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

   https://doi.org/10.1175/WAF-D-19-0184.1  

   Zhao, Sen; Stuecker, Malte F.; Jin, Fei-Fei; Feng, Juan; Ren, Hong-Li; Zhang, Wenjun; Li, Jianping (April 2020, Weather and Forecasting)

   Abstract 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. 

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2. Initialized Seasonal Prediction with the NCAR Models in the North American Multimodel Ensemble (NMME)

   https://doi.org/10.1175/WAF-D-24-0123.1  

   Becker, Emily; Kirtman, Ben P; Min, Dughong (June 2025, Weather and Forecasting)

   Abstract This study investigates the seasonal prediction capabilities of three models, all developed by the National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and implemented by the University of Miami, within the North American Multimodel Ensemble (NMME) framework. All three models, Community Climate System Model, version 3 (CCSM3), CCSM4, and Community Earth System Model, version 1 (CESM1), are initialized using the Climate Forecast System Reanalysis (CFSR) and have a common period of 1991–2018. The models’ performance in predicting key climate variables including surface temperature, precipitation, and El Niño–Southern Oscillation (ENSO) teleconnections is assessed. The models’ prediction skill is assessed using the sign test, a robust nonparametric method for comparing forecast errors. CCSM4 succeeded CCSM3 in 2014, bringing a much more accurate representation of global temperature trends and improved prediction of precipitation extremes and 2-m temperature over land. CESM1, introduced in 2023, shows further improvement relative to CCSM4 in the prediction of sea surface temperature in the tropical Pacific and precipitation extremes over land. The improvement in precipitation prediction skill is encouraging, as this field has seen little improvement over the life of the NMME. The modeled similarity to observed ENSO teleconnection patterns of 2-m temperature is somewhat less in CESM1 than in CCSM4, although precipitation teleconnection patterns are similar. CCSM4 and CESM1 show stronger surface temperature trends in the tropical Pacific and Southern Ocean than observed trends over the same period, a common problem for current state-of-the-art climate models with implications for prediction and for climate projections. Significance StatementThis study documents the improvements in seasonal climate prediction across three generations of coupled models developed by the National Science Foundation National Center for Atmospheric Research (NCAR) and implemented within the North American Multimodel Ensemble (NMME) by the University of Miami. Model upgrades are an important aspect of the NMME and have contributed to incremental increases in forecast skill. A thorough and ongoing assessment of individual models is critical to our understanding of the NMME system’s evolution and to future model improvements. 

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3. A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series

   https://doi.org/10.1029/2021GL093704  

   Chen, Nan; Gilani, Faheem; Harlim, John (August 2021, Geophysical Research Letters)

   Abstract A simple and efficient Bayesian machine learning (BML) training algorithm, which exploits only a 20‐year short observational time series and an approximate prior model, is developed to predict the Niño 3 sea surface temperature (SST) index. The BML forecast significantly outperforms model‐based ensemble predictions and standard machine learning forecasts. Even with a simple feedforward neural network (NN), the BML forecast is skillful for 9.5 months. Remarkably, the BML forecast overcomes the spring predictability barrier to a large extent: the forecast starting from spring remains skillful for nearly 10 months. The BML algorithm can also effectively utilize multiscale features: the BML forecast of SST using SST, thermocline, and windburst improves on the BML forecast using just SST by at least 2 months. Finally, the BML algorithm also reduces the forecast uncertainty of NNs and is robust to input perturbations. 

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4. Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

   https://doi.org/10.1175/JHM-D-23-0124.1  

   Lesinger, Kyle; Tian, Di; Wang, Hailan (July 2024, Journal of Hydrometeorology)

   Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ET_o), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ET_oand its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ET_oforecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance StatementFlash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections. 

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5. Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

   https://doi.org/10.1175/MWR-D-20-0194.1  

   Flora, Montgomery L.; Potvin, Corey K.; Skinner, Patrick S.; Handler, Shawn; McGovern, Amy (May 2021, Monthly Weather Review)

   Abstract A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0–3 h) severe weather forecasts. Postprocessing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for postprocessing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output. Our dataset includes WoFS ensemble forecasts available every 5 min out to 150 min of lead time from the 2017–19 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm-track identification method, we extracted three sets of predictors from the WoFS forecasts: intrastorm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based postprocessing of dynamical ensemble output can improve short-term, storm-scale severe weather probabilistic guidance. 

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