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1. Skillful All-Season S2S Prediction of U.S. Precipitation Using the MJO and QBO

   https://doi.org/10.1175/WAF-D-19-0232.1  

   Nardi, Kyle M. ; Baggett, Cory F. ; Barnes, Elizabeth A. ; Maloney, Eric D. ; Harnos, Daniel S. ; Ciasto, Laura M. ( October 2020 , Weather and Forecasting)

   null (Ed.)

   Abstract Although useful at short and medium ranges, current dynamical models provide little additional skill for precipitation forecasts beyond week 2 (14 days). However, recent studies have demonstrated that downstream forcing by the Madden–Julian oscillation (MJO) and quasi-biennial oscillation (QBO) influences subseasonal variability, and predictability, of sensible weather across North America. Building on prior studies evaluating the influence of the MJO and QBO on the subseasonal prediction of North American weather, we apply an empirical model that uses the MJO and QBO as predictors to forecast anomalous (i.e., categorical above- or below-normal) pentadal precipitation at weeks 3–6 (15–42 days). A novel aspect of our study is the application and evaluation of the model for subseasonal prediction of precipitation across the entire contiguous United States and Alaska during all seasons. In almost all regions and seasons, the model provides “skillful forecasts of opportunity” for 20%–50% of all forecasts valid weeks 3–6. We also find that this model skill is correlated with historical responses of precipitation, and related synoptic quantities, to the MJO and QBO. Finally, we show that the inclusion of the QBO as a predictor increases the frequency of skillful forecasts of opportunity over most of the contiguous United States and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks. 

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2. Deep learning for bias correction of MJO prediction

   https://doi.org/10.1038/s41467-021-23406-3  

   Kim, H. ; Ham, Y. G. ; Joo, Y. S. ; Son, S. W. ( December 2021 , Nature Communications)

   Abstract Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent. 

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3. Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

   https://doi.org/10.1175/AIES-D-23-0103.1  

   Orlova, Elena ; Liu, Haokun ; Rossellini, Raphael ; Cash, Benjamin_A ; Willett, Rebecca ( October 2024 , Artificial Intelligence for the Earth Systems)

   Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.

   Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.

    

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4. Quantifying sources of subseasonal prediction skill in CESM2

   https://doi.org/10.1038/s41612-024-00595-4  

   Richter, Jadwiga H. ; Glanville, Anne A. ; King, Teagan ; Kumar, Sanjiv ; Yeager, Stephen G. ; Davis, Nicholas A. ; Duan, Yanan ; Fowler, Megan D. ; Jaye, Abby ; Edwards, Jim ; et al ( March 2024 , npj Climate and Atmospheric Science)

   Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3–4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3–6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

    

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5. Subseasonal predictability of the North Atlantic Oscillation

   https://doi.org/10.1088/1748-9326/abe781  

   Albers, John R. ; Newman, Matthew ( March 2021 , Environmental Research Letters)

   Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.

    

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