Scientific Reports 6: Article number: 39169; published online: 15 December 2016; updated: 15 September 2017. This Article contains errors. In Equation 1, should read: In the Results section, the ...subheading ‘Detection using the multi-variate method and no filter (TfC0)’ should read: ‘Detection using the multi-variate method and no filter (TfNo)’.
The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be ...limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere.
CORRIGENDUM Mitchell, Daniel M.; Osprey, Scott M.; Gray, Lesley J. ...
Journal of the atmospheric sciences,
12/2012, Letnik:
69, Številka:
12
Journal Article
Geoengineering by stratospheric aerosol injection has been proposed as a policy response to warming from human emissions of greenhouse gases, but it may produce unequal regional impacts. We present a ...simple, intuitive risk-based framework for classifying these impacts according to whether geoengineering increases or decreases the risk of substantial climate change, with further classification by the level of existing risk from climate change from increasing carbon dioxide concentrations. This framework is applied to two climate model simulations of geoengineering counterbalancing the surface warming produced by a quadrupling of carbon dioxide concentrations, with one using a layer of sulphate aerosol in the lower stratosphere, and the other a reduction in total solar irradiance. The solar dimming model simulation shows less regional inequality of impacts compared with the aerosol geoengineering simulation. In the solar dimming simulation, 10% of the Earth's surface area, containing 10% of its population and 11% of its gross domestic product, experiences greater risk of substantial precipitation changes under geoengineering than under enhanced carbon dioxide concentrations. In the aerosol geoengineering simulation the increased risk of substantial precipitation change is experienced by 42% of Earth's surface area, containing 36% of its population and 60% of its gross domestic product.
Abstract The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale ...provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
There has been huge recent interest in the potential of making operational
weather forecasts using machine learning techniques. As they become a part of
the weather forecasting toolbox, there is a ...pressing need to understand how
well current machine learning models can simulate high-impact weather events.
We compare forecasts of Storm Ciar\'an, a European windstorm that caused
sixteen deaths and extensive damage in Northern Europe, made by machine
learning and numerical weather prediction models. The four machine learning
models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2)
produce forecasts that accurately capture the synoptic-scale structure of the
cyclone including the position of the cloud head, shape of the warm sector and
location of warm conveyor belt jet, and the large-scale dynamical drivers
important for the rapid storm development such as the position of the storm
relative to the upper-level jet exit. However, their ability to resolve the
more detailed structures important for issuing weather warnings is more mixed.
All of the machine learning models underestimate the peak amplitude of winds
associated with the storm, only some machine learning models resolve the warm
core seclusion and none of the machine learning models capture the sharp
bent-back warm frontal gradient. Our study shows there is a great deal about
the performance and properties of machine learning weather forecasts that can
be derived from case studies of high-impact weather events such as Storm
Ciar\'an.
This data assimilation study exploits infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland ...generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178 km almost due North from the explosion site. The difference between the true horizontal direction towards the source and the backazimuth direction (the horizontal direction of arrival) of the incoming infrasound wave-fronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross-wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. We demonstrate that information from both sources can be combined to obtain analysis (posterior) estimates of cross-winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12-60 km levels, with some changes with respect to the prior of the order of 0.1-1.0 m/s, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2-5%. This is the first published study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations - especially natural and continuous sources - to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products.
During volcanic eruptions, Volcanic Ash Advisory Centres issue ash advisories for aviation showing the forecasted outermost extent of the ash cloud. During the 2010 Icelandic volcano Eyjafjallajökull ...eruption, the Met Office produced supplementary forecasts of quantitative ash concentration, due to demand from airlines. Additionally, satellite retrievals of estimated volcanic ash concentration are now available. To test how these additional graphical representations of volcanic ash affect flight decisions, whether users infer uncertainty in graphical forecasts of volcanic ash, and how decisions are made when given conflicting forecasts, a survey was conducted of 25 delegates representing U.K. research and airline operations dealing with volcanic ash. Respondents were more risk seeking with safer flight paths and risk averse with riskier flight paths when given location and concentration forecasts compared to when given only the outermost extent of the ash. Respondents representing operations were more risk seeking than respondents representing research. Additionally, most respondents’ hand-drawn no-fly zones were larger than the areas of unsafe ash concentrations in the forecasts. This conservatism implies that respondents inferred uncertainty from the volcanic ash concentration forecasts. When given conflicting forecasts, respondents became more conservative than when given a single forecast. The respondents were also more risk seeking with high-risk flight paths and more risk averse with low-risk flight paths when given conflicting forecasts than when given a single forecast. The results show that concentration forecasts seem to reduce flight cancellations while maintaining safety. Open discussions with the respondents suggested that definitions of uncertainty may differ between research and operations.
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