Sentences with phrase «sea ice thickness as»

And scientists from NASA recently flew a series of missions over the Arctic during the IceBridge project, to study details of Arctic sea ice thickness as well as changing glaciers in Greenland.

Not exact matches

AWI researchers observed a considerable decrease in the thickness of the sea ice as early as the late summer of 2015, even though the overall ice covered area of the September minimum ultimately exceeded the record low of 2012 by approximately one million square kilometres.

Regarding my # 74: On sea ice thickness, here is an unreviewed but sensible discussion / analysis of Arctic sea ice volume and thickness as modeled by PIOMAS.

However, sea ice then grows very rapidly, since the growth rate for thin ice is much higher than for thick ice, which acts as a negative feedback on thickness during the growth season (Bitz and Roe, 2004; Notz, 2009).

This depends upon both the thickness of the ice, as well as the depression below sea level.

The sea ice component represents sea ice in multiple categories of thickness and accounts for changes in thickness due to growth and melt as well as mechanical deformation of ice (Thorndike et al. 1975, Hibler 1980).

The team, which Marc led and provided the logistical support for, deployed from Resolute to Nord Greenland before setting up a rustic field camp on the sea ice for six days, during which time we mechanically drilled the ice to measure thickness, measuring snow depth in a grid pattern along the flight lines as well as dragging instruments along the surface that produced the same measurements for comparison to the airborne data.

And variations in the thickness and extent of sea ice cloaking the Arctic Ocean are driven by yet another set of complicating factors, ranging from long - term shifts in atmospheric pressure patterns to events as close - focus as the potent Arctic superstorm I reported on earlier this month.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictorAs with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictoice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictoice predictors.

Currently, the NASA IceBridge mission supplies both sea ice thickness and snow depth measurements in spring, providing timely information on the state of the ice cover as the melt season begins.

Radar ice - thickness estimates of the Arctic Sea ice showed that it had been thinning for years, just as they had also shown that the northern coastal glaciers of Greenland were thinning.

In addition, Sentinel - 3B will as well measure sea ice thickness and significant wave heights, the latter will be assimilated into MET Norway's wave forecast model, also a contribution to the Copernicus Marine Services.

The pan-arctic ensemble runs with a coupled ice - ocean model by Kauker et al. also indicate a distinct ice thickness anomaly in the East Siberian Sea, where thicknesses at the end of June 2010 are shown to be higher by a factor of roughly two as compared to the previous three years.

Further: We calculate Arctic sea ice thickness and volume values from the standard, publically available CryoSat data as well as from near real time (NRT) CryoSat data provided directly to us from the European Space Agency.

Given the annual cycle of melting and refreezing for the majority of sea ice, thickness is not relevent as it would dissapear and re-appear annually.

Further investigation of ice thickness and free ice drift conditions, in addition to persistence of SLP maxima, will provide further insight as to whether convergence (divergence) of sea ice associated with SLP highs (lows) will give rise to increased ice retreat in the Arctic and the Beaufort Sea region in particulsea ice associated with SLP highs (lows) will give rise to increased ice retreat in the Arctic and the Beaufort Sea region in particulSea region in particular.

Kaleschke and Tian - Kunze, 3.6 (± 0.7), Heuristic / Statistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) million km2.

To make use of that potential we would need good estimates of sea ice thickness, such as might be obtained from ICESat or CryoSat (i.e., complete spatial coverage).

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictoice predictors.

As noted last month, this range depends in part on the relative weight that the respondents give to «initial conditions,» e.g., age and thickness of sea ice at the end of spring, versus whether summer winds in 2008 will be as supportive for ice loss as the favorable winds were in 200As noted last month, this range depends in part on the relative weight that the respondents give to «initial conditions,» e.g., age and thickness of sea ice at the end of spring, versus whether summer winds in 2008 will be as supportive for ice loss as the favorable winds were in 200as supportive for ice loss as the favorable winds were in 200as the favorable winds were in 2007.

The findings are based on satellite readings of Antarctic sea ice movement and thickness, as well as new, detailed interpretations of charts showing the shape of the sea bottom around Antarctica.

As a result of limited satellite observations of sea ice thickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thicknesea ice thickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneice thickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice tthickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneSea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneIce Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice tThickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thicknesea ice thickneice thicknessthickness.

As in 2012, sea ice thinning and not just anomalous weather should contribute to September 2013 sea ice loss (see the discussion of the IceBridge sea ice thickness data from the June Report).

The modeled evolution of Arctic sea ice volume appears to be much stronger correlated with changes in ice thickness than with ice extent as it shows a similar negative trend beginning around the mid-1990s.

Arctic sea ice extent and thickness have declined substantially, especially in late summer (September), when there is now only about half as much sea ice as at the beginning of the satellite record in 1979 (Ch.

However, as you'll see by the sea ice thickness maps below, there may be good reason for the lack of ringed seal lairs, and a general lack of seals except at the nearshore lead that forms because of tidal action: the ice just a bit further offshore ice looks too thick for a good crop of ringed seals in all three years of the study.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictorAs with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoras winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Any field - or ship - based updates on ice conditions in the different regions such as sea ice morphology (e.g., concentration, ice type, floe size, thickness, snow cover, melt pond characteristics, topography), meteorology (surface measurements) and oceanography (e.g., temperature, salinity, upper ocean temperature).

She says improved summer weather predictions as well as satellite measurements of sea ice thickness and concentration could help forecasting.

April 1, 2009 Sea ice cover over the Arctic Ocean typically reaches its maximum geographic extent and thickness just as spring begins in the Northern Hemisphere.

There is some evidence that the Arctic sea - ice cover has decreased about 6 % during the last two decades, and that the mean ice thickness has decreased as well.

As such, monitoring Arctic ice thickness may be useful for predicting rapid changes in sea ice.

As surface temperture is altitude dependent one might have thought the first thing to check would be a map, as the arctic ice lies at sea level + 9 % of its thickness, while the antarctic ice sits several kilometers high in the sky, and the surrounding apron of the stuff is immune to windage because of the circumpolar continent in its midsAs surface temperture is altitude dependent one might have thought the first thing to check would be a map, as the arctic ice lies at sea level + 9 % of its thickness, while the antarctic ice sits several kilometers high in the sky, and the surrounding apron of the stuff is immune to windage because of the circumpolar continent in its midsas the arctic ice lies at sea level + 9 % of its thickness, while the antarctic ice sits several kilometers high in the sky, and the surrounding apron of the stuff is immune to windage because of the circumpolar continent in its midst.

The time constants of albedo feedback from melting N America snow cover are shorter than the albedo feedback from melting Arctic sea ice, and the sea ice is changing response as its average thickness decreases, and the ratios of 1, 2, 3, 4, 5 year ice area changes.

Stroeve said that sea ice volume, which incorporates measurements of ice extent as well as thickness, is a more important metric than sea ice extent alone.

Nevertheless it should be readily apparent to all and sundry by now that the thickness of Arctic sea ice has been declining at the same time as its extensiveness.

Relating the age of Arctic sea ice to its thickness, as measured during NASA's ICESat and IceBridge campaigns.