Sentences with phrase «on sea ice thickness»

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

Zhang and Lindsay, 4.60 (4.00 - 5.20), Modeling Our seasonal prediction focuses not only on the total Arctic sea ice extent, but also on sea ice thickness field and ice edge location.

Some skill in predictability is possible based on the sea ice thickness distribution in spring.

Zhang, 5.1 (+ / - 0.6), Modeling The seasonal prediction focuses not only on the total Arctic sea ice extent, but also on sea ice thickness field and ice edge location.

In the Antarctic, there is very sparse data on sea ice thickness — not enough to judge one way or another, leaving only climate modeling results to work with.

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.

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.

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).

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.

You'll find some links to sea ice thickness data on my blog's most recent «Miscellanea» post.

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 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.

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.

Aspin et al., 4.0, Heuristic Sea ice extent is greater on 05 June 2013 than a year ago, however ice thicknesses and volumes are, on average, the lowest on record.

ESA's ice mission has become the first satellite to provide information on Arctic sea - ice thickness in near - real time to aid maritime activities in the polar region.

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.

However, our monthly sea ice volumes calculated from NRT and standard data agree to within 0.5 % on average, which shows that the NRT data allow us provide users with a reliable operational thickness and volume product.

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.

Arctic sea ice thickness variability and its influence on the atmospheric response to projected sea ice loss.

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....

Changes in sea ice extent, timing, ice thickness, and seasonal fluctuations are already having an impact on the people, plants, and animals that live in the Arctic.

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.

Zhang and Lindsay, 5.1 (± 0.4), Modeling Our seasonal prediction focuses not only on the total Arctic sea ice extent and ice concentration field, but also on ice thickness field and ice edge location.

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.

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.

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

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.

The mean ice concentration anomaly for June 2013 is 0.9 x 106 square kilometers greater than June 2012, however Arctic sea ice thicknesses and volumes continue to remain the lowest on record.

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