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  • 1
    Publication Date: 2022-05-26
    Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(10), (2020): E1842-E1850, doi:10.1175/BAMS-D-19-0263.1.
    Description: Process studies are designed to improve our understanding of poorly described physical processes that are central to the behavior of the climate system. They typically include coordinated efforts of intensive field campaigns in the atmosphere and/or ocean to collect a carefully planned set of in situ observations. Ideally the observational portion of a process study is paired with numerical modeling efforts that lead to better representation of a poorly simulated or previously neglected physical process in operational and research models. This article provides a framework of best practices to help guide scientists in carrying out more productive, collaborative, and successful process studies. Topics include the planning and implementation of a process study and the associated web of logistical challenges; the development of focused science goals and testable hypotheses; and the importance of assembling an integrated and compatible team with a diversity of social identity, gender, career stage, and scientific background. Guidelines are also provided for scientific data management, dissemination, and stewardship. Above all, developing trust and continual communication within the science team during the field campaign and analysis phase are key for process studies. We consider a successful process study as one that ultimately will improve our quantitative understanding of the mechanisms responsible for climate variability and enhance our ability to represent them in climate models.
    Description: We gratefully acknowledge U.S. CLIVAR for supporting the PSMI panel, as well as all the principal investigators that contributed to our PSMI panel webinars. JS was inspired by participation in the process studies funded by NASA NNH18ZDA001N-OSFC and NOAA NA17OAR4310257; GF was supported by base funds to NOAA/AOML’s Physical Oceanography Division; and HS was supported by NOAA NA19OAR4310376 and NA17OAR4310255.
    Description: 2021-04-01
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2019-02-01
    Description: Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway.
    Type: Article , PeerReviewed
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  • 3
    Publication Date: 2018-02-06
    Description: We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the stratopause and relatively fine stratospheric vertical resolution (high-top), and those that have a model top below the stratopause (low-top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low-top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low-top models with less than half the frequency of events observed in the reanalysis data and high-top models. The lack of stratospheric variability in the low-top models affects their stratosphere-troposphere coupling, resulting in short-lived anomalies in the Northern Annular Mode, which do not produce long-lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low-top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high-top models compared to the low-top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2017-10-24
    Description: Predictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Niño–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Niño and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather. This study explores the impact on Europe by separating the stratospheric pathway of the El Niño teleconnection. In the seasonal prediction system, the evolution of El Niño events is well captured for lead times of up to 6 months, and stratospheric variability is reproduced with a realistic frequency of SSW events. The model reproduces the El Niño teleconnection through the stratosphere, involving a deepened Aleutian low connected to a warm anomaly in the northern winter stratosphere. The stratospheric anomaly signal then propagates downward into the troposphere through the winter season. Predictability of 500-hPa geopotential height over Europe at lead times of up to 4 months is shown to be increased only for El Niño events that exhibit SSW events, and it is shown that the characteristic negative NAO signal is only obtained for winters also containing major SSW events for both the model and the reanalysis data.
    Type: Article , PeerReviewed
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  • 5
    Publication Date: 2023-02-08
    Description: Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
    Type: Article , PeerReviewed
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