scholarly journals ENSO Precipitation and Temperature Forecasts in the North American Multimodel Ensemble: Composite Analysis and Validation

2017 ◽  
Vol 30 (3) ◽  
pp. 1103-1125 ◽  
Author(s):  
Li-Chuan Chen ◽  
Huug van den Dool ◽  
Emily Becker ◽  
Qin Zhang
Keyword(s):  
North American ◽  
El Niño ◽  
El Nino ◽  
Composite Analysis ◽  
Multimodel Ensemble ◽  
Anomaly Correlation ◽  
Precipitation Patterns ◽  
The North ◽  
Precipitation And Temperature ◽  
North American Multimodel Ensemble

Abstract In this study, precipitation and temperature forecasts during El Niño–Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982–2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?

2017 ◽  
Vol 30 (20) ◽  
pp. 8335-8355 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett
Keyword(s):  
North American ◽  
Large Scale ◽  
Climate Model ◽  
Principal Component ◽  
Eastern Africa ◽  
Multimodel Ensemble ◽  
Anomaly Correlation ◽  
The North ◽  
The Individual ◽  
North American Multimodel Ensemble

Abstract Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October–December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today’s coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data.

scholarly journals The Pacific Meridional Mode as an ENSO Precursor and Predictor in the North American Multimodel Ensemble

2014 ◽  
Vol 27 (18) ◽  
pp. 7018-7032 ◽  
Author(s):  
Sarah M. Larson ◽  
Ben P. Kirtman
Keyword(s):  
El Niño ◽  
El Nino ◽  
Multimodel Ensemble ◽  
Enso Events ◽  
The North ◽  
The Pacific ◽  
El Niño Events ◽  
Pacific Meridional Mode ◽  
North American Multimodel Ensemble ◽  
El Nino Events

Abstract Although modeling and observational studies have highlighted a robust relationship between the Pacific meridional mode (PMM) and El Niño–Southern Oscillation (ENSO)—namely, that the PMM is often a precursor to El Niño events—it remains unclear if this relationship has any real predictive use. Bridging the gap between theory and practical application is essential, because the potential use of the PMM precursor as a supplemental tool for ENSO prediction has been implied but not yet implemented into a realistic forecast setting. In this paper, a suite of sea surface temperature hindcasts is utilized from the North American Multimodel Ensemble (NMME) prediction experiment between 1982 and 2010. The goal is first to assess the NMME’s ability to forecast the PMM precursor and second to examine the relationship between PMM and ENSO within a forecast framework. In terms of model performance, results are optimistic in that not only is PMM variability captured well by the multimodel ensemble mean, but it also appears as a precursor to ENSO events in the NMME. In forecast mode, positive PMM events predict eastern Pacific El Niño events in both observations and model forecasts with some skill, yet with less skill for central Pacific El Niño events. Conversely, negative PMM events poorly predict La Niña events in observations, yet the model forecasts fail to capture this observed representation. There proves to be considerable opportunity for improvement of the PMM–ENSO relationship in the forecast models; accordingly, the predictive use of PMM for certain types of ENSO events may also see improvement.

More reliable coastal SST forecasts from the North American multimodel ensemble

2017 ◽  
Vol 53 (12) ◽  
pp. 7153-7168 ◽  
Author(s):  
G. Hervieux ◽  
M. A. Alexander ◽  
C. A. Stock ◽  
M. G. Jacox ◽  
K. Pegion ◽  
...  
Keyword(s):  
North American ◽  
Multimodel Ensemble ◽  
The North ◽  
North American Multimodel Ensemble

scholarly journals Toward Identifying Subseasonal Forecasts of Opportunity Using North American Weather Regimes

2020 ◽  
Vol 148 (5) ◽  
pp. 1861-1875
Author(s):  
Andrew W. Robertson ◽  
Nicolas Vigaud ◽  
Jing Yuan ◽  
Michael K. Tippett
Keyword(s):  
North American ◽  
El Niño ◽  
Geopotential Height ◽  
Large Scale ◽  
El Nino ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
The North ◽  
The Pacific ◽  
Graphical Tool

Abstract Large-scale atmospheric circulation regime structures are used to diagnose subseasonal forecasts of wintertime geopotential height fields over the North American sector, from the NCEP CFSv2 model. Four large-scale daily circulation regimes derived from reanalysis 500-hPa geopotential height data using K-means clustering are used as a low-dimensional basis for diagnosing the model’s forecasts up to 45 days ahead. On average, hindcast skill in regime space is found to be limited to 10–15 days ahead, in terms of anomaly correlation of 5-day averages of regime counts, over the 1999–2010 period. However, skill up to 30 days ahead is identified in individual winters, and intraseasonal episodes of high skill are identified using a forecast-evolution graphical tool. A striking vacillation between the West Coast and Pacific ridge patterns during December–January 2008/09 is shown to be predicted 20–25 days in advance, illustrating the possibility to identify “forecasts of opportunity” when subseasonal forecast skill is much higher than the average. The forecast-evolution tool also provides insight into the poor seasonal forecasts of California precipitation by operational centers during the 2015/16 El Niño winter. The Pacific trough regime is shown to be greatly overpredicted beyond 1–2 weeks in advance during the 2015/16 winter, with weather-scale features dominating the forecast evolution at shorter lead times. A similar though less extreme situation took place during the weaker El Niño of 2009/10, with the Pacific trough overforecast at S2S lead times.

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

2014 ◽  
Vol 95 (4) ◽  
pp. 585-601 ◽  
Author(s):  
Ben P. Kirtman ◽  
Dughong Min ◽  
Johnna M. Infanti ◽  
James L. Kinter ◽  
Daniel A. Paolino ◽  
...  
Keyword(s):  
North American ◽  
Phase 1 ◽  
Phase 2 ◽  
Multimodel Ensemble ◽  
The North ◽  
North American Multimodel Ensemble

scholarly journals Comments on “Characterizing ENSO Coupled Variability and Its Impact on North American Seasonal Precipitation and Temperature”

2017 ◽  
Vol 30 (1) ◽  
pp. 427-436 ◽  
Author(s):  
D. E. Harrison ◽  
Andrew M. Chiodi
Keyword(s):  
North American ◽  
El Niño ◽  
Land Surface ◽  
El Nino ◽  
Longwave Radiation ◽  
La Niña ◽  
Seasonal Temperature ◽  
The North ◽  
La Nina ◽  
Linear Approach

El Niño and La Niña seasonal weather anomaly associations provide a useful basis for winter forecasting over the North American regions where they are sufficiently strong in amplitude and consistent in character from one event to another. When the associations during La Niña are different than El Niño, however, the obvious quasi-linear-statistical approach to modeling them has serious shortcomings. The linear approach of L’Heureux et al. is critiqued here based on observed land surface temperature and tropospheric circulation associations over North America. The La Niña associations are quite different in pattern from their El Niño counterparts. The El Niño associations dominate the statistics. This causes the linear approach to produce results that are inconsistent with the observed La Niña–averaged associations. Further, nearly all the useful North American associations have been contributed by the subset of El Niño and La Niña years that are identifiable by an outgoing longwave radiation (OLR) El Niño index and a distinct OLR La Niña index. The remaining “non-OLR events” exhibit winter weather anomalies with large event-to-event variability and contribute very little statistical utility to the composites. The result is that the linear analysis framework is sufficiently unable to fit the observations as to question its utility for studying La Niña and El Niño seasonal temperature and atmospheric circulation relationships. An OLR-event based approach that treats La Niña and El Niño separately is significantly more consistent with, and offers an improved statistical model for, the observed relationships.

scholarly journals Prediction of Seasonal Meteorological Drought Onset and Termination over the Southern Great Plains in the North American Multimodel Ensemble

2020 ◽  
Vol 21 (10) ◽  
pp. 2237-2255
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Great Plains ◽  
North American ◽  
Time Scale ◽  
Meteorological Drought ◽  
Atmospheric Variability ◽  
Multimodel Ensemble ◽  
Southern Great Plains ◽  
The North ◽  
Seasonal Time Scale ◽  
North American Multimodel Ensemble

AbstractThe predictability on the seasonal time scale of meteorological drought onsets and terminations over the southern Great Plains is examined within the North American Multimodel Ensemble. The drought onsets and terminations were those identified based on soil moisture transitions in land data assimilation systems and shown to be driven by precipitation anomalies. Sea surface temperature (SST) forcing explains about a quarter of variance of seasonal mean precipitation in the region. However, at lead times of a season, forecast SSTs only explain about 10% of seasonal mean precipitation variance. For the three identified drought onsets, fall 2010 is confidently predicted and spring 2012 is predicted with some skill, and fall 2005 was not predicted at all. None of the drought terminations were predicted on the seasonal time scale. Predictability of drought onset arises from La Niña–like conditions, but there is no indication that El Niño conditions lead to drought terminations in the southern Great Plains. Spring 2012 and fall 2000 are further examined. The limited predictability of onset in spring 2012 arises from cool tropical Pacific SSTs, but internal atmospheric variability played a very important role. Drought termination in fall 2000 was predicted at the 1-month time scale but not at the seasonal time scale, likely because of failure to predict warm SST anomalies directly east of subtropical Asia. The work suggests that improved SST prediction offers some potential for improved prediction of both drought onsets and terminations in the southern Great Plains, but that many onsets and terminations will not be predictable even a season in advance.

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