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A unique set of relaxation experiments with a forecast model initialized during December–January 1999–2019 is used to explore tropical influence on the Northern Hemisphere polar stratosphere and stratosphere–troposphere coupling, to quantify predictability benefits due to the perfect knowledge of the tropical variability. On average, predictability of the polar stratosphere, as represented by the 50 hPa geopotential height anomalies north of 50°N (Z50), increases from 17 days in freely running (control) forecasts to 21 days in the tropical relaxation experiments. At sub‐seasonal time‐scales, a statistically significant improvement in weekly mean skill scores can be demonstrated in 14%–20% of individual forecast ensembles, mostly in cases when the skill of the corresponding control forecast is worse than average. In these forecasts, root‐mean‐square errors and forecast spread of Z50 during forecast weeks 3–5 are decreased by 10%–15%. Stratospheric improvements are detected during periods of both vortex strengthening and vortex weakening, including most major Sudden Stratospheric Warmings that occurred during the study period, via modulation of the upward wave activity fluxes. An active Madden–Julian Oscillation (MJO) is found in most of these events with MJO phase 5–7 preceding vortex weakening and MJO phase 3–4 preceding vortex strengthening. Forecasts with improved stratospheric circulation also have improved tropospheric circulation during and after the periods when improvements are detected in the stratosphere. We attribute these improvements to both stratosphere–troposphere coupling and tropospheric tropical teleconnections. [ABSTRACT FROM AUTHOR]

A number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions. [ABSTRACT FROM AUTHOR]

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. [ABSTRACT FROM AUTHOR]