widely recognized (Wang et al. 2011; Xue et al. 2011, 2013; Zhang et al. 2012; Kumar et al. 2012).
Local and field significance evaluation for various correlation skill comparisons involving uncorrected and corrected versions of CFSv1 and/or CFSv2
Model versions for comparison, and their overall correlation over 12 seasons and 9 leads (“adj” = dual climatol.) | % Cells with increased (significantly increased) correlation | % Cells with decreased (significantly decreased) correlation | 108-Cell field significance: z-statistic | 108-Cell field significance: 1-sided p value for improvement |
---|---|---|---|---|
CFSv1–CFSv2 0.833–0.775 | 26 (5) | 74 (15) | −1.00 | 0.84 |
CFSv1–CFSv1 (adj) 0.833–0.870 | 99 (0) | 1 (0) | 0.81 | 0.21 |
CFSv2–CFSv2 (adj) 0.775–0.880 | 100 (33) | 0 (0) | 2.06 | 0.02 |
CFSv1–CFSv2 (adj) 0.833–0.880 | 85 (6) | 15 (0) | 1.06 | 0.15 |
CFSv1 (adj)–CFSv2 (adj) 0.870–0.880 | 54 (5) | 46 (0) | 0.25 | 0.40 |
While the above results may seem discouraging, one must keep in mind that they may reflect the modest sample size more than a lack of real incremental improvements in model quality between CFSv1 and CFSv2. Additionally, the tests weight all cells in the matrix equally, regardless of lead time and season. Such equal weighting ignores the existence of features considered of relatively greater importance, such as performance in predictions traversing the northern spring predictability barrier that suggests mostly higher skill in CFSv2.
3.2 RMSE
Local and field significance evaluation for various RMSE skill comparisons involving uncorrected and corrected versions of CFSv1 and/or CFSv2
Model versions for comparison, and their overall RMSE over 12 seasons and 9 leads (“adj” = dual climatol.) | % Cells with decreased (significantly decreased) RMSE | % Cells with increased (significantly increased) RMSE | 108-Cell field significance: average MSE ratio for F-test | 108-Cell field significance status (significant means p < 0.05) |
---|---|---|---|---|
CFSv1–CFSv2 1.14–1.09 | 44 (18) | 56 (24) | 1.14 | Not significant |
CFSv1–CFSv1 (adj) 1.14–0.58 | 86 (68) | 14 (0) | 3.97 | Significant p < 0.001 |
CFSv2–CFSv2 (adj) 1.09–0.49 | 100 (90) | 0 (0) | 5.60 | Significant p ≪ 0.001 |
CFSv1–CFSv2 (adj) 1.14–0.49 | 89 (72) | 11 (1) | 5.96 | Significant p ≪ 0.001 |
CFSv1 (adj)–CFSv2 (adj) 0.58–0.49 | 64 (31) | 36 (3) | 1.72 | Significant p < 0.01 |
3.3 Standard deviation ratio
The climatology correction results in small changes in the ratios for CFSv1, but a noticeable decrease toward unity is found in the case of CFSv2 for short to intermediate lead times for target months in the second half of the year. More importantly, the ratio of CFSv1 is noted to be too high (>1.5) even following the correction for intermediate lead predictions for northern spring season when the observed standard deviation is near its seasonal minimum. CFSv2 lacks this weakness and, following the bias correction, shows ratios fairly close to unity throughout many seasons and leads. In keeping with the lower skill expected for forecasts traversing the northern spring predictability barrier, ratios of less than unity are noted in CFSv2 for predictions for June–October made at medium and long leads.
Local and field significance evaluation of various standard deviation ratio comparisons involving uncorrected and corrected versions of CFSv1 and/or CFSv2
Model versions for comparison, and their overall SD ratio over 12 seasons and 9 leads (“adj” = dual climatol.) | 108-Cell field significance: avg variance ratio for F-test | 108-Cell field significance status (significant means p < 0.05) |
---|---|---|
CFSv1–CFSv2 1.21–1.03 | 1.51 | Significant p < 0.05 |
CFSv1–CFSv1 (adj) 1.21–1.18 | 1.07 | Not significant |
CFSv2–CFSv2 (adj) 1.03–0.97 | 1.13 | Not significant |
CFSv1–CFSv2 (adj) 1.21–0.97 | 1.66 | Significant p < 0.05 |
CFSv1 (adj)–CFSv2 (adj) 1.18–0.97 | 1.56 | Significant p < 0.05 |
3.4 Target month slippage
“Target month slippage” occurs when predictions verify with higher skill for target months earlier or later than those intended, such as a 4-month lead prediction intended for July verifying better using observations of May or June instead of July. Slippage typically occurs when predictions are late in reproducing observed changes, such as onsets or endings of ENSO episodes. In an extreme case, a prediction for a new event may not be made until the event is already present in the initial conditions. Slippage cannot be diagnosed by comparing forecasts with the verifying observations only for the intended target time. Although slippage is a systematic error, it is indistinguishable from a random error when forecasts at different leads are evaluated independently. It is most likely to occur when prediction is most difficult, such a prediction made in March for targets of July and beyond.
3.5 Trend bias and its seasonality
A reason for a remaining gradual positive trend in CFSv2 predictions relative to observations even after the discontinuity correction using dual climatologies is unknown. A problem involving radiation balance may be a candidate explanation, but additional study is required to explore such a hypothesis.
The trend bias in CFSv1 is negative for most months and leads, partly because of the discontinuity in 1991 but also due to gradual trends within the sub-periods. In contrast to CFSv2, trend biases in CFSv1 do not appear at short leads, indicating a likely lack of major biases in initial conditions. However, CFSv1 has the disadvantage of a non-evolving CO_{2} concentration setting, which could result in the slowly declining Nino3.4 SST predictions relative to observed SST.
Local and field significance evaluation for linear trend bias with respect to the observed trend in the Nino3.4 SST during 1982-2009
Model version | % Cells with negative trend bias (significantly negative) | % Cells with positive trend bias (significantly positive) | 108-Cell field significance: average z-statistic | 108-Cell field significance status (significant means p < 0.05) |
---|---|---|---|---|
CFSv1 | 86 (31) | 14 (0) | −1.05 | Significant p < 0.04 |
CFSv2 | 0 (0) | 100 (82) | 2.25 | Significant p ≪ 0.001 |
3.6 Reliability analysis
We assess the reliability and sharpness of the probabilistic predictions of Nino3.4 SST from the two CFS versions. For any prediction, probabilities for the below-, near- and above-normal categories are defined by counting the proportion of ensemble members whose predictions are in each respective category. The three categories are defined such that each has a one-third probability of occurring during the 28-year hindcast period (i.e., tercile cutoffs are used). For the models, terciles are defined using the individual ensemble members over the study period. The observations are also categorized. The categories may be thought to loosely represent El Nino, neutral and La Nina, although many ENSO classification systems are not tercile-based. Reliability analysis is carried out for each forecast category separately, but plotted together. For simplicity, here we focus only on the 6-month lead predictions. Furthermore, we ignore the near-normal category, which has been demonstrated to have weak performance (Van den Dool and Toth 1991).
As mentioned in Sect. 2, reliability analysis examines the correspondence between the forecast probabilities and their corresponding later observed relative frequencies. Ideally, the two should match. Over- and under-forecasting of the probability for a given category are specific forms of imperfect reliability. Forecast probability biases may depend on the probability level itself, or may be fairly constant over all forecast probabilities. The reliability diagram permits examination of such attributes of the set of probability forecasts. Because the forecast probabilities for each of the categories are binned into an array of probability intervals, reliability analysis requires a large sample of forecasts for each bin to be populated sufficiently for statistical robustness. Here we combine all target months, and form eleven 10 %-wide forecast probability bins centered on 0, 10, …, 90 and 100 % probability, to average about 31 predictions per probability bin, or (28) (12) /11. As indicated in Sect. 2 and discussed in Sect. 3.1, lack of independence among the forecasts results in far fewer than 336 independent forecast cases, so that the results are expected to paint a largely qualitative picture—a “sanity check” for probabilistic reliability.
For CFSv1 (Fig. 10a), positive skill is evidenced by the fact that predictions with increasing probabilities for both below and above normal SST tend to be associated with increasing observed relative frequencies of occurrence. The curves are not smooth because of sampling variability, but the average slope of both curves is seen to be somewhat less than unity. Thus, forecasts are “overconfident”, as very low (high) probabilities are not matched by comparably low (high) frequencies of observed occurrence. Overconfidence is particularly noticeable for probabilities between 0.7 and 0.9 for both categories, and for probabilities of 0.0 for above normal predictions. The inset plot at the bottom shows that the lowest bin (0–0.05) is by far the most frequently issued probability, followed by the highest bin (0.95–1.00) and the second lowest bin (0.05–0.15). The U-shaped curve described by the histogram bars indicates high forecast sharpness (i.e., probabilities deviating strongly and frequently from climatology), and the fact that the slope of the lines is <1 indicates that this degree of sharpness is not warranted, given the level of predictive skill achieved at the 6-month lead time.
The reliability result for the uncorrected CFSv2 (Fig. 10b) is somewhat similar to that of CFSv1, except that overconfidence appears milder, as the curves have slope closer (but still less than) unity, with smaller deviations below the ideal reliability (45°) line for bins for 0.50 and higher probability. Similarly, the lower inset shows that zero-probability predictions for above normal SST that are issued more than 41 % of the time by CFSv1 are issued 33 % of the time by CFSv2, indicating a greater expressed forecast uncertainty.
The somewhat more reliable probabilistic predictions seen in CFSv2 than in CFSv1 are attributable to a combination of its generally higher skill (Figs. 1, 3) and its slightly less sharp, more conservative probabilities that better reflect the true level of uncertainty in the ocean–atmosphere system. This outcome is consistent with the greater inflation above unity of the standard deviation ratio of the ensemble mean forecasts in CFSv1 than CFSv2 noted above (although high model variance, per se, would also contribute), especially at medium to long lead times (left panels of Fig. 5). Aside from model improvement, one reason for the better probabilistic forecast performance of CFSv2 than CFSv1 is the larger ensemble size of CFSv2 than CFSv1 (24 vs. 15 members), given that smaller ensemble sizes are associated with larger sampling variability in the ensemble mean and the ensemble distribution leading to the probability assignments.
Elimination of the discontinuity in the climatology of the predictions slightly helps to remedy the inflated standard deviation ratio of CFSv2 (lower right panel of Fig. 5). To determine the effect of the correction on CFSv2 reliability, the analysis is repeated using dual climatologies for the tercile boundary definitions for the model prediction category. Results (Fig. 10c) indeed indicate a slope closer to unity, and the observed relative frequencies for forecasts of zero probability become <2 %, suggesting that now such sharply low probabilities are justified in the absence of the spurious change in the forecast climatology within the hindcast period. Similarly, forecasts with 100 % probability are met with correctly verifying observations in about 95 % of cases when using the dual climatologies, but only about 80 % (90 %) for the above (below) normal category without the climatology adjustment. All told, the correction appears to improve probabilistic reliability for CFSv2. However, the small effective sample size of forecasts and observations must be noted. While results are suggestive, and consistent with findings shown earlier for the deterministic verifications, they are not likely to be statistically significant on their own, and are presented for qualitative interest.
4 Discussion and conclusion
Given the time and resources invested toward improvement, one would expect higher predictive skill in CFSv2 than in CFSv1. Here we examine skill differences between CFSv1 and CFSv2 in predictions of the ENSO state, as represented by the Nino3.4 SST anomaly.
Initial examination shows that CFSv2 is better able to predict the ENSO state than CFSv1 at long lead through the northern spring predictability barrier, the time of year when there is most need for improvement. By contrast, CFSv2 appears to fall short of CFSv1 in predictions for northern late summer and autumn start times—times for which ENSO prediction is known to be least challenging. Combining all times of year and all lead times, CFSv2 fails to show net improvement over CFSv1. However, CFSv2 is found to have a significant discontinuity in initial condition climatology near 1999 associated with discontinuities in the oceanic part of the Reanalysis observations generated using the high resolution CFSv2 (the CFSR). The size and impact of this discontinuity is most prominent in the tropical Pacific, in the form of an step-like increase in ENSO-related SST and associated changes in other tropical Pacific conditions around 1999, as described in Kumar et al. (2012), Xue et al. (2013), and other recent studies. This discontinuity is spurious, as it is not reflected in the observations. Here, we examine the consequences of the discontinuity for the performance of model predictions of the Nino3.4 SST anomaly. In identifying and removing those components of the differences in specific skill metrics likely related to the discontinuity, we aim to assess performance differences related to true model improvement (or lack thereof).
Summary of general results of the study in terms of comparative performance of CFSv1 versus CFSv2 in Nino3.4 prediction before and after climatology correction for both model versions
Metric | Better performing model | Comments | |
---|---|---|---|
Without correction | With correction | ||
Correlation | CFSv1 | CFSv2 | Better medium/long-lead boreal summer predictions from CFSv2 |
RMSE | CFSv2 | CFSv2** | Correction reduces CFSv2 RMSE dramatically |
Stand. deviation ratio | CFSv2* | CFSv2* | CFSv2 has better SD ratio by larger margin after correction |
Slippage | CFSv2^{NT} | NA | CFSv1 has 3-month slippage for long-lead predictions |
Linear trend bias | CFSv1: negative* CFSv2: positive** | NA | Trend biases are apparent in addition to the discontinuities |
Prob. reliability | CFSv2^{NT} | CFSv2^{NT} | CFSv2 has better reliability by larger margin after correction |
After discontinuity corrections, including correction of CFSv1’s less severe discontinuity, performance of CFSv2 is found to exceed that of CFSv1 at most times of the year in anomaly correlation (although the difference is not statistically field significant) and RMSE (with a highly significant difference), the two most basic and commonly used deterministic skill metrics. After correction, improvement in performance of CFSv2 over CFSv1 is also more strongly field significant in standard deviation ratio with respect to the observations, as CFSv2 lacks the forecast amplitude inflation of CFSv1 to a greater extent. While not confirmed statistically, CFSv2 also appears further improved in probabilistic reliability (Fig. 10).
A constant bias, correctable with a single adjustment, does not degrade measures such as the anomaly correlation or the confidence-indicating slope of the reliability curves. A changing bias, by contrast, is equivalent to a nonsystematic error, uncorrectable unless the problem is identified (e.g., by inspecting Fig. 2) and treated with a combination of human intervention and automation in choosing the point of discontinuity and the correction parameters. The timing of the discontinuity in CFSv2 has been linked to the advent of ATOVS radiance measurements in late 1998, and the lesser discontinuity in CFSv1 to issues with XBT measurements before 1991. Knowledge of the likely causes justifies identification of the temporal break points in the hindcast time series, reducing concern that they are subjectively based.
CFSv2 is shown to have a larger upward trend in Nino3.4 SST than that observed, apart from the 1999 discontinuity. This appears despite the specification of realistic time-evolving CO_{2} concentrations—an improvement over CFSv1, which had a fixed and outdated CO_{2} concentration, and a possibly related negative trend bias with respect to observations. The positive trend bias in CFSv2, with currently unknown cause, may indicate potential for improvement in a future version of CFS.
- 1.
CFSv2 makes more skillful long-lead predictions than CFSv1 from early in the calendar year, through the northern spring predictability barrier. But its shorter lead forecasts through the barrier (e.g., from March start time) remain no more skillful than those of CFSv1 at short and medium lead times. For predictions that do not traverse the barrier, skills of the two model versions are comparable. Overall differences in correlation skill between CFSv1 and CFSv2, while favoring CFSv2, are insufficient for statistical field significance over the 28-year hindcast period.
- 2.
CFSv2 predictions have more realistic (i.e., lower) amplitude, and correspondingly more reliable probabilistic forecasts, than CFSv1, especially during seasons and leads when predictability is relatively low. This significant improvement in calibration, combined with the slight overall improvement in correlation, leads to a highly statistically significant overall improvement in RMSE.
Although the discontinuity has clearly discernible effects on CFSv2 predictions of ENSO-related SST, they are not large enough to materially degrade the model’s predictions of climate across much of the globe, including those involving many of the ENSO-related climate teleconnections. Performance in climate predictions has been found significantly better than that of CFSv1 in many instances, including in the United States during winter when ENSO is a major governing factor (Peng et al. 2013), and in reproduction of the MJO (Weaver et al. 2011). The skill of CFSv2 is even found competitive with that of ECMWF system 4 for winter climate predictions over North America, despite its relative shortcomings in predictions of ENSO and the globally averaged tropical climate (Kim et al. 2012). A better CFSv2 than CFSv1 is expected on the basis of the factors shown in Table 1, including finer horizontal resolution, a more recent version of the GFS atmospheric model component, a more recent ocean model component, a larger ensemble size, more accurate (predicted) sea-ice, and evolving CO_{2} concentration. Last but not least, CFSv2 is initialized from a more realistic Reanalysis—except for the 1999 discontinuity whose correction using the dual-climatology approach has been demonstrated necessary to recognize some critical aspects of the improved performance.
Footnotes
- 1.
CFSv1 continued to be run, however, through most of 2012 in parallel with CFSv2.
- 2.
A discontinuity in CFSv1 is found also noted for Nino3.4 forecasts, but it is smaller in magnitude than that of CFSv2 and has a different cause.
- 3.
ATOVS refers to the Advanced Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder radiation data system.
- 4.
Here the RMSE is standardized for each season individually to scale it so that climatology forecasts (zero anomaly) would result in the same RMSE-based skill (of zero) for all seasons, and all seasons’ RMSE would contribute equally to a seasonally combined RMSE.
Notes
Acknowledgments
The authors appreciate the thoughtful comments and suggestions of the anonymous reviewers. This work was funded by a grant/cooperative agreement from the National Oceanic and Atmospheric Administration (NOAA) (NA10OAR4310210), and also two grants from the MAPP Program of NOAA (NA12OAR4310091 and NA12OAR4310082). The views expressed are those of the authors and do not necessarily reflect the views of NOAA or its subagencies.
References
- Barnston AG, Chelliah M, Goldenberg SB (1997) Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmos Ocean 35:367–383CrossRefGoogle Scholar
- Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–11: is our capability increasing? Bull Am Meteor Soc 93:631–651CrossRefGoogle Scholar
- Behringer DW, Xue Y (2004) Evaluation of the global ocean data assimilation system at NCEP: the Pacific Ocean. Eighth symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Washington, 11–15Google Scholar
- Chelliah M, Ebisuzaki W, Weaver S, Kumar A (2011) Evaluating the tropospheric variability in National Centers for Environmental Prediction’s Climate Forecast System Reanalysis. J Geophys Res (Atmos) 116 Art. No. D17107 doi:10.1029/2011JD015707
- Chen WY (1982) Fluctuations in Northern Hemisphere 700 mb height field associated with the Southern Oscillation. Mon Weather Rev 110:808–823CrossRefGoogle Scholar
- Davis RE (1976) Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. J Phys Oceanogr 6:249–266CrossRefGoogle Scholar
- Deser C, Philips AS, Alexander MA (2010) Twentieth century tropical sea surface temperature trends revisited. Geophys Res Lett 37 doi:10.1029/2010GL043321
- Ebisuzaki W, Zhang L (2011) Assessing the performance of the CFSR by an ensemble of analyses. Clim Dyn 37:2541–2550CrossRefGoogle Scholar
- Hayes WL (1973) Statistics for the social sciences. Rinehart and Winston, Holt 954Google Scholar
- Hoerling MP, Kumar A (2002) Atmospheric response pattern associated with tropical forcing. J. Clim 15:2184–2203CrossRefGoogle Scholar
- Jin EK, Kinter JL (2009) Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS. Clim Dyn 32:675–691CrossRefGoogle Scholar
- Jin EK et al (2008) Current status of ENSO prediction skill in coupled ocean–atmosphere models. Clim Dyn 31:647–664CrossRefGoogle Scholar
- Kanamitsu MW et al (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Meterol Soc 83:1631–1643. doi:10.1175/BAMS-83-11-1631 CrossRefGoogle Scholar
- Kim HM, Webster PJ, Curry JA (2012) Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere winter. Clim Dyn 39:2957–2973. doi:10.1007/s00382-012-1364-6 CrossRefGoogle Scholar
- Kumar A, Bhaskar J, L’heureux M (2010) Are tropical SST trends changing the global teleconnection during La Nina? Geophys Res Lett 37:L12702. doi:10.1029/2010GL043394 Google Scholar
- Kumar A, Chen M, Zhang L, Wang W, Xue Y, Wen C, Marx L, Huang B (2012) An analysis of the non-stationarity in the bias of sea surface temperature forecasts for the NCEP climate forecast system (CFS) version 2. Mon Weather Rev 140:3003–3016CrossRefGoogle Scholar
- Levitus S, Antonov JI, Boyer TP, Locarnini RA, Garcia HE, Mishonov AV (2009) Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys Res Lett 36:L07608. doi:10.1029/2008GL037155 CrossRefGoogle Scholar
- Livezey RE, Chen W-Y (1983) Field significance and its determination by Monte-Carlo techniques. Mon Weather Rev 111:46–59CrossRefGoogle Scholar
- Lyon B, DeWitt DG (2012) A recent and abrupt decline in the East African long rains. Geophys Res Lett 39:L02702. doi:10.1029/2011GL050337 CrossRefGoogle Scholar
- Lyon B, Barnston AG, DeWitt DG (2013) Tropical Pacific forcing of a 1998–99 climate shift: observational analysis and climate model results for the boreal spring season. Clim Dyn 26 (in press)Google Scholar
- Mason SJ, Goddard L (2001) Probabilistic precipitation anomalies associated with ENSO. Bull Am Meteorol Soc 82:619–638CrossRefGoogle Scholar
- Murphy AH (1973) A new vector partition of the probability score. J Appl Meteorol 12:595–600CrossRefGoogle Scholar
- Peng P, Barnston AG, Kumar A (2013) A comparison of skill between two versions of the NCEP climate forecast system (CFS) and CPC’s operational short-lead seasonal outlooks. Weather Forecast 28:445–462Google Scholar
- Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625CrossRefGoogle Scholar
- Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with the El Niño Southern Oscillation. Mon Weather Rev 115:1606–1626CrossRefGoogle Scholar
- Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517CrossRefGoogle Scholar
- Saha S et al (2010) The NCEP Climate Forecast System Reanalysis. Bull Am Meteorol Soc 91:1015–1057. doi:10.1175/2010BAMS3001.1 CrossRefGoogle Scholar
- Saha S et al (2013) The NCEP Climate Forecast System Version 2. J Clim 26 (unpublished)Google Scholar
- Tippett MK, Barnston AG, Li S (2012) Performance of recent multimodel ENSO forecasts. J Appl Meteorol Climatol 51:637–654CrossRefGoogle Scholar
- Van den Dool HM, Toth Z (1991) Why do forecasts for near normal often fail? Weather Forecast 6:76–85CrossRefGoogle Scholar
- Wang W, Xie P, Yo SH, Xue Y, Kumar A, Wu X (2011) An assessment of the surface climate in the NCEP Climate Forecast System Reanalysis. Clim Dyn 37:1601–1620. doi:10.1007/s00382-010-0935-7 CrossRefGoogle Scholar
- Weaver SJ, Wang WQ, Chen MY, Kumar A (2011) Representation of MJO variability in the NCEP climate forecast system. J Clim 24:4676–4694CrossRefGoogle Scholar
- Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, Oxford, p 648Google Scholar
- Xue Y, Huang B, Hu Z-Z, Kumar A, Wen C, Behringer D, Nadiga S (2011) An assessment of oceanic variability in the NCEP Climate Forecast System Reanalysis. Clim Dyn 37:2511–2539. doi:10.1007/s00382-010-0954-4 CrossRefGoogle Scholar
- Xue Y, Chen M, Kumar A, Hu Z-Z, Wang W (2013) Prediction skill and bias of tropical Pacific Sea surface temperatures in the NCEP Climate Forecast System version 2. J Clim 26. http://dx.doi.org/10.1175/JCLI-D-12-00600.1
- Zhang L, Kumar AK, Wang W (2012) Influence of changes in observations on precipitation: a case study for the Climate Forecast System Reanalysis (CFSR). J Geophys Res Atmos 117:D08105. doi:10.1029/2011JD017347 CrossRefGoogle Scholar
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