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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. Mapping Large-Scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal Prediction

   https://doi.org/10.1175/JCLI-D-20-0502.1  

   Tseng, Kai-Chih ; Johnson, Nathaniel C. ; Maloney, Eric D. ; Barnes, Elizabeth A. ; Kapnick, Sarah B. ( June 2021 , Journal of Climate)

   null (Ed.)

   Abstract The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads. 

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3. The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

   https://doi.org/10.1029/2019JD030923  

   Domeisen, Daniela I. V. ; Butler, Amy H. ; Charlton‐Perez, Andrew J. ; Ayarzagüena, Blanca ; Baldwin, Mark P. ; Dunn‐Sigouin, Etienne ; Furtado, Jason C. ; Garfinkel, Chaim I. ; Hitchcock, Peter ; Karpechko, Alexey Yu. ; et al ( January 2020 , Journal of Geophysical Research: Atmospheres)

   The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

    

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4. Skillful Coupled Atmosphere‐Ocean Forecasts on Interannual to Decadal Timescales Using a Linear Inverse Model

   https://doi.org/10.1029/2022EA002679  

   Taylor, L. M. ; Hakim, G. J. ( April 2023 , Earth and Space Science)

   There are two major challenges to improving interannual to decadal forecasts: (a) consistently initializing the coupled system so that variability is not dominated by initial imbalances, and (b) having a large sample of different initial conditions on which to test forecast skill. The second challenge requires consideration of time periods not only outside the recent period of intensive ocean observation, but also before the instrumental era, which increases the importance of the first challenge. Forecasts prior to the 1850s isolate internally generated sources of variability by removing the majority of anthropogenic forcing, and the sparse observational record during this time period motivates the use of paleoclimate proxy data. We address these issues by using a linear inverse model (LIM) approach and a recent proxy‐based reconstruction over the last millennium at annual resolution. The reconstruction is used to train, initialize, and validate LIM forecasts. The LIM trained on paleo‐data assimilated using a LIM trained on global climate model (GCM) simulation data outperforms a LIM trained on raw GCM data at forecast leads longer than 2 years for in‐sample experiments, and beyond 4‐year leads in most out‐of‐sample experiments validated on instrumental data. The most skillful normal mode of the paleo‐data LIM for the instrumental experiment represents a persistent pattern with a longer decay time than for the GCM‐LIM's modes, which accounts for the outperformance at longer leads. The paleo‐data LIM is consequently more sensitive to ocean initialization, which is reflected in forecasts during the instrumental era where ocean reanalyses exhibit large uncertainty.

    

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5. Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?

   https://doi.org/10.1002/qj.4822  

   Garfinkel, Chaim_I ; Knight, Jeff ; Taguchi, Masakazu ; Schwartz, Chen ; Cohen, Judah ; Chen, Wen ; Butler, Amy_H ; Domeisen, Daniela_I_V ( September 2024 , Quarterly Journal of the Royal Meteorological Society)

   Subseasonal forecast models are shown to suffer from the same inconsistency in the signal‐to‐noise ratio evident in climate models. Namely, predictable signals in these models are too weak, yet there is a relatively high level of agreement with observed variability of the atmospheric circulation. The net effect is subseasonal forecast models show higher correlation with observed variability than with their own simulations; that is, the signal‐to‐noise paradox. Also, similar to climate models, this paradox is particularly evident in the North Atlantic sector. The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal time‐scales. The paradox appears to be related to an overly fast decay of northern annular mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified: a too‐fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface in some models, and overly weak transient synoptic eddy feedbacks. Though the paradox is clearly evident in the North Atlantic, it is relatively muted in the Southern Hemisphere: southern annular mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal.

    

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