4 research outputs found

    Assimilation of atmospheric infrasound data to constrain tropospheric and stratospheric winds

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    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 source scan 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

    A multi-disciplinary perspective on climate model evaluation for Antarctica

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    A workshop was organized by Antarctic Climate 21 (AntClim21), with the topic 'evaluation of climate models' representation of Antarctic climate from the perspective of long-term twenty-first-century climate change.' The suggested approach for evaluating whether climate models over- or underestimate the effects of ozone depletion is to diagnose simulated historical trends in lower-stratospheric temperature and compare these to observational estimates. With regard to more regional changes over Antarctica, such as West Antarctic warming, the simulation of teleconnection patterns to the tropical Pacific was highlighted. To improve the evaluation of low-frequency variability and trends in climate models, the use and development of approaches to emulate ice-core proxies in models was recommended. It is recommended that effort be put into improving datasets of ice thickness, motion, and composition to allow for a more complete evaluation of sea ice in climate models. One process that was highlighted in particular is the representation of Antarctic clouds and resulting precipitation. It is recommended that increased effort be put into observations of clouds over Antarctica, such as the use of instruments that can detect cloud-base height or the use of remote sensing resources

    Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

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    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/lowerstratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each systemÂżs 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.This work uses S2S Project data. S2S is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). This work was initiated by the Stratospheric Network for the Assessment of Predictability (SNAP), a joint activity of SPARC (WCRP) and the S2S Project (WWRP–WCRP). The work of Rachel W.-Y. Wu is funded through ETH grant ETH-05 19-1. Support from the Swiss National Science Foundation through projects PP00P2_170523 and PP00P2_198896 to Daniela I. V. Domeisen is gratefully acknowledged. Chaim I. Garfinkel and Chen Schwartz are supported by the ISF–NSFC joint research program (grant no. 3259/19). The work of Marisol Osman was supported by UBACyT20020170100428BA and PICT-2018-03046 projects. The work of Alvaro de la CĂĄmara is funded by the Spanish Ministry of Science and Innovation through project PID2019-109107GB-I00. Blanca AyarzagĂŒena and Natalia Calvo acknowledge the support of the Spanish Ministry of Science and Innovation through the JeDiS (RTI2018-096402-B-I00) project. Froila M. Palmeiro and Javier GarcĂ­a-Serrano have been partially supported by the Spanish ATLANTE project (PID2019-110234RB-C21) and RamĂłn y Cajal program (RYC-2016-21181), respectively. Neil P. Hindley and Corwin J. Wright are supported by UK Natural Environment Research Council (NERC), grant number NE/S00985X/1. Corwin J. Wright is also supported by a Royal Society University Research Fellowship UF160545. Seok-Woo Son and Hera Kim are supported by the Basic Science Research Program through the National Research Foundation of Korea (2017R1E1A1A01074889). This material is based upon work supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling program under award no. DE-SC0022070 and National Science Foundation (NSF) IA 1947282. This work was also supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. Pu Lin is supported by award NA18OAR4320123 from the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. Zachary D. Lawrence was partially supported under NOAA award NA20NWS4680051; Zachary D. Lawrence and Judith Perlwitz also acknowledge support from US federally appropriated funds
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