Sentences with phrase «sea ice model using»

Basically, they have put a prognostic model for melt ponds into the CICE model (the most sophisticated of the sea ice models used in climate models).

Not exact matches

This gives confidence in the predictions of the current generation of ice - sheet models which are used to forecast future ice loss from Antarctica and resulting sea - level rise.»

The military uses the microwave information to detect ocean wind speeds to feed into weather models, among other uses, but the data happen to be nearly perfect for sensing sea ice, says Walt Meier, a sea - ice specialist with the NSIDC.

«A clear understanding of energy use and energy storage will help improve models of how bears will respond to future changes in the sea ice.»

Dr James Screen from the University of Exeter used a computer model to investigate how the dramatic retreat of Arctic sea ice influences the European summer climate.

«In our study we used satellite data for sea ice and sea surface temperatures to run some coordinated hindcast experiments with five different atmospheric models,» Ogawa says.

In the study, the researchers use an ice - ocean model created in Bremerhaven to decode the oceanographic and physical processes that could lead to an irreversible inflow of warm water under the ice shelf — a development that has already been observed in the Amundsen Sea.

Using the sophisticated UK Met Office climate model, Dr Screen conducted computer experiments to study the effects of Arctic sea - ice loss on the NAO and on Northern European winter temperatures.

The researchers then used a computer model of Earth that simulated growth in the Antarctic ice sheet to see what geophysical impacts this would have aside from generally lowering the sea level.

Bed topography data are vital for computer models used to project future changes to ice sheets and their contribution to sea level rise.

The team used a worldwide climate model that incorporated normal month - to - month variations in sea surface temperatures and sea ice coverage, among other climate factors, to simulate 12,000 years» worth of weather.

The data that Old Weather volunteer citizen scientists meticulously transcribe from the logbooks are used to drive climate and sea ice models to help understand changes and improve predictions.

Simulations using a climate model showed that several large, closely spaced eruptions could have cooled the Northern Hemisphere enough to spark sea - ice growth and the subsequent feedback loop.

Kuhn, from Germany's Alfred Wegener Institute, added, «This gives confidence in the predictions of the current generation of ice sheet models which are used to forecast future ice loss from Antarctica and resulting sea - level rise.»

The remote impacts of Arctic sea - ice loss can only be properly represented using models that simulate interactions among the ocean, sea ice, land and atmosphere.

Computational models that simulate the climate such as CAM5, which is the atmosphere component of the Community Earth System Model used in the Intergovernmental Panel on Climate Change 5th Assessment, are used to predict future climate changes, such as the Arctic sea ice loss.

A number of recent studies linking changes in the North Atlantic ocean circulation to sea ice extent led Yeager to think that it would also be possible to make decadal predictions for Arctic winter sea ice cover using the NCAR - based Community Earth System Model...

Previously, Kelly was a Postdoctoral Fellow and Research Associate at the University of Washington and the University of Victoria in British Columbia, Canada where she studied the role of the changing Arctic sea ice cover on global circulation, weather, and climate using a hierarchy of numerical global climate models.

Studies of the Arctic system, connections between atmosphere and sea ice, and between the Arctic and the global system using remote sensing, conventional measurements, and output from global climate models.

Finnish Meteorological Institute has been doing estimates of two essential sea ice parameters — namely, sea ice concentration (SIC) and sea ice thickness (SIT)-- for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometsea ice parameters — namely, sea ice concentration (SIC) and sea ice thickness (SIT)-- for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometsea ice concentration (SIC) and sea ice thickness (SIT)-- for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometsea ice thickness (SIT)-- for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometSea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometsea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer.

The new findings stem from an analysis that links a widely - used framework for projecting how sea level around the world will respond to climate change to a model that accounts for recently identified processes contributing to Antarctic ice loss.

In his paper, he proposed a few theories as to what might have gone wrong, including the idea that the ice age model he used was inaccurate or that the estimations of 20th - century sea - level rise were too high.

If we had done a simple back - of - the - envelope estimate, surely someone would have criticized us for not using a climate model... Besides we also looked into regional patterns and the sea - ice response in our paper, something one can not do without a climate model.

I'm not arguing that Tietsche is wrong vis a vis «tipping points» — I'm arguing that climate * is * driving sea ice loss and that the model Tietsche used lacked the proper mechanisms to reliably extrapolate forwards.

This is in contrast to fully - coupled models, such as those used in the IPCC projections, which make their own version of the weather and can only be expected to approximate the mean and general patterns of variability and the long - term trajectory of the sea ice evolution.

Given that impacts don't scale linearly — that's true both because of the statistics of normal distributions, which imply that (damaging) extremes become much more frequent with small shifts in the mean, and because significant breakpoints such as melting points for sea ice, wet - bulb temperatures too high for human survival, and heat tolerance for the most significant human food crops are all «in play» — the model forecasts using reasonable emissions inputs ought to be more than enough for anyone using sensible risk analysis to know that we making very bad choices right now.

With error bars provided, we can use the PIOMAS ice volume time series as a proxy record for reality and compare it against sea - ice simulations in global climate models.

This provides another tool in addition to more directly observed properties for the improvement and evaluation of these models and is in our view the best use of PIOMAS in the context of predicting the long - term trajectory of sea ice.

Mike's work, like that of previous award winners, is diverse, and includes pioneering and highly cited work in time series analysis (an elegant use of Thomson's multitaper spectral analysis approach to detect spatiotemporal oscillations in the climate record and methods for smoothing temporal data), decadal climate variability (the term «Atlantic Multidecadal Oscillation» or «AMO» was coined by Mike in an interview with Science's Richard Kerr about a paper he had published with Tom Delworth of GFDL showing evidence in both climate model simulations and observational data for a 50 - 70 year oscillation in the climate system; significantly Mike also published work with Kerry Emanuel in 2006 showing that the AMO concept has been overstated as regards its role in 20th century tropical Atlantic SST changes, a finding recently reaffirmed by a study published in Nature), in showing how changes in radiative forcing from volcanoes can affect ENSO, in examining the role of solar variations in explaining the pattern of the Medieval Climate Anomaly and Little Ice Age, the relationship between the climate changes of past centuries and phenomena such as Atlantic tropical cyclones and global sea level, and even a bit of work in atmospheric chemistry (an analysis of beryllium - 7 measurements).

But many of the key questions — regional variability, changes in the patterns of rainfall or statistics of drought, or the interplay of dynamics and thermodynamics in sea ice change — can only be approached using comprehensive models.

It is argued that uncertainty, differences and errors in sea ice model forcing sets complicate the use of models to determine the exact causes of the recently reported decline in Arctic sea ice thickness, but help in the determination of robust features if the models are tuned appropriately against observations.

Here are some possible choices — in order of increasing sophistication: * All (or most) scientists agree (the principal Gore argument) * The 20th century is the warmest in 1000 years (the «hockeystick» argument) * Glaciers are melting, sea ice is shrinking, polar bears are in danger, etc * Correlation — both CO2 and temperature are increasing * Sea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agsea ice is shrinking, polar bears are in danger, etc * Correlation — both CO2 and temperature are increasing * Sea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agSea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agree

Using comprehensive data sets of observations made between 1979 and 2001 of sea ice thickness, draft, extent, and speeds, we find that it is possible to tune model parameters to give satisfactory agreement with observed data, thereby highlighting the skill of modern sea ice models, though the parameter values chosen differ according to the model forcing used.

In a more recent paper, our own Stefan Rahmstorf used a simple regression model to suggest that sea level rise (SLR) could reach 0.5 to 1.4 meters above 1990 levels by 2100, but this did not consider individual processes like dynamic ice sheet changes, being only based on how global sea level has been linked to global warming over the past 120 years.

The paper uses evidence and modeling to explain how the sun - blocking impact from a 50 - year stretch of unusually intense eruptions of four tropical volcanoes caused sufficient cooling to produce a long - lasting shift in the generation and migration of Arctic Ocean sea ice, with substantial consequences for the Northern Hemisphere climate that lasted centuries and left a deep imprint on European history.

[Andy Revkin — The model runs used by the U.S. Geological Survey's Steven Amstrup to project sea ice conditions midcentury onward all presumed carbon dioxide concentrations continue to rise.

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.

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.

They used the very advanced ECMWF seasonal prediction model at high resolution and prescribed various sea - ice concentrations, ENSO states, as well SST and solar forcings.

Arbetter, 4.7, Statistical A statistical model using regional observations of sea ice area and global NCEP air temperature, sea level pressure, and freezing degree day estimates continues the trend of projecting below - average summer sea ice conditions for the Arctic.

This month's report includes details on the causes of the 2012 minimum, the use of sea ice volume versus extent, sea ice in climate models, and late spring 2013 conditions.

Klazes (Public), 3.6 (95 % confidence interval of + / - 0.9), Statistical September extent is predicted using an estimated minimum value of the PIOMAS arctic sea ice volume and a simple model for volume - extent relationship.

After all, the computer models used to predict a dire future for polar bears combined the Chukchi Sea with the Southern Beaufort, as having similar ice habitats («ice ecoregions»).

We quantify sea - level commitment in the baseline case by building on Levermann et al. (10), who used physical simulations to model the SLR within a 2,000 - y envelope as the sum of the contributions of (i) ocean thermal expansion, based on six coupled climate models; (ii) mountain glacier and ice cap melting, based on surface mass balance and simplified ice dynamic models; (iii) Greenland ice sheet decay, based on a coupled regional climate model and ice sheet dynamic model; and (iv) Antarctic ice sheet decay, based on a continental - scale model parameterizing grounding line ice flux in relation to temperature.

A new international study is the first to use a high - resolution, large - scale computer model to estimate how much ice the West Antarctic Ice Sheet could lose over the next couple of centuries, and how much that could add to sea - level riice the West Antarctic Ice Sheet could lose over the next couple of centuries, and how much that could add to sea - level riIce Sheet could lose over the next couple of centuries, and how much that could add to sea - level rise.

To assess these implications, we translate global into local SLR projections using a model of spatial variation in sea - level contributions caused by isostatic deformation and changes in gravity as the Greenland and Antarctic ice sheets lose mass (36 ⇓ — 38), represented as two global 0.5 ° matrices of scalar adjustment factors to the ice sheets» respective median global contributions to SLR and (squared) to their variances.

Corrigendum to «Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness» published in The Cryosphere, 8, 1839 - 1854, 2014.

Using a global climate model coupled with an ocean and a sea - ice model, we compare the effects of doubling CO2 and halving CO2 on sea - ice cover and connections with the atmosphere and ocean.

The current operational ensemble forecast systems model sea ice dynamically using the LIM2 model within NEMO ocean model to represent the dynamic and thermodynamic evolution of sea ice within the coupled forecast system.

«The use of a coupled ocean — atmosphere — sea ice model to hindcast (i.e., historical forecast) recent climate variability is described and illustrated for the cases of the 1976/77 and 1998/99 climate shift events in the Pacific.