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

The 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.

Improved Predictability of the Indian Ocean Dipole Using a Stochastic Dynamical Model Compared to the North American Multimodel Ensemble Forecast

This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.