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Peitao Peng and Arun Kumar

Abstract

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.

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Peitao Peng, Anthony G. Barnston, and Arun Kumar

Abstract

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.

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Arun Kumar, Peitao Peng, and Mingyue Chen

Abstract

In this paper, possible connections between actual and potential skill are discussed. Actual skill refers to when the prediction time series is validated against the observations as the verification while perfect skill refers to when the observed verification time series is replaced by one of the members from the ensemble of predictions. It is argued that (i) there need not be a relationship between potential and actual skill; (ii) potential skill is not constrained to be always greater than actual skill, and examples to the contrary can be found; and (iii) there are methods whereby statistical characteristics of predicted anomalies can be compared with the corresponding in the observations, and inferences about the validity of the (positive) gap between potential and actual skill as “room for improvement” can be better substantiated.

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Arun Kumar, Qin Zhang, Peitao Peng, and Bhaskar Jha

Abstract

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.

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Peitao Peng, Arun Kumar, Anthony G. Barnston, and Lisa Goddard

Abstract

The global responses of two atmospheric general circulation models (AGCM), the National Centers for Environmental Prediction–Medium Range Forecast (NCEP–MRF9) and the University of Hamburg climate model–3 (ECHAM), to simultaneous global SST forcing are examined on a 3-month timescale. Rotated principal components analysis of the model and observations is also used to identify and compare their leading modes of coherent variability. The scope of the present analyses is largely descriptive and does not attempt to explain the differences in model behavior in terms of their formulations. The authors’ main focus is to quantify the simulation skill of the two comprehensive AGCMs on seasonal timescales and compare it to skill obtained using empirical prediction models.

Both models are found to exhibit realistic responses to El Niño–Southern Oscillation (ENSO)-related forcing, with the ECHAM response slightly more accurate in the spatial phasing and structure of the atmospheric anomalies. The ECHAM model exhibits realistic atmospheric responses to tropical Pacific SST forcing as well as patterns associated with extratropical internal atmospheric dynamics [e.g., North Atlantic oscillation (NAO) and a high latitude north–south dipole in the Pacific]. It shows a slightly higher signal-to-noise ratio than that found in the real world, while the NCEP model’s signal-to-noise ratio is approximately equal to that in nature. The NCEP model responds with more zonally symmetric atmospheric patterns than observed, although this does not prevent it from forming realistic responses to ENSO over the Pacific–North American region. The NCEP model’s NAO variability is only about half as strong as that observed.

In terms of simulation skill with respect to observations, the ECHAM model generally tends to outperform the NCEP model for global 500-hPa geopotential height and surface climate. A decomposition of the observed and model data into rotated principal components indicates that both models reproduce the ENSO-related anomalies in circulation and surface climate of the real atmosphere quite well. The ECHAM model, which handles ENSO variability and impacts slightly better than the NCEP model, shows a larger increment of capability in reproducing other global climate processes. Two linear statistical benchmarks, which are used as skill control measures, sometimes outperform the NCEP model but are more comparable, on average, to the skill of the ECHAM model. Thus as noted in other recent studies, the dynamical models and the statistical models have roughly the same simulation skill and would be expected to have similar forecast skill if the models used forecasted SSTs as their boundary conditions.

To first order, the linear component of the relationships appears to be modeled well by the two dynamical models. It is undetermined whether instances of better performance of the dynamical models than the statistical benchmarks are partly attributable to the models’ effective exploitation of nonlinearities in the relationships between tropical SST and global climate. One reason for this inconclusiveness is that evidence for nonlinearities in the present analyses is not compelling. Hence, the question of whether dynamical models have untapped potential to consistently outperform statistical models on the seasonal timescale remains open and may require close examination of each physical formulation in the dynamical models.

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Peitao Peng, Arun Kumar, Michael S. Halpert, and Anthony G. Barnston

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.

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H. M. Van den Dool, Peitao Peng, Åke Johansson, Muthuvel Chelliah, Amir Shabbar, and Suranjana Saha

Abstract

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.

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Arun Kumar, Anthony G. Barnston, Peitao Peng, Martin P. Hoerling, and Lisa Goddard

Abstract

For a fixed sea surface temperature (SST) forcing, the variability of the observed seasonal mean atmospheric states in the extratropical latitudes can be characterized in terms of probability distribution functions (PDFs). Predictability of the seasonal mean anomalies related to interannual variations in the SSTs, therefore, entails understanding the influence of SST forcing on various moments of the probability distribution that characterize the variability of the seasonal means. Such an understanding for changes in the first moment of the PDF for the seasonal means with SSTs is well documented. In this paper the analysis is extended to include also the impact of SST forcing on the second moment of the PDFs.

The analysis is primarily based on ensemble atmospheric general circulation model (AGCM) simulations forced with observed SSTs for the period 1950–94. To establish the robustness of the results and to ensure that they are not unduly affected by biases in a particular AGCM, the analysis is based on simulations from four different AGCMs.

The analysis of AGCM simulations indicates that over the Pacific–North American region, the impact of interannual variations in SSTs on the spread of the seasonal mean atmospheric states (i.e., the second moment of the PDFs) may be small. This is in contrast to their well-defined impact on the first moment of the PDF for the seasonal mean atmospheric state that is manifested as an anomalous wave train over this region. For seasonal predictions, the results imply that the dominant contribution to seasonal predictability comes from the impact of SSTs on the first moment of the PDF, with the impact of SSTs on the second moment of the PDFs playing a secondary role.

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Zeng-Zhen Hu, Arun Kumar, Jieshun Zhu, Peitao Peng, and Bohua Huang

Abstract

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.

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Hui Wang, Jae-Kyung E. Schemm, Arun Kumar, Wanqiu Wang, Lindsey Long, Muthuvel Chelliah, Gerald D. Bell, and Peitao Peng

Abstract

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.

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