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.

scholarly journals Prospects for Advancing Drought Understanding, Monitoring, and Prediction

2015 ◽  
Vol 16 (4) ◽  
pp. 1636-1657 ◽  
Author(s):  
Eric F. Wood ◽  
Siegfried D. Schubert ◽  
Andrew W. Wood ◽  
Christa D. Peters-Lidard ◽  
Kingtse C. Mo ◽  
...  
Keyword(s):  
Great Plains ◽  
North American ◽  
Land Surface ◽  
Task Force ◽  
Atmospheric Variability ◽  
Southern Great Plains ◽  
The North ◽  
Drought Prediction ◽  
Seasonal Model

Abstract This paper summarizes and synthesizes the research carried out under the NOAA Drought Task Force (DTF) and submitted in this special collection. The DTF is organized and supported by NOAA’s Climate Program Office with the National Integrated Drought Information System (NIDIS) and involves scientists from across NOAA, academia, and other agencies. The synthesis includes an assessment of successes and remaining challenges in monitoring and prediction capabilities, as well as a perspective of the current understanding of North American drought and key research gaps. Results from the DTF papers indicate that key successes for drought monitoring include the application of modern land surface hydrological models that can be used for objective drought analysis, including extended retrospective forcing datasets to support hydrologic reanalyses, and the expansion of near-real-time satellite-based monitoring and analyses, particularly those describing vegetation and evapotranspiration. In the area of drought prediction, successes highlighted in the papers include the development of the North American Multimodel Ensemble (NMME) suite of seasonal model forecasts, an established basis for the importance of La Niña in drought events over the southern Great Plains, and an appreciation of the role of internal atmospheric variability related to drought events. Despite such progress, there are still important limitations in our ability to predict various aspects of drought, including onset, duration, severity, and recovery. Critical challenges include (i) the development of objective, science-based integration approaches for merging multiple information sources; (ii) long, consistent hydrometeorological records to better characterize drought; and (iii) extending skillful precipitation forecasts beyond a 1-month lead time.

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

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.

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 Erratum to: Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

2017 ◽  
Vol 53 (12) ◽  
pp. 7235-7235
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett ◽  
Meghana Ranganathan ◽  
Michelle L. L’Heureux
Keyword(s):  
North American ◽  
Multimodel Ensemble ◽  
The North ◽  
North American Multimodel Ensemble
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