Ice Conditions from Historical and Satellite Observations

Analysis of dynamics for anomalies of the ice cover in the Okhotsk Sea in the period from 1882 to 2015

Izvestiya TINRO ◽

10.26428/1606-9919-2016-185-228-239 ◽

2016 ◽

Vol 185 (2) ◽

pp. 228-239

Author(s):

Vladimir M. Pishchalnik ◽

Valery A. Romanyuk ◽

Igor G. Minervin ◽

Alevtina S. Batuhtina

Keyword(s):

Time Series ◽

Air Temperature ◽

Regime Shift ◽

Ice Cover ◽

Satellite Observations ◽

Okhotsk Sea ◽

Ice Conditions ◽

The Mean ◽

Analysis Of Dynamics

The time-series for the ice cover dynamics in the Okhotsk Sea in the period from 1882 to 2015 are reconstructed on the base of shipboard, airborne, and satellite observations and measurements of the air temperature at the coastal meteorological stations. Abnormality of the ice conditions is estimated relative to the “climate norm” determined as the mean seasonal variation for the 1961-1990. Long-term variability of the ice cover is analyzed. Its regime shift with change of trend is revealed in the late 1970s - early 1980s that corresponds to the regime shift of the air temperature variability in the northern hemisphere.

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Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2

10.5194/tc-2020-315 ◽

2020 ◽

Author(s):

Renée Mie Fredensborg Hansen ◽

Eero Rinne ◽

Sinéad Louise Farrell ◽

Henriette Skourup

Keyword(s):

Sea Ice ◽

Satellite Observations ◽

The Baltic Sea ◽

Ice Cloud ◽

Ice Conditions ◽

In Situ Observations ◽

The Difference ◽

The Baltic ◽

Time Critical

Abstract. The ability to provide ice navigators with reliable and timely information on sea ice conditions is crucial to ensure safe passage through rapidly changing ice-covered waters. Degree of ice ridging (DIR) is a particularly useful parameter for ice navigators, as it provides an idea of how difficult it is to navigate through an area based on e.g. sail heights and distribution of sea ice ridges. DIR estimates are included in ice charts of the Baltic Sea, and are based primarily on in situ observations from an active icebreaker fleet. DIR may also potentially be estimated from satellite observations, and warrants further investigation. Here, we present a comparison of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) geolocated photon heights and operational ice charts from the Finnish Ice Service in the Bay of Bothnia in spring 2019. We show that ICESat-2 (IS2) retrievals from ice areas with different ridging characteristics, more precisely DIR, differ significantly. Thus, we suggest that IS2 data can be of benefit to international ice services, especially if a time critical photon height product were to be made available. Furthermore, we show that the difference between highest and mean photon elevations (elevation anomalies) of IS2 correspond to expected ridge sail heights in our study area. Our study is one of the first steps in creating sea ice applications beyond the traditional goal of freeboard and thickness retrieval for IS2.

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Inter-comparison and evaluation of sea ice type concentration algorithms

10.5194/tc-2019-200 ◽

2019 ◽

Author(s):

Yufang Ye ◽

Mohammed Shokr ◽

Signe Aaboe ◽

Wiebke Aldenhoff ◽

Leif E. B. Eriksson ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Satellite Observations ◽

Ice Age ◽

Type Distribution ◽

Climate Monitoring ◽

Ice Conditions ◽

Open Question ◽

Varied Concentration

Abstract. Sea ice has been monitored in terms of concentration and types with microwave satellite observations since the late 1970s. However, it remains an open question as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with two sea ice age (SIA) and three sea ice type (SIT) products using microwave radiometer and scatterometer data from 2000 to 2015. Their performances were evaluated quantitatively with samples that are used for generating tie points, and qualitatively with the RADARSAT imagery. The methods that combined scatterometer and radiometer data have overall better performances on ice type discrimination. The best methods are ECICE-QSCAT-2 for the years 2000–2009 and ECICE-ASCAT for 2009–2015, both using scatterometer data along with radiometer data. Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions, for instance, homogeneous sea ice regions with little artifact for SIA algorithms to track.

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Summer Sea Ice Albedo in the Arctic in CMIP5 models

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-25219-2013 ◽

2013 ◽

Vol 13 (9) ◽

pp. 25219-25251 ◽

Cited By ~ 3

Author(s):

T. Koenigk ◽

A. Devasthale ◽

K.-G. Karlsson

Keyword(s):

Sea Ice ◽

Large Scale ◽

High Surface ◽

Temporal Variations ◽

Satellite Observations ◽

The Arctic ◽

Cmip5 Models ◽

Cold Temperatures ◽

Ice Conditions ◽

The Impact

Abstract. Spatial and temporal variations of summer sea ice albedo over the Arctic are analyzed using an ensemble of historical CMIP5 model simulations. The results are compared to the CLARA-SAL product that is based on long-term satellite observations. The summer sea ice albedo varies substantially among CMIP5 models and many models show large biases compared to the CLARA-SAL product. Single summer months show an extreme spread of ice albedo among models; July-values vary between 0.3 and 0.7 for individual models. The CMIP5 ensemble mean, however, agrees relatively well in the Central Arctic but shows too high ice albedo near the ice edges and coasts. In most models, the ice albedo is spatially too uniformly distributed. The summer to summer variations seem to be underestimated in many global models and almost no model is able to fully reproduce the temporal evolution of ice albedo throughout the summer. While the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August. Instead, the June values are often lower in the models than in the satellite observations. This is probably due to too high surface temperatures in June, leading to an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models. The summer sea ice albedo in the CMIP5 models is strongly governed by surface temperature and snow conditions, particularly during the period of melt onset in early summer and refreezing in late summer. The summer surface net solar radiation of the ice covered Arctic areas is highly related to the ice albedo in the CMIP5 models. However, the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible. This indicates the importance of other Arctic and large scale processes for the sea ice conditions.

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Summer Arctic sea ice albedo in CMIP5 models

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-1987-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 1987-1998 ◽

Cited By ~ 30

Author(s):

T. Koenigk ◽

A. Devasthale ◽

K.-G. Karlsson

Keyword(s):

Sea Ice ◽

Large Scale ◽

High Surface ◽

Arctic Sea Ice ◽

Satellite Observations ◽

The Arctic ◽

Cmip5 Models ◽

Cold Temperatures ◽

Ice Conditions ◽

The Impact

Abstract. Spatial and temporal variations of summer sea ice albedo over the Arctic are analyzed using an ensemble of historical CMIP5 model simulations. The results are compared to the CLARA-SAL product that is based on long-term satellite observations. The summer sea ice albedo varies substantially among CMIP5 models, and many models show large biases compared to the CLARA-SAL product. Single summer months show an extreme spread of ice albedo among models; July values vary between 0.3 and 0.7 for individual models. The CMIP5 ensemble mean, however, agrees relatively well in the central Arctic but shows too high ice albedo near the ice edges and coasts. In most models, the ice albedo is spatially too uniformly distributed. The summer-to-summer variations seem to be underestimated in many global models, and almost no model is able to reproduce the temporal evolution of ice albedo throughout the summer fully. While the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August. Instead, the June values are often lower in the models than in the satellite observations. This is probably due to too high surface temperatures in June, leading to an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models. The summer sea ice albedo in the CMIP5 models is strongly governed by surface temperature and snow conditions, particularly during the period of melt onset in early summer and refreezing in late summer. The summer surface net solar radiation of the ice-covered Arctic areas is highly related to the ice albedo in the CMIP5 models. However, the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible. This indicates the importance of other Arctic and large-scale processes for the sea ice conditions.

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Results of Latitude Observations with the Floating Zenith Telescope at Mizusawa: Comparisons between the Results Obtained from the Astronomical and Doppler Satellite Observations

International Astronomical Union Colloquium ◽

10.1017/s0252921100051241 ◽

1975 ◽

Vol 26 ◽

pp. 461-468

Author(s):

S. Takagi

Keyword(s):

Photographic Plate ◽

Satellite Observations ◽

Pole Motion

In this article, we intended to see whether we can obtain the same pole motion from two kinds of telescopes: the floating zenith telescope (PZT) and the ILS zenith telescope (VZT). The observations with the PZT have been pursued since 1967.0 with a star list whose star places are taken from the PK4 and its supplement. We revised the method of reduction of the observations with the PZT by adopting a variable scale value for the photographic plate (Takagi et al., 1974).

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