Peitao Peng

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

NCEP DYNAMICAL SEASONAL FORECAST SYSTEM 2000

The new National Centers for Environmental Prediction (NCEP) numerical seasonal forecast system is described in detail. The new system is aimed at a next-generation numerical seasonal prediction in which focus is placed on land processes, initial conditions, and ensemble methods, in addition to the tropical SST forcing. The atmospheric model physics is taken from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis model, which has more comprehensive land hydrology and improved physical processes. The model was further upgraded by introducing three new parameterization schemes: 1) the relaxed Arakawa–Schubert (RAS) convective parameterization, which improved middle latitude response to tropical heating; 2) Chous shortwave radiation, which corrected surface radiation fluxes; and 3) Chous longwave radiation scheme together with smoothed mean orography that reduced model warm bias. Atmospheric initial conditions were taken from the operational NCEP Global Data Assimilation System, allowing t...

An analysis of multimodel ensemble predictions for seasonal climate anomalies

[1] In this paper the potential advantages and relative performances of different techniques for constructing multimodel ensemble seasonal predictions are examined. Two commonly used methods of constructing multimodel ensemble predictions are analyzed. Particular emphasis is placed on the analysis of the schemes themselves. In the first technique--simple multimodel ensemble (SME) predictions-equal weights are assigned to the ensemble mean predictions of each of the atmospheric general circulation models (AGCM). In the second approach-optimal multimodel ensemble (OME) predictions the weights are obtained using a multiple linear regression. A theoretical analysis of these techniques is complemented by the analyses based on seasonal climate simulations for 45 January-February-March seasons over the 1950-1994 period. A comparison of seasonal simulation skill scores between SME and OME indicates that for the bias corrected data, i.e., when the seasonal anomalies of each of the AGCMs are computed with respect to its own mean state, the performance of seasonal predictions based on the simpler SME approach is comparable to that of the more complex OME approach. A major reason for this result is that the data record of historical predictions may not be long enough for a stable estimate of weights at individual geographical locations to be obtained. This problem can be reduced by extending the multiple linear regression approach to include the spatial domain. However, even with this algorithm change, the performance of OME in seasonal predictions does not improve over that using the SME approach. Results, therefore, indicate that the use of more sophisticated techniques for constructing multimodel ensembles may not be any more advantageous than the use of simpler approaches. Results also show that on average the skill scores for the predictions based on multimodel ensemble prediction techniques are only marginally better than those of the best AGCM. However, an advantage of multimodel ensemble prediction techniques may be that they retain the best performance of each AGCM on a regional basis in the merged forecasts.

Changes in the Spread of the Variability of the Seasonal Mean Atmospheric States Associated with ENSO

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 ba...

A Statistical Forecast Model for Atlantic Seasonal Hurricane Activity Based on the NCEP Dynamical Seasonal Forecast

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...

A large ensemble analysis of the influence of tropical SSTs on seasonal atmospheric variability

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 Nino-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 Nino 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 i...

Is There a Relationship between Potential and Actual Skill

AbstractIn 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.

SST-Forced Atmospheric Variability in an Atmospheric General Circulation Model

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 Nino–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...

Seasonal-to-Decadal Predictability and Prediction of North American Climate—The Atlantic Influence

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.

An Analysis of CPC’s Operational 0.5-Month Lead Seasonal Outlooks

AbstractAn 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 temperatur...