Sentences with phrase «observed sea ice extent»

Observed sea ice extent in the Russian Arctic, 1933 — 2006.

The prediction is initialised with the mean of the observed sea ice extent for September 2009 - 2013 and an ensemble prediction is created simply by adding all of the observed changes in the sea ice extent record from one September to the next over the historical period 1979 - 2013.

A key question was: were there systematic or physical changes that contributed to a greater observed sea ice extent this year or was it within the range of natural variability?

This is the main reason for our lower than observed sea ice extent prediction.

Furthermore, those 6 models failed to accurately simulate observed sea ice extent for individual Arctic basins.

We interpret the split of 2013 Outlooks above and below the 4.1 median to different interpretations of the guiding physics: those who considered that observed sea ice extent in 2012 being well below the 4.1 level indicates a shift in arctic conditions, especially with regard to reduced sea ice thickness and increased sea ice mobility; and those with estimates above 4.1 who support a return to the longer - term downward trend line (1979 - 2007).

We interpret the split of 2013 Outlooks above and below the 4.1 level to different interpretations of the guiding physics: those who considered that observed sea ice extent in 2012 being well below the 4.1 level indicates a shift in arctic conditions, especially with regard to reduced sea ice thickness and increased sea ice mobility; and those who have estimates above 4.1 who support a return to the longer - term downward trend line (1979 - 2007).

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.

Not exact matches

Substantial reductions in the extent of Arctic sea ice since 1978 (2.7 ± 0.6 percent per decade in the annual average, 7.4 ± 2.4 percent per decade for summer), increases in permafrost temperatures and reductions in glacial extent globally and in Greenland and Antarctic ice sheets have also been observed in recent decades.

Complementary analyses of the surface mass balance of Greenland (Tedesco et al, 2011) also show that 2010 was a record year for melt area extent... Extrapolating these melt rates forward to 2050, «the cumulative loss could raise sea level by 15 cm by 2050 ″ for a total of 32 cm (adding in 8 cm from glacial ice caps and 9 cm from thermal expansion)- a number very close to the best estimate of Vermeer & Rahmstorf (2009), derived by linking the observed rate of sea level rise to the observed warming.

Through satellite images, researchers have observed a steep decline in the average extent of Arctic sea ice for every month of the year.

Consistent with observed changes in surface temperature, there has been an almost worldwide reduction in glacier and small ice cap (not including Antarctica and Greenland) mass and extent in the 20th century; snow cover has decreased in many regions of the Northern Hemisphere; sea ice extents have decreased in the Arctic, particularly in spring and summer (Chapter 4); the oceans are warming; and sea level is rising (Chapter 5).

Observed decreases in arctic sea ice extent have been shown to be inconsistent with simulated internal variability, and consistent with the simulated response to human influence, but SH sea ice extent has not declined.

The draft report said, «There is low confidence in the scientific understanding of the small observed increase in Antarctic sea ice extent.»

A smaller ice sheet extent might still respond with the observed high rate of sea level rise (5 m per century) if the warming is much more rapid than when ice sheets were more extensive.

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 study, being published in Geophysical Research Letters, also looked back at recent ice behavior and concluded that «internal variability explains approximately half of the observed 1979 — 2005 September Arctic sea ice extent loss.»

«The very low summer extent of Arctic sea ice that has been observed in recent years is often casually interpreted as an early - warning sign of anthropogenic global warming.

Our results hence show that the observed evolution of Arctic sea - ice extent is consistent with the claim that virtually certainly the impact of an anthropogenic climate change is observable in Arctic sea ice already today.»

All climate models tell us that it is the Arctic sea ice cover that declines first, and that Antarctic ice extent falls only later, and may even (as observed) temporarily increase in response to changing patterns of atmospheric circulation.

This finding is consistent with the expected effect of increasing greenhouse gas concentrations and with other observed evidence of a changing climate such as reductions in Arctic sea ice extent, melting permafrost, rising sea levels, and increases in heavy downpours and heat waves.

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.

«Sea ice extent averaged over the Northern Hemisphere has decreased correspondingly over the past 50 years... The largest change has been observed in the summer months with decreases exceeding 30 %.

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.

Our model predicts that September 2015 Arctic sea ice extent will be 2.11 million km2 below the 1982 to 2011 observed average extent, but will not reach values as low as those observed in 2007 or 2012.

With regard to the Outlook estimates for the past two years, the median values for June outlooks for sea ice extent were within 0.1 million square kilometers (msk) of the observed values of 4.9 msk in 2010 and 4.6 msk in 2011.

With regard to the Outlook estimates for the past three years, the median values for June outlooks for sea ice extent were within 0.1 million square kilometers (msk) of the observed values of 4.9 msk in 2010 and 4.6 msk in 2011, but the June Outlook value of 4.4 msk in 2012 was well above the extreme observed September value of 3.6 msk.

Ice around Iceland (the number of weeks when ice was observed in this case) must correlate very well with the arctic sea ice extent / area, at least with the annual maximIce around Iceland (the number of weeks when ice was observed in this case) must correlate very well with the arctic sea ice extent / area, at least with the annual maximice was observed in this case) must correlate very well with the arctic sea ice extent / area, at least with the annual maximice extent / area, at least with the annual maximum.

This model has been proven skillful in reproducing the monthly arctic (and Antarctic) sea ice extent anomalies over the last 30 years, as well as the observed long - term downward trend.

An overall warming in the 2 × CO2 experiment causes reduction of sea - ice extent by 15 %, with maximum decrease in summer and autumn, consistent with observed seasonal sea - ice changes.

It is clear that these passages have been more open than today during certain periods of the past and also more closed during other periods, IOW the currently observed retreat of multi-year Arctic sea ice extent is nothing unusual or unprecedented.

Global mean temperatures in 2011 did not reach the record - setting levels of 2010, but were still the highest observed in a La Niña year, and Arctic sea - ice extent fell to near - record - low levels.

Figure 5: Observed (red line) and modelled September Arctic sea ice extent in millions of square kilometres.

The authors used very long control runs of both the Geophysical Fluid Dynamics Laboratory (GFDL) and Hadley Centre climate models (5,000 years for the GFDL model) to assess the probability that the observed and model - predicted trends in Arctic sea ice extent occur by chance as the result of natural climate variability.

Sea ice extent increased at a fairly steady rate throughout the month, staying slightly above the levels observed in December 2007.

In addition, chosen models had to simulate (hindcast), within 20 % accuracy, September sea ice extent observed from 1980 to 1999.

In fact, 2015 and early 2016 set records for the most sea ice extent observed.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) 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; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) 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 (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) 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 (MYI) 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.

Observed September minimum sea ice extent denoted by the red dashed line.

Conclusions Recently observed decadal trends in Arctic winter sea ice extent are not well explained by external forcing alone.

Thus, winter and spring atmospheric anomalies associated with the positive phase of the NAO may underlie the reduction of summer sea ice extent observed during the 1980s and 1990s.

Observed (black lines) and simulated (shading) surface temperatures, ocean heat content, and sea ice extent.

Looking at AR5, these seem to be the take away messages: «Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability could account for approximately half of the observed 1979 — 2005 September Arctic sea ice extent loss.»

(6) This study came out around the same time that we are observing greater sea ice extent.

Noting the black line ensemble member, there is a single year event that dramatically reduces the sea ice extent, similar to what was observed in 2007.

Wang, 5.0 (± 0.27), Modeling A projected September Arctic sea ice extent of 5.0 million km2 is based on a NCEP ensemble mean CFSv2 forecast initialized from the NCEP Climate Forecast System Reanalysis (CFSR) that assimilates observed sea ice concentrations and other atmospheric and oceanic observations.

The average arctic sea ice monthly extent for September 2012 was the lowest observed in the satellite era at 3.6 million square kilometers, based on National Snow and Ice Data Center (NSIDC) estimates — 50 % lower than the 1979 - 2000 average of 7.0 million square kilometeice monthly extent for September 2012 was the lowest observed in the satellite era at 3.6 million square kilometers, based on National Snow and Ice Data Center (NSIDC) estimates — 50 % lower than the 1979 - 2000 average of 7.0 million square kilometeIce Data Center (NSIDC) estimates — 50 % lower than the 1979 - 2000 average of 7.0 million square kilometers.

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.

In forcing the model with these winds, only the case of using winds from 2007 to project 2008 sea ice extent produce less sea ice than the observed 2007 ice extent (Ensemble member 7).

Our model predicts that September 2014 Arctic sea ice extent will be 1.45 million square kilometers below the 1981 to 2010 observed average extent, but will not reach values as low as those observed in 2007 or 2012.