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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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Based on hindcasts of seasonal forecast systems participating in the North American Multi-Model Ensemble, the seasonal dependence of predictability of the El Niño–Southern Oscillation (ENSO) was ...estimated. The results were consistent with earlier analyses in that the predictability of ENSO was highest in winter and lowest in spring and summer. Further, predictability as measured by the relative amplitude of predictable and unpredictable components was dominated by the ensemble mean instead of the spread (or dispersion) among ensemble members. This result was consistent with previous analysis that most of ENSO predictability resides in the shift of the probability density function (PDF) of ENSO sea surface temperature (SST) anomalies (i.e., changes in the first moment of the PDF that is associated with the ensemble mean of ENSO SST anomalies) rather than due to changes in the spread of the PDF. The analysis establishes our current best estimate of ENSO predictability that can serve as a benchmark for quantifying further improvements resulting from advances in observing, assimilation, and seasonal prediction systems.
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