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Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event's progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology. [ABSTRACT FROM AUTHOR]

Understanding and predicting midlatitude cold spells is of scientific and public interest, given often associated severe impacts. However, large-scale atmospheric dynamics related to these events are not fully understood. The winter of 2017/18 was characterized by several cold spells affecting large parts of North America and Eurasia. Here, the role of stratosphere–troposphere coupling for the occurrence of cold spells in this winter is investigated using different wave propagation diagnostics. While the European cold spell in late February 2018 was influenced by a major sudden stratospheric warming (SSW) associated with wave absorption, the cold spells over North America at the end of December 2017 and early February 2018 were related to downward reflected waves over the North Pacific. Previously proposed wave reflection indices, however, either miss these reflection events or are not able to distinguish them from the major SSW related to wave absorption. To overcome this, a novel simple index based on eddy heat flux is proposed here, capturing regional wave reflection over the North Pacific. Reflection events detected with this index are shown to be followed by North Pacific blocking and negative temperature anomalies over North America. An improved understanding of the contribution of wave reflection for cold spells is crucial to better predict such events in the future. [ABSTRACT FROM AUTHOR]

The skill of the Arctic stratospheric retrospective ensemble forecasts (hindcasts) of the European Centre for Medium-Range Weather Forecasts extended-range system is analyzed with a focus on the predictability of the major sudden stratospheric warmings (SSWs) during the period 1993-2016. Thirteen SSWs took place during this period. It is found that forecasts initialized 10-15 days before the SSWs show worse skill in the stratosphere than forecasts initialized during normal conditions in terms of root-mean-square errors but not in terms of anomaly correlation. Using the spread of ensemble members to estimate forecasted SSW probability, it is shown that some SSWs can be predicted with high (.0.9) probability at lead times of 12-13 days if a difference of 3 days between actual and forecasted SSW is allowed. Focusing on SSWs with significant impacts on the tropospheric circulation, on average, the forecasted SSW probability is found to increase from nearly 0 at 1-month lead time to 0.3 at day 13 before SSW, and then rapidly increases to nearly 1 at day 7. The period between days 8 and 12 is when most of the SSWs are predicted, with a probability of 0.5-0.9, which is considerably larger than the observed SSW occurrence frequency. Therefore, this period can be thought of as an estimate of the SSW predictability limit in this system. Indications that the predictability limit for some SSWs may be longer than 2 weeks are also found; however, this result is inconclusive and more studies are needed to understand when and why such long predictability is possible. [ABSTRACT FROM AUTHOR]

A technique is presented for meteorological modeling in which all variables are held on an unstaggered grid, but the winds are transformed to a staggered C grid for the gravity wave calculations. An important feature is the use of a new reversible interpolation procedure for the staggering–unstaggering of the winds. This reversible procedure has excellent dispersion properties for geostrophic adjustment of the linearized shallow-water equations, being generally superior to those of the A, B, and C grids. Its dispersion behavior is generally similar to that of the unstaggered Z grid of Randall, which carries divergence and vorticity as primary variables. The scheme has fewer computational overheads than the Z grid. [ABSTRACT FROM AUTHOR]