We investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height ...anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts.
Plain Language Summary
Seasonal forecasts are cast as anomalies as users want to know what can be expected beyond the typical seasonal swings of the climate. This necessitates a choice for the climatological base period relative to which forecast anomalies are computed. It, however, poses a challenge under rapid climate change. In this scenario, climate trends become part of the real‐time forecast anomalies, and if the climatological base period is sufficiently different, may even start to dominate. This was the case for the NMME DJF 2023/24 forecast of 200‐hPa heights which was forecast to be a strong El Niño, and yet, forecast for 200‐hPa heights differed from typical El Niño signal. The analysis implies that seasonal forecasts for some variables, consideration of trends is important and reliance on expected signal from El Niño—Southern Oscillation alone may not be sufficient.
Key Points
The North American Multi‐Model Ensemble (NMME) seasonal forecasts for December–February (DJF) 2023/24 upper‐level height differ from the expected El Niño signal
It is the combination of trends in heights and the expected El Niño signal that results in the forecast NMME ensemble mean heights anomalies
The forecast of trends is increasingly important to account for NMME forecast anomaly and their amplitude in recent years can be of same magnitude as the signal from El Niño
THE NORTH AMERICAN MULTIMODEL ENSEMBLE Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M. ...
Bulletin of the American Meteorological Society,
04/2014, Letnik:
95, Številka:
4
Journal Article
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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 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.
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In this study, the climate mean, variability, and dominant patterns of the Northern Hemisphere wintertime mean 200 hPa geopotential height (Z200) in a CMIP and a set of AMIP simulations from the ...National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) are analyzed and compared with the NCEP/NCAR reanalysis. For the climate mean, it is found that a component of the bias in stationary waves characterized with wave trains emanating from the tropics into both the hemispheres can be attributed to the precipitation deficit over the Maritime continent. The lack of latent heating associated with the precipitation deficit may have served as the forcing of the wave trains. For the variability of the seasonal mean, both the CMIP and AMIP successfully simulated the geographical locations of the major centers of action, but the simulated intensity was generally weaker than that in the reanalysis, particularly for the center over the Davis Strait-southern Greenland area. It is also noted that the simulated action center over Aleutian Islands was southeastward shifted to some extent. The shift was likely caused by the eastward extension of the Pacific jet. Differences also existed between the CMIP and the AMIP simulations, with the center of actions over the Aleutian Islands stronger in the AMIP and the center over the Davis Strait-southern Greenland area stronger in the CMIP simulation. In the mode analysis, the El Nino-Southern Oscillation (ENSO) teleconnection pattern in each dataset was first removed from the data, and a rotated empirical orthogonal function (REOF) analysis was then applied to the residual. The purpose of this separation was to avoid possible mixing between the ENSO mode and those generated by the atmospheric internal dynamics. It was found that the simulated ENSO teleconnection patterns from both model runs well resembled that from the reanalysis, except for a small eastward shift. Based on the REOF modes of the residual data, six dominant modes of the reanalysis data had counterparts in each model simulation, though with different rankings in explained variance and some distortions in spatial structure. By evaluating the temporal coherency of the REOF modes between the reanalysis and the AMIP, it was found that the time series associated with the equatorially displaced North Atlantic Oscillation in the two datasets were significantly correlated, suggesting a potential predictability for this mode.
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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Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the ...spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.
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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Abstract
This study, based on an analysis with observational and reanalysis data, highlights seasonal tropical–extratropical atmospheric teleconnections originating from tropical rainfall modes ...unrelated to the Niño-3.4 index for northern winters. The mode decomposition for tropical rainfall is done by first removing the Niño-3.4 index–related variability from the tropical rainfall and then applying rotated empirical orthogonal function (REOF) analysis to the residual. The corresponding teleconnection patterns are obtained by regressing global atmospheric fields against the time series of the rainfall modes. Analyses of the tropical heating–atmospheric circulation relationship indicate that the circulation anomalies corresponding to the rainfall modes are forced responses to the corresponding rainfall mode. The teleconnection patterns reveal some new features and show that some intrinsic midlatitude patterns can be triggered by tropical forcing with different rainfall patterns. Results from this study are relevant to seasonal climate attribution and prediction analyses and climate model evaluation. As an illustration, the teleconnections from the rainfall modes, together with that related to the Niño-3.4 index and linear trend, are applied to the attribution analyses for the global circulation anomalies of 2019/20 winter and the California dry condition during the strong El Niño winter of 2015/16. The overall impact of these modes in the period of 1980–2021 is also discussed.
Significance Statement
This study highlights the seasonal tropical–extratropical atmospheric teleconnections independent of the Niño-3.4 index using tropical rainfall modes for northern winters. The reason for using rainfall rather than SST in the mode decomposition is that rainfall represents vertically integrated latent heat, which is the direct forcing of the tropical atmosphere, while SST may have no definite relationship with rainfall in the Indo-Pacific warm pool region. The results of this study are applicable to the analysis of climate attribution and prediction and climate model evaluation, and further, may also have the potential to help improve seasonal climate forecasts.
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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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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.
NCEP dynamical seasonal forecast system 2000 KANAMITSU, Masao; KUMAR, Arun; JI, Ming ...
Bulletin of the American Meteorological Society,
07/2002, Letnik:
83, Številka:
7
Journal Article
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