Sentences with phrase «sea ice model with»

Two groups (Kauker, et al., and Zhang) ran sea ice models with an ensemble (many years) of summer weather conditions from previous years.

Not exact matches

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.

Dirk Notz and Julienne Stroeve have now compared corresponding model calculations with data from satellite measurements, and discovered that the climate models underestimate the loss of Arctic sea ice.

With coordinated experiments with six atmospheric general circulation models, forced by observed daily sea - ice concentration and sea surface temperatuWith coordinated experiments with six atmospheric general circulation models, forced by observed daily sea - ice concentration and sea surface temperatuwith six atmospheric general circulation models, forced by observed daily sea - ice concentration and sea surface temperatures.

«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.

What's left to figure out is whether this is happening with other subglacial lakes around the Greenland ice sheet, as well as whether and how to incorporate the findings into models that are aimed at gauging how much Greenland might change with the warming climate and how much water it could add to the rising seas.

«The primary uncertainty in sea level rise is what are the ice sheets going to do over the coming century,» said Mathieu Morlighem, an expert in ice sheet modeling at the University of California, Irvine, who led the paper along with dozens of other contributors from institutions around the world.

In examining the ultimate transdisciplinary issue, humanity's evolving two - way relationship with the climate, I've had the rare privilege of studying the whole picture, from the climate models running on supercomputers in Boulder in 1985 to the burning rain forests of the western Amazon in 1989 to the shifting sea ice around the North Pole in 2003 to the contentious climate treaty talks in one city after another.

For the new study, Thomas Rackow and his colleagues fed actual position and size data of 6,912 Antarctic icebergs into the Finite Element Sea Ice - Ocean Model FESOM, which they combined with a dynamic - thermodynamic iceberg model (both of which were developed at the Model FESOM, which they combined with a dynamic - thermodynamic iceberg model (both of which were developed at the model (both of which were developed at the AWI).

development of a regional scale earth system model that includes coupling WRF with other earth system components such as ocean, sea ice, land surface hydrology, ecosystem, and chemistry; and

Simulations with general circulation ocean models do not fully support the gas exchange - sea ice hypothesis.

Joughin et al. (2010) applied a numerical ice sheet model to predicting the future of PIG, their model suggested ongoing loss of ice mass from PIG, with a maximum rate of global sea level rise of 2.7 cm per century.

A new study combines the latest observations with an ice sheet model to estimate that melting ice on the Antarctic ice sheet is likely to add 10 cm to global sea levels by 2100, but it could be as much as 30 cm.

Just look at the plots taken from CMIP4 and CMIP5 models when they are compared with measured extents from NSIDC data then tell us where you would place your bet for a summer free of sea ice.

She has shown, in an ice sheet model with gravitationally self - consistent sea level, there is actually a sea level fall at the grounding line, which acts to stabilize against the marine ice sheet instability.

This mixing model is well suited to quantify fractional contributions (f) from various sources — in this case, meteoric fresh water and sea ice meltwater mixing with seawater.

It's a long paper with a long title: «Ice melt, sea level rise and superstorms: evidence from paleoclimate data, climate modeling, and modern observations that 2 oC global warming could be dangerous».

Researchers also combine their observations and data with computer models that try to replicate weather and sea ice conditions in the Arctic.

Natural climate variability of the Arctic atmosphere, the impact of Greenland and PBL stability changes K. Dethloff *, A. Rinke *, W. Dorn *, D. Handorf *, J. H. Christensen ** * AWI Potsdam, ** DMI Copenhagen Unforced and forced long - term model integrations from 500 to 1000 years with global coupled atmosphere - ocean - sea - ice models have been analysed in order to find out whether the different models are able to simulate the North Atlantic Oscillation (NAO) similar to the real atmosphere.

Our modelled values are consistent with current rates of Antarctic ice loss and sea - level rise, and imply that accelerated mass loss from marine - based portions of Antarctic ice sheets may ensue when an increase in global mean air temperature of only 1.4 - 2.0 deg.

Zhang, J., D. R. Thomas, D. A. Rothrock, R. W. Lindsay, Y. Yu, and R. Kwok (2003), Assimilation of ice motion observations and comparisons with submarine ice thickness data, J.Geophys.Res., 108 (C6), 3170, DOI: 3110.1029 / 2001JC001041 Zhang, J., and D. A. Rothrock (2003), Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Monthly Weather Review, 131 (5), 845 - 861.

A comparison of observed sea ice decline with the model ensemble spread can tell us only how likely an observed trend is relative to that ensemble.

I don't know if it would be possible to force a climate model with the observed sea ice extent evolution (and an extrapolation) to get some information what this might produce.

See the Winton 2011 reference for an attempt to assess whether or not the observed sea ice time series fits with expected declines from the coupled models.

The eventual demise of the summer sea ice is a common feature of nearly every climate model projection (the exceptions are models with very inappropriate initial conditions).

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.

Global climate model projections (in CMIP3 at least) appear to underestimate sea ice extent losses with respect to observations, though this is not universally true for all models and some of them actually have ensemble spreads that are compatible with PIOMAS ice volume estimates and satellite observations of sea ice extent.

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).

These models consist of connected sub-modules that deal with radiative transfer, the circulation of the atmosphere and oceans, the physics of moist convection and cloud formation, sea ice, soil moisture and the like.

Just look at the plots taken from CMIP4 and CMIP5 models when they are compared with measured extents from NSIDC data then tell us where you would place your bet for a summer free of sea ice.

But this scenario was created with models that may underestimate warming because they underestimate feedbacks, such as sea - ice albedo.

Polar bears haven't seen what the ice models are predicting if we don't deal with the warming patterns and sea ice loss.

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.

The sea - level estimates are consistent with those from delta18O curves and numerical ice sheet models, and imply a significant sensitivity of the WAIS and the coastal margins of the EAIS to orbital oscillations in insolation during the Mid-Pliocene period of relative global warmth.

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.

The models also suggested that the rise in Arctic temperatures at the time, corresponded with the observed reduction in sea ice.

This one at least is consistent with other data, like the rapid decline of Arctic sea ice, way ahead of any model predictions I've seen published prior to the decline.

By analyzing climate anomalies in the model that are similar to those that occurred in the early twentieth century, it was found that the simulated temperature increase in the Arctic was related to enhanced wind - driven oceanic inflow into the Barents Sea with an associated sea ice retreSea with an associated sea ice retresea ice retreat.

Just to clarify: It is important to distinguish between attributing the differences between models (with anthropogenic forcing) and observations to natural variability and attributing the 33 + year decline in sea ice to anthropogenic forcing.

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.

It is not that the polar regions are amplifying the warming «going on» at lower latitudes, it is that any warming going on AT THE POLES is amplified through inherent positive feedback processes AT THE POLES, and specifically this is primarily the ice - albedo positive feedback process whereby more open water leads to more warming leads to more open water, etc. *** «Climate model simulations have shown that ice albedo feedbacks associated with variations in snow and sea - ice coverage are a key factor in positive feedback mechanisms which amplify climate change at high northern latitudes...»

The fact that I can point to the majority of models not predicting this fast of a decline, and you can point out a few that say that it might have started by now and it hasn't, totally ignores the fact that either way, sea ice is diminishing and that is consistent with accumulating heat in the Earth's climate system.

The Sea Ice Outlook (SIO) is well established; participation remains high, with contributions based on methodologies ranging from numerical models to informed estimates.

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.

Yuan et al. (LDEO Columbia University), 5.08 (+ / - 0.51), Statistical The prediction is made by statistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonable skills.

«Climate model simulations have shown that ice albedo feedbacks associated with variations in snow and sea - ice coverage are a key factor in positive feedback mechanisms which amplify climate change at high northern latitudes...»

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, composites of several approaches, estimates based on various non sea ice datasets and trends, and subjective information (the heuristic category).

Our strategy is to initialize the sea ice anomalies with respect to the model mean that are good approximations to actual arctic sea ice anomalies.

«A General Circulation Experiment with a Coupled Atmosphere, Ocean and Sea Ice Model.»