Sentences with phrase «of sea ice thickness on»

In this study, we conduct sensitivity experiments to isolate the role of sea ice thickness on the atmospheric circulation.

Instead, we are interested in isolating the role of sea ice thickness on the atmosphere and quantifying its contribution compared to sea ice concentration.

Not exact matches

Researchers from Norway and China have collaborated on developing an autonomous buoy with instruments that can more precisely measure the optical properties of Arctic sea ice while also taking measurements of ice thickness and temperature.

In addition to the thickness of the snow cover on top of the sea ice, the buoys also measure the air temperature and air pressure.

From an altitude of just over 700 km, CryoSat will precisely monitor changes in the thickness of sea ice and variations in the thickness of the ice sheets on land.

First, we expect the ice thickness distribution in April 30 from redistribution (divergence / convergence) of sea ice during December and April, based on the daily ice velocity data.

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.

So what we need is detailed topo maps of the bed and thickness of the GIS, and to work out a map of the «net buoyancy», or some such (i.e. total ice area density subtracted from the area density of a hypothetical column of water resting on the bed and extending up to sea level).

And you are correct that it depends on the thickness of the ice and the depression below sea level.

Professor Peter Wadhams, member of AMEG, expert on Arctic sea ice and a reviewer for the IPCC AR5 report, says that the PIOMAS data is based on actual thickness measurements.

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 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 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.

A threshold h applied on the (thickness Feb = Mar concentration Sept) field yields the predicted September extent after the regression with the past four years of sea ice extent observations.

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.

Sea ice extent, thickness and volume are all normal, yet the Flat Earth Society of climate scientists drones on endlessly about an ice - free Arctic — which they will never live to see.

The thickness of Arctic sea ice has also been on a steady decline over the last several decades.

On the text on the extent of Arctic sea ice, the UK asked about changes in Arctic sea ice thickness and the US about summer sea ice extent, to which the CLAs replied that this information is discussed in detail in the underlying assessmenOn the text on the extent of Arctic sea ice, the UK asked about changes in Arctic sea ice thickness and the US about summer sea ice extent, to which the CLAs replied that this information is discussed in detail in the underlying assessmenon the extent of Arctic sea ice, the UK asked about changes in Arctic sea ice thickness and the US about summer sea ice extent, to which the CLAs replied that this information is discussed in detail in the underlying assessment.

A comparison of the modeled ice thickness on 1 June 2007, 2008, and 2009, and the initial ice thickness on 28 May 2010 reveals considerably larger ice thickness mainly in the East Siberian Sea, north of the East Siberian Sea, and in the vicinity of the North Pole in 2010 compared to 2007 — 2009.

Peter Wadhams, President of the International Association on Sea Ice and Head of the Polar Ocean Physics Group / Department of Applied Mathematics and Theoretical Physics, University of Cambridge, says: «It is quite urgent that we recognize what is going on... the ice has been getting thinner over the last 40 years since I have been measuring it, and it has lost about one - half of its thickness... five years ago the shrinkage started to acceleraIce and Head of the Polar Ocean Physics Group / Department of Applied Mathematics and Theoretical Physics, University of Cambridge, says: «It is quite urgent that we recognize what is going on... the ice has been getting thinner over the last 40 years since I have been measuring it, and it has lost about one - half of its thickness... five years ago the shrinkage started to acceleraice has been getting thinner over the last 40 years since I have been measuring it, and it has lost about one - half of its thickness... five years ago the shrinkage started to accelerate.

It is hypothesized that these delayed responses reflect the dynamical influence of the AO on the thickness of the wintertime sea ice, whose persistent «footprint» is reflected in the heat fluxes during the subsequent spring, in the extent of open water during the subsequent summer, and the heat liberated in the freezing of the open water during the subsequent autumn.»

Reasoning for a decrease in sea ice extent from recent years, perhaps approaching new record - low minimum, focuses on the below - normal sea ice thickness overall, the thinning of sea ice in coastal seas, rotting of old multi-year sea ice, warm temperatures in April and May 2010, and the rapid loss of sea ice area seen during May.

Reasoning for a new record minimum focuses on the below - normal ice thickness overall, the thinning of sea ice in coastal seas, rotting of old multi-year sea ice, and the rapid loss of sea ice area seen during May.

One of the important ingredients of the new model is data on the thickness of ice floating on the sea.

The new ice thickness estimates will also be used to improve on - going seasonal predictions of sea ice extent.

On page 16 here: https://curryja.files.wordpress.com/2014/10/sea-ice-physical-processes.pdf There is the «Annual cycle of net surface heat flux for various ice thicknesses» Roughly interpolating the no sea ice flux I got an average of — 310 Wm2 over the course of a year.

``... examination of records of fast ice thickness and ice extent from four Arctic marginal seas (Kara, Laptev, East Siberian, and Chukchi) indicates that long - term trends are small and generally statistically insignificant, while trends for shorter records are not indicative of the long - term tendencies due to strong low - frequency variability in these time series, which places a strong limitation on our ability to resolve long - term trends....

5.3.7 In the Polar Regions, the main effect foreseen is a reduction in thickness and extent of glaciers, ice sheets, sea ice, and permafrost, and associated impacts on infrastructures, ecosystems, and traditional ways of life.

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.

The Outlook also underscored important lessons for improvements in future efforts, including: a need for additional work on remote sensing of spring and summer sea ice conditions; sea ice thickness data; and more formal forecasting and evaluation methods.

«Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System» J. Climate 0, (https://doi.org/10.1175/JCLI-D-17-0093.1).

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 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.

So, prompted by reports of the heaviest sea ice conditions on the East Coast «in decades» and news that ice on the Great Lakes is, for mid-April, the worst it's been since records began, I took a close look at ice thickness charts for the Arctic.

Since 1950, Arctic sea ice has lost half its area and half its thickness, helping to cause the phenomenon of Arctic amplification − the greater temperature rise (approaching 3 °C) observed in the Arctic than anywhere else on Earth.

Based on winter air temperatures and sea ice extents and thickness, a September 2016 minimum ice extent value of 4.3 million km2 is heuristically predicted.

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 predictors.

Is it your contention that the thickness of fast ice on these sea shores is the same from year to year?

Submissions of melt pond fraction, ice thickness, and any other sea ice parameter based on early - season data that could contribute to a status summary of pre-season conditions and help inform subsequent contributions to the regular SIO monthly report.

From the atmospheric temperature rise to the acidification of the sea, from ice thickness and extent to sea levels, we really need to continue to know what is going on.

NOAA@NSIDC is pleased to announce the release of On - Ice Arctic Sea Ice Thickness Measurements by Auger, Core, and Electromagnetic Induction, From the Fram Expedition Onward.

Until then, we have some new observational data of Canadian sea ice thickness and this remarkable figure of sea ice volume since 1979 from Neven's Arctic Sea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargsea ice thickness and this remarkable figure of sea ice volume since 1979 from Neven's Arctic Sea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargice thickness and this remarkable figure of sea ice volume since 1979 from Neven's Arctic Sea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargsea ice volume since 1979 from Neven's Arctic Sea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargice volume since 1979 from Neven's Arctic Sea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargSea Ice Blog, based on data from the University of Washington's Polar Science Center [click to enlargIce Blog, based on data from the University of Washington's Polar Science Center [click to enlarge]:

Based on the last 20 years, the reduction in sea ice cover and its thickness have enhanced the warming of the Arctic throughout the year.

Based on the understanding of both the physical processes that control key climate feedbacks (see Section 8.6.3), and also the origin of inter-model differences in the simulation of feedbacks (see Section 8.6.2), the following climate characteristics appear to be particularly important: (i) for the water vapour and lapse rate feedbacks, the response of upper - tropospheric RH and lapse rate to interannual or decadal changes in climate; (ii) for cloud feedbacks, the response of boundary - layer clouds and anvil clouds to a change in surface or atmospheric conditions and the change in cloud radiative properties associated with a change in extratropical synoptic weather systems; (iii) for snow albedo feedbacks, the relationship between surface air temperature and snow melt over northern land areas during spring and (iv) for sea ice feedbacks, the simulation of sea ice thickness.

With regard to proxy studies, same basic questions, are these direct or passive correlations, what evidence that tree ring core thickness depends only on temperature (what about precipitation, cloud cover, volcanic activity, sea surface temperatue changes, sea current changes, solar irradiance changes, cloud cover, etc.) How are these variables accounted for when analysis of ice cores is completed, or for that matter when computer models, and / or proxy studies are completed.

Shibata et al. (Kitami Institute of Technology); 5.4; Heuristic, Statistical Prediction is based on sea ice thickness, summer melt, outflow, and cloudiness.

Ship - based observations from the NASA Icescape cruise with USCG Healy (Perovich and Polashenski) indicate that the ice cover in the Chukchi Sea is in an advanced state of melt with melt ponds melted through in many areas and ice thicknesses on the order of 1m (Figure 3).

Further research is needed particularly on role of natural internal variability in influencing sea ice thickness and extent.