ALBERT

Description: An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

Description: This work demonstrates the influence of the initial amplitude of the sea surface temperature anomaly (SSTA) associated with El Niño–Southern Oscillation (ENSO) following its evolutionary phase on the forecast skill of ENSO in retrospective predictions of the Climate Forecast System, version 2. It is noted that the prediction skill varies with the phase of the ENSO cycle. The averaged skill (linear correlation) of Niño-3.4 index is in a range of 0.15–0.55 for the amplitude of Niño-3.4 index smaller than 0.5°C (e.g., initial phase or neutral condition of ENSO), and 0.74–0.93 for the amplitude larger than 0.5°C (e.g., mature condition of ENSO) for 0–6-month lead predictions. The dependence of the prediction skills of ENSO on its phase is linked to the variation of signal-to-noise ratio (SNR). This variation is found to be mainly due to the changes in the amplitude of the signal (prediction of the ensemble mean) during different phases of the ENSO cycle, as the noise (forecast spread among the ensemble members), both in the Niño-3.4 region and the whole Pacific, does not depend much on the Niño-3.4 amplitude. It is also shown that the spatial pattern of unpredictable noise in the Pacific is similar to the predictable signal. These results imply that skillful prediction of the ENSO cycle, either at the initial time of an event or during the transition phase of the ENSO cycle, when the anomaly signal is weak and the SNR is small, is an inherent challenge.

Description: From ensembles of 80 AGCM simulations for every December–January–February (DJF) seasonal mean in the 1980–2000 period, interannual variability in atmospheric response to interannual variations in observed sea surface temperature (SST) is analyzed. A unique facet of this study is the use of large ensemble size that allows identification of the atmospheric response to SSTs for each DJF in the analysis period. The motivation of this study was to explore what atmospheric response patterns beyond the canonical response to El Niño–Southern Oscillation (ENSO) SST anomalies exist, and to which SST forcing such patterns may be related. A practical motivation for this study was to seek sources of atmospheric predictability that may lead to improvements in seasonal predictability efforts. This analysis was based on the EOF technique applied to the ensemble mean 200-mb height response. The dominant mode of the atmospheric response was indeed the canonical atmospheric response to ENSO; however, this mode only explained 53% of interannual variability of the ensemble means (often referred to as the external variability). The second mode, explaining 19% of external variability, was related to a general increase (decrease) in the 200-mb heights related to a Tropicwide warming (cooling) in SSTs. The third dominant mode, explaining 12% of external variability, was similar to the mode identified as the “nonlinear” response to ENSO in earlier studies. The realism of different atmospheric response patterns was also assessed from a comparison of anomaly correlations computed between different renditions of AGCM-simulated atmospheric responses and the observed 200-mb height anomalies. For example, the anomaly correlation between the atmospheric response reconstructed from the first mode alone and the observations was compared with the anomaly correlation when the atmospheric response was reconstructed including modes 2 and 3. If the higher-order atmospheric response patterns obtained from the AGCM simulations had observational counterparts, their inclusion in the reconstructed atmospheric response should lead to higher anomaly correlations. Indeed, at some geographical regions, an increase in anomaly correlation with the inclusion of higher modes was found, and it is concluded that the higher-order atmospheric response patterns found in this study may be realistic and may represent additional sources of atmospheric seasonal predictability.

Description: A hybrid dynamical–statistical model is developed for predicting Atlantic seasonal hurricane activity. The model is built upon the empirical relationship between the observed interannual variability of hurricanes and the variability of sea surface temperatures (SSTs) and vertical wind shear in 26-yr (1981–2006) hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The number of Atlantic hurricanes exhibits large year-to-year fluctuations and an upward trend over the 26 yr. The latter is characterized by an inactive period prior to 1995 and an active period afterward. The interannual variability of the Atlantic hurricanes significantly correlates with the CFS hindcasts for August–October (ASO) SSTs and vertical wind shear in the tropical Pacific and tropical North Atlantic where CFS also displays skillful forecasts for the two variables. In contrast, the hurricane trend shows less of a correlation to the CFS-predicted SSTs and vertical wind shear in the two tropical regions. Instead, it strongly correlates with observed preseason SSTs in the far North Atlantic. Based on these results, three potential predictors for the interannual variation of seasonal hurricane activity are constructed by averaging SSTs over the tropical Pacific (TPCF; 5°S–5°N, 170°E–130°W) and the Atlantic hurricane main development region (MDR; 10°–20°N, 20°–80°W), respectively, and vertical wind shear over the MDR, all of which are from the CFS dynamical forecasts for the ASO season. In addition, two methodologies are proposed to better represent the long-term trend in the number of hurricanes. One is the use of observed preseason SSTs in the North Atlantic (NATL; 55°–65°N, 30°–60°W) as a predictor for the hurricane trend, and the other is the use of a step function that breaks up the hurricane climatology into a generally inactive period (1981–94) and a very active period (1995–2006). The combination of the three predictors for the interannual variation, along with the two methodologies for the trend, is explored in developing an empirical forecast system for Atlantic hurricanes. A cross validation of the hindcasts for the 1981–2006 hurricane seasons suggests that the seasonal hurricane forecast with the TPCF SST as the only CFS predictor is more skillful in inactive hurricane seasons, while the forecast with only the MDR SST is more skillful in active seasons. The forecast using both predictors gives better results. The most skillful forecast uses the MDR vertical wind shear as the only CFS predictor. A comparison with forecasts made by other statistical models over the 2002–07 seasons indicates that this hybrid dynamical–statistical forecast model is competitive with the current statistical forecast models.

Description: Based on a 40-member ensemble for the January–March (JFM) seasonal mean for the 1980–2000 period using an atmospheric general circulation model (AGCM), interannual variability in the first and second moments of probability density function (PDF) of atmospheric seasonal means with sea surface temperatures (SSTs) is analyzed. Based on the strength of the SST anomaly in the Niño-3.4 index region, the years between 1980 and 2000 were additionally categorized into five separate bins extending from strong cold to strong warm El Niño events. This procedure further enhances the size of the ensemble for each SST category. All the AGCM simulations were forced with the observed SSTs, and different ensemble members for specified SST boundary forcing were initiated from different atmospheric initial conditions. The main focus of this analysis is on the changes in the seasonal mean and the internal variability of tropical rainfall and extratropical 200-mb heights with SSTs. For the tropical rainfall, results indicate that in the equatorial tropical Pacific, internal variability of the tropical rainfall anomaly decreases (increases) for the La Niña (El Niño) events. On the other hand, seasonal mean variability of extratropical 200-mb height decreases for the El Niño events. Although there is increase in the seasonal mean variability of 200-mb heights for the La Niña events, results are rather inclusive. Analysis also indicates that for the variables studied, the influence of the interannual variability in SSTs is much stronger on the first moment of seasonal means compared to their influence on the internal variability. As a consequence, seasonal predictability due to changes in SSTs can be attributed primarily to the shift in the PDFs of the seasonal atmospheric means and less to changes in their spread. Modes of internal variability for 200-mb extratropical seasonal mean heights for different SST categories are also analyzed. The dominant mode of internal variability has little dependence on the tropical SST forcing, while larger influence on the second mode of internal variability is found. For SST forcing changing from a La Niña to El Niño state, the spatial pattern of the second mode shifts eastward. For the cold events, the spatial patterns bear more resemblance to the Pacific–North American (PNA) pattern, while for the warm events, it more resembles the tropical–North Hemispheric (TNH) pattern. Change in the spatial pattern of this mode from strong cold to a strong warm event resembles the change in the spatial pattern of response in the mean state.

Description: The question of the impact of the Atlantic on North American (NA) seasonal prediction skill and predictability is examined. Basic material is collected from the literature, a review of seasonal forecast procedures in Canada and the United States, and some fresh calculations using the NCEP–NCAR reanalysis data. The general impression is one of low predictability (due to the Atlantic) for seasonal mean surface temperature and precipitation over NA. Predictability may be slightly better in the Caribbean and the (sub)tropical Americas, even for precipitation. The NAO is widely seen as an agent making the Atlantic influence felt in NA. While the NAO is well established in most months, its prediction skill is limited. Year-round evidence for an equatorially displaced version of the NAO (named ED_NAO) carrying a good fraction of the variance is also found. In general the predictability from the Pacific is thought to dominate over that from the Atlantic sector, which explains the minimal number of reported Atmospheric Model Intercomparison Project (AMIP) runs that explore Atlantic-only impacts. Caveats are noted as to the question of the influence of a single predictor in a nonlinear environment with many predictors. Skill of a new one-tier global coupled atmosphere–ocean model system at NCEP is reviewed; limited skill is found in midlatitudes and there is modest predictability to look forward to. There are several signs of enthusiasm in the community about using “trends” (low-frequency variations): (a) seasonal forecast tools include persistence of last 10 years’ averaged anomaly (relative to the official 30-yr climatology), (b) hurricane forecasts are based largely on recognizing a global multidecadal mode (which is similar to an Atlantic trend mode in SST), and (c) two recent papers, one empirical and one modeling, giving equal roles to the (North) Pacific and Atlantic in “explaining” variations in drought frequency over NA on a 20 yr or longer time scale during the twentieth century.

Description: Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr common period of 1995–2009. The analyses are applied to predictions of seasonal mean surface temperature and total precipitation over the conterminous United States for the shortest and most commonly used lead time of 0.5 months. The assessments include both categorical and probabilistic verification diagnostics—their seasonalities, spatial distributions, and probabilistic reliability. Attribution of skill to specific physical sources is attempted when possible. Motivations for the analyses are to document improvements in skill between two generations of NOAA’s dynamical seasonal prediction system and to inform the forecast producers, but more importantly the user community, of the skill of the CFS model now in use (CFSv2) to help guide the users’ decision-making processes. The CFSv2 model is found to deliver generally higher mean predictive skill than CFSv1. This result is strongest for surface temperature predictions, and may be related to the use of time-evolving CO2 concentration in CFSv2, in contrast to a fixed (and now outdated) concentration used in CFSv1. CFSv2, and especially CFSv1, exhibit more forecast “overconfidence” than the official seasonal outlooks, despite that the CFSv2 hindcasts have outperformed the outlooks more than half of the time. Results justify the greater weight given to CFSv2 in developing the final outlooks than given to previous dynamical input tools (e.g., CFSv1) and indicate that CFSv2 should be of greater interest to users.

Description: 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.