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Abstract:

The quasi-biennial oscillation (QBO) of tropical stratospheric winds was disrupted during the 2019/20 Northern Hemisphere winter. We show that this latest disruption to the regular QBO cycling was similar in many respects to that seen in 2016, but initiated by horizontal momentum transport from the Southern Hemisphere. The predictable signal associated with the QBO's quasi-regular phase progression is lost during disruptions and the oscillation reemerges after a few months significantly shifted in phase from what would be expected if it had progressed uninterrupted. We infer from an increased wave-momentum flux into equatorial latitudes seen in climate model projections that disruptions to the QBO are likely to become more common in future. Consequently, it is possible that in the future, the QBO could be a less reliable source of predictability on lead times extending out to several years than it currently is.

Abstract:

Global warming has clearly affected the occurrence of extreme events in recent years. Here, we assess changes in the frequency of temperature extremes and their causes, using percentile-based indices. Cold extremes are defined as temperatures below the 10th percentile of daily minimum (TN10) and maximum (TX10) temperatures while hot extremes exceed the 90th percentile of daily minimum (TN90) and maximum (TX90) temperatures. We analyze Berkeley Earth Surface Temperature (BEST) for observed changes in the last four decades 1981-2020, for two extended seasons, boreal summer April–September (AMJJAS) and boreal winter October–March (ONDJFM), and evaluate results using several reanalysis data sets. For the attribution of causes we use CMIP6 climate model simulations, analyzing natural-only and anthropogenic-only forcings. We use an attribution method that accounts for climate modeling uncertainty in both amplitude and pattern of responses.The observations show detectable changes in both cold and hot extreme temperatures. Hot extremes have increased in all regions and in both seasons while cold extremes have decreased over the past decades. Our attribution analysis revealed anthropogenic forcings are robustly detectable and the main drivers of observed changes in all indices for all regions, consistently in all data sets. Contributions from natural forcings are found small and detectable only in a few regions mainly for daytime cold extremes in ONDJFM. Anthropogenic forcing contributed to an increase of 3.4 days per decade in TN90 and of 2.7 days per decade in TX90, on average, at the global scale. Regionally, the anthropogenic contribution caused a range of decrease of 2–4.7 days per decade in TN10, 1.5–3.6 days per decade in TX10 while it caused an increase of 2.2–4.8 days per decade for TN90 and 2–3.3 days per decade in TX90. Anthropogenic-only warming in ONDJFM is slightly less than in AMJJAS

Abstract:

The quasi‐biennial oscillation (QBO) dominates the interannual variability of the tropical stratosphere and influences other regions of the atmosphere. The high predictability of the QBO implies that its teleconnections could lead to increased skill of seasonal and decadal forecasts provided the relevant mechanisms are accurately represented in models. Here modelling and sampling uncertainties of QBO teleconnections are examined using a multi‐model ensemble of QBO‐resolving atmospheric general circulation models that have carried out a set of coordinated experiments as part of the Stratosphere‐troposphere Processes And their Role in Climate (SPARC) QBO initiative (QBOi). During Northern Hemisphere winter the stratospheric polar vortex in most of these models strengthens when the QBO near 50 hPa is westerly and weakens when it is easterly, consistent with, but weaker than, the observed response. These weak responses are likely due to model errors, such as systematically weak QBO amplitudes near 50 hPa, affecting the teleconnection. The teleconnection to the North Atlantic Oscillation is less well captured overall, but of similar strength to the observed signal in the few models that do show it. The models do not show clear evidence of a QBO teleconnection to the Northern Hemisphere Pacific‐sector subtropical jet.

Abstract:

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.