Impact of radiosonde observations on forecasting summertime Arctic cyclone formation

Yamazaki, A.; Inoue, Jun; Dethloff, Klaus; Maturilli, Marion; König-Langlo, Gert

doi:10.1002/2014jd022925

Cited by 56 publications

(84 citation statements)

References 57 publications

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“…Recent studies have highlighted the importance of tropopause vorticity anomalies for the development of Arctic summer cyclones (Simmonds and Rudeva 2012;Yamazaki et al 2015). However, relatively little is known about the mechanisms responsible for the development of Arctic synoptic scale summer cyclones and few observations exist (Aizawa and Tanaka 2016).…”

Section: Discussionmentioning

confidence: 99%

Seasonal differences in the response of Arctic cyclones to climate change in CESM1

2017

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The dramatic warming of the Arctic over the last three decades has reduced both the thickness and extent of sea ice, opening opportunities for business in diverse sectors and increasing human exposure to meteorological hazards in the Arctic. It has been suggested that these changes in environmental conditions have led to an increase in extreme cyclones in the region, therefore increasing this hazard. In this study, we investigate the response of Arctic synoptic scale cyclones to climate change in a large initial value ensemble of future climate projections with the CESM1-CAM5 climate model (CESM-LE). We find that the response of Arctic cyclones in these simulations varies with season, with significant reductions in cyclone dynamic intensity across the Arctic basin in winter, but with contrasting increases in summer intensity within the region known as the Arctic Ocean cyclone maximum. There is also a significant reduction in winter cyclogenesis events within the Greenland-Iceland-Norwegian sea region. We conclude that these differences in the response of cyclone intensity and cyclogenesis, with season, appear to be closely linked to changes in surface temperature gradients in the high latitudes, with Arctic poleward temperature gradients increasing in summer, but decreasing in winter.

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Section: Discussionmentioning

confidence: 99%

Seasonal differences in the response of Arctic cyclones to climate change in CESM1

2017

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“…To carry out these experiments, a sustained observing period is required with significantly enhanced spatial and temporal coverage-a Year of Polar Prediction (see below). In this respect, increasing the frequency of observations from existing stations and vessels (e.g., Inoue et al 2013;Yamazaki et al 2015;Inoue et al 2015) and adding additional mobile observing systems such as buoys (Inoue et al 2009;Meredith Forecasting system research. The elements of forecasting system research-namely, observations, modeling, data assimilation, and ensemble forecasting (Fig.…”

Section: How To Improve Polar Prediction Capacity?mentioning

confidence: 99%

Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales

Jung

Gordon²,

Bauer

et al. 2016

Bulletin of the American Meteorological Society

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The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.

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“…The low prediction accuracy of the meridional wind and ice speed suggested that the meridional component of sea ice advection contributes substantially to the SIT distribution in the ESS. Since it was reported that additional radiosonde observations over the Arctic Ocean have considerable impact on the prediction accuracy in synoptic-scale fluctuations Yamazaki et al, 2015), additional radiosonde observations acquired over the Arctic Ocean could lead to a further extension of the lead time for medium-range forecast of SIT distribution.…”

Section: Summary and Discussionmentioning

confidence: 99%

Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system

et al. 2018

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Abstract. Accelerated retreat of Arctic Ocean summertime sea ice has focused attention on the potential use of the Northern Sea Route (NSR), for which sea ice thickness (SIT) information is crucial for safe maritime navigation. This study evaluated the medium-range (lead time below 10 days) forecast of SIT distribution in the East Siberian Sea (ESS) in early summer (June-July) based on the TOPAZ4 iceocean data assimilation system. A comparison of the operational model SIT data with reliable SIT estimates (hindcast, satellite and in situ data) showed that the TOPAZ4 reanalysis qualitatively reproduces the tongue-like distribution of SIT in ESS in early summer and the seasonal variations. Pattern correlation analysis of the SIT forecast data over 3 years (2014)(2015)(2016) reveals that the early summer SIT distribution is accurately predicted for a lead time of up to 3 days, but that the prediction accuracy drops abruptly after the fourth day, which is related to a dynamical process controlled by synoptic-scale atmospheric fluctuations. For longer lead times ( > 4 days), the thermodynamic melting process takes over, which contributes to most of the remaining prediction accuracy. In July 2014, during which an iceblocking incident occurred, relatively thick SIT (∼ 150 cm) was simulated over the ESS, which is consistent with the reduction in vessel speed. These results suggest that TOPAZ4 sea ice information has great potential for practical applications in summertime maritime navigation via the NSR.

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Impact of radiosonde observations on forecasting summertime Arctic cyclone formation

Cited by 56 publications

References 57 publications

Seasonal differences in the response of Arctic cyclones to climate change in CESM1

Seasonal differences in the response of Arctic cyclones to climate change in CESM1

Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales

Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system

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