Sentences with phrase «ice models based»

It's now clear that Mitch Taylor was right to be skeptical of sea ice models based on pessimistic climate change assumptions; he was also right to be more optimistic than his PBSG colleagues about the ability of polar bears to adapt to changing sea ice conditions (Taylor and Dowsley 2008), since the bears have turned out to be more resilient than even he expected.

Not exact matches

Yet these model - based estimates do not include the possible acceleration of recently observed increases in ice loss from the Greenland and Antarctic ice sheets.

On the basis of physical factors, it is claimed that these models can be used to predict when and where the ice may collapse.

The international team of co-authors, led by Peter Clark of Oregon State University, generated new scenarios for temperature rise, glacial melting, sea - level rise and coastal flooding based on state - of - the - art climate and ice sheet models.

Visitors can enter models of the explorers» base camps and view a computer map of Antarctica outlining the ocean currents around the continent and the land masses and mountains that lie hidden beneath the ice.

«When we included projected Antarctic wind shifts in a detailed global ocean model, we found water up to 4 °C warmer than current temperatures rose up to meet the base of the Antarctic ice shelves,» said lead author Dr Paul Spence from the ARC Centre of Excellence for Climate System Science (ARCCSS).

The model simulates melting at the base of the Amundsen Sea ice shelves at current rates over several decades.

Based on the southern core we thought this was a localized low heat - flux region — but our model shows that a much larger part of the southern ice sheet has low heat flux.

The researchers» forecasts are based on the AWI's BRIOS (Bremerhaven Regional Ice - Ocean Simulations) model, a coupled ice - ocean model that the team forced with atmospheric data from the SRES - A1B climate scenario, created at Britain's Met Office Hadley Centre in ExetIce - Ocean Simulations) model, a coupled ice - ocean model that the team forced with atmospheric data from the SRES - A1B climate scenario, created at Britain's Met Office Hadley Centre in Exetice - ocean model that the team forced with atmospheric data from the SRES - A1B climate scenario, created at Britain's Met Office Hadley Centre in Exeter.

Moon describes the many ways researchers study glacier dynamics, from in - place measurements on the ice to satellite - based monitoring campaigns to models.

The ice sheets themselves are the biggest challenge for climate modelling since we don't have direct evidence of the many of the key processes that occur at the ice sheet base (for obvious reasons), nor even of what the topography or conditions are at the base itself.

As models improved, the Fifth Assessment Report, released in 2013, was able to provide numerical estimates of future ice loss but still based on the informal judgment of a limited number of participants.

Scientists are involved in the evaluation of global - scale climate models, regional studies of the coupled atmosphere / ocean / ice systems, regional severe weather detection and prediction, measuring the local and global impact of the aerosols and pollutants, detecting lightning from space and the general development of remotely - sensed data bases.

Based on a model that excludes ice sheet flow due to a lack of basis in published literature, it is estimated that sea level rise will be, in a low scenario, 18 to 38 cm (7 to 15 inches) and in a high scenario, 26 to 59 cm (10 to 23 inches).

Hoose, C. et al. (2010): A classical - theory - based parameterization of heterogeneous ice nucleation by mineral dust, soot, and biological particles in a global climate model, J. Atmos.

After obtaining precise ice shelf height data, the researchers used a regional climate model to work out how much of the variability on a year - to - year basis was due to snowfall (which causes ice shelves to grow taller) versus ocean - driven melting (which causes ice shelves to thin from below).

170 (Symposium on Physical Basis of Ice Sheet Modelling, Vancouver), p. 313 - 322, 1987.

Improving digital elevation models over ice sheets using AVHRR - based photoclinometry.

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...

In The Physical Basis of Ice Sheet Modelling, pp. 81 - 91.

We compared the observed depth of the ice absorption feature with the disk model based on \ cite -LCB- Oka2012 -RCB- including water ice photodesorption effect by stellar UV photons.

Scientific knowledge input into process based models has much improved, reducing uncertainty of known science for some components of sea - level rise (e.g. steric changes), but when considering other components (e.g. ice melt from ice sheets, terrestrial water contribution) science is still emerging, and uncertainties remain high.

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The Discovery Sport showed me excellent snow and ice utility available on its base model.

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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.

The periods considered were mainly the Pleistocene ice age cycles, the LGM and the Pliocene, but Paul Valdes provided some interesting modeling that also included the Oligocene, the Turonian, the Maastrichtian and Eocene, indicating the importance of the base continental configuration, ice sheet position, and ocean circulation for sensitivity.

Based on their choice of time - shift rather than volume shift to «correct» the models, I'd suspect that an assumption of lingering ice is built into the models.

This is quite subtle though — weather forecast models obviously do better if they have initial conditions that are closer to the observations, and one might argue that for particular climate model predictions that are strongly dependent on the base climatology (such as for Arctic sea ice) tuning to the climatology will be worthwhile.

The authors compared recently constructed temperature data sets from Antarctica, based on data from ice cores and ground weather stations, to 20th century simulations from computer models used by scientists to simulate global climate.

(By the way, the I.P.C.C. didn't exclude the ice - sheet dynamics but they did say that the models currently were not good enough to do anything but to linearly extrapolate, and therefore they simply assumed the flow from Greenland and Antarctica based on the period 1993 - 2003.)

One possible explanation is that the CMIP5 models underestimate the strength of the feedback as did the CMIP3 models based upon the systematic errors in simulated sea ice coverage decline relative to observed rates (Boe et al., 2009b).

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.

Along with David Schilling, I had developed a model to reconstruct former ice sheets with ice elevations based on the strength of ice - bed coupling determined by glacial geology.

Our physical patterns are based on the physics of glacier / ice sheet melt (static equioibrium fingerprints), glacial isostatic adjustment models, and an ensemble of GCMs to inform the ocean dynamic contribution.

Mr. Kempthorne and other administration officials, while saying the decision to list is based on those models, have consistently not mentioned the role of rising carbon dioxide levels in driving the projected ice loss.]

Wu et al., 4.8 + / -0.2, Modeling The one we submit here is based on the correction of ice thickness initial condition due to too thick ice in the real time CFSv2 initial condition.

Based on last year's estimate, the initial condition change (thinning the ice pack by 60 cm) did appear to have improved the model's behavior and skill.

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

Arctic sea ice has reached record lows this winter around Greenland and elsewhere, following the predictions of remarkably accurate models based on global warming.

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 predicbased 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 predicBased (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 predictoice predictors.

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).

Since its inception 8 years ago, the NCAR / CU sea ice pool has easily rivaled much more sophisticated efforts based on statistical methods and physical models to predict the September monthly mean Arctic sea ice extent (e.g. see appendix of Stroeve et al. 2014 in GRL doi: 10.1002 / 2014GL059388; Witness the Arctic article by Hamilton et al. 2014 http://www.arcus.org/witness-the-arctic/2014/2/article/21066).

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 predicbased 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 predicBased 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 extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice predictors.

Zhang and Lindsay, 4.3 ± 0.8, Model The forecasting system is based on a synthesis of a model, the NCEP / NCAR reanalysis data, and satellite observations of ice concentration and sea surface temperaModel The forecasting system is based on a synthesis of a model, the NCEP / NCAR reanalysis data, and satellite observations of ice concentration and sea surface temperamodel, the NCEP / NCAR reanalysis data, and satellite observations of ice concentration and sea surface temperature.

Barthélemy et al., 5.0 (range from 4.1 to 5.5), Modeling Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model NEMO - LIM3.

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