The impact of radiosonde data on forecasting sea‐ice distribution along the Northern Sea Route during an extremely developed cyclone

Abstract

To investigate the impact of radiosonde data on the sea-ice forecast in the Northern Sea Route during an extremely developed cyclone on 6 August 2012, a series of numerical experiments were conducted using an ice-ocean coupled model with fine horizontal resolution (approximately 2.5 km). The atmospheric forcing data used for the model were forecast data with (CTL) and without (OSE) initialization by radiosonde data over the Fram Strait, obtained by the German R/V Polarstern, and the European Centre for Medium-Range Weather Forecasts Interim reanalysis data. All numerical experiments were run from 06:00 UTC on 3 August 2012 to 00:00 UTC on 8 August 2012 with an initial sea-ice concentration and thickness derived from the Advanced Microwave Scanning Radiometer 2 satellite data. The root-mean-square error and correlation coefficient for the sea-ice distribution showed that the CTL simulation predicted better the sea-ice distribution in the Northern Sea Route than the OSE simulation. This occurred in particular from 6 to 7 August when the cyclone became strong. The thermodynamic processes resulted in the difference in the sea-ice thickness due to changes in the vertical energy fluxes. However, the differences in the sea-ice concentration and velocity were caused mainly by the dynamics, particularly the difference in wind fields, rather than the thermodynamics. These results suggest that radiosonde data are effective in improving the forecast accuracy of the sea-ice distribution. Therefore, errors in the weather forecast data would have a substantial impact on the safety of ship navigation and the sea-ice distribution.

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

“…Their results suggested that the large reduction in prediction accuracy could be attributed to the process of dynamical advection of sea ice; thus the prediction of early summer SIT distribution will depend on the precise prediction of the surface wind. Our comprehensive analysis supports an earlier study that suggested the dynamical processes have an essential role in the prediction accuracy of sea ice distribution on short timescales (Ono et al, 2016). The time evolution of SIT and the related ice velocity relates the large difference between the forecast and analysis data at a lead time of 4 days to the low forecasts for an Arctic cyclone event.…”

“…Because the ESS corresponds to the route of Arctic cyclones generated over the Eurasian continent (Orsolini and Sorteberg, 2009), it is expected that synoptic-scale atmospheric fluctuations would substantially influence the spatial distributions of SIT and ice motion in the ESS. Ono et al (2016) highlighted the importance of atmospheric prediction accuracy on medium-range forecasts of sea ice distribution in the ESS based on a case of an extreme cyclone that occurred on 6 August 2012. Mohammadi-Aragh et al (2018) suggest that the chaotic behaviour of atmospheric prediction accuracy controls the short-term predictability of sea ice deformation in the Arctic Ocean.…”

Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations

“…The reproducibility of atmospheric circulations over the Arctic region in reanalysis data, which assimilate observation data (e.g., land‐based and satellite) using a data assimilation system, depends not only on model performance [ Inoue et al ., ] but also on the quantity of observations [ Inoue et al ., ]. Additional data from radiosondes and dropsondes contribute to more accurate reproduction of atmospheric fields [ Kristjánsson et al ., ; Yamazaki et al ., ], which in turn improve reproducibility and prediction of the Arctic sea ice distribution because of wind‐driven sea‐ice drift related to the atmospheric circulation [ Ono et al ., ]. Although it has been found through OSEs that radiosonde observation data over the Arctic Ocean significantly improve the analysis ensemble mean and reduce the spread of ensemble members (i.e., uncertainty) in upper tropospheric circulations during summer [ Inoue et al ., ; Yamazaki et al ., ], their impact on circulations at midlatitudes would be very limited, partly because of the relatively small size of the tropospheric polar vortex during summer.…”

Towards reliable Arctic sea ice prediction using multivariate data assimilation

“…The above mentioned changes of Arctic sea ice have captured attention and pose significant challenges to many stakeholders, including shipping, oil/gas exploration, and coastal communities facing the prospect of relocation [12][13][14][15]. As a result, there is an increasing need of Arctic sea ice prediction ranging from daily to seasonal time scales [13,16,17], driven in particular by an increasing accessibility of the Arctic associated with global climate change. Moreover, the rapid decline of Arctic sea ice in recent years has coincided with more frequent extreme events in parts of northern mid-latitude continents, i.e., several winters following anomalously low Arctic sea ice cover had strong cold surges along with heavy snowfall [18][19][20][21][22][23].…”